System

Abstract

An object of the system according to the embodiment is to analyze student information and provide objective and theoretical course guidance.SOLUTION: A system includes an input part, an analysis part, a provision part, a teacher cooperation part, and a data collection part. The input unit inputs information of the student. The analysis unit analyzes the information input by the input unit. The providing unit provides advice based on the analysis result obtained by the analysis unit. The teacher cooperation part provides the advice provided by the provision part to the teacher, and supports the teacher to perform course guidance based on the advice. A data collection part collects data on the score, personality and taste of the student.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, career guidance tends to depend on the subjective judgment of teachers, and there is room for improvement in providing objective and theoretical advice.

[0005] The system according to the embodiment aims to analyze student information and provide objective and theoretical career guidance. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a provision unit, a teacher collaboration unit, and a data collection unit. The input unit inputs information about the student. The analysis unit analyzes the information input by the input unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The teacher collaboration unit provides the advice provided by the provision unit to teachers, and supports teachers in providing career guidance based on that advice. The data collection unit collects data on the student's grades, personality, and preferences. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information about students and provide them with objective and theoretical career guidance. [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 career guidance support system according to an embodiment of the present invention is a system in which a student's information is input, analyzed by a generation AI, and advice is provided. The career guidance support system inputs the student's information, analyzes it, and advises the student on an optimal life plan. For example, the career guidance support system inputs the student's thoughts, personality, grades, preferences, and future aspirations. The career guidance support system then analyzes the input information using a generation AI. The generation AI then advises the student on an optimal life plan based on the student's information. For example, if the generation AI indicates a student's desire to "become a doctor in the future," the system provides specific advice on the necessary major, university, and skills. This enables the career guidance support system to efficiently input, analyze, provide advice, collaborate with teachers, and collect data on the student's information. This enables the career guidance support system to efficiently input, analyze, provide advice, collaborate with teachers, and collect data on the student's information. For example, the generation AI analyzes the student's information and advises on an optimal life plan, enabling more objective and theoretical career guidance. Furthermore, teachers can provide more appropriate career guidance by referring to the advice provided by the generation AI.

[0029] A career guidance support system according to an embodiment includes an input unit, an analysis unit, a provision unit, a teacher collaboration unit, and a data collection unit. The input unit inputs information about the student. The information about the student includes, but is not limited to, thoughts, personality, grades, preferences, and future aspirations. For example, the student himself / herself can input the information into the input unit, or a teacher can input the information. The analysis unit uses a generation AI to analyze the information input by the input unit. The generation AI comprehensively assesses the student's information using, for example, natural language processing technology or a machine learning algorithm. The generation AI advises the student on an optimal life plan based on, for example, the student's thoughts, personality, grades, preferences, and future aspirations. The provision unit provides the student with advice based on the analysis results obtained by the analysis unit. The provision unit advises the student on an optimal life plan based on, for example, data analyzed by the generation AI. The teacher collaboration unit provides the advice provided by the provision unit to teachers and supports the teachers in providing career guidance based on the advice. The teacher collaboration unit, for example, provides advice from the generation AI to teachers and supports teachers in providing career guidance based on that advice. The data collection unit collects data such as the student's grades, personality, and preferences. The data collection unit collects data using methods such as questionnaire surveys, sensor data collection, and online activity monitoring. This enables the career guidance support system according to the embodiment to efficiently input, analyze, provide advice, collaborate with teachers, and collect data about the student.

[0030] The data collection unit can collect data on the student's grades, personality, and preferences. The data collection unit, for example, collects the student's grades. Grades include, but are not limited to, semester grades, subject grades, and overall evaluations. The data collection unit can also collect the student's personality. Personality includes, but is not limited to, personality diagnostic tests, psychological evaluations, and behavioral observations. The data collection unit can also collect the student's preferences. Preferences include, but are not limited to, hobbies, areas of interest, and favorite activities. By collecting data such as the student's grades, personality, and preferences, more accurate advice can be provided. Some or all of the above-described processing in the data collection unit can be performed using, or without, AI. For example, the data collection unit can input the results of a questionnaire survey into a generation AI, which then analyzes the data.

[0031] The analysis unit can use the generation AI to comprehensively determine the student's thoughts, personality, grades, preferences, and future aspirations. The analysis unit, for example, uses the generation AI to determine the student's thoughts. The generation AI, for example, uses natural language processing technology to analyze the student's thoughts from their writing. The analysis unit can also use the generation AI to determine the student's personality. The generation AI, for example, analyzes the student's personality based on the results of a personality diagnostic test. The analysis unit can also use the generation AI to determine the student's grades. The generation AI analyzes the student's academic ability based on, for example, semester grades and subject grades. The analysis unit can also use the generation AI to determine the student's preferences. The generation AI analyzes preferences based on, for example, the student's hobbies and areas of interest. The analysis unit can also use the generation AI to determine the student's future aspirations. For example, the generation AI analyzes the required departments, university to enter, and necessary skills based on the student's future aspirations. This allows the generation AI to comprehensively assess the student's information, enabling more appropriate advice to be provided. Some or all of the above-described processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the analysis unit can input the student's information into the generation AI, which then makes a comprehensive assessment.

[0032] The provision unit can advise the student on an appropriate life plan based on the data analyzed by the generation AI. For example, the provision unit advises the student on an optimal life plan based on the data analyzed by the generation AI. For example, the generation AI provides specific advice on the required department, university, and necessary skills based on the student's thoughts, personality, grades, preferences, and future aspirations. The provision unit can also advise the student on a career plan based on the data analyzed by the generation AI. For example, the generation AI provides specific advice on the required qualifications, experience, and career path for the student's desired career. Furthermore, the provision unit can also advise the student on a study plan based on the data analyzed by the generation AI. For example, the generation AI provides specific advice on effective study methods and study schedules based on the student's grades and learning style. This allows for more specific career guidance by advising the student on an optimal life plan based on the data analyzed by the generation AI. Some or all of the above-described processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit provides advice to the student based on the data analyzed by the generation AI.

[0033] The Faculty Collaboration Unit can provide teachers with advice from the generative AI and support teachers in providing career guidance based on that advice. For example, the Faculty Collaboration Unit provides teachers with advice from the generative AI. The teachers can provide career guidance to the students in question based on the advice from the generative AI. The Faculty Collaboration Unit also supports teachers in providing career guidance based on the advice from the generative AI. For example, the Faculty Collaboration Unit can provide teachers with information on how to interpret and utilize the advice from the generative AI. Furthermore, the Faculty Collaboration Unit has a function for facilitating collaboration between teachers and the generative AI. For example, the Faculty Collaboration Unit can enable teachers to check the advice from the generative AI in real time. The Faculty Collaboration Unit can also enable teachers to provide feedback on the advice from the generative AI. This allows teachers to receive advice from the generative AI and support teachers in providing career guidance based on that advice, thereby enabling more objective and theoretical career guidance. Some or all of the above-described processing in the Faculty Collaboration Unit may be performed using AI, or may be performed without AI. For example, the Faculty Collaboration Department will provide advice from the generative AI to teachers and support them in providing career guidance based on that advice.

[0034] The provision unit can refer to the career paths chosen by students with similar aspirations and the results they achieved based on past data. For example, the provision unit refers to the career paths chosen by students with similar aspirations based on past data. Past data includes, but is not limited to, past career choice data, success stories, and failure stories. The provision unit can also refer to the results achieved by students with similar aspirations based on past data. For example, the provision unit analyzes the career paths chosen by students with similar aspirations and the results they achieved based on past data. Furthermore, the provision unit can provide more reliable advice to the student based on the past data. For example, the provision unit provides specific career choice advice to the student based on the past data. By referring to the past data, more reliable advice can be provided. Some or all of the above-described processing by the provision unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the provision unit can input past data into a generation AI and have the generation AI perform analysis.

[0035] The input unit can analyze the student's past input history and select the optimal input method. The input unit, for example, analyzes the student's past input history. The input history includes, for example, past input data, input frequency, and input content trends, but is not limited to these examples. Next, the input unit selects the optimal input method based on the past input history. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the student has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the student's past input history. Furthermore, the input unit can customize the input method based on the student's past input content. This enables more efficient information input by analyzing the student's past input history. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input past input history data into a generation AI, which can select the optimal input method.

[0036] The input unit can filter the data based on the student's current learning situation and areas of interest when inputting the data. The input unit, for example, grasps the student's current learning situation. Examples of the learning situation include, but are not limited to, learning progress, level of understanding, and study time. The input unit can also grasp the student's areas of interest. Examples of the areas of interest include, but are not limited to, subjects of interest, hobbies, and future goals. Next, the input unit performs filtering based on the student's current learning situation and areas of interest. For example, only relevant questions can be displayed based on the student's current learning situation. Also, interesting questions can be preferentially displayed based on the student's areas of interest. Furthermore, questions of an appropriate level of difficulty can be displayed depending on the student's learning progress. This allows for more relevant information to be input by filtering based on the student's current learning situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using AI or without AI. For example, the input unit can input the student's learning situation data to a generation AI, which then performs filtering.

