System

Abstract

An object of a system according to an embodiment is to propose a career path suitable for a student and reduce a burden on a parent or a teacher.SOLUTION: A system includes a reception part, an analysis part, a proposal part, and a support part. The reception part inputs information such as the interest of the student and a future desired occupation. The analysis unit analyzes the information input by the reception unit and proposes a career path suitable for the student. The suggestion unit provides the student with the career path suggested by the analysis unit. The support department reduces the work of the master, the teacher, the university, and the like.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult to provide students with appropriate advice on their career development, placing a heavy burden on parents and teachers.

[0005] The system according to the embodiment aims to suggest suitable career paths for students and reduce the burden on parents and teachers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a support unit. The reception unit inputs information such as students' interests and desired future careers. The analysis unit analyzes the information input by the reception unit and proposes career paths suitable for the students. The proposal unit provides the students with the career paths proposed by the analysis unit. The support unit reduces the workload of parents, teachers, universities, etc. [Effects of the Invention]

[0007] The system according to the embodiment can suggest suitable career paths for students and reduce the burden on parents and teachers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, a career advice system allows students to input information such as their interests and desired future careers, and a generation AI analyzes the information to suggest optimal career paths. In a career advice system, students input information such as their interests and desired future careers, and a generation AI analyzes the information to suggest optimal career paths. For example, a career advice system answers questions such as "Science or Humanities?", "What do you like?", and "What career do you want to pursue in the future?" This information is input into a generation AI, which then analyzes the input information. The generation AI then proposes optimal career paths based on the student's interests, aptitude, and future job market trends. For example, if a student is interested in science and hopes to become an engineer, the generation AI suggests specific steps and necessary skills for that student to become an engineer. Furthermore, the career advice system also contributes to reducing the workload of parents, teachers, universities, and other organizations. For example, by providing appropriate advice to students, the generation AI eliminates the need for parents and teachers to respond individually. Remote support via the Internet is also possible, allowing students to be supported without geographical constraints. This allows the career advice system to help students find a career path based on their interests and aptitudes, reducing the workload of parents, teachers, universities, etc. This allows students to build more fulfilling careers by choosing their future careers based on their interests and aptitudes. It also improves work efficiency by eliminating the need for parents and teachers to respond individually. Furthermore, remote support using the Internet allows support to be provided to students without being restricted by geographical location.

[0029] A career advice system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a support unit. The reception unit receives information from students, such as their interests and desired future careers. The information received from students includes, but is not limited to, their academic fields, hobbies, activities, and desired future careers. The reception unit receives information in the form of students answering questions such as, "Are you a science or humanities major?", "What do you like?", and "What career do you want to pursue in the future?" The reception unit can also use a generation AI to convert the information received from students into a format that is easy to analyze. The analysis unit uses the generation AI to analyze the information received from the reception unit. The analysis is performed based on, for example, a data analysis method or an algorithm, but is not limited to, examples. For example, the generation AI proposes an optimal career path based on the student's interests, aptitudes, future job market trends, and the like. The generation AI can analyze the input information using a text generation AI (e.g., LLM) or a multimodal generation AI. The proposal unit provides the student with the career path proposed by the generation AI. The suggestions are made based on, for example, specific career paths and required skills and qualifications, but are not limited to, such examples. For example, the suggestion unit provides students with career paths suggested by the generation AI and supports the students in taking specific actions based on the career paths. The support unit reduces the workload of parents, teachers, universities, etc. and provides remote support. The support is provided based on, for example, shortening work time and reducing burden, but is not limited to, such examples. For example, the support unit uses the generation AI to provide appropriate advice to students, eliminating the need for parents and teachers to provide individual support. Furthermore, the support unit provides remote support using the Internet, allowing students to support each other without being restricted by geography. As a result, the career advice system according to the embodiment allows students to find career paths based on their interests and aptitudes, thereby reducing the workload of parents, teachers, universities, etc. For example, the career advice system allows students to choose future careers based on their interests and aptitudes, allowing them to build more fulfilling careers. Furthermore, eliminating the need for parents and teachers to provide individual support improves work efficiency.Furthermore, remote support via the Internet allows us to support students without being restricted by geographical location.

[0030] The reception unit can analyze the student's past input history and select an appropriate input method. The reception unit uses a generation AI to analyze the student's past input history. The input history includes, but is not limited to, past input data, frequency, and patterns. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the student has frequently used in the past. The reception unit can also automatically generate related questions based on information the student has previously input. The reception unit can also suggest the most efficient input method based on the student's past input history. For example, if the student has frequently used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also automatically generate related questions based on information the student has previously input, allowing for efficient information collection. This enables efficient information collection by selecting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history data to the generation AI and have the generation AI select the optimal input method.

[0031] When inputting information, the reception unit can select information based on the student's current learning status and areas of interest. The reception unit uses generative AI to select information based on the student's current learning status and areas of interest. Learning status includes, but is not limited to, grades, progress, and learning content. Areas of interest include, but are not limited to, specific academic fields, hobbies, and activities. For example, the reception unit can prioritize questions related to the subject the student is currently studying. The reception unit can also display questions about related occupations and career paths based on the student's areas of interest. Furthermore, the reception unit can display questions of an appropriate level of difficulty according to the student's learning progress. For example, the reception unit prioritizes questions related to the subject the student is currently studying to efficiently collect information. The reception unit can also display questions about related occupations and career paths based on the student's areas of interest to attract the student's interest. Furthermore, the reception unit can display questions of an appropriate level of difficulty according to the student's learning progress to collect information according to the student's level of understanding. This allows highly relevant information to be collected by filtering information based on the student's learning status and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the student's learning status data and area of ​​interest data into the generation AI, and has the generation AI select the information.

[0032] The reception unit can select an appropriate input means depending on the student's input method when inputting information. The reception unit uses the generation AI to select an appropriate input means depending on the student's input method when inputting information. Input methods include, but are not limited to, text input, voice input, and image input. For example, the reception unit provides a voice recognition function when a student requests voice input. The reception unit can also provide keyboard input when a student requests text input. The reception unit can also provide an image recognition function when a student requests image input. For example, the reception unit provides a voice recognition function to convert voice to text when a student requests voice input. The reception unit can also provide keyboard input when a student requests text input, allowing for efficient information collection. The reception unit can also provide an image recognition function to analyze images when a student requests image input. This improves the efficiency of information input by selecting the optimal means depending on the student's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's input method data into the generation AI and have the generation AI select the optimal input means.

[0033] The reception unit can prioritize inputting highly relevant information based on the student's geographical location information when inputting information. The reception unit uses the generation AI to prioritize inputting highly relevant information based on the student's geographical location information when inputting information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the reception unit prioritizes inputting job market trends in the area where the student lives. The reception unit can also prioritize inputting information related to the curriculum of the school the student attends. The reception unit can also prioritize inputting information about areas in which the student is interested. For example, the reception unit prioritizes inputting job market trends in the area where the student lives to collect highly relevant information. The reception unit can also prioritize inputting information related to the curriculum of the school the student attends to efficiently collect information. The reception unit prioritizes inputting information about areas in which the student is interested to attract the student's interest. This allows highly relevant information to be collected preferentially by taking geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception department can input students' geographic location data into the generation AI and have the generation AI select highly relevant information.

