system

Abstract

The system according to the embodiment aims to suggest appropriate schools and departments based on the user's wishes and requests, and to provide information from prospective students. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs a user's wishes and requests. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes appropriate schools and departments based on the information analyzed by the analysis unit. The provision unit provides information from prospective students according to the schools and departments proposed by the proposal unit.

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 does not adequately support the selection of schools and the provision of information from students entering higher education, so there is room for improvement.

[0005] The system according to the embodiment aims to suggest appropriate schools and departments based on the user's wishes and requests, and to provide information from prospective students. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs the user's wishes and requests. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes appropriate schools and departments based on the information analyzed by the analysis unit. The provision unit provides information from prospective students according to the schools and departments proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate schools and departments based on the user's wishes and requests, and provide information from prospective students. [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) A school selection support system according to an embodiment of the present invention proposes optimal schools and departments based on users' input of their hopes and desires (e.g., what they want to study, what they want to do, location, tuition fees, etc.). The system uses AI tools to analyze user input and propose optimal schools and departments. Furthermore, depending on the selected school, the system shares recommended study methods and tips for passing collected from recent students who have advanced to the university. For example, if a user inputs specific requests such as "I want to attend a science department in Tokyo" or "I'm looking for a school with low tuition fees," the AI ​​tool analyzes the information and suggests candidates such as the University of Tokyo and the Tokyo Institute of Technology. Schools with comprehensive scholarship programs and relatively low tuition fees are also suggested. Furthermore, depending on the selected school, study methods and tips for passing from students who have been accepted to the University of Tokyo are provided. This allows users to study efficiently and increase their chances of passing. This system allows users to find schools and departments that match their hopes and desires, enabling them to study efficiently. Furthermore, by utilizing information from recent students who have advanced to the university, the path to success can be clearly defined. This allows the school selection support system to suggest the most suitable schools and departments based on the user's wishes and requests, and by providing information from students who are going on to higher education, it is possible to efficiently select a school to attend.

[0029] A school selection support system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs a user's wishes and requests. The user's wishes and requests include, but are not limited to, a desired school, a choice of department, and a request for tuition fees. The reception unit allows the user to input specific requests, such as, for example, "I want to attend a science department at a university in Tokyo" or "I'm looking for a school with low tuition fees." The analysis unit analyzes the information input by the reception unit. The analysis may be performed using, for example, text analysis, data mining, machine learning algorithms, or other methods, but is not limited to these. The analysis unit performs text analysis to propose optimal schools and departments based on the user's wishes and requests. The analysis unit may also use data mining technology to extract data related to the user's wishes and requests. The analysis unit may also use machine learning algorithms to build a model for proposing optimal schools and departments based on the user's wishes and requests. The proposal unit proposes optimal schools and departments based on the information analyzed by the analysis unit. Suggestions are made based on criteria such as, for example, the user's academic ability, interests, and future career path, but are not limited to these examples. The suggestion unit, for example, suggests the most suitable school or department based on the user's academic ability. The suggestion unit can also suggest related schools and departments based on the user's interests. The suggestion unit can also suggest appropriate schools and departments based on the user's future career path. The providing unit provides information from the students continuing on to higher education in accordance with the schools and departments suggested by the suggestion unit. The information from the students continuing on to higher education includes, for example, evaluations of the school, their learning experiences, and employment status, but is not limited to these examples. The providing unit, for example, provides evaluations of the school. The providing unit can also share the learning experiences of the students continuing on to higher education. The providing unit can also provide information on the employment status of the students continuing on to higher education. As a result, the school selection support system according to the embodiment can efficiently select a school to continue on to by suggesting the most suitable school or department based on the user's wishes and requests and providing information from the students continuing on to higher education.

[0030] The reception unit can analyze the user's past history of wishes and requests and select an appropriate input method. For example, the reception unit can automatically display wishes and requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest wishes and requests to be used in a specific time period based on the user's past history of wishes and requests. In this way, by analyzing the past history, the optimal input method can be provided to the user. 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 can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0031] When inputting hopes and requests, the reception unit can filter the requests based on the user's current learning situation and areas of interest. For example, the reception unit can prioritize displaying hopes and requests related to the subject the user is currently studying. The reception unit can also filter and display information about related schools and faculties based on the user's areas of interest. The reception unit can also provide appropriate input options for hopes and requests depending on the user's learning progress. This makes it possible to provide appropriate information based on the user's learning situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's learning situation data into a generation AI and have the generation AI perform the filtering.

[0032] When inputting a user's hopes and requests, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize displaying information about nearby schools and departments based on the user's current location. The reception unit can also suggest schools and departments within commuting distance based on the user's geographical location information. The reception unit can also provide region-specific scholarship information and tuition assistance information taking into account the user's geographical location information. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's location information data into a generation AI and cause the generation AI to select highly relevant information.

[0033] When inputting a user's hopes and requests, the reception unit can analyze the user's social media activity and input relevant information. For example, the reception unit can analyze the user's social media posts and suggest faculties or schools of interest. The reception unit can also suggest schools or faculties attended by friends based on the user's social media following relationships. The reception unit can also provide information about relevant events and open campus events based on the user's social media activity history. In this way, by analyzing social media activity, relevant information can be provided to the user. Some or all of the above-described processing by 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 user's social media data into a generation AI and have the generation AI select relevant information.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the wishes and requests. For example, the analysis unit can provide detailed analysis results for wishes and requests with high importance. The analysis unit can also provide concise analysis results for wishes and requests with low importance. The analysis unit can also determine the priority of the analysis and adjust the level of detail according to the importance of the wishes and requests. This makes it possible to provide appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the wishes and requests. 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 the user's wishes and requests data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of hopes and requests. For example, the analysis unit can apply an education-related analysis algorithm to hopes and requests regarding what the user wants to learn. The analysis unit can also apply a career-related analysis algorithm to hopes and requests regarding what the user wants to do. The analysis unit can also apply an economics-related analysis algorithm to hopes and requests regarding tuition fees. This allows for the application of an appropriate analysis algorithm depending on the category of hopes and requests, thereby providing highly accurate analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's hopes and requests data into the generation AI and have the generation AI apply the analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the hopes and requests. For example, the analysis unit prioritizes the analysis of the most recently submitted hopes and requests. The analysis unit can also postpone the analysis of hopes and requests that were submitted earlier. The analysis unit can also adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time. 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 the user's submission time data into the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the wishes and requests. For example, the analysis unit prioritizes analysis of highly relevant wishes and requests. The analysis unit can also postpone analysis of less relevant wishes and requests. The analysis unit can also adjust the order of analysis based on the relevance of the wishes and requests. This enables efficient analysis by adjusting the order of analysis based on the relevance. 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 the user's wishes and requests data into the generation AI and have the generation AI adjust the order of analysis.

[0038] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the school or department. For example, the suggestion unit provides detailed proposals for schools or departments with high importance. The suggestion unit can also provide concise proposals for schools or departments with low importance. The suggestion unit can also determine the priority of the proposal and adjust the level of detail according to the importance of the school or department. This enables appropriate proposals to be made by adjusting the level of detail of the proposal according to the importance of the school or department. 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 importance data of schools and departments into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0039] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of school or faculty. For example, the proposal unit can apply a science-related proposal algorithm to a proposal related to a science faculty. The proposal unit can also apply a humanities-related proposal algorithm to a proposal related to a humanities faculty. The proposal unit can also apply an arts-related proposal algorithm to a proposal related to an arts faculty. This enables highly accurate proposals by applying an appropriate proposal algorithm depending on the category of school or faculty. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input school and faculty category data into a generation AI and cause the generation AI to apply a proposal algorithm.

