system

Abstract

The system according to the embodiment aims to provide appropriate advice to help students find the best career path. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an estimation unit, a provision unit, and an agency unit. The collection unit collects data on students' hobbies, preferences, interests, and grades. The analysis unit analyzes the data collected by the collection unit. The estimation unit estimates an appropriate career path that matches the student's aptitude and interests based on the data analyzed by the analysis unit. The provision unit provides specific advice based on the career path estimated by the estimation unit. The agency unit provides career counseling on behalf of the student based on the advice provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for students to obtain sufficient information and appropriate advice when deciding on their career path, which can lead to regret.

[0005] The system according to the embodiment aims to provide appropriate advice to help students find the best career path. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, a provision unit, and an agency unit. The collection unit collects data on students' hobbies, preferences, interests, and grades. The analysis unit analyzes the data collected by the collection unit. The estimation unit estimates an appropriate career path that matches the student's aptitude and interests based on the data analyzed by the analysis unit. The provision unit provides specific advice based on the career path estimated by the estimation unit. The agency unit provides career counseling on behalf of the student based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate advice to help students find the best career path. [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) The AI ​​career coaching system according to an embodiment of the present invention digitizes career counseling for students, reducing the burden on teachers while guiding them toward optimal career paths based on their future aspirations and values. This AI career coaching system can solve the challenges students face when deciding on their career paths and reduce the number of students who end up regretting their choices. For example, when deciding on their career path, students face limited opportunities to learn about society, occupations, and lifestyles; the limited knowledge and experience of their teachers and parents; and a lack of teachers, making it difficult to allocate sufficient time for career counseling. These challenges lead students to make career decisions based on limited information, which can lead to later regrets. The AI ​​career coach can solve these challenges and reduce the number of students who end up regretting their choices. Specifically, the AI ​​career coach interacts with students to understand their hobbies, preferences, interests, and grades, and then provides optimal career advice on behalf of their teachers. Furthermore, the AI ​​career coach leverages its multimodal capabilities to handle all career counseling needs, including job interview practice and document editing for job and entrance exams. For example, when a student asks about their future career, the AI ​​career coach provides detailed information about that job and advice on the necessary skills, qualifications, and career paths. Additionally, when practicing interviews, the AI ​​career coach conducts mock interviews and provides feedback on the student's answers. In this way, the AI ​​career coach helps students obtain sufficient information about their future careers and reduces regrets about their career choices. This allows the AI ​​career coach system to digitize career counseling for students, reducing the burden on teachers while guiding them to the optimal career path based on their future aspirations and values.

[0029] An AI career coaching system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, a provision unit, and an agent unit. The collection unit collects data on students' hobbies, preferences, interests, and grades. For example, the collection unit collects data on hobbies, preferences, and interests through questionnaires or online forms filled out by students. The collection unit can also obtain grade information from a school's grade database. The collection unit can also collect students' past career counseling histories. For example, the collection unit digitizes records of past career counseling sessions and stores them in a database. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze patterns of students' hobbies, preferences, and interests. The analysis unit can also analyze grade data to determine students' academic ability and favorite subjects. The analysis unit can also analyze past career counseling histories to determine students' career choice trends. The estimation unit estimates a career path that matches the student's aptitude and interests based on the data analyzed by the analysis unit. For example, the estimation unit uses AI to predict appropriate occupations based on a student's hobbies, preferences, and interests. The estimation unit can also predict career paths that students are likely to succeed in based on academic performance data. Furthermore, the estimation unit can predict career paths that students should avoid based on their past career counseling history. The provision unit provides specific advice based on the career paths predicted by the estimation unit. For example, when a student asks about their future career, the provision unit provides detailed information about that occupation. The provision unit can also provide advice on necessary skills, qualifications, and career paths. Furthermore, the provision unit can provide information on occupations that may interest the student. The proxy unit provides career counseling on behalf of the student based on the advice provided by the provision unit. For example, the proxy unit conducts interview practice and provides feedback on the student's answers. The proxy unit can also correct required documents to improve the quality of the documents submitted by the student. Furthermore, when a student asks about their future career, the proxy unit can provide detailed information about that occupation.As a result, the AI ​​career coaching system of the embodiment can collect and analyze data such as students' hobbies, preferences, interests, and grades, predict career paths that match their aptitudes and interests, provide specific advice, and act as career counseling agents.