[0037] The input unit can select the optimal input means depending on the student's input method when inputting data. The input unit, for example, identifies the student's input method. The input method includes, but is not limited to, voice input, text input, and image input. Next, the input unit selects the optimal input means depending on the student's input method. For example, if the student desires voice input, a voice recognition function can be provided. Also, if the student desires text input, keyboard input can be provided. Furthermore, if the student desires image input, an image recognition function can be provided. This allows for smoother information input by selecting the optimal input means depending on the student's input method. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the student's input method data into a generation AI, which can select the optimal input means.

[0038] The input unit can prioritize inputting highly relevant information by taking into account the student's geographical location information. The input unit, for example, grasps the student's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history. Next, the input unit prioritizes inputting highly relevant information by taking into account the student's geographical location information. For example, based on the student's current location, the input unit prioritizes inputting nearby educational destinations and occupational information. Regional career path information can also be provided based on the student's geographical location information. Furthermore, educational destinations with good transportation access can be prioritized by taking into account the student's geographical location information. This enables more relevant information to be input by taking into account the student's geographical location information. Some or all of the above-described processing in the input unit may be performed using AI or without AI. For example, the input unit can input the student's geographical location information data to a generation AI, which then prioritizes inputting highly relevant information.

[0039] The input unit can analyze the student's social media activity and input relevant information at the time of input. The input unit, for example, analyzes the student's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of followers, and the engagement rate. Next, the input unit inputs relevant information based on the student's social media activity. For example, the input unit analyzes the content of the student's social media posts and inputs relevant career information. The input unit can also input relevant career information based on the student's social media friends' activities. Furthermore, the input unit can input relevant career information based on the student's social media check-in information. This allows for more relevant information to be input by analyzing the student's social media activity. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the student's social media data to a generation AI, which then inputs relevant information.

[0040] The input unit can customize the input method by reflecting the student's past feedback during input. The input unit, for example, collects the student's past feedback. The feedback includes, but is not limited to, user opinions, evaluations, and areas for improvement. Next, the input unit customizes the input method based on the past feedback. For example, the input method is improved based on feedback provided by the student in the past. The input unit can also customize the input interface by referring to the student's past feedback. Furthermore, the input unit can also optimize the input procedure by reflecting the student's past feedback. This provides a more appropriate input method by reflecting the student's past feedback. Some or all of the above-described processing in the input unit may be performed using AI or without AI. For example, the input unit can input the student's feedback data into a generation AI, which can customize the input method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, evaluates the importance of the information. The importance of the information includes, for example, impact, urgency, and relevance, but is not limited to these examples. Next, the analysis unit adjusts the level of detail of the analysis based on the importance of the information. For example, a detailed analysis can be performed for important information. On the other hand, a brief analysis can be performed for less important information. Furthermore, the level of detail of the analysis can be adjusted in stages depending on the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more important information can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the analysis unit can input information importance data into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit, for example, classifies the categories of information. Information categories include, but are not limited to, academic information, personality information, and interest information. Next, the analysis unit applies different analysis algorithms depending on the category of information. For example, an analysis algorithm dedicated to academic performance can be applied to information about academic performance. An analysis algorithm dedicated to personality analysis can be applied to information about personality. Furthermore, an analysis algorithm dedicated to career planning can be applied to information about future aspirations. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the analysis unit can input information category data into a generation AI and apply different analysis algorithms depending on the generation AI.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results. The analysis unit, for example, collects the student's past analysis results. The past analysis results include, but are not limited to, past data points, analysis accuracy, and result reliability. Next, the analysis unit corrects the current analysis result based on the past analysis results. For example, the analysis algorithm can be optimized by referring to the student's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the past analysis results. In this way, by referring to the student's past analysis results, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the analysis unit can input past analysis result data into a generation AI, which can then improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the information was submitted. The analysis unit, for example, grasps the time when the information was submitted. The submission time includes, but is not limited to, the submission deadline, the frequency of submission, and the timing of submission. Next, the analysis unit determines the analysis priority based on the time when the information was submitted. For example, the analysis unit prioritizes the analysis of recently submitted information. Also, information submitted earlier can be analyzed later. Furthermore, the analysis priority can be gradually adjusted depending on the time of submission. In this way, by determining the analysis priority based on the time when the information was submitted, more timely analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the analysis unit can input data on the time when the information was submitted into a generation AI, and the generation AI can determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, evaluates the relevance of the information. The relevance of the information includes, but is not limited to, common themes, related data points, and correlations. Next, the analysis unit adjusts the order of analysis based on the relevance of the information. For example, highly relevant information is analyzed preferentially. Also, less relevant information can be analyzed later. Furthermore, the order of analysis can be adjusted in stages depending on the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, more relevant information is analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the analysis unit can input relevance data of the information into a generation AI and adjust the order of analysis using the generation AI.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise. The analysis unit, for example, evaluates the student's level of expertise. Examples of technical terminology include, but are not limited to, academic background, work history, and knowledge of a specialized field. Next, the analysis unit adjusts the use of technical terminology in the analysis according to the student's level of expertise. For example, a student with a high level of expertise can be provided with analysis results that use a lot of technical terminology. Furthermore, a student with a low level of expertise can be provided with analysis results that avoid technical terminology. Furthermore, the use of technical terminology in the analysis results can be gradually adjusted according to the student's level of expertise. By adjusting the use of technical terminology in the analysis according to the student's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or without using a generation AI. For example, the analysis unit can input the student's level of expertise data into a generation AI and have the generation AI adjust the use of technical terminology in the analysis.

[0047] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the information. The providing unit, for example, evaluates the importance of the information. The importance of the information includes, for example, impact, urgency, and relevance, but is not limited to these examples. Next, the providing unit adjusts the level of detail of the advice based on the importance of the information. For example, detailed advice can be provided for important information. Concise advice can be provided for less important information. Furthermore, the level of detail of the advice can be adjusted in stages depending on the importance of the information. In this way, by adjusting the level of detail of the advice based on the importance of the information, detailed advice can be provided for more important information. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (for example, a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit can input information importance data to a generation AI and have the generation AI adjust the level of detail of the advice.

[0048] When providing advice, the providing unit can apply different advice algorithms depending on the category of information. The providing unit, for example, classifies the category of information. The categories of information include, but are not limited to, academic information, personality information, and interest information. Next, the providing unit applies different advice algorithms depending on the category of information. For example, an advice algorithm dedicated to academic performance can be applied to advice regarding academic performance. Furthermore, an advice algorithm dedicated to personality analysis can be applied to advice regarding personality. Furthermore, an advice algorithm dedicated to career planning can be applied to advice regarding future aspirations. In this way, by applying different advice algorithms depending on the category of information, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the providing unit can input information category data into a generation AI and apply different advice algorithms using the generation AI.

[0049] When providing advice, the providing unit can improve the accuracy of the advice by referring to the student's past advice results. The providing unit, for example, collects the student's past advice results. The past advice results include, for example, but are not limited to, the success rate of past advice, the content of feedback, and areas for improvement. Next, the providing unit corrects the current advice based on the past advice results. For example, the providing unit optimizes the advice algorithm by referring to the student's past advice results. The providing unit can also improve the accuracy of the advice by using the past advice results. As a result, more accurate advice is provided by referring to the student's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit can input past advice result data into a generation AI, which can improve the accuracy of the advice.

[0050] When providing advice, the providing unit can determine the priority of advice based on the time of submission of information. The providing unit, for example, grasps the time of submission of information. The submission time includes, for example, a submission deadline, a submission frequency, and a submission timing, but is not limited to these examples. Next, the providing unit determines the priority of advice based on the time of submission of information. For example, recently submitted information is reflected preferentially in the advice. Also, advice can be provided later for information that was submitted earlier. Furthermore, the priority of advice can be gradually adjusted depending on the time of submission. In this way, by determining the priority of advice based on the time of submission of information, more timely advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit can input information submission time data into a generation AI and have the generation AI determine the priority of advice.

[0051] When providing advice, the providing unit can adjust the order of advice based on the relevance of the information. The providing unit, for example, evaluates the relevance of the information. The relevance of the information includes, for example, a common theme, related data points, correlations, etc., but is not limited to these examples. Next, the providing unit adjusts the order of advice based on the relevance of the information. For example, highly relevant information is reflected preferentially in the advice. Also, less relevant information can be provided later in advice. Furthermore, the order of advice can be adjusted in stages depending on the relevance of the information. In this way, by adjusting the order of advice based on the relevance of the information, more relevant information is given priority in advice. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit can input information relevance data into a generation AI and adjust the order of advice using the generation AI.

[0052] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the student's level of expertise. The providing unit, for example, evaluates the student's level of expertise. Examples of the level of expertise include, but are not limited to, academic background, work history, and knowledge of a specialized field. Next, the providing unit adjusts the use of technical terms in the advice according to the student's level of expertise. For example, advice that uses a lot of technical terms can be provided to a student with a high level of expertise. Furthermore, advice that avoids technical terms can be provided to a student with a low level of expertise. Furthermore, the use of technical terms in the advice can be gradually adjusted according to the student's level of expertise. By adjusting the use of technical terms in the advice according to the student's level of expertise, more understandable advice can be provided. Some or all of the above-described processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the providing unit can input the student's level of expertise data into a generation AI, which can adjust the use of technical terms in the advice.