[0040] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission dates of the schools and faculties. For example, the proposal unit can prioritize the most recently submitted schools and faculties. The proposal unit can also postpone the proposal of schools and faculties that were submitted earlier. The proposal unit can also adjust the proposal schedule based on the submission dates. This enables efficient proposals by determining the priority of proposals based on the submission dates. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input submission date data of schools and faculties into the generation AI and have the generation AI determine the priority of the proposals.

[0041] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the schools and faculties. For example, the proposal unit prioritizes the proposal of highly relevant schools and faculties. The proposal unit can also postpone the proposal of less relevant schools and faculties. The proposal unit can also adjust the order of proposals based on the relevance of the schools and faculties. This enables efficient proposals by adjusting the order of proposals based on the relevance. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input relevance data of schools and faculties into a generation AI and cause the generation AI to adjust the order of proposals.

[0042] At the time of provision, the provision unit can analyze the past information of the student going on to higher education and select an appropriate provision method. The provision unit, for example, can suggest the optimal study method based on the past information of the student going on to higher education. The provision unit can also analyze the past information of the student going on to higher education and provide tips for passing the exam. The provision unit can also select the optimal information provision method by referring to the past information of the student going on to higher education. In this way, the optimal provision method can be selected by analyzing past information. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the past information data of the student going on to higher education into a generation AI and have the generation AI select the provision method.

[0043] The providing unit can customize the information to be provided based on the student's current situation at the time of providing the information. The providing unit can, for example, suggest appropriate study methods based on the student's current learning progress. The providing unit can also provide tuition and scholarship information based on the student's current living situation. The providing unit can also provide health management information based on the student's current health condition. This makes it possible to provide appropriate information based on the student's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the student's current situation data into the generating AI and have the generating AI customize the information.

[0044] The provision unit can select the optimal provision method by taking into consideration the geographical location information of the student when providing the information. For example, the provision unit can prioritize providing information on nearby schools and faculties based on the geographical location information of the student. The provision unit can also suggest schools and faculties within commuting distance by taking into consideration the geographical location information of the student. The provision unit can also provide region-specific scholarship information and tuition assistance information based on the geographical location information of the student. This makes it possible to provide optimal information based on the geographical location information. Some or all of the above-mentioned processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the geographical location information data of the student to the generation AI and have the generation AI select the provision method.

[0045] At the time of providing the information, the providing unit can analyze the social media activity of the prospective student and suggest information to be provided. For example, the providing unit can analyze the content of the prospective student's social media posts and suggest faculties or schools of interest. The providing unit can also suggest schools or faculties attended by the prospective student's friends based on the prospective student's social media following relationships. The providing unit can also provide information about related events and open campus events based on the prospective student's social media activity history. In this way, by analyzing social media activity, relevant information can be provided to the prospective student. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the prospective student's social media data into a generation AI and have the generation AI execute information suggestions.

[0046] When entering details, the detail input unit can select the optimal input method by referring to the user's past input history. For example, the detail input unit can automatically display details that the user has frequently entered in the past as candidates. The detail input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The detail input unit can also predict and suggest details to be used in a specific time period based on the user's past input history. This makes it possible to provide the optimal input method by referring to the past input history. Some or all of the above-described processing in the detail input unit may be performed using, or without, AI, for example. For example, the detail input unit can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0047] The detailed input unit can select the optimal input method by taking into account the user's device information when entering details. For example, if the user is using a smartphone, the detailed input unit can provide an input method that matches the screen size. Furthermore, if the user is using a tablet, the detailed input unit can also provide an input method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the detailed input unit can also provide an input method that is simple and highly visible. This makes it possible to provide the optimal input method based on the device information. Some or all of the above-described processing in the detailed input unit may be performed using, for example, AI, or may be performed without using AI. For example, the detailed input unit can input the user's device information data to the generation AI and cause the generation AI to select the optimal input method.

[0048] When selecting an analysis algorithm, the analysis algorithm unit can select the optimal algorithm by referring to past analysis data. The analysis algorithm unit, for example, selects the most effective algorithm based on past analysis data. The analysis algorithm unit can also analyze past analysis data and select the most efficient algorithm. The analysis algorithm unit can also select the optimal analysis algorithm by referring to past analysis data. In this way, the optimal analysis algorithm can be selected by referring to past analysis data. Some or all of the above-mentioned processing in the analysis algorithm unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis algorithm unit can input past analysis data to the generation AI and cause the generation AI to select the optimal algorithm.

[0049] When selecting an analysis algorithm, the analysis algorithm unit can weight the algorithm based on the submission date of the hopes and requests. For example, the analysis algorithm unit can assign a higher weight to the hopes and requests submitted most recently. The analysis algorithm unit can also assign a lower weight to the hopes and requests submitted earlier. The analysis algorithm unit can also adjust the weighting of the analysis algorithm based on the submission date. This enables appropriate analysis by weighting the algorithm based on the submission date. Some or all of the above-described processing in the analysis algorithm unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis algorithm unit can input data on the submission date of the hopes and requests to the generation AI and have the generation AI execute the weighting of the algorithm.

[0050] When providing information, the information providing unit can select the optimal information providing method by referring to the user's past information providing history. For example, the information providing unit can prioritize providing information providing methods that the user has frequently used in the past. The information providing unit can also select the optimal information providing method based on the user's past information providing history. The information providing unit can also analyze the user's past information providing history and select the most effective information providing method. In this way, the optimal information providing method can be selected by referring to the past information providing history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past information providing history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0051] When providing information, the information providing unit can select the optimal information providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the information providing unit can provide an information providing method that matches the screen size. Furthermore, if the user is using a tablet, the information providing unit can also provide an information providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the information providing unit can also provide an information providing method that is concise and highly visible. This makes it possible to provide the optimal information providing method based on the device information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal information providing method.

[0052] When collecting information, the information collection unit can select the optimal collection method by referring to the student's past information collection history. For example, the information collection unit selects the optimal information collection method based on the student's past information collection history. The information collection unit can also analyze the student's past information collection history and select the most effective information collection method. The information collection unit can also select the optimal information collection method by referring to the student's past information collection history. In this way, the optimal collection method can be provided by referring to the past information collection history. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the student's past information collection history data into the generation AI and have the generation AI select the optimal collection method.

[0053] When collecting information, the information collection unit can select the optimal collection method taking into account the geographical location information of the student. For example, the information collection unit prioritizes collecting information on nearby schools and faculties based on the geographical location information of the student. The information collection unit can also collect schools and faculties within commuting distance taking into account the geographical location information of the student. The information collection unit can also collect region-specific scholarship information and tuition assistance information based on the geographical location information of the student. This allows optimal information to be collected based on the geographical location information. Some or all of the above-mentioned processing by the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the geographical location information data of the student into the generation AI and have the generation AI select the optimal collection method.