[0030] The collection unit can collect data on students' hobbies, preferences, interests, grades, and past career counseling histories. The collection unit collects data on hobbies, preferences, and interests through, for example, questionnaires or online forms filled out by students. For example, the collection unit can collect information on students' hobbies, preferences, such as favorite sports, music, and reading. The collection unit can also collect information on students' academic fields and career fields of interest. For example, the collection unit can collect information on students' fields of interest, such as science, literature, and medicine. The collection unit can also obtain grade information from a school's grade database. For example, the collection unit can collect information on students' grade averages and subject-specific grades. The collection unit can also collect past career counseling histories. For example, the collection unit can digitize records of past career counseling sessions and store them in a database. By collecting data such as students' hobbies, preferences, interests, grades, and past career counseling histories, the collection unit can predict career paths based on more detailed information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input questionnaire data filled out by students into AI, which may then analyze the data and extract information on hobbies, preferences, and interests.

[0031] The analysis unit analyzes the collected data and can predict a career path that matches the student's aptitude and interests. The analysis unit, for example, uses AI to analyze the student's hobbies, preferences, and interest patterns. For example, the analysis unit can use AI to analyze the student's hobbies and preferences data to determine what activities the student is interested in. The analysis unit can also analyze grade data to determine the student's academic ability and favorite subjects. For example, the analysis unit can use AI to analyze the grade data to determine which subjects the student excels in. The analysis unit can also analyze past career counseling histories to determine the student's career choices. For example, the analysis unit can use AI to analyze past career counseling histories and analyze the career paths the student has chosen. This allows the analysis unit to analyze the collected data and predict a career path that matches the student's aptitude and interests, thereby providing more appropriate career advice. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, which can then analyze the data and predict career paths that match a person's aptitudes and interests.

[0032] The provision unit can provide specific advice based on the estimated career path. For example, when a student asks about their future career, the provision unit provides detailed information about that career. For example, the provision unit can use AI to refer to a database of careers and provide information such as the job description, required skills, and qualifications of the career. The provision unit can also provide advice on required skills, qualifications, and career paths. For example, the provision unit can use AI to advise the student on what skills and qualifications are needed based on the student's aptitude and interests. Furthermore, the provision unit can provide information on careers that may interest the student. For example, the provision unit can use AI to provide information on related careers based on the student's hobbies, preferences, and interests. As a result, the provision unit can provide specific advice based on the estimated career path, allowing the student to obtain guidelines for taking specific actions. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can input the student's question into AI, which then provides information on careers.

[0033] The proxy unit can perform tasks normally performed by teachers, such as interview practice and document correction. For example, the proxy unit may conduct interview practice and provide feedback on students' responses. For example, the proxy unit may use AI to conduct mock interviews, evaluate students' responses, and suggest areas for improvement. The proxy unit may also correct required documents to improve the quality of the documents submitted by students. For example, the proxy unit may use AI to check students' documents and correct grammar and expressions. Furthermore, when students ask about their future careers, the proxy unit may provide detailed information about those careers. For example, the proxy unit may use AI to reference a database of careers and provide information such as the job description, required skills, and qualifications. This reduces the burden on teachers by performing tasks normally performed by teachers, such as interview practice and document correction, thereby providing students with more support. Some or all of the above-described processing performed by the proxy unit may be performed using AI, or may be performed without AI. For example, the agency can input students' answers into the AI, which can then provide feedback.