[0053] During teacher collaboration, the teacher collaboration unit can select the optimal collaboration method by referring to the teacher's past teaching history. The teacher collaboration unit, for example, collects the teacher's past teaching history. The teaching history includes, for example, but is not limited to, past teaching content, teaching results, feedback, etc. Next, the teacher collaboration unit proposes the optimal collaboration method based on the past teaching history. For example, an effective collaboration method is selected from the teacher's past teaching history. The teacher collaboration unit can also customize the collaboration method by referring to the past teaching history. This enables more effective collaboration by referring to the teacher's past teaching history. Some or all of the above-mentioned processing in the teacher collaboration unit may be performed using AI, or may be performed without using AI. For example, the teacher collaboration unit can input the teacher's teaching history data into a generation AI, which can select the optimal collaboration method.

[0054] During teacher collaboration, the teacher collaboration unit can customize the collaboration method based on the teacher's current teaching status. The teacher collaboration unit, for example, grasps the teacher's current teaching status. The teaching status includes, but is not limited to, the current teaching content, the progress of the teaching, and the results of the teaching. Next, the teacher collaboration unit customizes the collaboration method based on the teacher's current teaching status. For example, the unit provides the optimal collaboration method based on the teacher's current teaching status. The unit can also grasp the teacher's teaching status in real time and adjust the collaboration method. Furthermore, the collaboration method can be customized according to the teacher's teaching status. Customizing the collaboration method based on the teacher's current teaching status enables more appropriate collaboration. Some or all of the above-described processing in the teacher collaboration unit may be performed using AI, or may be performed without AI. For example, the teacher collaboration unit can input the teacher's teaching status data into a generation AI and have the generation AI customize the collaboration method.

[0055] The Faculty Collaboration Unit can improve the collaboration method by reflecting faculty feedback during faculty collaboration. For example, the Faculty Collaboration Unit collects faculty feedback. The feedback includes, but is not limited to, faculty opinions, evaluations, and areas for improvement. Next, the Faculty Collaboration Unit improves the collaboration method based on the feedback. For example, the collaboration method can be optimized by referring to past faculty feedback. The Faculty Collaboration Unit can also customize the collaboration method by reflecting the feedback. This enables more effective collaboration by reflecting faculty feedback. Some or all of the above-mentioned processing in the Faculty Collaboration Unit may be performed using AI, or may be performed without using AI. For example, the Faculty Collaboration Unit can input faculty feedback data into a generation AI, which can then improve the collaboration method.

[0056] During teacher collaboration, the teacher collaboration unit can select the optimal collaboration method by taking into account the teacher's geographic location information. The teacher collaboration unit, for example, grasps the teacher's geographic location information. Geographic location information includes, but is not limited to, GPS data, address information, and location history. Next, the teacher collaboration unit selects the optimal collaboration method by taking into account the teacher's geographic location information. For example, the optimal collaboration method can be provided based on the teacher's current location. Regionally specific collaboration methods can also be proposed based on the teacher's geographic location information. Furthermore, an efficient collaboration method can be selected by taking into account the teacher's geographic location information. This enables more appropriate collaboration by taking into account the teacher's geographic location information. Some or all of the above-described processing in the teacher collaboration unit may be performed using AI, or may be performed without using AI. For example, the teacher collaboration unit can input the teacher's geographic location information data into a generation AI, which then selects the optimal collaboration method.

[0057] During faculty collaboration, the Faculty Collaboration Unit can analyze faculty members' social media activities and propose collaboration methods. The Faculty Collaboration Unit, for example, analyzes the faculty members' social media activities. Social media activities include, but are not limited to, the content of posts, the number of followers, and engagement rates. Next, the Faculty Collaboration Unit proposes collaboration methods based on the faculty members' social media activities. For example, the Faculty Collaboration Unit analyzes the faculty members' social media activities and proposes optimal collaboration methods. The Faculty Collaboration Unit can also customize collaboration methods based on the content of the faculty members' social media posts. Furthermore, collaboration methods can be proposed based on the activities of the faculty members' friends on social media. In this way, more appropriate collaboration methods are proposed by analyzing the faculty members' social media activities. Some or all of the above-described processing in the Faculty Collaboration Unit may be performed using AI, or may be performed without AI. For example, the Faculty Collaboration Unit can input the faculty members' social media data into a generation AI, which then proposes collaboration methods.

[0058] During teacher collaboration, the teacher collaboration unit can customize the collaboration method by reflecting teachers' past feedback. For example, the teacher collaboration unit collects teachers' past feedback. The feedback includes, but is not limited to, teachers' opinions, evaluations, and areas for improvement. Next, the teacher collaboration unit improves the collaboration method based on the past feedback. For example, the collaboration method is optimized by referring to teachers' past feedback. The teacher collaboration unit can also customize the collaboration means by reflecting the feedback. This enables more appropriate collaboration by reflecting teachers' past feedback. Some or all of the above-mentioned processing in the teacher collaboration unit may be performed using AI, or may be performed without using AI. For example, the teacher collaboration unit can input teachers' feedback data into a generation AI, which can then improve the collaboration method.

[0059] When collecting data, the data collection unit can select the optimal collection method by referring to the student's past data collection history. The data collection unit, for example, collects the student's past data collection history. The data collection history includes, for example, but is not limited to, the content of past data collection, the frequency of collection, and the effectiveness of the collection method. Next, the data collection unit proposes the optimal collection method based on the past data collection history. For example, an effective collection method is selected from the student's past data collection history. The data collection unit can also customize the collection method by referring to the past data collection history. This enables more effective data collection by referring to the student's past data collection history. Some or all of the above-mentioned processing in the data collection unit may be performed using AI or without AI. For example, the data collection unit can input the student's data collection history data into a generation AI, which can select the optimal collection method.

[0060] The data collection unit can customize the collection means based on the student's current learning situation when collecting data. The data collection unit, for example, grasps the student's current learning situation. The learning situation includes, but is not limited to, learning progress, level of understanding, and study time. Next, the data collection unit customizes the collection means based on the student's current learning situation. For example, the data collection unit provides the optimal collection means based on the student's current learning situation. The data collection unit can also grasp the student's learning situation in real time and adjust the collection means. Furthermore, the data collection unit can customize the collection means according to the student's learning situation. This enables more appropriate data collection by customizing the collection means based on the student's current learning situation. Some or all of the above-mentioned processing in the data collection unit may be performed using AI or without AI. For example, the data collection unit can input the student's learning situation data into a generation AI and have the generation AI customize the collection means.

[0061] The data collection unit can improve the collection method by reflecting student feedback during data collection. The data collection unit, for example, collects student feedback. The feedback includes, but is not limited to, student opinions, evaluations, and areas for improvement. The data collection unit then improves the collection method based on the feedback. For example, the data collection unit can optimize the collection method by referring to students' past feedback. The data collection unit can also customize the collection means by reflecting the feedback. This enables more effective data collection by reflecting student feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can input student feedback data into a generation AI, which can then improve the collection method.

[0062] The data collection unit can select the optimal collection method by taking into account the student's geographic location information when collecting data. The data collection unit, for example, grasps the student's geographic location information. The geographic location information includes, but is not limited to, GPS data, address information, and location history. Next, the data collection unit selects the optimal collection method by taking into account the student's geographic location information. For example, the optimal collection method can be provided based on the student's current location. Furthermore, a region-specific data collection method can be suggested based on the student's geographic location information. Furthermore, an efficient collection method can be selected by taking into account the student's geographic location information. This enables more appropriate data collection by taking into account the student's geographic location information. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can input the student's geographic location information data into a generation AI, which then selects the optimal collection method.

[0063] When collecting data, the data collection unit can analyze the student's social media activity and suggest collection methods. The data collection unit, for example, analyzes the student's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of followers, and the engagement rate. Next, the data collection unit suggests collection methods based on the student's social media activity. For example, the data collection unit analyzes the student's social media activity and suggests the optimal collection method. The data collection unit can also customize the collection method based on the content of the student's social media posts. Furthermore, the data collection unit can also suggest collection methods based on the activity of the student's friends on social media. In this way, by analyzing the student's social media activity, more appropriate data collection methods are suggested. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can input the student's social media data into a generation AI, which then suggests collection methods.

[0064] The data collection unit can customize the collection method by reflecting students' past feedback when collecting data. The data collection unit, for example, collects students' past feedback. The feedback includes, but is not limited to, students' opinions, evaluations, and areas for improvement. Next, the data collection unit improves the collection method based on the past feedback. For example, the data collection unit optimizes the collection method by referring to students' past feedback. The data collection unit can also customize the collection means by reflecting the feedback. This enables more appropriate data collection by reflecting students' past feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using AI or without AI. For example, the data collection unit can input students' feedback data into a generation AI, which can then improve the collection method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The career guidance support system can also collect student health data and use it for analysis. For example, the data collection unit can collect health data such as students' sleep patterns, exercise levels, and dietary habits. The analysis unit can then evaluate students' learning efficiency and stress levels based on this health data. The provision unit can provide students with advice on maintaining a healthy lifestyle based on the health data. This allows the career guidance support system to provide comprehensive career guidance that takes students' health conditions into consideration.