[0054] When selecting a delivery method, the delivery method can select the optimal method by referring to the student's past delivery method history. For example, the delivery method selects the optimal delivery method based on the student's past delivery method history. The delivery method can also analyze the student's past delivery method history and select the most effective delivery method. The delivery method can also select the optimal delivery method by referring to the student's past delivery method history. In this way, the optimal delivery method can be selected by referring to the past delivery method history. Some or all of the above-mentioned processing in the delivery method can be performed, for example, using AI, or can be performed without using AI. For example, the delivery method can input the student's past delivery method history data into a generation AI and have the generation AI select the optimal delivery method.

[0055] The provision method can select the optimal provision method by taking into consideration the geographical location information of the student. For example, the provision method can prioritize providing information on nearby schools and faculties based on the geographical location information of the student. The provision method can also provide schools and faculties within a commuting distance based on the geographical location information of the student. The provision method can also provide region-specific scholarship information and tuition assistance information based on the geographical location information of the student. This makes it possible to provide optimal information based on geographical location information. Some or all of the above-described processing in the provision method can be performed using, for example, AI, or can be performed without using AI. For example, the provision method can input the geographical location information data of the student into a generation AI and have the generation AI select the optimal provision method.

[0056] During privacy protection, the privacy protection unit can select the optimal protection method by referring to the user's past privacy protection history. For example, the privacy protection unit selects the optimal protection method based on the user's past privacy protection history. The privacy protection unit can also analyze the user's past privacy protection history and select the most effective protection method. The privacy protection unit can also select the optimal protection method by referring to the user's past privacy protection history. In this way, the optimal protection method can be selected by referring to the past privacy protection history. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input the user's past privacy protection history data into a generation AI and have the generation AI select the optimal protection method.

[0057] The privacy protection unit can select the optimal privacy protection method by taking into account the user's device information during privacy protection. For example, if the user is using a smartphone, the privacy protection unit can provide a privacy protection method tailored to the screen size. Furthermore, if the user is using a tablet, the privacy protection unit can provide a privacy protection method optimized for a large screen. Furthermore, if the user is using a smartwatch, the privacy protection unit can provide a simple and highly visible privacy protection method. This makes it possible to provide the optimal privacy protection method based on the device information. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal protection method.

[0058] When providing input guidance, the input guidance unit can select the optimal guidance method by referring to the user's past input guidance history. The input guidance unit selects the optimal guidance method based on, for example, the user's past input guidance history. The input guidance unit can also analyze the user's past input guidance history and select the most effective guidance method. The input guidance unit can also select the optimal guidance method by referring to the user's past input guidance history. In this way, the optimal guidance method can be provided by referring to the past input guidance history. Some or all of the above-described processing in the input guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the input guidance unit can input the user's past input guidance history data to a generation AI and cause the generation AI to select the optimal guidance method.

[0059] When providing input guidance, the input guide unit can select the optimal guidance method by taking into account the user's device information. For example, if the user is using a smartphone, the input guide unit provides input guidance tailored to the screen size. Furthermore, if the user is using a tablet, the input guide unit can provide input guidance optimized for a large screen. Furthermore, if the user is using a smartwatch, the input guide unit can provide input guidance that is concise and highly visible. This makes it possible to provide the optimal guidance method based on the device information. Some or all of the above-described processing in the input guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the input guide unit can input the user's device information data into a generation AI and cause the generation AI to select the optimal guidance method.

[0060] When displaying a flowchart, the flowchart unit can select the optimal display method by referring to the user's past operation history. The flowchart unit, for example, selects the optimal flowchart display method based on the user's past operation history. The flowchart unit can also analyze the user's past operation history and select the most effective flowchart display method. The flowchart unit can also select the optimal flowchart display method by referring to the user's past operation history. In this way, the optimal flowchart display method can be provided by referring to the past operation history. Some or all of the above-mentioned processing in the flowchart unit may be performed using, for example, AI, or may be performed without using AI. For example, the flowchart unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal display method.

[0061] When displaying a flowchart, the flowchart unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the flowchart unit can provide a flowchart that matches the screen size. Furthermore, if the user is using a tablet, the flowchart unit can provide a flowchart that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the flowchart unit can provide a concise and highly visible flowchart. This allows the optimal flowchart display method to be provided based on the device information. Some or all of the above-described processing in the flowchart unit may be performed using, for example, AI, or may be performed without using AI. For example, the flowchart unit can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The school selection support system can further include a learning style analysis unit that suggests the most suitable school or department based on the user's learning style. The learning style analysis unit collects and analyzes data on the user's preferred learning methods and learning materials in the past. For example, if the user prefers visual learning, the system can suggest schools or departments with a wide range of visual learning materials. If the user prefers group learning, the system can also suggest schools or departments with many opportunities for group learning. Furthermore, if the user prefers online learning, the system can suggest schools or departments with a wide range of online courses. This makes it possible to maximize learning effectiveness by suggesting schools and departments that suit the user's learning style.

[0064] The school selection support system can further include a career path analysis unit that suggests the most suitable schools and departments based on the user's future career path. The career path analysis unit collects and analyzes data related to the user's desired occupation and industry. For example, if the user is aiming to become an engineer, the system can suggest schools and departments with a strong engineering program. If the user is interested in the medical field, the system can also suggest schools and departments with a strong medical-related program. Furthermore, if the user is interested in the business field, the system can suggest schools and departments with a strong business program. This allows the system to support the user's career success by suggesting schools and departments that match the user's future career path.

[0065] The reception unit can provide options for inputting hopes and requests based on the user's current learning situation and areas of interest. For example, hopes and requests related to the subject the user is currently studying can be displayed with priority. Information on related schools and departments can also be filtered and displayed based on the user's areas of interest. Furthermore, the reception unit can provide appropriate options for inputting hopes and requests depending on the user's learning progress. This makes it possible to provide appropriate information based on the user's learning situation and areas of interest.

[0066] The school selection support system can further include a learning progress tracking unit that tracks the user's learning progress and suggests the most suitable school or department based on the progress. The learning progress tracking unit collects and analyzes data on the subjects and grades the user is studying. For example, if the user is achieving excellent grades in a particular subject, the unit can suggest schools and departments related to that subject. Also, if the user has a subject that they are weak at, the unit can suggest schools and departments that offer comprehensive support for that subject. Furthermore, the unit can provide appropriate study methods and resources according to the user's learning progress. This can maximize learning effectiveness by suggesting the most suitable school or department based on the user's learning progress.

[0067] The reception unit can analyze the user's social media activity and input related information. For example, it can analyze the content of the user's social media posts and suggest faculties or schools of interest. It can also suggest schools or faculties attended by friends based on the user's social media following. It can also provide information about related events and open campuses based on the user's social media activity history. In this way, it is possible to provide information relevant to the user by analyzing social media activity.

[0068] The school selection support system can further include a learning environment analysis unit that suggests the most suitable school or department based on the user's learning environment. The learning environment analysis unit collects and analyzes data related to the location and time of day the user studies. For example, if the user often studies at home, the system can suggest schools and departments that offer a wide range of online courses suitable for home study. If the user often studies in libraries or cafes, the system can also suggest schools and departments that offer such learning environments. Furthermore, if the user often studies at night, the system can suggest schools and departments that offer a wide range of night courses. This makes it possible to maximize learning effectiveness by suggesting the most suitable school or department based on the user's learning environment.