[0034] When a student asks about their future career, the provision unit can provide detailed information about the career and advice on skills, qualifications, and career paths. For example, when a student asks about their future career, the provision unit provides detailed information about the career. For example, the provision unit can use AI to refer to a database of careers and provide information such as the job description, required skills, and qualifications of the career. The provision unit can also provide advice on the required skills, qualifications, and career paths. For example, the provision unit can use AI to advise the student on what skills and qualifications are needed based on the student's aptitude and interests. Furthermore, the provision unit can provide information on careers that may interest the student. For example, the provision unit can use AI to provide information on related careers based on the student's hobbies, preferences, and interests. As a result, when a student asks about their future career, the provision unit can provide detailed information about the career and advice on the required skills, qualifications, and career paths, making it easier for the student to formulate a specific career plan. Some or all of the above-described processing by the provision unit may be performed using AI, for example, or without AI. For example, the provider can input students' questions into the AI, which can then provide information about careers.

[0035] The proxy unit can conduct mock interviews and provide feedback to students on their answers. For example, the proxy unit can conduct mock interviews and provide feedback to students on their answers. For example, the proxy unit can have AI conduct mock interviews, evaluate students' answers, and point out areas for improvement. The proxy unit can also have AI provide real-time feedback to students when they practice interviews. For example, the proxy unit can have AI analyze students' answers and provide appropriate advice. Furthermore, the proxy unit can have AI generate mock interview scenarios when students practice interviews, allowing students to adapt to various situations. For example, the proxy unit can provide AI mock interview scenarios for different occupations or industries. This allows the proxy unit to conduct mock interviews and provide feedback to students on their answers, allowing students to more effectively prepare for interviews. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input students' answers into AI, which then provides feedback.

[0036] The collection unit can analyze the student's past career counseling history and select the optimal data collection method. For example, the collection unit selects the student's preferred data collection method (such as a questionnaire or interview) from the past career counseling history. For example, the collection unit can use AI to analyze the past career counseling history and identify the student's preferred data collection method. The collection unit can also collect data during the student's most relaxed time based on the past career counseling history. For example, the collection unit can use AI to analyze the past career counseling history and identify the student's most relaxed time. The collection unit can also analyze the past career counseling history and prioritize the method in which the student provided the most information. For example, the collection unit can use AI to analyze the past career counseling history and identify the data collection method in which the student provided the most information. This allows the collection unit to analyze the student's past career counseling history and select the optimal data collection method, enabling more effective data collection. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection department can input past career counseling history into the AI, which can then select the most appropriate data collection method.

[0037] When collecting data, the collection unit can filter the data based on the student's current learning situation and living environment. For example, the collection unit can collect data during times that do not affect the student's academic work, taking into account the student's current learning situation. For example, the collection unit can use AI to analyze the student's learning schedule and identify the optimal timing for data collection. The collection unit can also select an appropriate data collection method by taking into account the student's living environment (home environment, commuting environment, etc.). For example, the collection unit can use AI to analyze the student's living environment data and identify the optimal data collection method. Furthermore, the collection unit can adjust the type and amount of data to be collected based on the student's learning situation and living environment. For example, the collection unit can use AI to analyze the student's learning situation and living environment data and identify the optimal range of data collection. This allows the collection unit to collect more relevant data by filtering the data based on the student's current learning situation and living environment. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection department can input data on students' learning status and living environment into the AI, which can then filter the data.

[0038] When collecting data, the collection unit can prioritize collecting relevant data by taking into account the student's geographic location information. For example, if the student lives in an urban area, the collection unit can prioritize collecting data related to urban occupations. For example, the collection unit can use AI to analyze the student's geographic location information and identify data related to urban occupations. Furthermore, if the student lives in a rural area, the collection unit can prioritize collecting data related to agricultural-related occupations. For example, the collection unit can use AI to analyze the student's geographic location information and identify data related to agricultural-related occupations. Furthermore, if the student lives overseas, the collection unit can prioritize collecting data related to occupations in that country. For example, the collection unit can use AI to analyze the student's geographic location information and identify data related to occupations in that country. This allows the collection unit to prioritize collecting relevant data by taking into account the student's geographic location information, thereby providing more appropriate career advice. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's geographic location information into AI, which can identify relevant data.