[0067] The career guidance support system can further analyze students' learning styles and suggest optimal learning methods. For example, the data collection unit can collect data such as students' learning history, learning environment, and study time. Next, the analysis unit can analyze students' learning styles based on this data. The provision unit can suggest effective learning methods and study schedules to students based on their learning styles. This allows the career guidance support system to provide career guidance that meets the individual learning needs of students.

[0068] The career guidance support system can further evaluate students' social skills and provide advice for improvement. For example, the data collection unit can collect data on students' social skills, such as communication ability, leadership, and cooperation. Next, the analysis unit can evaluate students' social skills based on this data. The provision unit can provide students with specific advice for improving their social skills based on the results of the social skill evaluation. In this way, the career guidance support system can provide support for improving students' social skills.

[0069] The career guidance support system can further evaluate students' creativity and provide advice to develop their creativity. For example, the data collection unit can collect data on students' creative activities, ideas, projects, etc. Next, the analysis unit can evaluate students' creativity based on this data. The provision unit can provide students with specific advice to develop their creativity based on the results of the creativity evaluation. In this way, the career guidance support system can provide support to maximize students' creativity.

[0070] The career guidance support system can further evaluate the student's time management ability and provide advice for efficient time management. For example, the data collection unit can collect data on the student's schedule, task management, and time usage. Next, the analysis unit can evaluate the student's time management ability based on this data. The provision unit can provide the student with specific advice for efficient time management based on the evaluation results of the time management ability. In this way, the career guidance support system can provide support for improving the student's time management ability.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The input unit inputs the student's information, including their thoughts, personality, grades, preferences, and future aspirations. The input unit can be entered by the student themselves or by a teacher. Step 2: The analysis unit uses the generation AI to analyze the information entered by the input unit. The generation AI uses natural language processing technology and machine learning algorithms to comprehensively assess the student's information and provide advice on the optimal life plan. Step 3: The provision unit provides advice to the student based on the analysis results obtained by the analysis unit. The provision unit advises the student on the optimal life plan based on the data analyzed by the generation AI. Step 4: The Faculty Collaboration Department provides the advice provided by the Advice Providing Department to faculty members and supports them in providing career guidance based on that advice.The Faculty Collaboration Department provides the advice provided by the Generative AI to faculty members and supports them in providing career guidance based on that advice. Step 5: The data collection unit collects data such as the student's grades, personality, preferences, etc. The data collection unit collects data using methods such as questionnaire surveys, sensor data collection, and online activity monitoring.

[0073] (Example 2) A career guidance support system according to an embodiment of the present invention is a system in which a student's information is input, analyzed by a generation AI, and advice is provided. The career guidance support system inputs the student's information, analyzes it, and advises the student on an optimal life plan. For example, the career guidance support system inputs the student's thoughts, personality, grades, preferences, and future aspirations. The career guidance support system then analyzes the input information using a generation AI. The generation AI then advises the student on an optimal life plan based on the student's information. For example, if the generation AI indicates a student's desire to "become a doctor in the future," the system provides specific advice on the necessary major, university, and skills. This enables the career guidance support system to efficiently input, analyze, provide advice, collaborate with teachers, and collect data on the student's information. This enables the career guidance support system to efficiently input, analyze, provide advice, collaborate with teachers, and collect data on the student's information. For example, the generation AI analyzes the student's information and advises on an optimal life plan, enabling more objective and theoretical career guidance. Furthermore, teachers can provide more appropriate career guidance by referring to the advice provided by the generation AI.

[0074] A career guidance support system according to an embodiment includes an input unit, an analysis unit, a provision unit, a teacher collaboration unit, and a data collection unit. The input unit inputs information about the student. The information about the student includes, but is not limited to, thoughts, personality, grades, preferences, and future aspirations. For example, the student himself / herself can input the information into the input unit, or a teacher can input the information. The analysis unit uses a generation AI to analyze the information input by the input unit. The generation AI comprehensively assesses the student's information using, for example, natural language processing technology or a machine learning algorithm. The generation AI advises the student on an optimal life plan based on, for example, the student's thoughts, personality, grades, preferences, and future aspirations. The provision unit provides the student with advice based on the analysis results obtained by the analysis unit. The provision unit advises the student on an optimal life plan based on, for example, data analyzed by the generation AI. The teacher collaboration unit provides the advice provided by the provision unit to teachers and supports the teachers in providing career guidance based on the advice. The teacher collaboration unit, for example, provides advice from the generation AI to teachers and supports teachers in providing career guidance based on that advice. The data collection unit collects data such as the student's grades, personality, and preferences. The data collection unit collects data using methods such as questionnaire surveys, sensor data collection, and online activity monitoring. This enables the career guidance support system according to the embodiment to efficiently input, analyze, provide advice, collaborate with teachers, and collect data about the student.

[0075] The data collection unit can collect data on the student's grades, personality, and preferences. The data collection unit, for example, collects the student's grades. Grades include, but are not limited to, semester grades, subject grades, and overall evaluations. The data collection unit can also collect the student's personality. Personality includes, but is not limited to, personality diagnostic tests, psychological evaluations, and behavioral observations. The data collection unit can also collect the student's preferences. Preferences include, but are not limited to, hobbies, areas of interest, and favorite activities. By collecting data such as the student's grades, personality, and preferences, more accurate advice can be provided. Some or all of the above-described processing in the data collection unit can be performed using, or without, AI. For example, the data collection unit can input the results of a questionnaire survey into a generation AI, which then analyzes the data.

[0076] The analysis unit can use the generation AI to comprehensively determine the student's thoughts, personality, grades, preferences, and future aspirations. The analysis unit, for example, uses the generation AI to determine the student's thoughts. The generation AI, for example, uses natural language processing technology to analyze the student's thoughts from their writing. The analysis unit can also use the generation AI to determine the student's personality. The generation AI, for example, analyzes the student's personality based on the results of a personality diagnostic test. The analysis unit can also use the generation AI to determine the student's grades. The generation AI analyzes the student's academic ability based on, for example, semester grades and subject grades. The analysis unit can also use the generation AI to determine the student's preferences. The generation AI analyzes preferences based on, for example, the student's hobbies and areas of interest. The analysis unit can also use the generation AI to determine the student's future aspirations. For example, the generation AI analyzes the required departments, university to enter, and necessary skills based on the student's future aspirations. This allows the generation AI to comprehensively assess the student's information, enabling more appropriate advice to be provided. Some or all of the above-described processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the analysis unit can input the student's information into the generation AI, which then makes a comprehensive assessment.

[0077] The provision unit can advise the student on an appropriate life plan based on the data analyzed by the generation AI. For example, the provision unit advises the student on an optimal life plan based on the data analyzed by the generation AI. For example, the generation AI provides specific advice on the required department, university, and necessary skills based on the student's thoughts, personality, grades, preferences, and future aspirations. The provision unit can also advise the student on a career plan based on the data analyzed by the generation AI. For example, the generation AI provides specific advice on the required qualifications, experience, and career path for the student's desired career. Furthermore, the provision unit can also advise the student on a study plan based on the data analyzed by the generation AI. For example, the generation AI provides specific advice on effective study methods and study schedules based on the student's grades and learning style. This allows for more specific career guidance by advising the student on an optimal life plan based on the data analyzed by the generation AI. Some or all of the above-described processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit provides advice to the student based on the data analyzed by the generation AI.

[0078] The Faculty Collaboration Unit can provide teachers with advice from the generative AI and support teachers in providing career guidance based on that advice. For example, the Faculty Collaboration Unit provides teachers with advice from the generative AI. The teachers can provide career guidance to the students in question based on the advice from the generative AI. The Faculty Collaboration Unit also supports teachers in providing career guidance based on the advice from the generative AI. For example, the Faculty Collaboration Unit can provide teachers with information on how to interpret and utilize the advice from the generative AI. Furthermore, the Faculty Collaboration Unit has a function for facilitating collaboration between teachers and the generative AI. For example, the Faculty Collaboration Unit can enable teachers to check the advice from the generative AI in real time. The Faculty Collaboration Unit can also enable teachers to provide feedback on the advice from the generative AI. This allows teachers to receive advice from the generative AI and support teachers in providing career guidance based on that advice, thereby enabling more objective and theoretical career guidance. Some or all of the above-described processing in the Faculty Collaboration Unit may be performed using AI, or may be performed without AI. For example, the Faculty Collaboration Department will provide advice from the generative AI to teachers and support them in providing career guidance based on that advice.

[0079] The provision unit can refer to the career paths chosen by students with similar aspirations and the results they achieved based on past data. For example, the provision unit refers to the career paths chosen by students with similar aspirations based on past data. Past data includes, but is not limited to, past career choice data, success stories, and failure stories. The provision unit can also refer to the results achieved by students with similar aspirations based on past data. For example, the provision unit analyzes the career paths chosen by students with similar aspirations and the results they achieved based on past data. Furthermore, the provision unit can provide more reliable advice to the student based on the past data. For example, the provision unit provides specific career choice advice to the student based on the past data. By referring to the past data, more reliable advice can be provided. Some or all of the above-described processing by the provision unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the provision unit can input past data into a generation AI and have the generation AI perform analysis.