[0069] During analysis, the analysis unit can apply different analysis algorithms depending on the category of hopes and desires. For example, an education-related analysis algorithm can be applied to hopes and desires regarding what a person wants to learn. A career-related analysis algorithm can also be applied to hopes and desires regarding what a person wants to do. Furthermore, an economics-related analysis algorithm can be applied to hopes and desires regarding tuition fees. This makes it possible to provide highly accurate analysis results by applying the appropriate analysis algorithm depending on the category of hopes and desires.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit inputs the user's wishes and requests. The user's wishes and requests include the desired school, choice of faculty, and tuition fees. For example, the user can input specific requests such as "I want to go to a science faculty at a university in Tokyo" or "I'm looking for a school with low tuition fees." Step 2: The analysis unit analyzes the information input by the reception unit. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis is performed to suggest the most suitable schools and departments based on the user's wishes and requests. Data mining techniques can also be used to extract data related to the user's wishes and requests. Furthermore, machine learning algorithms can be used to build models to suggest the most suitable schools and departments based on the user's wishes and requests. Step 3: The suggestion unit suggests the most suitable schools and departments based on the information analyzed by the analysis unit. The suggestions are made based on criteria such as the user's academic ability, interests, and future career path. For example, the suggestion unit suggests the most suitable schools and departments based on the user's academic ability. It can also suggest related schools and departments based on the user's interests. It can also suggest appropriate schools and departments based on the user's future career path. Step 4: The provision unit provides information from the students who will be continuing on to the next school according to the schools and faculties proposed by the proposal unit. The information from the students who will be continuing on to the next school includes evaluations of the school, their learning experiences, and employment status. For example, the provision unit provides evaluations of the school they will be continuing on to. It can also share the learning experiences of the students who will be continuing on to the next school. It can also provide information on the employment status of the students who will be continuing on to the next school.

[0072] (Example 2) A school selection support system according to an embodiment of the present invention proposes optimal schools and departments based on users' input of their hopes and desires (e.g., what they want to study, what they want to do, location, tuition fees, etc.). The system uses AI tools to analyze user input and propose optimal schools and departments. Furthermore, depending on the selected school, the system shares recommended study methods and tips for passing collected from recent students who have advanced to the university. For example, if a user inputs specific requests such as "I want to attend a science department in Tokyo" or "I'm looking for a school with low tuition fees," the AI ​​tool analyzes the information and suggests candidates such as the University of Tokyo and the Tokyo Institute of Technology. Schools with comprehensive scholarship programs and relatively low tuition fees are also suggested. Furthermore, depending on the selected school, study methods and tips for passing from students who have been accepted to the University of Tokyo are provided. This allows users to study efficiently and increase their chances of passing. This system allows users to find schools and departments that match their hopes and desires, enabling them to study efficiently. Furthermore, by utilizing information from recent students who have advanced to the university, the path to success can be clearly defined. This allows the school selection support system to suggest the most suitable schools and departments based on the user's wishes and requests, and by providing information from students who are going on to higher education, it is possible to efficiently select a school to attend.

[0073] A school selection support system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs a user's wishes and requests. The user's wishes and requests include, but are not limited to, a desired school, a choice of department, and a request for tuition fees. The reception unit allows the user to input specific requests, such as, for example, "I want to attend a science department at a university in Tokyo" or "I'm looking for a school with low tuition fees." The analysis unit analyzes the information input by the reception unit. The analysis may be performed using, for example, text analysis, data mining, machine learning algorithms, or other methods, but is not limited to these. The analysis unit performs text analysis to propose optimal schools and departments based on the user's wishes and requests. The analysis unit may also use data mining technology to extract data related to the user's wishes and requests. The analysis unit may also use machine learning algorithms to build a model for proposing optimal schools and departments based on the user's wishes and requests. The proposal unit proposes optimal schools and departments based on the information analyzed by the analysis unit. Suggestions are made based on criteria such as, for example, the user's academic ability, interests, and future career path, but are not limited to these examples. The suggestion unit, for example, suggests the most suitable school or department based on the user's academic ability. The suggestion unit can also suggest related schools and departments based on the user's interests. The suggestion unit can also suggest appropriate schools and departments based on the user's future career path. The providing unit provides information from the students continuing on to higher education in accordance with the schools and departments suggested by the suggestion unit. The information from the students continuing on to higher education includes, for example, evaluations of the school, their learning experiences, and employment status, but is not limited to these examples. The providing unit, for example, provides evaluations of the school. The providing unit can also share the learning experiences of the students continuing on to higher education. The providing unit can also provide information on the employment status of the students continuing on to higher education. As a result, the school selection support system according to the embodiment can efficiently select a school to continue on to by suggesting the most suitable school or department based on the user's wishes and requests and providing information from the students continuing on to higher education.

[0074] The reception unit can estimate the user's emotions and adjust the timing of inputting the user's wishes and requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can simplify the input process to allow the user to complete the process in a short time. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to quickly input their wishes and requests. This allows for more appropriate input by adjusting the input timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0075] The reception unit can analyze the user's past history of wishes and requests and select an appropriate input method. For example, the reception unit can automatically display wishes and requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest wishes and requests to be used in a specific time period based on the user's past history of wishes and requests. In this way, by analyzing the past history, the optimal input method can be provided to the user. 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 can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0076] When inputting hopes and requests, the reception unit can filter the requests based on the user's current learning situation and areas of interest. For example, the reception unit can prioritize displaying hopes and requests related to the subject the user is currently studying. The reception unit can also filter and display information about related schools and faculties based on the user's areas of interest. The reception unit can also provide appropriate input options for hopes and requests depending on the user's learning progress. This makes it possible to provide appropriate information based on the user's learning situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's learning situation data into a generation AI and have the generation AI perform the filtering.

[0077] The reception unit can estimate the user's emotions and determine the priority of input hopes and requests based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize input of important hopes and requests. Furthermore, when the user is relaxed, the reception unit can also prioritize input of detailed hopes and requests. Furthermore, when the user is in a hurry, the reception unit can also prioritize input of the most important hopes and requests. Thus, by determining the priority of hopes and requests according to the user's emotions, important information can be input preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0078] When inputting a user's hopes and requests, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize displaying information about nearby schools and departments based on the user's current location. The reception unit can also suggest schools and departments within commuting distance based on the user's geographical location information. The reception unit can also provide region-specific scholarship information and tuition assistance information taking into account the user's geographical location information. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's location information data into a generation AI and cause the generation AI to select highly relevant information.