[0039] During data collection, the collection unit can analyze students' social media activities and collect related data. For example, the collection unit can collect data on interests from students' social media activities. For example, the collection unit can use AI to analyze students' social media posts and identify topics the students are interested in. The collection unit can also analyze students' social media activities and collect data related to their future careers. For example, the collection unit can use AI to analyze students' social media activities and identify what careers the students are interested in. Furthermore, the collection unit can collect data on hobbies and preferences based on students' social media activities. For example, the collection unit can use AI to analyze students' social media posts and identify what hobbies the students have. This allows the collection unit to analyze students' social media activities and collect related data, thereby predicting career paths based on more detailed information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input students' social media data into AI, which then collects related data.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit can use AI to evaluate the importance of data and perform a detailed analysis on the data of high importance. The analysis unit can also perform a brief analysis on data of low importance. For example, the analysis unit can use AI to evaluate the importance of data and perform a brief analysis on the data of low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit can use AI to evaluate the importance of data and perform an analysis with a moderate level of detail on data of medium importance. As a result, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data, thereby performing a detailed analysis on more important data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which can evaluate the importance of the data and adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a statistical analysis algorithm to grade data. For example, the analysis unit can have AI analyze the grade data and analyze the data using statistical methods. The analysis unit can also apply a clustering algorithm to hobby and preference data. For example, the analysis unit can have AI analyze the hobby and preference data and classify the data using clustering methods. The analysis unit can also apply a natural language processing algorithm to interest data. For example, the analysis unit can have AI analyze the interest data and analyze the data using natural language processing technology. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category into AI, and the AI ​​can apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of recently collected data. For example, the analysis unit can use AI to evaluate the time when the data was collected and prioritize analysis of recently collected data. The analysis unit can also prioritize analysis of data collected immediately before an important event. For example, the analysis unit can use AI to evaluate the time when the data was collected and prioritize analysis of data collected immediately before an important event. The analysis unit can also prioritize analysis of data collected over a long period of time. For example, the analysis unit can use AI to evaluate the time when the data was collected and prioritize analysis of data collected over a long period of time. This allows the analysis unit to determine the analysis priority based on the time when the data was collected, thereby providing more timely analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit can have AI evaluate the relevance of the data and prioritize analysis of data with high relevance. The analysis unit can also analyze data with moderate relevance next. For example, the analysis unit can have AI evaluate the relevance of the data and analyze data with moderate relevance next. Furthermore, the analysis unit can analyze data with low relevance last. For example, the analysis unit can evaluate the relevance of the data and analyze data with low relevance last. In this way, the analysis unit can prioritize analysis of more highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, and the AI ​​can adjust the order of analysis.

[0044] During estimation, the estimation unit can analyze the student's past career selection history to estimate an optimal career path. The estimation unit, for example, estimates a career path that the student is likely to be interested in based on the student's past career selection history. For example, the estimation unit can use AI to analyze the student's past career selection history and identify which career path the student is likely to be interested in. The estimation unit can also analyze the student's past career selection history to estimate a career path that the student is likely to be successful in. For example, the estimation unit can use AI to analyze the student's past career selection history and identify which career path the student is likely to be successful in. Furthermore, the estimation unit can also estimate a career path the student should avoid based on the student's past career selection history. For example, the estimation unit can use AI to analyze the student's past career selection history and identify which career path the student should avoid. This allows the estimation unit to analyze the student's past career selection history to estimate an optimal career path, thereby providing more appropriate career advice. Some or all of the above-described processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input past career choice history into the AI, which can then estimate the optimal career path.

[0045] The estimation unit can customize the career path based on the student's current learning situation and living environment during estimation. The estimation unit, for example, takes into account the student's current learning situation to estimate an appropriate career path. For example, the estimation unit can use AI to analyze the student's learning data and identify the student's learning situation. The estimation unit can also estimate a realistic career path by taking into account the student's living environment (home environment, commuting environment, etc.). For example, the estimation unit can use AI to analyze the student's living environment data and identify the student's living environment. The estimation unit can also customize career path options based on the student's learning situation and living environment. For example, the estimation unit can use AI to analyze the student's learning situation and living environment data and identify the optimal career path for the student. This allows the estimation unit to provide more realistic career advice by customizing the career path based on the student's current learning situation and living environment. Some or all of the above-described processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input data on students' learning status and living environment into the AI, allowing the AI ​​to customize their career path.