[0080] The input unit can estimate a student's emotions and adjust the timing of input based on the estimated student's emotions. The input unit estimates the student's emotions, for example. Emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, and text analysis technology. For example, the input unit can capture the student's facial expression with a camera and estimate the emotion using facial expression recognition technology. The input unit can also record the student's voice and estimate the emotion using voice analysis technology. The input unit can also analyze the student's text input and estimate the emotion using text analysis technology. Next, the input unit adjusts the timing of input based on the estimated student's emotions. For example, if the student is stressed, the timing of prompting input can be delayed to allow the student to enter in a relaxed state. If the student is relaxed, the timing of prompting input can be advanced to allow the student to enter information smoothly. Furthermore, if the student is concentrating, the timing of prompting input can be optimized to allow the student to enter information efficiently. This allows the timing of input to be adjusted according to the student's emotions, enabling more appropriate information entry. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit may input facial expression data of a student to the generation AI, which may then estimate the emotion.

[0081] The input unit can analyze the student's past input history and select the optimal input method. The input unit, for example, analyzes the student's past input history. The input history includes, for example, past input data, input frequency, and input content trends, but is not limited to these examples. Next, the input unit selects the optimal input method based on the past input history. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the student has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the student's past input history. Furthermore, the input unit can customize the input method based on the student's past input content. This enables more efficient information input by analyzing the student's past input history. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input past input history data into a generation AI, which can select the optimal input method.

[0082] The input unit can filter the data based on the student's current learning situation and areas of interest when inputting the data. The input unit, for example, grasps the student's current learning situation. Examples of the learning situation include, but are not limited to, learning progress, level of understanding, and study time. The input unit can also grasp the student's areas of interest. Examples of the areas of interest include, but are not limited to, subjects of interest, hobbies, and future goals. Next, the input unit performs filtering based on the student's current learning situation and areas of interest. For example, only relevant questions can be displayed based on the student's current learning situation. Also, interesting questions can be preferentially displayed based on the student's areas of interest. Furthermore, questions of an appropriate level of difficulty can be displayed depending on the student's learning progress. This allows for more relevant information to be input by filtering based on the student's current learning situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using AI or without AI. For example, the input unit can input the student's learning situation data to a generation AI, which then performs filtering.

[0083] The input unit can select the optimal input means depending on the student's input method when inputting data. The input unit, for example, identifies the student's input method. The input method includes, but is not limited to, voice input, text input, and image input. Next, the input unit selects the optimal input means depending on the student's input method. For example, if the student desires voice input, a voice recognition function can be provided. Also, if the student desires text input, keyboard input can be provided. Furthermore, if the student desires image input, an image recognition function can be provided. This allows for smoother information input by selecting the optimal input means depending on the student's input method. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the student's input method data into a generation AI, which can select the optimal input means.

[0084] The input unit can estimate the student's emotions and determine the priority of information to be input based on the estimated student's emotions. The input unit estimates the student's emotions, for example. Emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, and text analysis technology. For example, the input unit can capture the student's facial expression with a camera and estimate the emotion using facial expression recognition technology. The input unit can also record the student's voice and estimate the emotion using voice analysis technology. Furthermore, the input unit can analyze the student's text input and estimate the emotion using text analysis technology. Next, the input unit determines the priority of information to be input based on the estimated student's emotions. For example, if the student is stressed, important information can be input first. Also, if the student is relaxed, detailed information can be input first. Furthermore, if the student is concentrating, the priority can be adjusted to allow information to be input efficiently. In this way, by determining the priority of information to be input according to the student's emotions, more important information can be input first. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit may input facial expression data of a student to the generation AI, which may then estimate the emotion.

[0085] The input unit can prioritize inputting highly relevant information by taking into account the student's geographical location information. The input unit, for example, grasps the student's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and location history. Next, the input unit prioritizes inputting highly relevant information by taking into account the student's geographical location information. For example, based on the student's current location, the input unit prioritizes inputting nearby educational destinations and occupational information. Regional career path information can also be provided based on the student's geographical location information. Furthermore, educational destinations with good transportation access can be prioritized by taking into account the student's geographical location information. This enables more relevant information to be input by taking into account the student's geographical location information. Some or all of the above-described processing in the input unit may be performed using AI or without AI. For example, the input unit can input the student's geographical location information data to a generation AI, which then prioritizes inputting highly relevant information.

[0086] The input unit can analyze the student's social media activity and input relevant information at the time of input. The input unit, for example, analyzes the student's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of followers, and the engagement rate. Next, the input unit inputs relevant information based on the student's social media activity. For example, the input unit analyzes the content of the student's social media posts and inputs relevant career information. The input unit can also input relevant career information based on the student's social media friends' activities. Furthermore, the input unit can input relevant career information based on the student's social media check-in information. This allows for more relevant information to be input by analyzing the student's social media activity. Some or all of the above-described processing in the input unit may be performed using AI, or may be performed without using AI. For example, the input unit can input the student's social media data to a generation AI, which then inputs relevant information.

[0087] The input unit can customize the input method by reflecting the student's past feedback during input. The input unit, for example, collects the student's past feedback. The feedback includes, but is not limited to, user opinions, evaluations, and areas for improvement. Next, the input unit customizes the input method based on the past feedback. For example, the input method is improved based on feedback provided by the student in the past. The input unit can also customize the input interface by referring to the student's past feedback. Furthermore, the input unit can also optimize the input procedure by reflecting the student's past feedback. This provides a more appropriate input method by reflecting the student's past feedback. Some or all of the above-described processing in the input unit may be performed using AI or without AI. For example, the input unit can input the student's feedback data into a generation AI, which can customize the input method.

[0088] The analysis unit can estimate the student's emotions and adjust the presentation method of the analysis based on the estimated student's emotions. The analysis unit, for example, estimates the student's emotions. Emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, and text analysis technology. For example, the analysis unit can capture the student's facial expressions with a camera and estimate the emotion using facial expression recognition technology. The analysis unit can also record the student's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can analyze the student's text input and estimate the emotion using text analysis technology. Next, the analysis unit adjusts the presentation method of the analysis based on the estimated student's emotions. For example, if the student is relaxed, detailed analysis results can be provided. If the student is stressed, concise and to the point analysis results can be provided. Furthermore, if the student is concentrating, analysis results that are easy to understand visually can be provided. In this way, by adjusting the presentation method of the analysis according to the student's emotions, more understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input facial expression data of a student into the generation AI, which may then estimate the emotion.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, evaluates the importance of the information. The importance of the information includes, for example, impact, urgency, and relevance, but is not limited to these examples. Next, the analysis unit adjusts the level of detail of the analysis based on the importance of the information. For example, a detailed analysis can be performed for important information. On the other hand, a brief analysis can be performed for less important information. Furthermore, the level of detail of the analysis can be adjusted in stages depending on the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more important information can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the analysis unit can input information importance data into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit, for example, classifies the categories of information. Information categories include, but are not limited to, academic information, personality information, and interest information. Next, the analysis unit applies different analysis algorithms depending on the category of information. For example, an analysis algorithm dedicated to academic performance can be applied to information about academic performance. An analysis algorithm dedicated to personality analysis can be applied to information about personality. Furthermore, an analysis algorithm dedicated to career planning can be applied to information about future aspirations. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the analysis unit can input information category data into a generation AI and apply different analysis algorithms depending on the generation AI.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results. The analysis unit, for example, collects the student's past analysis results. The past analysis results include, but are not limited to, past data points, analysis accuracy, and result reliability. Next, the analysis unit corrects the current analysis result based on the past analysis results. For example, the analysis algorithm can be optimized by referring to the student's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the past analysis results. In this way, by referring to the student's past analysis results, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the analysis unit can input past analysis result data into a generation AI, which can then improve the accuracy of the analysis.

[0092] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated student's emotions. The analysis unit estimates the student's emotions, for example. Emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, and text analysis technology. For example, the analysis unit can capture the student's facial expressions with a camera and estimate the emotion using facial expression recognition technology. The analysis unit can also record the student's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can analyze the student's text input and estimate the emotion using text analysis technology. Next, the analysis unit adjusts the length of the analysis based on the estimated student's emotions. For example, if the student is in a hurry, the analysis unit can provide a short and concise analysis result. On the other hand, if the student is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the student is concentrating, the analysis unit can provide an analysis result of appropriate length. In this way, by adjusting the length of the analysis according to the student's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input facial expression data of a student into the generation AI, which may then estimate the emotion.