[0034] The reception unit can analyze the student's social media activity and input relevant information when the information is input. The reception unit uses a generation AI to analyze the student's social media activity and input relevant information when the information is input. Social media activity includes, but is not limited to, post content, follower count, and engagement. For example, the reception unit inputs information related to occupations in which the student expressed interest on social media. The reception unit can also suggest suitable occupations based on the student's social media activity. The reception unit can also input related information based on the activity of the student's friends on social media. For example, the reception unit inputs information related to occupations in which the student expressed interest on social media, thereby efficiently collecting information. The reception unit can also suggest suitable occupations based on the student's social media activity, thereby attracting the student's interest. The reception unit can also input related information based on the activity of the student's friends on social media, thereby attracting the student's interest. In this way, highly relevant information can be collected by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception department can input students' social media activity data into the generation AI and have the generation AI select relevant information.

[0035] The reception unit can customize the input method by reflecting the student's past feedback when inputting information. The reception unit uses the generation AI to customize the input method by reflecting the student's past feedback when inputting information. Feedback includes, but is not limited to, past evaluations, comments, and areas for improvement. For example, the reception unit improves the input method based on feedback previously provided by the student. The reception unit can also suggest the optimal input method based on the student's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the student's feedback. For example, the reception unit improves the input method based on the student's past feedback and efficiently collects information. The reception unit can also suggest the optimal input method based on the student's past feedback, thereby attracting the student's interest. Furthermore, the reception unit can customize the input interface by reflecting the student's feedback and improving ease of use. In this way, the input method can be optimized by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception department can input the student's past feedback data into the generation AI and have the generation AI customize the input method.

[0036] The analysis unit can adjust the level of detail of the analysis based on the student's interests and aptitudes during the analysis. The analysis unit uses the generative AI to adjust the level of detail of the analysis based on the student's interests and aptitudes during the analysis. Interests include, but are not limited to, academic fields, hobbies, and activities. Aptitudes include, but are not limited to, skills, abilities, and personality traits. For example, the analysis unit provides detailed analysis results related to the student's fields of interest. The analysis unit can also provide an appropriate level of analysis results based on the student's aptitudes. The analysis unit can also adjust the level of detail of the analysis results based on the student's interests and aptitudes. For example, the analysis unit can provide detailed analysis results related to the student's fields of interest to attract the student's attention. The analysis unit can also provide an appropriate level of analysis results based on the student's aptitudes and provide information according to the student's level of understanding. The analysis unit can also adjust the level of detail of the analysis results based on the student's interests and aptitudes and provide information according to the student's level of understanding. By adjusting the level of detail of the analysis based on the student's interests and aptitudes, more relevant analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input student interest data and aptitude data into the generation AI and have the generation AI adjust the level of analysis detail.

[0037] The analysis unit can apply different analysis algorithms depending on the student's category during analysis. The analysis unit uses the generation AI to apply different analysis algorithms depending on the student's category during analysis. Examples of categories include, but are not limited to, year of study, major, and field of interest. For example, the analysis unit can apply an analysis algorithm specialized for science to science students. The analysis unit can also apply an analysis algorithm specialized for humanities to humanities students. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the student's interests and aptitude. For example, the analysis unit can apply an analysis algorithm specialized for science to science students to efficiently analyze information. The analysis unit can also apply an analysis algorithm specialized for humanities to humanities students to efficiently analyze information. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the student's interests and aptitude to efficiently analyze information. As a result, by applying the optimal analysis algorithm depending on the student's category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input student category data into the generation AI and have the generation AI apply the optimal analysis algorithm.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results during analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to the student's past analysis results during analysis. Past analysis results include, but are not limited to, past data and analysis reports. For example, the analysis unit adjusts the analysis algorithm based on the student's past analysis results. The analysis unit can also select an optimal analysis method from the student's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the student's past analysis results to improve the accuracy of the analysis. The analysis unit can select an optimal analysis method from the student's past analysis results and efficiently analyze information. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results and provide more accurate analysis results. As a result, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI. For example, the analysis unit can input a student's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] The analysis unit can determine the analysis priority based on the time of information submission. The analysis unit uses the generation AI to determine the analysis priority based on the time of information submission. The submission time includes, but is not limited to, for example, a submission deadline and a submission frequency. For example, the analysis unit prioritizes analysis of information with an upcoming deadline. The analysis unit can also prioritize analysis of information with an early submission time. Furthermore, the analysis unit can adjust the analysis priority based on the submission time. For example, the analysis unit prioritizes analysis of information with an upcoming deadline and analyzes the information efficiently. The analysis unit can also prioritize analysis of information with an early submission time and analyze the information efficiently. Furthermore, the analysis unit can adjust the analysis priority based on the submission time and analyze the information efficiently. Thus, determining the analysis priority based on the submission time enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit uses the generation AI to adjust the order of analysis based on the relevance of the information. Relevance includes, for example, a relevance score, a correlation, etc., but is not limited to these examples. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information to efficiently analyze the information. Furthermore, the analysis unit can postpone analysis of less relevant information to efficiently analyze the information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the information to efficiently analyze the information. As a result, adjusting the order of analysis based on the relevance of the information enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] The analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise. The analysis unit uses a generation AI to adjust the use of technical terminology in the analysis according to the student's level of expertise. Examples of technical terminology include, but are not limited to, knowledge test results and learning histories. For example, the analysis unit can use simple terminology for students with little technical knowledge. The analysis unit can also use detailed technical terminology for students with abundant technical knowledge. Furthermore, the analysis unit can adjust the technical terminology of the analysis results according to the student's level of expertise. For example, the analysis unit can use simple terminology to provide easy-to-understand analysis results for students with little technical knowledge. The analysis unit can also use detailed technical terminology to provide detailed analysis results for students with abundant technical knowledge. Furthermore, the analysis unit can adjust the technical terminology of the analysis results according to the student's level of expertise to provide easy-to-understand analysis results. By adjusting the technical terminology according to the student's level of expertise, it is possible to provide easy-to-understand analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the student's expertise level data into the generation AI and have the generation AI adjust the terminology.

[0042] The suggestion unit can adjust the level of detail of the proposal based on the importance of the career path when making the proposal. The suggestion unit uses the generation AI to adjust the level of detail of the proposal based on the importance of the career path when making the proposal. Examples of the importance of a career path include, but are not limited to, future demand and personal goals. For example, the suggestion unit provides detailed suggestions for career paths with high importance. The suggestion unit can also provide brief suggestions for career paths with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal based on the importance of the career path. For example, the suggestion unit provides detailed suggestions for career paths with high importance to help students take specific actions. The suggestion unit can also provide brief suggestions for career paths with low importance to efficiently provide information. Furthermore, the suggestion unit can adjust the level of detail of the proposal based on the importance of the career path to help students take specific actions. As a result, adjusting the level of detail of the proposal based on the importance of the career path can provide more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input career path importance data into the generation AI and have the generation AI adjust the level of detail of the proposal.

[0043] The suggestion unit can apply different suggestion algorithms depending on the category of the career path when making a suggestion. The suggestion unit uses the generation AI to apply different suggestion algorithms depending on the category of the career path when making a suggestion. Examples of categories include, but are not limited to, occupational categories and industries. For example, the suggestion unit can apply a suggestion algorithm specialized for science to science career paths. The suggestion unit can also apply a suggestion algorithm specialized for humanities to humanities career paths. Furthermore, the suggestion unit can select an optimal suggestion algorithm depending on the category of the career path. For example, the suggestion unit can apply a suggestion algorithm specialized for science to science career paths to efficiently provide information. The suggestion unit can also apply a suggestion algorithm specialized for humanities to humanities career paths to efficiently provide information. Furthermore, the suggestion unit can select an optimal suggestion algorithm depending on the category of the career path to efficiently provide information. As a result, by applying the optimal suggestion algorithm depending on the category of the career path, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input career path category data into the generation AI and cause the generation AI to apply the optimal suggestion algorithm.