[0046] The estimation unit can estimate an optimal career path by taking into account the student's geographical location information. For example, if the student lives in an urban area, the estimation unit estimates a career path related to urban occupations. For example, the estimation unit can use AI to analyze the student's geographical location information and identify a career path related to urban occupations. Furthermore, if the student lives in a rural area, the estimation unit can estimate a career path related to agricultural-related occupations. For example, the estimation unit can use AI to analyze the student's geographical location information and identify a career path related to agricultural-related occupations. Furthermore, if the student lives overseas, the estimation unit can estimate a career path related to occupations in that country. For example, the estimation unit can use AI to analyze the student's geographical location information and identify a career path related to occupations in that country. This allows the estimation unit to estimate an optimal career path by taking the student's geographical location information into account, thereby providing more realistic career advice. Some or all of the above-described processing in the estimation unit may be performed using AI, for example, or may be performed without using AI. For example, the estimation unit can input the student's geographical location information into AI, which can then estimate an optimal career path.

[0047] During estimation, the estimation unit can analyze the student's social media activity to estimate a career path. The estimation unit, for example, estimates a career path based on the student's interests from the student's social media activity. For example, the estimation unit can use AI to analyze the student's social media posts and identify topics the student is interested in. The estimation unit can also analyze the student's social media activity to estimate a career path related to the student's future career. For example, the estimation unit can use AI to analyze the student's social media activity and identify what occupation the student is interested in. Furthermore, the estimation unit can estimate a career path based on the student's hobbies and preferences based on the student's social media activity. For example, the estimation unit can use AI to analyze the student's social media posts and identify what hobbies the student has. This allows the estimation unit to estimate a career path based on more detailed information by analyzing the student's social media activity. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without AI. For example, the estimation unit can input the student's social media data into AI, which then estimates the student's career path.

[0048] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the career path. The providing unit, for example, provides detailed advice for a highly important career path. For example, the providing unit can use AI to evaluate the importance of a career path and provide detailed advice for a highly important career path. The providing unit can also provide concise advice for a low-importance career path. For example, the providing unit can use AI to evaluate the importance of a career path and provide concise advice for a low-importance career path. The providing unit can also provide advice with an appropriate level of detail for a medium-importance career path. For example, the providing unit can use AI to evaluate the importance of a career path and provide advice with an appropriate level of detail for a medium-importance career path. As a result, the providing unit can adjust the level of detail of the advice based on the importance of the career path, thereby providing detailed advice for a more important career path. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provider can input the importance of a career path into the AI, which can then adjust the level of detail in its advice.

[0049] When providing advice, the providing unit can apply different advice algorithms depending on the career path category. For example, the providing unit can provide advice on technical skills and qualifications to technical occupations. For example, the providing unit can use AI to analyze data related to technical occupations and provide advice on technical skills and qualifications. The providing unit can also provide advice on portfolio creation and creative skills to creative occupations. For example, the providing unit can use AI to analyze data related to creative occupations and provide advice on portfolio creation and creative skills. The providing unit can also provide advice on leadership and management skills to management occupations. For example, the providing unit can use AI to analyze data related to management occupations and provide advice on leadership and management skills. This allows the providing unit to provide more appropriate advice by applying different advice algorithms depending on the career path category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the career path category into AI, which then applies an appropriate advice algorithm.

[0050] When providing advice, the providing unit can determine the priority of advice based on the submission time of the career path. The providing unit, for example, can prioritize advice for career paths with high urgency. For example, the providing unit can use AI to evaluate the submission time of the career path and prioritize advice for career paths with high urgency. The providing unit can also quickly provide advice for career paths with an approaching submission deadline. For example, the providing unit can use AI to evaluate the submission time of the career path and quickly provide advice for career paths with an upcoming submission deadline. Furthermore, the providing unit can also provide detailed advice for career paths with a distant submission deadline. For example, the providing unit can use AI to evaluate the submission time of the career path and provide detailed advice for career paths with a distant submission deadline. This allows the providing unit to determine the priority of advice based on the submission time of the career path, thereby providing more timely advice. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the submission time of the career path into AI, and the AI ​​can determine the priority of advice.