[0093] During analysis, the analysis unit can determine the analysis priority based on the time when the information was submitted. The analysis unit, for example, grasps the time when the information was submitted. The submission time includes, but is not limited to, the submission deadline, the frequency of submission, and the timing of submission. Next, the analysis unit determines the analysis priority based on the time when the information was submitted. For example, the analysis unit prioritizes the analysis of recently submitted information. Also, information submitted earlier can be analyzed later. Furthermore, the analysis priority can be gradually adjusted depending on the time of submission. In this way, by determining the analysis priority based on the time when the information was submitted, more timely analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the analysis unit can input data on the time when the information was submitted into a generation AI, and the generation AI can determine the analysis priority.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, evaluates the relevance of the information. The relevance of the information includes, but is not limited to, common themes, related data points, and correlations. Next, the analysis unit adjusts the order of analysis based on the relevance of the information. For example, highly relevant information is analyzed preferentially. Also, less relevant information can be analyzed later. Furthermore, the order of analysis can be adjusted in stages depending on the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, more relevant information is analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the analysis unit can input relevance data of the information into a generation AI and adjust the order of analysis using the generation AI.

[0095] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise. The analysis unit, for example, evaluates the student's level of expertise. Examples of technical terminology include, but are not limited to, academic background, work history, and knowledge of a specialized field. Next, the analysis unit adjusts the use of technical terminology in the analysis according to the student's level of expertise. For example, a student with a high level of expertise can be provided with analysis results that use a lot of technical terminology. Furthermore, a student with a low level of expertise can be provided with analysis results that avoid technical terminology. Furthermore, the use of technical terminology in the analysis results can be gradually adjusted according to the student's level of expertise. By adjusting the use of technical terminology in the analysis according to the student's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or without using a generation AI. For example, the analysis unit can input the student's level of expertise data into a generation AI and have the generation AI adjust the use of technical terminology in the analysis.

[0096] The providing unit can estimate the student's emotions and adjust the way the advice is presented based on the estimated student's emotions. The providing unit, for example, estimates the student's emotions. The emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, or text analysis technology. For example, the providing unit captures the student's facial expressions with a camera and estimates the emotion using facial expression recognition technology. The providing unit can also record the student's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can analyze the student's text input and estimate the emotion using text analysis technology. Next, the providing unit adjusts the way the advice is presented based on the estimated student's emotions. For example, if the student is relaxed, detailed advice can be provided. If the student is stressed, concise and to-the-point advice can be provided. Furthermore, if the student is concentrating, visually easy-to-understand advice can be provided. In this way, more appropriate advice can be provided by adjusting the way the advice is presented according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input facial expression data of a student into the generation AI, which may then estimate the emotion.

[0097] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the information. The providing unit, for example, evaluates the importance of the information. The importance of the information includes, for example, impact, urgency, and relevance, but is not limited to these examples. Next, the providing unit adjusts the level of detail of the advice based on the importance of the information. For example, detailed advice can be provided for important information. Concise advice can be provided for less important information. Furthermore, the level of detail of the advice can be adjusted in stages depending on the importance of the information. In this way, by adjusting the level of detail of the advice based on the importance of the information, detailed advice can be provided for more important information. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (for example, a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit can input information importance data to a generation AI and have the generation AI adjust the level of detail of the advice.

[0098] When providing advice, the providing unit can apply different advice algorithms depending on the category of information. The providing unit, for example, classifies the category of information. The categories of information include, but are not limited to, academic information, personality information, and interest information. Next, the providing unit applies different advice algorithms depending on the category of information. For example, an advice algorithm dedicated to academic performance can be applied to advice regarding academic performance. Furthermore, an advice algorithm dedicated to personality analysis can be applied to advice regarding personality. Furthermore, an advice algorithm dedicated to career planning can be applied to advice regarding future aspirations. In this way, by applying different advice algorithms depending on the category of information, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the providing unit can input information category data into a generation AI and apply different advice algorithms using the generation AI.

[0099] When providing advice, the providing unit can improve the accuracy of the advice by referring to the student's past advice results. The providing unit, for example, collects the student's past advice results. The past advice results include, for example, but are not limited to, the success rate of past advice, the content of feedback, and areas for improvement. Next, the providing unit corrects the current advice based on the past advice results. For example, the providing unit optimizes the advice algorithm by referring to the student's past advice results. The providing unit can also improve the accuracy of the advice by using the past advice results. As a result, more accurate advice is provided by referring to the student's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit can input past advice result data into a generation AI, which can improve the accuracy of the advice.

[0100] The providing unit can estimate the student's emotions and adjust the length of the advice based on the estimated student's emotions. The providing unit, for example, estimates the student's emotions. The emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, or text analysis technology. For example, the providing unit captures the student's facial expressions with a camera and estimates the emotion using facial expression recognition technology. The providing unit can also record the student's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can analyze the student's text input and estimate the emotion using text analysis technology. Next, the providing unit adjusts the length of the advice based on the estimated student's emotions. For example, if the student is in a hurry, the providing unit can provide short, to-the-point advice. If the student is relaxed, the providing unit can provide detailed advice. Furthermore, if the student is concentrating, the providing unit can provide advice of an appropriate length. In this way, by adjusting the length of the advice according to the student's emotions, more appropriate advice can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input facial expression data of a student into the generation AI, which may then estimate the emotion.

[0101] When providing advice, the providing unit can determine the priority of advice based on the time of submission of information. The providing unit, for example, grasps the time of submission of information. The submission time includes, for example, a submission deadline, a submission frequency, and a submission timing, but is not limited to these examples. Next, the providing unit determines the priority of advice based on the time of submission of information. For example, recently submitted information is reflected preferentially in the advice. Also, advice can be provided later for information that was submitted earlier. Furthermore, the priority of advice can be gradually adjusted depending on the time of submission. In this way, by determining the priority of advice based on the time of submission of information, more timely advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit can input information submission time data into a generation AI and have the generation AI determine the priority of advice.

[0102] When providing advice, the providing unit can adjust the order of advice based on the relevance of the information. The providing unit, for example, evaluates the relevance of the information. The relevance of the information includes, for example, a common theme, related data points, correlations, etc., but is not limited to these examples. Next, the providing unit adjusts the order of advice based on the relevance of the information. For example, highly relevant information is reflected preferentially in the advice. Also, less relevant information can be provided later in advice. Furthermore, the order of advice can be adjusted in stages depending on the relevance of the information. In this way, by adjusting the order of advice based on the relevance of the information, more relevant information is given priority in advice. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the providing unit can input information relevance data into a generation AI and adjust the order of advice using the generation AI.

[0103] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the student's level of expertise. The providing unit, for example, evaluates the student's level of expertise. Examples of the level of expertise include, but are not limited to, academic background, work history, and knowledge of a specialized field. Next, the providing unit adjusts the use of technical terms in the advice according to the student's level of expertise. For example, advice that uses a lot of technical terms can be provided to a student with a high level of expertise. Furthermore, advice that avoids technical terms can be provided to a student with a low level of expertise. Furthermore, the use of technical terms in the advice can be gradually adjusted according to the student's level of expertise. By adjusting the use of technical terms in the advice according to the student's level of expertise, more understandable advice can be provided. Some or all of the above-described processing in the providing unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI) or may be performed without using a generation AI. For example, the providing unit can input the student's level of expertise data into a generation AI, which can adjust the use of technical terms in the advice.

[0104] The teacher collaboration unit can estimate the teacher's emotions and adjust the collaboration method based on the estimated teacher's emotions. The teacher collaboration unit, for example, estimates the teacher's emotions. Emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, and text analysis technology. For example, the teacher collaboration unit can capture the teacher's facial expression with a camera and estimate the emotion using facial expression recognition technology. The teacher collaboration unit can also record the teacher's voice and estimate the emotion using voice analysis technology. Furthermore, the teacher collaboration unit can analyze the teacher's text input and estimate the emotion using text analysis technology. Next, the teacher collaboration unit adjusts the collaboration method based on the estimated teacher's emotions. For example, if the teacher is stressed, a simple and efficient collaboration method can be provided. If the teacher is relaxed, a detailed collaboration method can be provided. Furthermore, if the teacher is concentrating, a visually easy-to-understand collaboration method can be provided. This enables more appropriate collaboration by adjusting the collaboration method according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 faculty collaboration unit may be performed using AI, or may be performed without using AI. For example, the faculty collaboration unit may input facial expression data of a teacher into the generation AI, which may then estimate the emotion.

[0105] During teacher collaboration, the teacher collaboration unit can select the optimal collaboration method by referring to the teacher's past teaching history. The teacher collaboration unit, for example, collects the teacher's past teaching history. The teaching history includes, for example, but is not limited to, past teaching content, teaching results, feedback, etc. Next, the teacher collaboration unit proposes the optimal collaboration method based on the past teaching history. For example, an effective collaboration method is selected from the teacher's past teaching history. The teacher collaboration unit can also customize the collaboration method by referring to the past teaching history. This enables more effective collaboration by referring to the teacher's past teaching history. Some or all of the above-mentioned processing in the teacher collaboration unit may be performed using AI, or may be performed without using AI. For example, the teacher collaboration unit can input the teacher's teaching history data into a generation AI, which can select the optimal collaboration method.