[0044] The suggestion unit can determine the priority of proposals based on the submission time of the career paths when making the proposals. The suggestion unit, using the generation AI, can determine the priority of proposals based on the submission time of the career paths when making the proposals. The submission time includes, but is not limited to, for example, a submission deadline and a submission frequency. For example, the suggestion unit can prioritize proposals based on the deadline approaching. The suggestion unit can also prioritize proposals based on the earliest submission time. Furthermore, the suggestion unit can adjust the priority of proposals based on the submission time. For example, the suggestion unit can prioritize proposals based on the deadline approaching and efficiently provide information. The suggestion unit can prioritize proposals based on the earliest submission time and efficiently provide information. Furthermore, the suggestion unit can adjust the priority of proposals based on the submission time and efficiently provide information. Thus, by determining the priority of proposals based on the submission time, efficient proposals are possible. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission time of the career path into the generation AI and have the generation AI determine the priority of the proposals.

[0045] The suggestion unit can adjust the order of proposals based on the relevance of the career paths when making a proposal. The suggestion unit uses the generation AI to adjust the order of proposals based on the relevance of the career paths when making a proposal. Relevance includes, but is not limited to, relevance scores, correlations, and the like. For example, the suggestion unit prioritizes proposing highly relevant career paths. The suggestion unit can also postpone less relevant career paths. Furthermore, the suggestion unit can adjust the order of proposals based on the relevance of the career paths. For example, the suggestion unit prioritizes proposing highly relevant career paths and efficiently provides information. The suggestion unit can also postpone less relevant career paths and efficiently provide information. Furthermore, the suggestion unit can adjust the order of proposals based on the relevance of the career paths and efficiently provide information. As a result, adjusting the order of proposals based on the relevance of the career paths enables efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without using AI. For example, the proposal unit can input career path association data into the generation AI and have the generation AI adjust the order of proposals.

[0046] The suggestion unit can adjust the use of technical terminology in the proposal according to the student's level of expertise when making a proposal. The suggestion unit uses the generation AI to adjust the use of technical terminology in the proposal according to the student's level of expertise when making a proposal. Examples of technical expertise levels include, but are not limited to, knowledge test results and learning histories. For example, the suggestion unit can use simpler terms for students with less technical expertise. The suggestion unit can also use more detailed technical terminology for students with more specialized expertise. The suggestion unit can also adjust the technical terminology in the proposal results according to the student's level of expertise. For example, the suggestion unit can use simpler terms to provide easy-to-understand proposals for students with less specialized expertise. The suggestion unit can use more detailed technical terminology to provide more detailed proposals for students with more specialized expertise. The suggestion unit can also adjust the technical terminology in the proposal results according to the student's level of expertise to provide easy-to-understand proposals. As a result, by adjusting the technical terminology according to the student's level of expertise, it is possible to provide easy-to-understand proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the student's expertise level data into the generation AI and have the generation AI adjust the terminology.

[0047] The support unit can analyze a student's past support history and select the optimal support method when providing support. The support unit uses the generation AI to analyze a student's past support history and select the optimal support method when providing support. The support history includes, but is not limited to, past support content, effects, and feedback. For example, the support unit can propose the optimal support method based on the support methods the student received in the past. The support unit can also select an effective support method from the student's past support history. Furthermore, the support unit can improve the support method by referring to the student's past support history. For example, the support unit can propose the optimal support method based on the support methods the student received in the past and provide support efficiently. The support unit can select an effective support method from the student's past support history and provide support efficiently. Furthermore, the support unit can improve the support method by referring to the student's past support history and provide more effective support. By selecting the optimal support method based on the past support history, effective support is possible. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support department can input a student's past support history data into the generation AI and have the generation AI select the optimal support method.

[0048] The support unit can customize support measures based on the student's current learning situation when providing support. The support unit uses the generation AI to customize support measures based on the student's current learning situation when providing support. Learning situations include, but are not limited to, grades, progress, and learning content. For example, the support unit provides appropriate support measures according to the student's current learning progress. The support unit can also suggest an optimal support method based on the student's learning situation. Furthermore, the support unit can customize support measures taking into account the student's learning situation. For example, the support unit can provide appropriate support measures according to the student's current learning progress and provide efficient support. The support unit can also suggest an optimal support method based on the student's learning situation and support the student's learning. Furthermore, the support unit can customize support measures taking into account the student's learning situation and support the student's learning. In this way, by customizing support measures based on the student's learning situation, more appropriate support can be provided. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support department can input student learning status data into the generation AI and have the generation AI customize the support methods.

[0049] The support unit can improve the support method by reflecting student feedback during support. The support unit uses the generation AI to improve the support method by reflecting student feedback during support. Feedback includes, but is not limited to, past evaluations, comments, and areas for improvement. For example, the support unit improves the support method based on student feedback. The support unit can also select the optimal support method based on students' past feedback. Furthermore, the support unit can customize the support method by reflecting student feedback. For example, the support unit improves the support method based on student feedback and provides support efficiently. The support unit can select the optimal support method based on students' past feedback and provide support efficiently. Furthermore, the support unit can customize the support method by reflecting student feedback and provide support that meets the student's needs. In this way, the support method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input student feedback data into the generation AI and have the generation AI improve the support method.

[0050] The support unit can select the optimal support method by taking into account the student's geographical location information when providing support. The support unit uses the generation AI to select the optimal support method by taking into account the student's geographical location information when providing support. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the support unit provides support by taking into account trends in the job market in the area where the student lives. The support unit can also provide support related to the curriculum of the school the student attends. Furthermore, the support unit can provide support based on information about the area in which the student is interested. For example, the support unit provides support by taking into account trends in the job market in the area where the student lives, thereby providing highly relevant support. The support unit can also provide support related to the curriculum of the school the student attends, thereby providing efficient support. Furthermore, the support unit can provide support based on information about the area in which the student is interested, thereby attracting the student's interest. In this way, highly relevant support can be provided by taking into account the geographical location information. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without using AI. For example, the support department can input a student's geographic location data into the generation AI and have the generation AI select the optimal support method.

[0051] The support unit can analyze the student's social media activity and suggest support measures during support. The support unit uses generative AI to analyze the student's social media activity and suggest support measures during support. Social media activity includes, but is not limited to, post content, follower count, and engagement. For example, the support unit provides support related to occupations that the student has expressed interest in on social media. The support unit can also suggest suitable occupations based on the student's social media activity. Furthermore, the support unit can provide relevant support by referring to the activity of the student's friends on social media. For example, the support unit can provide support related to occupations that the student has expressed interest in on social media, thereby providing efficient support. The support unit can also suggest suitable occupations based on the student's social media activity to attract the student's interest. Furthermore, the support unit can provide relevant support by referring to the activity of the student's friends on social media. In this way, highly relevant support can be provided by analyzing social media activity. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support department can input students' social media activity data into the generation AI and have the generation AI suggest support methods.

[0052] The support unit can customize the support method by reflecting the student's past feedback when providing support. The support unit uses the generation AI to customize the support method by reflecting the student's past feedback when providing support. Feedback includes, but is not limited to, past evaluations, comments, and areas for improvement. For example, the support unit improves the support method based on the student's past feedback. The support unit can also select the optimal support method from the student's past feedback. Furthermore, the support unit can customize the support method by reflecting the student's feedback. For example, the support unit can improve the support method based on the student's past feedback and provide support efficiently. The support unit can select the optimal support method from the student's past feedback and provide support efficiently. Furthermore, the support unit can customize the support method by reflecting the student's feedback and provide support that meets the student's needs. In this way, the support method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without using AI. For example, the support department can input student feedback data into the generation AI and have the generation AI customize the support method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can estimate a student's learning style based on the student's input information and adjust the way information is presented based on the estimated learning style. For example, students with a visual learning style can be provided with information that makes extensive use of graphs and charts. Students with an auditory learning style can be provided with information in the form of audio guides or podcasts. Furthermore, students with an experiential learning style can be provided with interactive simulations and practical assignments. This makes it possible to present information according to each student's learning style, improving learning effectiveness.