[0079] When inputting a user's hopes and requests, the reception unit can analyze the user's social media activity and input relevant information. For example, the reception unit can analyze the user's social media posts and suggest faculties or schools of interest. The reception unit can also suggest schools or faculties attended by friends based on the user's social media following relationships. The reception unit can also provide information about relevant events and open campus events based on the user's social media activity history. In this way, by analyzing social media activity, relevant information can be provided to the user. Some or all of the above-described processing by 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 user's social media data into a generation AI and have the generation AI select relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the wishes and requests. For example, the analysis unit can provide detailed analysis results for wishes and requests with high importance. The analysis unit can also provide concise analysis results for wishes and requests with low importance. The analysis unit can also determine the priority of the analysis and adjust the level of detail according to the importance of the wishes and requests. This makes it possible to provide appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the wishes and requests. 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 the user's wishes and requests data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of hopes and requests. For example, the analysis unit can apply an education-related analysis algorithm to hopes and requests regarding what the user wants to learn. The analysis unit can also apply a career-related analysis algorithm to hopes and requests regarding what the user wants to do. The analysis unit can also apply an economics-related analysis algorithm to hopes and requests regarding tuition fees. This allows for the application of an appropriate analysis algorithm depending on the category of hopes and requests, thereby providing highly accurate analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's hopes and requests data into the generation AI and have the generation AI apply the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the length of the analysis.

[0084] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the hopes and requests. For example, the analysis unit prioritizes the analysis of the most recently submitted hopes and requests. The analysis unit can also postpone the analysis of hopes and requests that were submitted earlier. The analysis unit can also adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time. 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 the user's submission time data into the generation AI and have the generation AI determine the analysis priority.

[0085] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the wishes and requests. For example, the analysis unit prioritizes analysis of highly relevant wishes and requests. The analysis unit can also postpone analysis of less relevant wishes and requests. The analysis unit can also adjust the order of analysis based on the relevance of the wishes and requests. This enables efficient analysis by adjusting the order of analysis based on the relevance. 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 the user's wishes and requests data into the generation AI and have the generation AI adjust the order of analysis.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. This allows for more appropriate suggestions by adjusting the way suggestions are expressed according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0087] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the school or department. For example, the suggestion unit provides detailed proposals for schools or departments with high importance. The suggestion unit can also provide concise proposals for schools or departments with low importance. The suggestion unit can also determine the priority of the proposal and adjust the level of detail according to the importance of the school or department. This enables appropriate proposals to be made by adjusting the level of detail of the proposal according to the importance of the school or department. 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 importance data of schools and departments into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0088] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of school or faculty. For example, the proposal unit can apply a science-related proposal algorithm to a proposal related to a science faculty. The proposal unit can also apply a humanities-related proposal algorithm to a proposal related to a humanities faculty. The proposal unit can also apply an arts-related proposal algorithm to a proposal related to an arts faculty. This enables highly accurate proposals by applying an appropriate proposal algorithm depending on the category of school or faculty. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input school and faculty category data into a generation AI and cause the generation AI to apply a proposal algorithm.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This enables appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using AI, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0090] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission dates of the schools and faculties. For example, the proposal unit can prioritize the most recently submitted schools and faculties. The proposal unit can also postpone the proposal of schools and faculties that were submitted earlier. The proposal unit can also adjust the proposal schedule based on the submission dates. This enables efficient proposals by determining the priority of proposals based on the submission dates. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input submission date data of schools and faculties into the generation AI and have the generation AI determine the priority of the proposals.

[0091] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the schools and faculties. For example, the proposal unit prioritizes the proposal of highly relevant schools and faculties. The proposal unit can also postpone the proposal of less relevant schools and faculties. The proposal unit can also adjust the order of proposals based on the relevance of the schools and faculties. This enables efficient proposals by adjusting the order of proposals based on the relevance. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input relevance data of schools and faculties into a generation AI and cause the generation AI to adjust the order of proposals.

[0092] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can provide detailed information. Furthermore, when the user is in a hurry, the providing unit can provide information that focuses on the main points. This allows important information to be provided preferentially by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of information.

[0093] At the time of provision, the provision unit can analyze the past information of the student going on to higher education and select an appropriate provision method. The provision unit, for example, can suggest the optimal study method based on the past information of the student going on to higher education. The provision unit can also analyze the past information of the student going on to higher education and provide tips for passing the exam. The provision unit can also select the optimal information provision method by referring to the past information of the student going on to higher education. In this way, the optimal provision method can be selected by analyzing past information. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the past information data of the student going on to higher education into a generation AI and have the generation AI select the provision method.

[0094] The providing unit can customize the information to be provided based on the student's current situation at the time of providing the information. The providing unit can, for example, suggest appropriate study methods based on the student's current learning progress. The providing unit can also provide tuition and scholarship information based on the student's current living situation. The providing unit can also provide health management information based on the student's current health condition. This makes it possible to provide appropriate information based on the student's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the student's current situation data into the generating AI and have the generating AI customize the information.

[0095] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This enables highly visible information to be provided by adjusting the information display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the information display method.

[0096] The provision unit can select the optimal provision method by taking into consideration the geographical location information of the student when providing the information. For example, the provision unit can prioritize providing information on nearby schools and faculties based on the geographical location information of the student. The provision unit can also suggest schools and faculties within commuting distance by taking into consideration the geographical location information of the student. The provision unit can also provide region-specific scholarship information and tuition assistance information based on the geographical location information of the student. This makes it possible to provide optimal information based on the geographical location information. Some or all of the above-mentioned processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the geographical location information data of the student to the generation AI and have the generation AI select the provision method.

[0097] At the time of providing the information, the providing unit can analyze the social media activity of the prospective student and suggest information to be provided. For example, the providing unit can analyze the content of the prospective student's social media posts and suggest faculties or schools of interest. The providing unit can also suggest schools or faculties attended by the prospective student's friends based on the prospective student's social media following relationships. The providing unit can also provide information about related events and open campus events based on the prospective student's social media activity history. In this way, by analyzing social media activity, relevant information can be provided to the prospective student. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the prospective student's social media data into a generation AI and have the generation AI execute information suggestions.

[0098] The detail input unit can estimate the user's emotions and adjust the detail input method based on the estimated user emotions. For example, if the user is stressed, the detail input unit simplifies input so that it can be completed in a short time. Furthermore, if the user is relaxed, the detail input unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the detail input unit can prioritize voice input and enable quick detail input. This allows for appropriate input by adjusting the detail input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 detail input unit can be performed using, for example, an AI, or without an AI. For example, the detail input unit can input the user's facial expression data into the generation AI and have the generation AI adjust the detail input method.

[0099] When entering details, the detail input unit can select the optimal input method by referring to the user's past input history. For example, the detail input unit can automatically display details that the user has frequently entered in the past as candidates. The detail input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The detail input unit can also predict and suggest details to be used in a specific time period based on the user's past input history. This makes it possible to provide the optimal input method by referring to the past input history. Some or all of the above-described processing in the detail input unit may be performed using, or without, AI, for example. For example, the detail input unit can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0100] The detail input unit can estimate the user's emotions and prioritize detail input based on the estimated user emotions. For example, when the user is stressed, the detail input unit can prioritize input of important details. Furthermore, when the user is relaxed, the detail input unit can also prioritize input of detailed information. Furthermore, when the user is in a hurry, the detail input unit can prioritize input of the most important details. Thus, by prioritizing detail input according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 detail input unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the detail input unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of detail input.

[0101] The detailed input unit can select the optimal input method by taking into account the user's device information when entering details. For example, if the user is using a smartphone, the detailed input unit can provide an input method that matches the screen size. Furthermore, if the user is using a tablet, the detailed input unit can also provide an input method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the detailed input unit can also provide an input method that is simple and highly visible. This makes it possible to provide the optimal input method based on the device information. Some or all of the above-described processing in the detailed input unit may be performed using, for example, AI, or may be performed without using AI. For example, the detailed input unit can input the user's device information data to the generation AI and cause the generation AI to select the optimal input method.