[0051] When providing advice, the providing unit can adjust the order of advice based on the relevance of the career paths. For example, the providing unit can prioritize providing advice for career paths with high relevance. For example, the providing unit can use AI to evaluate the relevance of career paths and prioritize providing advice for career paths with high relevance. The providing unit can also provide advice next for career paths with medium relevance. For example, the providing unit can use AI to evaluate the relevance of career paths and provide advice next for career paths with medium relevance. Furthermore, the providing unit can also provide advice last for career paths with low relevance. For example, the providing unit can use AI to evaluate the relevance of career paths and provide advice last for career paths with low relevance. In this way, the providing unit can prioritize providing advice for more relevant career paths by adjusting the order of advice based on the relevance of the career paths. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of career paths to AI, and the AI ​​can adjust the order of advice.

[0052] During the proxy service, the proxy unit can analyze the student's past career counseling history and select the optimal proxy method. For example, the proxy unit selects the proxy method preferred by the student based on the past career counseling history. For example, the proxy unit can use AI to analyze the past career counseling history and identify the proxy method preferred by the student. The proxy unit can also analyze the past career counseling history and perform the proxy service using the method that makes the student most relaxed. For example, the proxy unit can use AI to analyze the past career counseling history and identify the method that makes the student most relaxed. Furthermore, the proxy unit can select the method that allows the student to provide the most information based on the past career counseling history. For example, the proxy unit can use AI to analyze the past career counseling history and identify the proxy method that allows the student to provide the most information. This allows the proxy unit to analyze the student's past career counseling history and select the optimal proxy method, thereby providing more effective proxy service. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or without AI. For example, the agency can input past career counseling history into the AI, which can then select the most appropriate agency method.

[0053] The proxy unit can customize the proxy work based on the student's current learning situation and living environment when performing proxy work. For example, the proxy unit performs proxy work in a manner that does not affect the student's academic performance, taking into account the student's current learning situation. For example, the proxy unit can use AI to analyze the student's learning data and identify the student's learning situation. The proxy unit can also select an appropriate proxy method by taking into account the student's living environment (e.g., home environment, commuting environment, etc.). For example, the proxy unit can use AI to analyze the student's living environment data and identify the student's living environment. Furthermore, the proxy unit can adjust the content and method of the proxy work based on the student's learning situation and living environment. For example, the proxy unit can use AI to analyze the student's learning situation and living environment data and identify the proxy method that is optimal for the student. This allows the proxy unit to customize the proxy work based on the student's current learning situation and living environment, thereby providing more appropriate proxy work. Some or all of the above-mentioned processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy department can input data on students' learning status and living environment into the AI, which can then customize the proxy work.

[0054] When performing a proxy service, the proxy unit can select the optimal proxy method by taking into account the student's geographic location information. For example, if the student lives in an urban area, the proxy unit can perform proxy services related to urban occupations. For example, the proxy unit can use AI to analyze the student's geographic location information and identify proxy services related to urban occupations. Furthermore, if the student lives in a rural area, the proxy unit can perform proxy services related to agriculture-related occupations. For example, the proxy unit can use AI to analyze the student's geographic location information and identify proxy services related to agriculture-related occupations. Furthermore, if the student lives overseas, the proxy unit can perform proxy services related to occupations in that country. For example, the proxy unit can use AI to analyze the student's geographic location information and identify proxy services related to occupations in that country. This allows the proxy unit to select the optimal proxy method by taking into account the student's geographic location information, thereby providing more realistic proxy services. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or may be performed without using AI. For example, the proxy department can input the student's geographical location information into the AI, which can then select the most appropriate proxy method.