[0055] The reception unit can analyze a student's past input history and suggest the optimal input timing. For example, if a student has previously input data at night, it can send a notification encouraging them to input data at night. Also, if a student has previously input data on weekends, it can send a notification encouraging them to input data on weekends. Furthermore, if a student has concentrated their input data during a specific time period, it can send a notification encouraging them to input data during that time period. This improves the efficiency of information input by suggesting the optimal input timing based on the student's past input history.

[0056] The reception department can provide relevant learning resources based on the student's current learning situation and areas of interest. For example, it can provide online courses and materials related to the subject the student is currently studying. It can also recommend related books and papers based on the student's areas of interest. It can also provide learning resources of an appropriate level of difficulty according to the student's learning progress. This improves learning effectiveness by providing appropriate learning resources based on the student's learning situation and areas of interest.

[0057] When entering information, the reception department can provide input assistance tools according to the student's input method. For example, voice recognition software can be provided to students who wish to input by voice. A keyboard with a predictive conversion function can also be provided to students who wish to input by text. Furthermore, image recognition software can be provided to students who wish to input by image. In this way, the efficiency of information entry can be improved by providing input assistance tools according to the student's input method.

[0058] When entering information, the reception unit can provide region-specific career information based on the student's geographic location information. For example, it can provide information on major industries and employment opportunities in the area where the student lives. It can also provide information on local universities and vocational schools. It can also provide information on local companies and internships. This makes it possible to propose more specific career paths by providing region-specific career information based on the student's geographic location information.

[0059] When entering information, the reception department can analyze the student's social media activity and suggest relevant career events and seminars. For example, it can suggest events related to careers that the student has expressed interest in on social media. It can also suggest events that the student's followers and friends are attending. Furthermore, it can suggest seminars related to careers that the student is suited to based on the student's social media activity. This makes it possible to suggest highly relevant career events and seminars by analyzing social media activity.

[0060] The reception unit can customize the input interface by reflecting students' past feedback when they input information. For example, it can improve the design and functionality of the interface based on feedback provided by students in the past. It can also suggest the optimal input method based on students' past feedback. It can also improve the ease of use of the input interface by reflecting students' feedback. In this way, the input interface can be optimized by reflecting past feedback, and the efficiency of information input can be improved.

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

[0062] Step 1: The reception department allows students to input information such as their interests and desired future career. The information students input includes their academic field, hobbies, activities, and desired future career. For example, students input information in the form of answering questions such as "Science or Humanities?", "What do you like?", and "What career do you want to pursue in the future?". The reception department can also use generative AI to convert the information students input into a format that is easier to analyze. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is based on the data analysis method and algorithm used. For example, the generation AI considers the student's interests and aptitudes, future job market trends, etc., to suggest the optimal career path. The generation AI can analyze the input information using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The suggestion unit provides the student with the career path proposed by the generative AI. The suggestion is based on specific career paths and the required skills and qualifications. For example, the suggestion unit provides the student with the career path proposed by the generative AI and helps the student take specific actions based on that career path. Step 4: The support department will reduce the workload of parents, teachers, universities, and others and provide remote support. Support will be based on reducing work time and reducing the burden. For example, the support department will use generative AI to provide appropriate advice to students, eliminating the need for parents and teachers to respond individually. The support department will also provide remote support using the internet, allowing it to support students without being restricted by geography.

[0063] (Example 2) In an embodiment of the present invention, a career advice system allows students to input information such as their interests and desired future careers, and a generation AI analyzes the information to suggest optimal career paths. In a career advice system, students input information such as their interests and desired future careers, and a generation AI analyzes the information to suggest optimal career paths. For example, a career advice system answers questions such as "Science or Humanities?", "What do you like?", and "What career do you want to pursue in the future?" This information is input into a generation AI, which then analyzes the input information. The generation AI then proposes optimal career paths based on the student's interests, aptitude, and future job market trends. For example, if a student is interested in science and hopes to become an engineer, the generation AI suggests specific steps and necessary skills for that student to become an engineer. Furthermore, the career advice system also contributes to reducing the workload of parents, teachers, universities, and other organizations. For example, by providing appropriate advice to students, the generation AI eliminates the need for parents and teachers to respond individually. Remote support via the Internet is also possible, allowing students to be supported without geographical constraints. This allows the career advice system to help students find a career path based on their interests and aptitudes, reducing the workload of parents, teachers, universities, etc. This allows students to build more fulfilling careers by choosing their future careers based on their interests and aptitudes. It also improves work efficiency by eliminating the need for parents and teachers to respond individually. Furthermore, remote support using the Internet allows support to be provided to students without being restricted by geographical location.

[0064] A career advice system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a support unit. The reception unit receives information from students, such as their interests and desired future careers. The information received from students includes, but is not limited to, their academic fields, hobbies, activities, and desired future careers. The reception unit receives information in the form of students answering questions such as, "Are you a science or humanities major?", "What do you like?", and "What career do you want to pursue in the future?" The reception unit can also use a generation AI to convert the information received from students into a format that is easy to analyze. The analysis unit uses the generation AI to analyze the information received from the reception unit. The analysis is performed based on, for example, a data analysis method or an algorithm, but is not limited to, examples. For example, the generation AI proposes an optimal career path based on the student's interests, aptitudes, future job market trends, and the like. The generation AI can analyze the input information using a text generation AI (e.g., LLM) or a multimodal generation AI. The proposal unit provides the student with the career path proposed by the generation AI. The suggestions are made based on, for example, specific career paths and required skills and qualifications, but are not limited to, such examples. For example, the suggestion unit provides students with career paths suggested by the generation AI and supports the students in taking specific actions based on the career paths. The support unit reduces the workload of parents, teachers, universities, etc. and provides remote support. The support is provided based on, for example, shortening work time and reducing burden, but is not limited to, such examples. For example, the support unit uses the generation AI to provide appropriate advice to students, eliminating the need for parents and teachers to provide individual support. Furthermore, the support unit provides remote support using the Internet, allowing students to support each other without being restricted by geography. As a result, the career advice system according to the embodiment allows students to find career paths based on their interests and aptitudes, thereby reducing the workload of parents, teachers, universities, etc. For example, the career advice system allows students to choose future careers based on their interests and aptitudes, allowing them to build more fulfilling careers. Furthermore, eliminating the need for parents and teachers to provide individual support improves work efficiency.Furthermore, remote support via the Internet allows us to support students without being restricted by geographical location.

[0065] The reception unit estimates the student's emotions and adjusts the timing of information input based on the estimated student emotions. The reception unit estimates the student's emotions using generative AI. Emotion estimation is performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the reception unit captures the student's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the student's voice and estimate the emotion using voice analysis technology. The reception unit can also analyze the student's text input to estimate the emotion. The reception unit adjusts the timing of information input based on the estimated emotion. For example, if a student is feeling stressed, the reception unit can prompt the student to enter information at a time when they can relax. If a student is concentrating, the reception unit can prompt the student to enter information immediately, allowing for efficient data collection. If a student is tired, the reception unit can prompt the student to enter information after a break. This allows for more appropriate information collection by adjusting the timing of information input according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input image data of a student taken with a camera into the generation AI and cause the generation AI to estimate the student's emotions.