[0102] The analysis algorithm unit can estimate the user's emotions and select an analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, the analysis algorithm unit can select an algorithm that performs detailed analysis. Furthermore, if the user is in a hurry, the analysis algorithm unit can select an algorithm that performs quick analysis. Furthermore, if the user is excited, the analysis algorithm unit can select an algorithm that provides visually stimulating analysis results. By selecting an analysis algorithm according to the user's emotions, appropriate analysis becomes possible. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 algorithm unit can be performed using, for example, an AI, or without an AI. For example, the analysis algorithm unit can input the user's facial expression data into the generation AI and have the generation AI select an analysis algorithm.

[0103] When selecting an analysis algorithm, the analysis algorithm unit can select the optimal algorithm by referring to past analysis data. The analysis algorithm unit, for example, selects the most effective algorithm based on past analysis data. The analysis algorithm unit can also analyze past analysis data and select the most efficient algorithm. The analysis algorithm unit can also select the optimal analysis algorithm by referring to past analysis data. In this way, the optimal analysis algorithm can be selected by referring to past analysis data. Some or all of the above-mentioned processing in the analysis algorithm unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis algorithm unit can input past analysis data to the generation AI and cause the generation AI to select the optimal algorithm.

[0104] The analysis algorithm unit can estimate the user's emotions and adjust the frequency of application of the analysis algorithm based on the estimated user emotions. For example, if the user is in a hurry, the analysis algorithm unit can apply an algorithm that performs frequent analysis. Furthermore, if the user is relaxed, the analysis algorithm unit can apply an algorithm that performs detailed analysis. Furthermore, if the user is excited, the analysis algorithm unit can apply an algorithm that provides visually stimulating analysis results. This enables appropriate analysis by adjusting the frequency of application of the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 algorithm unit can be performed using, for example, an AI, or without an AI. For example, the analysis algorithm unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the frequency of application of the analysis algorithm.

[0105] When selecting an analysis algorithm, the analysis algorithm unit can weight the algorithm based on the submission date of the hopes and requests. For example, the analysis algorithm unit can assign a higher weight to the hopes and requests submitted most recently. The analysis algorithm unit can also assign a lower weight to the hopes and requests submitted earlier. The analysis algorithm unit can also adjust the weighting of the analysis algorithm based on the submission date. This enables appropriate analysis by weighting the algorithm based on the submission date. Some or all of the above-described processing in the analysis algorithm unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis algorithm unit can input data on the submission date of the hopes and requests to the generation AI and have the generation AI execute the weighting of the algorithm.

[0106] The information providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, if the user is nervous, the information providing unit can provide a simple, highly visible information provision method. Furthermore, if the user is relaxed, the information providing unit can provide an information provision method that includes detailed information. Furthermore, if the user is in a hurry, the information providing unit can provide an information provision method that focuses on the main points. This enables appropriate information provision by adjusting the information provision method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the information provision method.

[0107] When providing information, the information providing unit can select the optimal information providing method by referring to the user's past information providing history. For example, the information providing unit can prioritize providing information providing methods that the user has frequently used in the past. The information providing unit can also select the optimal information providing method based on the user's past information providing history. The information providing unit can also analyze the user's past information providing history and select the most effective information providing method. In this way, the optimal information providing method can be selected by referring to the past information providing history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past information providing history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0108] The information providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, when the user is feeling stressed, the information providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the information providing unit can also provide detailed information. Furthermore, when the user is in a hurry, the information providing unit can also provide information that focuses on the main points. In this way, by determining the priority of information provision according to the user's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit can input the user's facial expression data into the generation AI and cause the generation AI to determine the priority of information provision.

[0109] When providing information, the information providing unit can select the optimal information providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the information providing unit can provide an information providing method that matches the screen size. Furthermore, if the user is using a tablet, the information providing unit can also provide an information providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the information providing unit can also provide an information providing method that is concise and highly visible. This makes it possible to provide the optimal information providing method based on the device information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal information providing method.

[0110] The information collection unit can estimate the user's emotions and adjust the information collection method based on the estimated user emotions. For example, if the user is nervous, the information collection unit can provide a simple and highly visible information collection method. Furthermore, if the user is relaxed, the information collection unit can provide an information collection method that includes detailed information. Furthermore, if the user is in a hurry, the information collection unit can provide an information collection method that focuses on the main points. This enables appropriate information collection by adjusting the information collection method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the information collection unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the information collection method.

[0111] When collecting information, the information collection unit can select the optimal collection method by referring to the student's past information collection history. For example, the information collection unit selects the optimal information collection method based on the student's past information collection history. The information collection unit can also analyze the student's past information collection history and select the most effective information collection method. The information collection unit can also select the optimal information collection method by referring to the student's past information collection history. In this way, the optimal collection method can be provided by referring to the past information collection history. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the student's past information collection history data into the generation AI and have the generation AI select the optimal collection method.

[0112] The information collection unit can estimate the user's emotions and determine the priority of information collection based on the estimated user emotions. For example, when the user is feeling stressed, the information collection unit prioritizes collecting important information. Furthermore, when the user is relaxed, the information collection unit can also collect detailed information. Furthermore, when the user is in a hurry, the information collection unit can collect information that focuses on the main points. Thus, by determining the priority of information collection according to the user's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 information collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information collection unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of information collection.

[0113] When collecting information, the information collection unit can select the optimal collection method taking into account the geographical location information of the student. For example, the information collection unit prioritizes collecting information on nearby schools and faculties based on the geographical location information of the student. The information collection unit can also collect schools and faculties within commuting distance taking into account the geographical location information of the student. The information collection unit can also collect region-specific scholarship information and tuition assistance information based on the geographical location information of the student. This allows optimal information to be collected based on the geographical location information. Some or all of the above-mentioned processing by the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can input the geographical location information data of the student into the generation AI and have the generation AI select the optimal collection method.

[0114] The information providing method can estimate the user's emotions and adjust the information providing method based on the estimated user emotions. For example, if the user is nervous, the information providing method can provide a simple, highly visible information providing method. Furthermore, if the user is relaxed, the information providing method can provide a detailed information providing method. Furthermore, if the user is in a hurry, the information providing method can provide a basic information providing method. This enables appropriate information provision by adjusting the information providing method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information providing method can be performed using, for example, AI, or without AI. For example, the information providing method can input the user's facial expression data into the generation AI and have the generation AI adjust the information providing method.

[0115] When selecting a delivery method, the delivery method can select the optimal method by referring to the student's past delivery method history. For example, the delivery method selects the optimal delivery method based on the student's past delivery method history. The delivery method can also analyze the student's past delivery method history and select the most effective delivery method. The delivery method can also select the optimal delivery method by referring to the student's past delivery method history. In this way, the optimal delivery method can be selected by referring to the past delivery method history. Some or all of the above-mentioned processing in the delivery method can be performed, for example, using AI, or can be performed without using AI. For example, the delivery method can input the student's past delivery method history data into a generation AI and have the generation AI select the optimal delivery method.