[0106] During teacher collaboration, the teacher collaboration unit can customize the collaboration method based on the teacher's current teaching status. The teacher collaboration unit, for example, grasps the teacher's current teaching status. The teaching status includes, but is not limited to, the current teaching content, the progress of the teaching, and the results of the teaching. Next, the teacher collaboration unit customizes the collaboration method based on the teacher's current teaching status. For example, the unit provides the optimal collaboration method based on the teacher's current teaching status. The unit can also grasp the teacher's teaching status in real time and adjust the collaboration method. Furthermore, the collaboration method can be customized according to the teacher's teaching status. Customizing the collaboration method based on the teacher's current teaching status enables more appropriate collaboration. Some or all of the above-described processing in the teacher collaboration unit may be performed using AI, or may be performed without AI. For example, the teacher collaboration unit can input the teacher's teaching status data into a generation AI and have the generation AI customize the collaboration method.

[0107] The Faculty Collaboration Unit can improve the collaboration method by reflecting faculty feedback during faculty collaboration. For example, the Faculty Collaboration Unit collects faculty feedback. The feedback includes, but is not limited to, faculty opinions, evaluations, and areas for improvement. Next, the Faculty Collaboration Unit improves the collaboration method based on the feedback. For example, the collaboration method can be optimized by referring to past faculty feedback. The Faculty Collaboration Unit can also customize the collaboration method by reflecting the feedback. This enables more effective collaboration by reflecting faculty feedback. Some or all of the above-mentioned processing in the Faculty Collaboration Unit may be performed using AI, or may be performed without using AI. For example, the Faculty Collaboration Unit can input faculty feedback data into a generation AI, which can then improve the collaboration method.

[0108] The teacher collaboration unit can estimate the teacher's emotions and determine collaboration priorities based on the estimated teacher's emotions. The teacher collaboration unit estimates the teacher's emotions, for example. Emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, and text analysis technology. For example, the teacher collaboration unit can capture the teacher's facial expression with a camera and estimate the emotion using facial expression recognition technology. The teacher collaboration unit can also record the teacher's voice and estimate the emotion using voice analysis technology. Furthermore, the teacher collaboration unit can analyze the teacher's text input and estimate the emotion using text analysis technology. Next, the teacher collaboration unit determines collaboration priorities based on the estimated teacher's emotions. For example, if the teacher is stressed, important collaborations can be prioritized. Also, if the teacher is relaxed, detailed collaborations can be prioritized. Furthermore, if the teacher is concentrating, the priority can be adjusted to efficiently perform collaborations. In this way, by determining collaboration priorities according to the teacher's emotions, more important collaborations can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 faculty collaboration unit may be performed using AI, or may be performed without using AI. For example, the faculty collaboration unit may input facial expression data of a teacher into the generation AI, which may then estimate the emotion.

[0109] During teacher collaboration, the teacher collaboration unit can select the optimal collaboration method by taking into account the teacher's geographic location information. The teacher collaboration unit, for example, grasps the teacher's geographic location information. Geographic location information includes, but is not limited to, GPS data, address information, and location history. Next, the teacher collaboration unit selects the optimal collaboration method by taking into account the teacher's geographic location information. For example, the optimal collaboration method can be provided based on the teacher's current location. Regionally specific collaboration methods can also be proposed based on the teacher's geographic location information. Furthermore, an efficient collaboration method can be selected by taking into account the teacher's geographic location information. This enables more appropriate collaboration by taking into account the teacher's geographic location information. Some or all of the above-described processing in the teacher collaboration unit may be performed using AI, or may be performed without using AI. For example, the teacher collaboration unit can input the teacher's geographic location information data into a generation AI, which then selects the optimal collaboration method.

[0110] During faculty collaboration, the Faculty Collaboration Unit can analyze faculty members' social media activities and propose collaboration methods. The Faculty Collaboration Unit, for example, analyzes the faculty members' social media activities. Social media activities include, but are not limited to, the content of posts, the number of followers, and engagement rates. Next, the Faculty Collaboration Unit proposes collaboration methods based on the faculty members' social media activities. For example, the Faculty Collaboration Unit analyzes the faculty members' social media activities and proposes optimal collaboration methods. The Faculty Collaboration Unit can also customize collaboration methods based on the content of the faculty members' social media posts. Furthermore, collaboration methods can be proposed based on the activities of the faculty members' friends on social media. In this way, more appropriate collaboration methods are proposed by analyzing the faculty members' social media activities. Some or all of the above-described processing in the Faculty Collaboration Unit may be performed using AI, or may be performed without AI. For example, the Faculty Collaboration Unit can input the faculty members' social media data into a generation AI, which then proposes collaboration methods.

[0111] During teacher collaboration, the teacher collaboration unit can customize the collaboration method by reflecting teachers' past feedback. For example, the teacher collaboration unit collects teachers' past feedback. The feedback includes, but is not limited to, teachers' opinions, evaluations, and areas for improvement. Next, the teacher collaboration unit improves the collaboration method based on the past feedback. For example, the collaboration method is optimized by referring to teachers' past feedback. The teacher collaboration unit can also customize the collaboration means by reflecting the feedback. This enables more appropriate collaboration by reflecting teachers' past feedback. Some or all of the above-mentioned processing in the teacher collaboration unit may be performed using AI, or may be performed without using AI. For example, the teacher collaboration unit can input teachers' feedback data into a generation AI, which can then improve the collaboration method.

[0112] The data collection unit can estimate the student's emotions and adjust the data collection method based on the estimated student's emotions. The data collection unit, for example, estimates the student's emotions. Emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, and text analysis technology. For example, the data collection unit can capture the student's facial expressions with a camera and estimate the emotion using facial expression recognition technology. The data collection unit can also record the student's voice and estimate the emotion using voice analysis technology. Furthermore, the data collection unit can analyze the student's text input and estimate the emotion using text analysis technology. Next, the data collection unit adjusts the data collection method based on the estimated student's emotions. For example, if the student is relaxed, detailed data can be collected. On the other hand, if the student is stressed, concise and to the point data can be collected. Furthermore, if the student is concentrating, the method can be adjusted to efficiently collect data. This allows for more appropriate data collection by adjusting the data collection method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit may input student facial expression data into the generation AI, which may then estimate emotions.

[0113] When collecting data, the data collection unit can select the optimal collection method by referring to the student's past data collection history. The data collection unit, for example, collects the student's past data collection history. The data collection history includes, for example, but is not limited to, the content of past data collection, the frequency of collection, and the effectiveness of the collection method. Next, the data collection unit proposes the optimal collection method based on the past data collection history. For example, an effective collection method is selected from the student's past data collection history. The data collection unit can also customize the collection method by referring to the past data collection history. This enables more effective data collection by referring to the student's past data collection history. Some or all of the above-mentioned processing in the data collection unit may be performed using AI or without AI. For example, the data collection unit can input the student's data collection history data into a generation AI, which can select the optimal collection method.

[0114] The data collection unit can customize the collection means based on the student's current learning situation when collecting data. The data collection unit, for example, grasps the student's current learning situation. The learning situation includes, but is not limited to, learning progress, level of understanding, and study time. Next, the data collection unit customizes the collection means based on the student's current learning situation. For example, the data collection unit provides the optimal collection means based on the student's current learning situation. The data collection unit can also grasp the student's learning situation in real time and adjust the collection means. Furthermore, the data collection unit can customize the collection means according to the student's learning situation. This enables more appropriate data collection by customizing the collection means based on the student's current learning situation. Some or all of the above-mentioned processing in the data collection unit may be performed using AI or without AI. For example, the data collection unit can input the student's learning situation data into a generation AI and have the generation AI customize the collection means.

[0115] The data collection unit can improve the collection method by reflecting student feedback during data collection. The data collection unit, for example, collects student feedback. The feedback includes, but is not limited to, student opinions, evaluations, and areas for improvement. The data collection unit then improves the collection method based on the feedback. For example, the data collection unit can optimize the collection method by referring to students' past feedback. The data collection unit can also customize the collection means by reflecting the feedback. This enables more effective data collection by reflecting student feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can input student feedback data into a generation AI, which can then improve the collection method.

[0116] The data collection unit can estimate the student's emotions and determine the priority of data collection based on the estimated student's emotions. The data collection unit, for example, estimates the student's emotions. Emotion estimation is performed using, for example, facial expression recognition technology, voice analysis technology, and text analysis technology. For example, the data collection unit can capture the student's facial expressions with a camera and estimate the emotion using facial expression recognition technology. The data collection unit can also record the student's voice and estimate the emotion using voice analysis technology. Furthermore, the data collection unit can analyze the student's text input and estimate the emotion using text analysis technology. Next, the data collection unit determines the priority of data collection based on the estimated student's emotions. For example, if the student is relaxed, detailed data collection can be prioritized. Also, if the student is stressed, important data collection can be prioritized. Furthermore, if the student is concentrating, the priority can be adjusted to efficiently collect data. In this way, by determining the priority of data collection according to the student's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit may input student facial expression data into the generation AI, which may then estimate emotions.

[0117] The data collection unit can select the optimal collection method by taking into account the student's geographic location information when collecting data. The data collection unit, for example, grasps the student's geographic location information. The geographic location information includes, but is not limited to, GPS data, address information, and location history. Next, the data collection unit selects the optimal collection method by taking into account the student's geographic location information. For example, the optimal collection method can be provided based on the student's current location. Furthermore, a region-specific data collection method can be suggested based on the student's geographic location information. Furthermore, an efficient collection method can be selected by taking into account the student's geographic location information. This enables more appropriate data collection by taking into account the student's geographic location information. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can input the student's geographic location information data into a generation AI, which then selects the optimal collection method.