[0066] The reception unit can analyze the student's past input history and select an appropriate input method. The reception unit uses a generation AI to analyze the student's past input history. The input history includes, but is not limited to, past input data, frequency, and patterns. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the student has frequently used in the past. The reception unit can also automatically generate related questions based on information the student has previously input. The reception unit can also suggest the most efficient input method based on the student's past input history. For example, if the student has frequently used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also automatically generate related questions based on information the student has previously input, allowing for efficient information collection. This enables efficient information collection by selecting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history data to the generation AI and have the generation AI select the optimal input method.

[0067] When inputting information, the reception unit can select information based on the student's current learning status and areas of interest. The reception unit uses generative AI to select information based on the student's current learning status and areas of interest. Learning status includes, but is not limited to, grades, progress, and learning content. Areas of interest include, but are not limited to, specific academic fields, hobbies, and activities. For example, the reception unit can prioritize questions related to the subject the student is currently studying. The reception unit can also display questions about related occupations and career paths based on the student's areas of interest. Furthermore, the reception unit can display questions of an appropriate level of difficulty according to the student's learning progress. For example, the reception unit prioritizes questions related to the subject the student is currently studying to efficiently collect information. The reception unit can also display questions about related occupations and career paths based on the student's areas of interest to attract the student's interest. Furthermore, the reception unit can display questions of an appropriate level of difficulty according to the student's learning progress to collect information according to the student's level of understanding. This allows highly relevant information to be collected by filtering information based on the student's learning status and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the student's learning status data and area of ​​interest data into the generation AI, and has the generation AI select the information.

[0068] The reception unit can select an appropriate input means depending on the student's input method when inputting information. The reception unit uses the generation AI to select an appropriate input means depending on the student's input method when inputting information. Input methods include, but are not limited to, text input, voice input, and image input. For example, the reception unit provides a voice recognition function when a student requests voice input. The reception unit can also provide keyboard input when a student requests text input. The reception unit can also provide an image recognition function when a student requests image input. For example, the reception unit provides a voice recognition function to convert voice to text when a student requests voice input. The reception unit can also provide keyboard input when a student requests text input, allowing for efficient information collection. The reception unit can also provide an image recognition function to analyze images when a student requests image input. This improves the efficiency of information input by selecting the optimal means depending on the student's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's input method data into the generation AI and have the generation AI select the optimal input means.

[0069] The reception unit can estimate the student's emotions and prioritize the information to be input based on the estimated student's emotions. The reception unit uses a generative AI to estimate the student's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the reception unit can capture the student's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the student's voice and estimate the emotion using voice analysis technology. The reception unit can also analyze the student's text input to estimate the emotion. Based on the estimated emotion, the reception unit can prioritize the information to be input. For example, if the student is feeling anxious, information that gives a sense of security can be prioritized. If the student is excited, information that is interesting can be prioritized. If the student is relaxed, detailed information can be prioritized. This enables more appropriate information collection by prioritizing information according to the student's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input image data of a student taken with a camera into the generation AI and cause the generation AI to estimate the student's emotions.

[0070] The reception unit can prioritize inputting highly relevant information based on the student's geographical location information when inputting information. The reception unit uses the generation AI to prioritize inputting highly relevant information based on the student's geographical location information when inputting information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the reception unit prioritizes inputting job market trends in the area where the student lives. The reception unit can also prioritize inputting information related to the curriculum of the school the student attends. The reception unit can also prioritize inputting information about areas in which the student is interested. For example, the reception unit prioritizes inputting job market trends in the area where the student lives to collect highly relevant information. The reception unit can also prioritize inputting information related to the curriculum of the school the student attends to efficiently collect information. The reception unit prioritizes inputting information about areas in which the student is interested to attract the student's interest. This allows highly relevant information to be collected preferentially by taking geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception department can input students' geographic location data into the generation AI and have the generation AI select highly relevant information.

[0071] The reception unit can analyze the student's social media activity and input relevant information when the information is input. The reception unit uses a generation AI to analyze the student's social media activity and input relevant information when the information is input. Social media activity includes, but is not limited to, post content, follower count, and engagement. For example, the reception unit inputs information related to occupations in which the student expressed interest on social media. The reception unit can also suggest suitable occupations based on the student's social media activity. The reception unit can also input related information based on the activity of the student's friends on social media. For example, the reception unit inputs information related to occupations in which the student expressed interest on social media, thereby efficiently collecting information. The reception unit can also suggest suitable occupations based on the student's social media activity, thereby attracting the student's interest. The reception unit can also input related information based on the activity of the student's friends on social media, thereby attracting the student's interest. In this way, highly relevant information can be collected by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception department can input students' social media activity data into the generation AI and have the generation AI select relevant information.

[0072] The reception unit can customize the input method by reflecting the student's past feedback when inputting information. The reception unit uses the generation AI to customize the input method by reflecting the student's past feedback when inputting information. Feedback includes, but is not limited to, past evaluations, comments, and areas for improvement. For example, the reception unit improves the input method based on feedback previously provided by the student. The reception unit can also suggest the optimal input method based on the student's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the student's feedback. For example, the reception unit improves the input method based on the student's past feedback and efficiently collects information. The reception unit can also suggest the optimal input method based on the student's past feedback, thereby attracting the student's interest. Furthermore, the reception unit can customize the input interface by reflecting the student's feedback and improving ease of use. In this way, the input method can be optimized by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception department can input the student's past feedback data into the generation AI and have the generation AI customize the input method.

[0073] The analysis unit can estimate the student's emotions and adjust the analysis presentation method based on the estimated student's emotions. The analysis unit uses generative AI to estimate the student's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can capture the student's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the student's voice and estimate the emotion using voice analysis technology. The analysis unit can also analyze the student's text input to estimate the emotion. Based on the estimated emotion, the analysis unit adjusts the analysis presentation method. For example, if the student is nervous, a simple and highly visible analysis result can be provided. If the student is relaxed, a detailed analysis result can be provided. If the student is excited, a visually stimulating analysis result can be provided. By adjusting the analysis presentation method according to the student's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input image data of a student taken with a camera into the generation AI and cause the generation AI to estimate the student's emotions.

[0074] The analysis unit can adjust the level of detail of the analysis based on the student's interests and aptitudes during the analysis. The analysis unit uses the generative AI to adjust the level of detail of the analysis based on the student's interests and aptitudes during the analysis. Interests include, but are not limited to, academic fields, hobbies, and activities. Aptitudes include, but are not limited to, skills, abilities, and personality traits. For example, the analysis unit provides detailed analysis results related to the student's fields of interest. The analysis unit can also provide an appropriate level of analysis results based on the student's aptitudes. The analysis unit can also adjust the level of detail of the analysis results based on the student's interests and aptitudes. For example, the analysis unit can provide detailed analysis results related to the student's fields of interest to attract the student's attention. The analysis unit can also provide an appropriate level of analysis results based on the student's aptitudes and provide information according to the student's level of understanding. The analysis unit can also adjust the level of detail of the analysis results based on the student's interests and aptitudes and provide information according to the student's level of understanding. By adjusting the level of detail of the analysis based on the student's interests and aptitudes, more relevant analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input student interest data and aptitude data into the generation AI and have the generation AI adjust the level of analysis detail.

[0075] The analysis unit can apply different analysis algorithms depending on the student's category during analysis. The analysis unit uses the generation AI to apply different analysis algorithms depending on the student's category during analysis. Examples of categories include, but are not limited to, year of study, major, and field of interest. For example, the analysis unit can apply an analysis algorithm specialized for science to science students. The analysis unit can also apply an analysis algorithm specialized for humanities to humanities students. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the student's interests and aptitude. For example, the analysis unit can apply an analysis algorithm specialized for science to science students to efficiently analyze information. The analysis unit can also apply an analysis algorithm specialized for humanities to humanities students to efficiently analyze information. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the student's interests and aptitude to efficiently analyze information. As a result, by applying the optimal analysis algorithm depending on the student's category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input student category data into the generation AI and have the generation AI apply the optimal analysis algorithm.