[0116] The delivery method can estimate a user's emotions and prioritize delivery methods based on the estimated user emotions. For example, the delivery method can prioritize providing important information when the user is stressed. The delivery method can also provide detailed information when the user is relaxed. The delivery method can also provide information that focuses on the main points when the user is in a hurry. This allows important information to be prioritized by determining the priority of delivery methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the delivery method can be performed using, for example, an AI, or can be performed without using an AI. For example, the delivery method can input user facial expression data into the generation AI and have the generation AI determine the priority of delivery methods.

[0117] The provision method can select the optimal provision method by taking into consideration the geographical location information of the student. For example, the provision method can prioritize providing information on nearby schools and faculties based on the geographical location information of the student. The provision method can also provide schools and faculties within a commuting distance based on the geographical location information of the student. The provision method can also provide region-specific scholarship information and tuition assistance information based on the geographical location information of the student. This makes it possible to provide optimal information based on geographical location information. Some or all of the above-described processing in the provision method can be performed using, for example, AI, or can be performed without using AI. For example, the provision method can input the geographical location information data of the student into a generation AI and have the generation AI select the optimal provision method.

[0118] The privacy protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated user's emotions. For example, if the user is nervous, the privacy protection unit can provide a simple, highly visible privacy protection method. Furthermore, if the user is relaxed, the privacy protection unit can provide a detailed privacy protection method. Furthermore, if the user is in a hurry, the privacy protection unit can provide a privacy protection method that focuses on the key points. This enables appropriate privacy protection by adjusting the privacy protection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input the user's facial expression data into the generation AI and have the generation AI adjust the privacy protection method.

[0119] During privacy protection, the privacy protection unit can select the optimal protection method by referring to the user's past privacy protection history. For example, the privacy protection unit selects the optimal protection method based on the user's past privacy protection history. The privacy protection unit can also analyze the user's past privacy protection history and select the most effective protection method. The privacy protection unit can also select the optimal protection method by referring to the user's past privacy protection history. In this way, the optimal protection method can be selected by referring to the past privacy protection history. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input the user's past privacy protection history data into a generation AI and have the generation AI select the optimal protection method.

[0120] The privacy protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated user emotions. For example, if the user is stressed, the privacy protection unit can prioritize providing important privacy protection. The privacy protection unit can also provide detailed privacy protection when the user is relaxed. The privacy protection unit can also provide privacy protection that focuses on the main points when the user is in a hurry. Thus, by determining the priority of privacy protection according to the user's emotions, important privacy protection can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of privacy protection.

[0121] The privacy protection unit can select the optimal privacy protection method by taking into account the user's device information during privacy protection. For example, if the user is using a smartphone, the privacy protection unit can provide a privacy protection method tailored to the screen size. Furthermore, if the user is using a tablet, the privacy protection unit can provide a privacy protection method optimized for a large screen. Furthermore, if the user is using a smartwatch, the privacy protection unit can provide a simple and highly visible privacy protection method. This makes it possible to provide the optimal privacy protection method based on the device information. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal protection method.

[0122] The input guide unit can estimate the user's emotions and adjust the input guidance method based on the estimated user's emotions. For example, if the user is nervous, the input guide unit can provide a simple, highly visible input guide. Furthermore, if the user is relaxed, the input guide unit can provide detailed input guidance. Furthermore, if the user is in a hurry, the input guide unit can provide an input guidance that focuses on the main points. This enables appropriate input guidance by adjusting the input guidance method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 input guide unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input guide unit can input the user's facial expression data into the generation AI and have the generation AI adjust the input guidance method.

[0123] When providing input guidance, the input guidance unit can select the optimal guidance method by referring to the user's past input guidance history. The input guidance unit selects the optimal guidance method based on, for example, the user's past input guidance history. The input guidance unit can also analyze the user's past input guidance history and select the most effective guidance method. The input guidance unit can also select the optimal guidance method by referring to the user's past input guidance history. In this way, the optimal guidance method can be provided by referring to the past input guidance history. Some or all of the above-described processing in the input guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the input guidance unit can input the user's past input guidance history data to a generation AI and cause the generation AI to select the optimal guidance method.

[0124] The input guide unit can estimate the user's emotions and determine the priority of input guides based on the estimated user emotions. For example, when the user is stressed, the input guide unit can prioritize providing important input guides. Furthermore, when the user is relaxed, the input guide unit can also provide detailed input guides. Furthermore, when the user is in a hurry, the input guide unit can also provide input guides that focus on the main points. Thus, by determining the priority of input guides according to the user's emotions, important input guides can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 input guide unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the input guide unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of input guides.

[0125] When providing input guidance, the input guide unit can select the optimal guidance method by taking into account the user's device information. For example, if the user is using a smartphone, the input guide unit provides input guidance tailored to the screen size. Furthermore, if the user is using a tablet, the input guide unit can provide input guidance optimized for a large screen. Furthermore, if the user is using a smartwatch, the input guide unit can provide input guidance that is concise and highly visible. This makes it possible to provide the optimal guidance method based on the device information. Some or all of the above-described processing in the input guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the input guide unit can input the user's device information data into a generation AI and cause the generation AI to select the optimal guidance method.

[0126] The flowchart unit can estimate the user's emotions and adjust the display method of the flowchart based on the estimated user emotions. For example, if the user is nervous, the flowchart unit can provide a simple, highly visible flowchart. Furthermore, if the user is relaxed, the flowchart unit can provide a detailed flowchart. Furthermore, if the user is in a hurry, the flowchart unit can provide a flowchart that focuses on the main points. This allows for an appropriate flowchart display by adjusting the display method of the flowchart according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 flowchart unit can be performed using, for example, AI, or without AI. For example, the flowchart unit can input the user's facial expression data into the generation AI and have the generation AI adjust the display method of the flowchart.

[0127] When displaying a flowchart, the flowchart unit can select the optimal display method by referring to the user's past operation history. The flowchart unit, for example, selects the optimal flowchart display method based on the user's past operation history. The flowchart unit can also analyze the user's past operation history and select the most effective flowchart display method. The flowchart unit can also select the optimal flowchart display method by referring to the user's past operation history. In this way, the optimal flowchart display method can be provided by referring to the past operation history. Some or all of the above-mentioned processing in the flowchart unit may be performed using, for example, AI, or may be performed without using AI. For example, the flowchart unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal display method.

[0128] The flowchart unit can estimate the user's emotions and adjust the operation procedures of the flowchart based on the estimated user's emotions. For example, if the user is feeling stressed, the flowchart unit can prioritize providing important operation procedures. The flowchart unit can also provide detailed operation procedures when the user is relaxed. The flowchart unit can also provide operation procedures that focus on the main points when the user is in a hurry. This allows the operation procedures of the flowchart to be adjusted according to the user's emotions, thereby providing appropriate operation procedures. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the flowchart unit can be performed using AI, or without AI. For example, the flowchart unit can input the user's facial expression data into the generation AI and have the generation AI adjust the operation procedures.