[0118] When collecting data, the data collection unit can analyze the student's social media activity and suggest collection methods. The data collection unit, for example, analyzes the student's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of followers, and the engagement rate. Next, the data collection unit suggests collection methods based on the student's social media activity. For example, the data collection unit analyzes the student's social media activity and suggests the optimal collection method. The data collection unit can also customize the collection method based on the content of the student's social media posts. Furthermore, the data collection unit can also suggest collection methods based on the activity of the student's friends on social media. In this way, by analyzing the student's social media activity, more appropriate data collection methods are suggested. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can input the student's social media data into a generation AI, which then suggests collection methods.

[0119] The data collection unit can customize the collection method by reflecting students' past feedback when collecting data. The data collection unit, for example, collects students' past feedback. The feedback includes, but is not limited to, students' opinions, evaluations, and areas for improvement. Next, the data collection unit improves the collection method based on the past feedback. For example, the data collection unit optimizes the collection method by referring to students' past feedback. The data collection unit can also customize the collection means by reflecting the feedback. This enables more appropriate data collection by reflecting students' past feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using AI or without AI. For example, the data collection unit can input students' feedback data into a generation AI, which can then improve the collection method. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, provision unit, faculty collaboration unit, and data collection unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input information about the student using the reception device 38 of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using the generation AI. For example, the provision unit can provide advice to the student using the output device 40 of the smart device 14. For example, the faculty collaboration unit can be realized by the specific processing unit 290 of the data processing device 12 and provides the advice of the generation AI to the faculty. For example, the data collection unit can collect data about the student using the camera 42 or sensor of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned input unit, analysis unit, provision unit, faculty collaboration unit, and data collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input information about the student using the microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the input information using the generation AI. For example, the provision unit can provide advice to the student using the speaker 240 of the smart glasses 214. For example, the faculty collaboration unit can be realized by the specific processing unit 290 of the data processing device 12 and provide the advice of the generation AI to the faculty. For example, the data collection unit can collect data about the student using the camera 42 or sensor of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, provision unit, teacher collaboration unit, and data collection unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit can input information about the student using the microphone 238 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using the generation AI. For example, the provision unit can provide advice to the student using the speaker 240 of the headset-type terminal 314. For example, the teacher collaboration unit is realized by the specific processing unit 290 of the data processing device 12 and provides the advice of the generation AI to the teacher. For example, the data collection unit can collect data about the student using the camera 42 or sensor of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, provision unit, teacher collaboration unit, and data collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input information about the student using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. For example, the provision unit can provide advice to the student using the speaker 240 of the robot 414. For example, the teacher collaboration unit is realized by the specific processing unit 290 of the data processing device 12 and provides the advice of the generation AI to the teacher. For example, the data collection unit can collect data about the student using the camera 42 or sensor of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The career guidance support system can also collect student health data and use it for analysis. For example, the data collection unit can collect health data such as students' sleep patterns, exercise levels, and dietary habits. The analysis unit can then evaluate students' learning efficiency and stress levels based on this health data. The provision unit can provide students with advice on maintaining a healthy lifestyle based on the health data. This allows the career guidance support system to provide comprehensive career guidance that takes students' health conditions into consideration.

[0122] The career guidance support system can further analyze students' learning styles and suggest optimal learning methods. For example, the data collection unit can collect data such as students' learning history, learning environment, and study time. Next, the analysis unit can analyze students' learning styles based on this data. The provision unit can suggest effective learning methods and study schedules to students based on their learning styles. This allows the career guidance support system to provide career guidance that meets the individual learning needs of students.

[0123] The career guidance support system can further evaluate students' social skills and provide advice for improvement. For example, the data collection unit can collect data on students' social skills, such as communication ability, leadership, and cooperation. Next, the analysis unit can evaluate students' social skills based on this data. The provision unit can provide students with specific advice for improving their social skills based on the results of the social skill evaluation. In this way, the career guidance support system can provide support for improving students' social skills.

[0124] The career guidance support system can further evaluate students' creativity and provide advice to develop their creativity. For example, the data collection unit can collect data on students' creative activities, ideas, projects, etc. Next, the analysis unit can evaluate students' creativity based on this data. The provision unit can provide students with specific advice to develop their creativity based on the results of the creativity evaluation. In this way, the career guidance support system can provide support to maximize students' creativity.

[0125] The career guidance support system can further evaluate the student's time management ability and provide advice for efficient time management. For example, the data collection unit can collect data on the student's schedule, task management, and time usage. Next, the analysis unit can evaluate the student's time management ability based on this data. The provision unit can provide the student with specific advice for efficient time management based on the evaluation results of the time management ability. In this way, the career guidance support system can provide support for improving the student's time management ability.

[0126] The career guidance support system can estimate a student's emotions and adjust the learning environment based on the estimated emotions. For example, the data collection unit can estimate the student's emotions from their facial expressions, voice, text input, etc. Next, the analysis unit can suggest a learning environment that allows the student to relax based on the estimated emotions. The provision unit can advise the student on how to set up an appropriate learning environment based on the emotions. In this way, the career guidance support system can provide a learning environment that suits the student's emotions.

[0127] The career guidance support system can estimate a student's emotions and provide advice to improve motivation based on the estimated emotions. For example, the data collection unit can estimate emotions from the student's facial expressions, voice, text input, etc. Next, the analysis unit can suggest a method to improve the student's motivation based on the estimated emotions. The provision unit can provide the student with specific advice to increase motivation based on the emotions. In this way, the career guidance support system can support the improvement of motivation according to the student's emotions.

[0128] The career guidance support system can estimate a student's emotions and provide advice for stress management based on the estimated emotions. For example, the data collection unit can estimate the student's emotions from their facial expressions, voice, text input, etc. Next, the analysis unit can evaluate the student's stress level based on the estimated emotions. The provision unit can provide the student with specific advice for stress management based on the emotions. This allows the career guidance support system to support stress management according to the student's emotions.

[0129] The career guidance support system can estimate a student's emotions and adjust the student's learning progress based on the estimated emotions. For example, the data collection unit can estimate the student's emotions from the student's facial expressions, voice, text input, etc. Next, the analysis unit can evaluate the student's learning progress based on the estimated emotions. The provision unit can provide the student with specific advice for adjusting the student's learning progress based on the emotions. This allows the career guidance support system to adjust the student's learning progress according to the student's emotions.

[0130] The career guidance support system can estimate a student's emotions and adjust the feedback method based on the estimated emotions. For example, the data collection unit can estimate the student's emotions from their facial expressions, voice, text input, etc. Next, the analysis unit can suggest a feedback method to the student based on the estimated emotions. The provision unit can advise the student on an appropriate feedback method based on the emotions. This allows the career guidance support system to provide feedback according to the student's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The input unit inputs the student's information, including their thoughts, personality, grades, preferences, and future aspirations. The input unit can be entered by the student themselves or by a teacher. Step 2: The analysis unit uses the generation AI to analyze the information entered by the input unit. The generation AI uses natural language processing technology and machine learning algorithms to comprehensively assess the student's information and provide advice on the optimal life plan. Step 3: The provision unit provides advice to the student based on the analysis results obtained by the analysis unit. The provision unit advises the student on the optimal life plan based on the data analyzed by the generation AI. Step 4: The Faculty Collaboration Department provides the advice provided by the Advice Providing Department to faculty members and supports them in providing career guidance based on that advice.The Faculty Collaboration Department provides the advice provided by the Generative AI to faculty members and supports them in providing career guidance based on that advice. Step 5: The data collection unit collects data such as the student's grades, personality, preferences, etc. The data collection unit collects data using methods such as questionnaire surveys, sensor data collection, and online activity monitoring.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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. an input section for inputting the student's information; an analysis unit that analyzes the information input by the input unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; a faculty cooperation department that provides the advice provided by the advice providing department to faculty members and supports the faculty members in providing career guidance based on the advice; A data collection unit that collects data on the grades, personality, and preferences of the students. A system characterized by:

2. The data collection unit Collect data on the student's grades, personality, and preferences 2. The system of claim 1.

3. The analysis unit Generative AI makes a comprehensive judgment based on the student's thoughts, personality, grades, preferences, and future aspirations.

2. The system of claim 1.

4. The providing unit Based on the data analyzed by the generative AI, the relevant student will be given advice on an appropriate life plan.

2. The system of claim 1.

5. The Faculty Collaboration Department Providing generative AI advice to teachers and supporting them in providing career guidance based on that advice 2. The system of claim 1.

6. The providing unit Based on past data, look at what career paths students with similar aspirations have chosen and what results they have achieved.

2. The system of claim 1.

7. The input unit Estimate the student's emotions and adjust the timing of input based on the estimated student emotions 2. The system of claim 1.

8. The input unit Analyze students' past input history and select the optimal input method 2. The system of claim 1.

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