[0076] The analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results during analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to the student's past analysis results during analysis. Past analysis results include, but are not limited to, past data and analysis reports. For example, the analysis unit adjusts the analysis algorithm based on the student's past analysis results. The analysis unit can also select an optimal analysis method from the student's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the student's past analysis results to improve the accuracy of the analysis. The analysis unit can select an optimal analysis method from the student's past analysis results and efficiently analyze information. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results and provide more accurate analysis results. As a result, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI. For example, the analysis unit can input a student's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0077] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated student's emotions. The analysis unit uses generative AI to estimate the student's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can capture the student's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the student's voice and estimate the emotion using voice analysis technology. The analysis unit can also analyze the student's text input to estimate the emotion. Based on the estimated emotion, the analysis unit adjusts the length of the analysis. For example, if the student is in a hurry, the analysis unit can provide a short and concise analysis result. If the student is relaxed, the analysis unit can provide a detailed analysis result. If the student is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the student's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input image data of a student taken with a camera into the generation AI and cause the generation AI to estimate the student's emotions.

[0078] The analysis unit can determine the analysis priority based on the time of information submission. The analysis unit uses the generation AI to determine the analysis priority based on the time of information submission. The submission time includes, but is not limited to, for example, a submission deadline and a submission frequency. For example, the analysis unit prioritizes analysis of information with an upcoming deadline. The analysis unit can also prioritize analysis of information with an early submission time. Furthermore, the analysis unit can adjust the analysis priority based on the submission time. For example, the analysis unit prioritizes analysis of information with an upcoming deadline and analyzes the information efficiently. The analysis unit can also prioritize analysis of information with an early submission time and analyze the information efficiently. Furthermore, the analysis unit can adjust the analysis priority based on the submission time and analyze the information efficiently. Thus, determining the analysis priority based on the submission time enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit uses the generation AI to adjust the order of analysis based on the relevance of the information. Relevance includes, for example, a relevance score, a correlation, etc., but is not limited to these examples. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information to efficiently analyze the information. Furthermore, the analysis unit can postpone analysis of less relevant information to efficiently analyze the information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the information to efficiently analyze the information. As a result, adjusting the order of analysis based on the relevance of the information enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0080] The analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise. The analysis unit uses a generation AI to adjust the use of technical terminology in the analysis according to the student's level of expertise. Examples of technical terminology include, but are not limited to, knowledge test results and learning histories. For example, the analysis unit can use simple terminology for students with little technical knowledge. The analysis unit can also use detailed technical terminology for students with abundant technical knowledge. Furthermore, the analysis unit can adjust the technical terminology of the analysis results according to the student's level of expertise. For example, the analysis unit can use simple terminology to provide easy-to-understand analysis results for students with little technical knowledge. The analysis unit can also use detailed technical terminology to provide detailed analysis results for students with abundant technical knowledge. Furthermore, the analysis unit can adjust the technical terminology of the analysis results according to the student's level of expertise to provide easy-to-understand analysis results. By adjusting the technical terminology according to the student's level of expertise, it is possible to provide easy-to-understand analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the student's expertise level data into the generation AI and have the generation AI adjust the terminology.

[0081] The suggestion unit can estimate the student's emotions and adjust the way the suggestions are presented based on the estimated student's emotions. The suggestion unit uses a generative AI to estimate the student's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the suggestion unit can capture the student's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also record the student's voice and estimate the emotion using voice analysis technology. Furthermore, the suggestion unit can analyze the student's text input to estimate the emotion. Based on the estimated emotion, the suggestion unit can adjust the way the suggestions are presented. For example, if the student is nervous, the suggestion unit can provide simple, highly visible suggestions. If the student is relaxed, the suggestion unit can provide detailed suggestions. If the student is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the way the suggestions are presented based on the student's emotions, more appropriate suggestions can be provided. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit may input image data of a student taken with a camera into the generation AI and cause the generation AI to estimate the student's emotions.

[0082] The suggestion unit can adjust the level of detail of the proposal based on the importance of the career path when making the proposal. The suggestion unit uses the generation AI to adjust the level of detail of the proposal based on the importance of the career path when making the proposal. Examples of the importance of a career path include, but are not limited to, future demand and personal goals. For example, the suggestion unit provides detailed suggestions for career paths with high importance. The suggestion unit can also provide brief suggestions for career paths with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal based on the importance of the career path. For example, the suggestion unit provides detailed suggestions for career paths with high importance to help students take specific actions. The suggestion unit can also provide brief suggestions for career paths with low importance to efficiently provide information. Furthermore, the suggestion unit can adjust the level of detail of the proposal based on the importance of the career path to help students take specific actions. As a result, adjusting the level of detail of the proposal based on the importance of the career path can provide more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input career path importance data into the generation AI and have the generation AI adjust the level of detail of the proposal.

[0083] The suggestion unit can apply different suggestion algorithms depending on the category of the career path when making a suggestion. The suggestion unit uses the generation AI to apply different suggestion algorithms depending on the category of the career path when making a suggestion. Examples of categories include, but are not limited to, occupational categories and industries. For example, the suggestion unit can apply a suggestion algorithm specialized for science to science career paths. The suggestion unit can also apply a suggestion algorithm specialized for humanities to humanities career paths. Furthermore, the suggestion unit can select an optimal suggestion algorithm depending on the category of the career path. For example, the suggestion unit can apply a suggestion algorithm specialized for science to science career paths to efficiently provide information. The suggestion unit can also apply a suggestion algorithm specialized for humanities to humanities career paths to efficiently provide information. Furthermore, the suggestion unit can select an optimal suggestion algorithm depending on the category of the career path to efficiently provide information. As a result, by applying the optimal suggestion algorithm depending on the category of the career path, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input career path category data into the generation AI and cause the generation AI to apply the optimal suggestion algorithm.

[0084] The suggestion unit can estimate the student's emotion and adjust the length of the suggestion based on the estimated student's emotion. The suggestion unit uses a generative AI to estimate the student's emotion. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the suggestion unit can capture the student's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also record the student's voice and estimate the emotion using voice analysis technology. The suggestion unit can also analyze the student's text input to estimate the emotion. The suggestion unit adjusts the length of the suggestion based on the estimated emotion. For example, if the student is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. If the student is relaxed, the suggestion unit can provide a detailed suggestion. If the student is excited, the suggestion unit can provide a visually stimulating suggestion. By adjusting the length of the suggestion based on the student's emotion, more appropriate suggestions can be provided. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit may input image data of a student taken with a camera into the generation AI and cause the generation AI to estimate the student's emotions.

[0085] The suggestion unit can determine the priority of proposals based on the submission time of the career paths when making the proposals. The suggestion unit, using the generation AI, can determine the priority of proposals based on the submission time of the career paths when making the proposals. The submission time includes, but is not limited to, for example, a submission deadline and a submission frequency. For example, the suggestion unit can prioritize proposals based on the deadline approaching. The suggestion unit can also prioritize proposals based on the earliest submission time. Furthermore, the suggestion unit can adjust the priority of proposals based on the submission time. For example, the suggestion unit can prioritize proposals based on the deadline approaching and efficiently provide information. The suggestion unit can prioritize proposals based on the earliest submission time and efficiently provide information. Furthermore, the suggestion unit can adjust the priority of proposals based on the submission time and efficiently provide information. Thus, by determining the priority of proposals based on the submission time, efficient proposals are possible. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission time of the career path into the generation AI and have the generation AI determine the priority of the proposals.