[0129] When displaying a flowchart, the flowchart unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the flowchart unit can provide a flowchart that matches the screen size. Furthermore, if the user is using a tablet, the flowchart unit can provide a flowchart that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the flowchart unit can provide a concise and highly visible flowchart. This allows the optimal flowchart display method to be provided based on the device information. Some or all of the above-described processing in the flowchart unit may be performed using, for example, AI, or may be performed without using AI. For example, the flowchart unit can input the user's device information data into a generation AI and have the generation AI select the optimal display method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and can input the user's wishes and requests. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable school or department based on the analysis results. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the user with information from prospective students. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and allows the user to input their wishes and requests by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests the most suitable school or department based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the user with information from prospective students. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit 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 is realized by the microphone 238 of the headset-type terminal 314, and allows the user to input their wishes and requests by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the most suitable school or department based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314, and provides the user with information from prospective students. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and allows the user to input their wishes and requests by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests the most suitable school or department based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the user with information from prospective students.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The school selection support system can further include a learning style analysis unit that suggests the most suitable school or department based on the user's learning style. The learning style analysis unit collects and analyzes data on the user's preferred learning methods and learning materials in the past. For example, if the user prefers visual learning, the system can suggest schools or departments with a wide range of visual learning materials. If the user prefers group learning, the system can also suggest schools or departments with many opportunities for group learning. Furthermore, if the user prefers online learning, the system can suggest schools or departments with a wide range of online courses. This makes it possible to maximize learning effectiveness by suggesting schools and departments that suit the user's learning style.

[0132] The reception unit can estimate the user's emotions and customize the input method for wishes and requests based on the estimated user emotions. For example, if the user is feeling stressed, the input can be simplified to allow the user to complete the input in a short time. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input wishes and requests. This allows the input method to be customized according to the user's emotions, enabling more appropriate input.

[0133] The school selection support system can further include a career path analysis unit that suggests the most suitable schools and departments based on the user's future career path. The career path analysis unit collects and analyzes data related to the user's desired occupation and industry. For example, if the user is aiming to become an engineer, the system can suggest schools and departments with a strong engineering program. If the user is interested in the medical field, the system can also suggest schools and departments with a strong medical-related program. Furthermore, if the user is interested in the business field, the system can suggest schools and departments with a strong business program. This allows the system to support the user's career success by suggesting schools and departments that match the user's future career path.

[0134] The reception unit can provide options for inputting hopes and requests based on the user's current learning situation and areas of interest. For example, hopes and requests related to the subject the user is currently studying can be displayed with priority. Information on related schools and departments can also be filtered and displayed based on the user's areas of interest. Furthermore, the reception unit can provide appropriate options for inputting hopes and requests depending on the user's learning progress. This makes it possible to provide appropriate information based on the user's learning situation and areas of interest.

[0135] The reception unit can estimate the user's emotions and determine the priority of the hopes and requests to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize inputting important hopes and requests. If the user is relaxed, the reception unit can also prioritize inputting detailed hopes and requests. Furthermore, if the user is in a hurry, the reception unit can prioritize inputting the most important hopes and requests. In this way, by determining the priority of hopes and requests according to the user's emotions, important information can be input preferentially.

[0136] The school selection support system can further include a learning progress tracking unit that tracks the user's learning progress and suggests the most suitable school or department based on the progress. The learning progress tracking unit collects and analyzes data on the subjects and grades the user is studying. For example, if the user is achieving excellent grades in a particular subject, the unit can suggest schools and departments related to that subject. Also, if the user has a subject that they are weak at, the unit can suggest schools and departments that offer comprehensive support for that subject. Furthermore, the unit can provide appropriate study methods and resources according to the user's learning progress. This can maximize learning effectiveness by suggesting the most suitable school or department based on the user's learning progress.

[0137] The reception unit can analyze the user's social media activity and input related information. For example, it can analyze the content of the user's social media posts and suggest faculties or schools of interest. It can also suggest schools or faculties attended by friends based on the user's social media following. It can also provide information about related events and open campuses based on the user's social media activity history. In this way, it is possible to provide information relevant to the user by analyzing social media activity.

[0138] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, if the user is in a hurry, it can also provide concise analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide more appropriate analysis results.

[0139] The school selection support system can further include a learning environment analysis unit that suggests the most suitable school or department based on the user's learning environment. The learning environment analysis unit collects and analyzes data related to the location and time of day the user studies. For example, if the user often studies at home, the system can suggest schools and departments that offer a wide range of online courses suitable for home study. If the user often studies in libraries or cafes, the system can also suggest schools and departments that offer such learning environments. Furthermore, if the user often studies at night, the system can suggest schools and departments that offer a wide range of night courses. This makes it possible to maximize learning effectiveness by suggesting the most suitable school or department based on the user's learning environment.

[0140] During analysis, the analysis unit can apply different analysis algorithms depending on the category of hopes and desires. For example, an education-related analysis algorithm can be applied to hopes and desires regarding what a person wants to learn. A career-related analysis algorithm can also be applied to hopes and desires regarding what a person wants to do. Furthermore, an economics-related analysis algorithm can be applied to hopes and desires regarding tuition fees. This makes it possible to provide highly accurate analysis results by applying the appropriate analysis algorithm depending on the category of hopes and desires.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The reception unit inputs the user's wishes and requests. The user's wishes and requests include the desired school, choice of faculty, and tuition fees. For example, the user can input specific requests such as "I want to go to a science faculty at a university in Tokyo" or "I'm looking for a school with low tuition fees." Step 2: The analysis unit analyzes the information input by the reception unit. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis is performed to suggest the most suitable schools and departments based on the user's wishes and requests. Data mining techniques can also be used to extract data related to the user's wishes and requests. Furthermore, machine learning algorithms can be used to build models to suggest the most suitable schools and departments based on the user's wishes and requests. Step 3: The suggestion unit suggests the most suitable schools and departments based on the information analyzed by the analysis unit. The suggestions are made based on criteria such as the user's academic ability, interests, and future career path. For example, the suggestion unit suggests the most suitable schools and departments based on the user's academic ability. It can also suggest related schools and departments based on the user's interests. It can also suggest appropriate schools and departments based on the user's future career path. Step 4: The provision unit provides information from the students who will be continuing on to the next school according to the schools and faculties proposed by the proposal unit. The information from the students who will be continuing on to the next school includes evaluations of the school, their learning experiences, and employment status. For example, the provision unit provides evaluations of the school they will be continuing on to. It can also share the learning experiences of the students who will be continuing on to the next school. It can also provide information on the employment status of the students who will be continuing on to the next school.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the 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.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 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.

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

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0194] 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.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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).

[0200] 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.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0213] 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.

[0214] [Explanation of symbols]

[0215] 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 unit for inputting user wishes and requests; an analysis unit that analyzes the information input by the reception unit; a suggestion unit that suggests an appropriate school or department based on the information analyzed by the analysis unit; a provision unit that provides information from students who will continue on to higher education in accordance with the schools and faculties proposed by the proposal unit; A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of inputting their wishes and requests based on the estimated emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past wishes and requests and select the appropriate input method 2. The system of claim 1.

4. The reception unit Filtering requests based on the user's current learning status and interests as they are entered 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the input wishes and requests based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When entering requests and wishes, the system takes into account the user's geographic location to prioritize the most relevant information.

2. The system of claim 1.

7. The reception unit When you enter your preferences and requests, analyze your social media activity and enter relevant information 2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

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