[0086] The suggestion unit can adjust the order of proposals based on the relevance of the career paths when making a proposal. The suggestion unit uses the generation AI to adjust the order of proposals based on the relevance of the career paths when making a proposal. Relevance includes, but is not limited to, relevance scores, correlations, and the like. For example, the suggestion unit prioritizes proposing highly relevant career paths. The suggestion unit can also postpone less relevant career paths. Furthermore, the suggestion unit can adjust the order of proposals based on the relevance of the career paths. For example, the suggestion unit prioritizes proposing highly relevant career paths and efficiently provides information. The suggestion unit can also postpone less relevant career paths and efficiently provide information. Furthermore, the suggestion unit can adjust the order of proposals based on the relevance of the career paths and efficiently provide information. As a result, adjusting the order of proposals based on the relevance of the career paths enables efficient proposals. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without using AI. For example, the proposal unit can input career path association data into the generation AI and have the generation AI adjust the order of proposals.

[0087] The suggestion unit can adjust the use of technical terminology in the proposal according to the student's level of expertise when making a proposal. The suggestion unit uses the generation AI to adjust the use of technical terminology in the proposal according to the student's level of expertise when making a proposal. Examples of technical expertise levels include, but are not limited to, knowledge test results and learning histories. For example, the suggestion unit can use simpler terms for students with less technical expertise. The suggestion unit can also use more detailed technical terminology for students with more specialized expertise. The suggestion unit can also adjust the technical terminology in the proposal results according to the student's level of expertise. For example, the suggestion unit can use simpler terms to provide easy-to-understand proposals for students with less specialized expertise. The suggestion unit can use more detailed technical terminology to provide more detailed proposals for students with more specialized expertise. The suggestion unit can also adjust the technical terminology in the proposal results according to the student's level of expertise to provide easy-to-understand proposals. As a result, by adjusting the technical terminology according to the student's level of expertise, it is possible to provide easy-to-understand proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the student's expertise level data into the generation AI and have the generation AI adjust the terminology.

[0088] The support unit can estimate a student's emotions and adjust support methods based on the estimated student emotions. The support unit uses generative AI to estimate the student's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the support unit can capture the student's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The support unit can also record the student's voice and estimate the emotion using voice analysis technology. The support unit can also analyze the student's text input to estimate the emotion. Based on the estimated emotion, the support unit can adjust the support method. For example, if the student is nervous, the support unit can provide a support method that helps the student relax. If the student is relaxed, the support unit can provide a detailed support method. If the student is excited, the support unit can provide a visually stimulating support method. This allows the support method to be adjusted according to the student's emotions, thereby providing more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit may input image data of a student taken with a camera into the generation AI and cause the generation AI to estimate the student's emotions.

[0089] The support unit can analyze a student's past support history and select the optimal support method when providing support. The support unit uses the generation AI to analyze a student's past support history and select the optimal support method when providing support. The support history includes, but is not limited to, past support content, effects, and feedback. For example, the support unit can propose the optimal support method based on the support methods the student received in the past. The support unit can also select an effective support method from the student's past support history. Furthermore, the support unit can improve the support method by referring to the student's past support history. For example, the support unit can propose the optimal support method based on the support methods the student received in the past and provide support efficiently. The support unit can select an effective support method from the student's past support history and provide support efficiently. Furthermore, the support unit can improve the support method by referring to the student's past support history and provide more effective support. By selecting the optimal support method based on the past support history, effective support is possible. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support department can input a student's past support history data into the generation AI and have the generation AI select the optimal support method.

[0090] The support unit can customize support measures based on the student's current learning situation when providing support. The support unit uses the generation AI to customize support measures based on the student's current learning situation when providing support. Learning situations include, but are not limited to, grades, progress, and learning content. For example, the support unit provides appropriate support measures according to the student's current learning progress. The support unit can also suggest an optimal support method based on the student's learning situation. Furthermore, the support unit can customize support measures taking into account the student's learning situation. For example, the support unit can provide appropriate support measures according to the student's current learning progress and provide efficient support. The support unit can also suggest an optimal support method based on the student's learning situation and support the student's learning. Furthermore, the support unit can customize support measures taking into account the student's learning situation and support the student's learning. In this way, by customizing support measures based on the student's learning situation, more appropriate support can be provided. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support department can input student learning status data into the generation AI and have the generation AI customize the support methods.

[0091] The support unit can improve the support method by reflecting student feedback during support. The support unit uses the generation AI to improve the support method by reflecting student feedback during support. Feedback includes, but is not limited to, past evaluations, comments, and areas for improvement. For example, the support unit improves the support method based on student feedback. The support unit can also select the optimal support method based on students' past feedback. Furthermore, the support unit can customize the support method by reflecting student feedback. For example, the support unit improves the support method based on student feedback and provides support efficiently. The support unit can select the optimal support method based on students' past feedback and provide support efficiently. Furthermore, the support unit can customize the support method by reflecting student feedback and provide support that meets the student's needs. In this way, the support method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input student feedback data into the generation AI and have the generation AI improve the support method.

[0092] The support unit can estimate a student's emotions and prioritize support based on the estimated student's emotions. The support unit uses generative AI to estimate the student's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the support unit can capture the student's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The support unit can also record the student's voice and estimate the emotion using voice analysis technology. The support unit can also analyze the student's text input to estimate the emotion. Based on the estimated emotions, the support unit can prioritize support. For example, if a student is feeling anxious, it can prioritize providing support that provides a sense of security. If a student is excited, it can prioritize providing support that is interesting. If a student is relaxed, it can prioritize providing detailed support. This allows for more appropriate support to be provided by prioritizing support based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit may input image data of a student taken with a camera into the generation AI and cause the generation AI to estimate the student's emotions.

[0093] The support unit can select the optimal support method by taking into account the student's geographical location information when providing support. The support unit uses the generation AI to select the optimal support method by taking into account the student's geographical location information when providing support. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the support unit provides support by taking into account trends in the job market in the area where the student lives. The support unit can also provide support related to the curriculum of the school the student attends. Furthermore, the support unit can provide support based on information about the area in which the student is interested. For example, the support unit provides support by taking into account trends in the job market in the area where the student lives, thereby providing highly relevant support. The support unit can also provide support related to the curriculum of the school the student attends, thereby providing efficient support. Furthermore, the support unit can provide support based on information about the area in which the student is interested, thereby attracting the student's interest. In this way, highly relevant support can be provided by taking into account the geographical location information. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without using AI. For example, the support department can input a student's geographic location data into the generation AI and have the generation AI select the optimal support method.

[0094] The support unit can analyze the student's social media activity and suggest support measures during support. The support unit uses generative AI to analyze the student's social media activity and suggest support measures during support. Social media activity includes, but is not limited to, post content, follower count, and engagement. For example, the support unit provides support related to occupations that the student has expressed interest in on social media. The support unit can also suggest suitable occupations based on the student's social media activity. Furthermore, the support unit can provide relevant support by referring to the activity of the student's friends on social media. For example, the support unit can provide support related to occupations that the student has expressed interest in on social media, thereby providing efficient support. The support unit can also suggest suitable occupations based on the student's social media activity to attract the student's interest. Furthermore, the support unit can provide relevant support by referring to the activity of the student's friends on social media. In this way, highly relevant support can be provided by analyzing social media activity. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support department can input students' social media activity data into the generation AI and have the generation AI suggest support methods.

[0095] The support unit can customize the support method by reflecting the student's past feedback when providing support. The support unit uses the generation AI to customize the support method by reflecting the student's past feedback when providing support. Feedback includes, but is not limited to, past evaluations, comments, and areas for improvement. For example, the support unit improves the support method based on the student's past feedback. The support unit can also select the optimal support method from the student's past feedback. Furthermore, the support unit can customize the support method by reflecting the student's feedback. For example, the support unit can improve the support method based on the student's past feedback and provide support efficiently. The support unit can select the optimal support method from the student's past feedback and provide support efficiently. Furthermore, the support unit can customize the support method by reflecting the student's feedback and provide support that meets the student's needs. In this way, the support method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without using AI. For example, the support department can input student feedback data into the generation AI and have the generation AI customize the support method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can detect the student's facial expression and voice using the camera 42 and microphone 38B of the smart device 14, and estimate the student's emotion using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the student's input information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides the student with a career path suggested by the generation AI. The support unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and reduces the workload of parents and teachers and provides remote support. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can detect a student's facial expression and voice using the camera 42 and microphone 238 of the smart glasses 214 and estimate their emotions using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the student's input information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides the student with a career path suggested by the generation AI. The support unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and reduces the workload of parents and teachers and provides remote support. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, analysis unit, suggestion unit, and support unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can detect the student's facial expression and voice using the camera 42 and microphone 238 of the headset-type terminal 314 and estimate the student's emotion using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the student's input information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides the student with a career path suggested by the generation AI. The support unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and reduces the workload of parents and teachers and provides remote support. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, suggestion unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can detect the student's facial expressions and voice using the camera 42 and microphone 238 of the robot 414 and estimate the student's emotions using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the student's input information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides the student with a career path suggested by the generation AI. The support unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and reduces the workload of parents and teachers and provides remote support.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can estimate a student's learning style based on the student's input information and adjust the way information is presented based on the estimated learning style. For example, students with a visual learning style can be provided with information that makes extensive use of graphs and charts. Students with an auditory learning style can be provided with information in the form of audio guides or podcasts. Furthermore, students with an experiential learning style can be provided with interactive simulations and practical assignments. This makes it possible to present information according to each student's learning style, improving learning effectiveness.

[0098] The reception unit can estimate the student's emotions and customize the information input interface based on the estimated student emotions. For example, if the student is nervous, a simple and intuitive interface can be provided. Alternatively, if the student is relaxed, an interface that allows detailed information input can be provided. Furthermore, if the student is excited, a visually appealing interface can be provided. In this way, the efficiency of information input can be improved by providing an interface that corresponds to the student's emotions.

[0099] The reception unit can analyze a student's past input history and suggest the optimal input timing. For example, if a student has previously input data at night, it can send a notification encouraging them to input data at night. Also, if a student has previously input data on weekends, it can send a notification encouraging them to input data on weekends. Furthermore, if a student has concentrated their input data during a specific time period, it can send a notification encouraging them to input data during that time period. This improves the efficiency of information input by suggesting the optimal input timing based on the student's past input history.

[0100] The reception department can provide relevant learning resources based on the student's current learning situation and areas of interest. For example, it can provide online courses and materials related to the subject the student is currently studying. It can also recommend related books and papers based on the student's areas of interest. It can also provide learning resources of an appropriate level of difficulty according to the student's learning progress. This improves learning effectiveness by providing appropriate learning resources based on the student's learning situation and areas of interest.

[0101] When entering information, the reception department can provide input assistance tools according to the student's input method. For example, voice recognition software can be provided to students who wish to input by voice. A keyboard with a predictive conversion function can also be provided to students who wish to input by text. Furthermore, image recognition software can be provided to students who wish to input by image. In this way, the efficiency of information entry can be improved by providing input assistance tools according to the student's input method.

[0102] The reception unit can estimate the student's emotions and adjust the feedback for information input based on the estimated student's emotions. For example, if the student is feeling anxious, positive feedback can be provided preferentially. Also, if the student is relaxed, detailed feedback can be provided. Furthermore, if the student is excited, visually appealing feedback can be provided. In this way, providing feedback according to the student's emotions improves the student's motivation to input information.

[0103] When entering information, the reception unit can provide region-specific career information based on the student's geographic location information. For example, it can provide information on major industries and employment opportunities in the area where the student lives. It can also provide information on local universities and vocational schools. It can also provide information on local companies and internships. This makes it possible to propose more specific career paths by providing region-specific career information based on the student's geographic location information.

[0104] When entering information, the reception department can analyze the student's social media activity and suggest relevant career events and seminars. For example, it can suggest events related to careers that the student has expressed interest in on social media. It can also suggest events that the student's followers and friends are attending. Furthermore, it can suggest seminars related to careers that the student is suited to based on the student's social media activity. This makes it possible to suggest highly relevant career events and seminars by analyzing social media activity.

[0105] The reception unit can customize the input interface by reflecting students' past feedback when they input information. For example, it can improve the design and functionality of the interface based on feedback provided by students in the past. It can also suggest the optimal input method based on students' past feedback. It can also improve the ease of use of the input interface by reflecting students' feedback. In this way, the input interface can be optimized by reflecting past feedback, and the efficiency of information input can be improved.

[0106] The analysis unit can estimate the student's emotions and adjust the way in which the analysis results are presented based on the estimated student's emotions. For example, if the student is nervous, it can provide simple, highly visible analysis results. If the student is relaxed, it can provide detailed analysis results. Furthermore, if the student is excited, it can provide visually stimulating analysis results. In this way, by adjusting the way in which the analysis results are presented according to the student's emotions, it is possible to provide more appropriate analysis results.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception department allows students to input information such as their interests and desired future career. The information students input includes their academic field, hobbies, activities, and desired future career. For example, students input information in the form of answering questions such as "Science or Humanities?", "What do you like?", and "What career do you want to pursue in the future?". The reception department can also use generative AI to convert the information students input into a format that is easier to analyze. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is based on the data analysis method and algorithm used. For example, the generation AI considers the student's interests and aptitudes, future job market trends, etc., to suggest the optimal career path. The generation AI can analyze the input information using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The suggestion unit provides the student with the career path proposed by the generative AI. The suggestion is based on specific career paths and the required skills and qualifications. For example, the suggestion unit provides the student with the career path proposed by the generative AI and helps the student take specific actions based on that career path. Step 4: The support department will reduce the workload of parents, teachers, universities, and others and provide remote support. Support will be based on reducing work time and reducing the burden. For example, the support department will use generative AI to provide appropriate advice to students, eliminating the need for parents and teachers to respond individually. The support department will also provide remote support using the internet, allowing it to support students without being restricted by geography.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where students input information such as their interests and future career aspirations, an analysis unit that analyzes the information input by the reception unit and suggests career paths suitable for students; a suggestion unit that provides students with career paths suggested by the analysis unit; We have a support department that reduces the workload of parents, teachers, universities, etc. A system characterized by:

2. The reception unit Estimate the student's emotions and adjust the timing of information input based on the estimated student emotions.

2. The system of claim 1.

3. The reception unit Analyze students' past input history and select the most appropriate input method 2. The system of claim 1.

4. The reception unit As information is entered, it is filtered based on the student's current learning status and areas of interest 2. The system of claim 1.

5. The reception unit When entering information, select the appropriate input method depending on the student's input method.

2. The system of claim 1.

6. The reception unit Estimate the student's feelings and prioritize the information to be entered based on the estimated student's feelings 2. The system of claim 1.

7. The reception unit Prioritize relevant information based on student geographic location when entering information 2. The system of claim 1.

8. The reception unit When entering information, analyze students' social media activity and enter relevant information 2. The system of claim 1.

Images