[0055] When performing a task, the proxy unit can analyze the student's social media activity and suggest a task based on the student's interests. For example, the proxy unit can use AI to analyze the student's social media posts and identify topics the student is interested in. The proxy unit can also analyze the student's social media activity and suggest a task related to the student's future career. For example, the proxy unit can use AI to analyze the student's social media activity and identify the type of career the student is interested in. Furthermore, the proxy unit can suggest a task based on the student's hobbies and preferences based on the student's social media activity. For example, the proxy unit can use AI to analyze the student's social media posts and identify the student's hobbies. This allows the proxy unit to analyze the student's social media activity and suggest a task based on more detailed information. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or without AI. For example, the agency can input students' social media data into AI, which can then suggest substitute tasks.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The AI ​​career coaching system can further include a feedback unit. The feedback unit can collect feedback from students regarding the advice and proxy services they receive and use the information to improve the system as a whole. For example, the feedback unit can conduct a survey after students receive advice and collect the results. The feedback unit can also provide a form for students to fill out after taking a mock interview and analyze the content of the form. Furthermore, the feedback unit can introduce an evaluation system to evaluate students' satisfaction with the results of the corrections to their submitted documents. This allows the feedback unit to collect students' opinions and feedback and use the information to improve the system, thereby providing more effective career support.

[0058] The AI ​​career coaching system can further include a networking department. The networking department can help students network with experts and peers related to the occupations and fields in which they are interested. For example, the networking department can provide information about industry events and seminars that students can attend. The networking department can also provide a platform for students to book mentoring sessions with experts. Furthermore, the networking department can operate an online community where students can connect with other students who share the same interests. In this way, the networking department can provide students with an opportunity not only to obtain career-related information but also to actually connect with industry experts and peers.

[0059] The AI ​​career coaching system can further include a motivation unit. The motivation unit can provide support to increase students' motivation for learning and career goals. For example, the motivation unit can track the progress of students toward goals set by them and provide rewards according to the degree of achievement. The motivation unit can also send encouraging messages to students when they face difficulties. Furthermore, the motivation unit can send congratulatory messages to students when they succeed, helping them feel a sense of accomplishment. In this way, the motivation unit can provide more effective career support by increasing students' motivation for learning and career goals.

[0060] The AI ​​career coaching system may further include a resource unit. The resource unit may provide resources for students to obtain career-related information. For example, the resource unit may operate an online library that students can access and provide career-related books, papers, articles, etc. The resource unit may also provide information on online courses and training programs that students can use. Furthermore, the resource unit may operate a forum where students can post career-related questions and receive answers from experts. This allows the resource unit to provide more effective career support by providing a variety of resources for students to obtain career-related information.

[0061] The AI ​​career coaching system can further include an internship department. The internship department can provide internship opportunities for students to gain real-world work experience. For example, the internship department can provide a list of internships that students can apply for and support the application process. The internship department can also provide training programs for students to learn the skills and knowledge they need during their internship. Furthermore, the internship department can provide feedback to students after they complete their internship and advice on how to proceed to the next step. This allows the internship department to provide more realistic career support by allowing students to gain real-world work experience.

[0062] The AI ​​career coach system can further include a career events department. The career events department can plan and run career-related events that students can participate in. For example, the career events department can hold career fairs and company information sessions that students can participate in. The career events department can also plan panel discussions and workshops where students can interact directly with industry experts. Furthermore, the career events department can hold social events for students to network. In this way, the career events department can provide students with an opportunity not only to obtain career-related information but also to actually connect with industry experts and peers.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects data on students' hobbies, preferences, interests, and grades. For example, the collection unit collects data on hobbies, preferences, and interests through questionnaires or online forms filled out by students. The collection unit can also obtain grade information from the school's grade database. Furthermore, the collection unit can collect students' past career counseling histories. For example, the collection unit digitizes records of past career counseling sessions and stores them in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze patterns of students' hobbies, preferences, and interests. The analysis unit can also analyze grade data to understand students' academic ability and favorite subjects. Furthermore, the analysis unit can analyze past career counseling history to understand students' career choice trends. Step 3: The estimation unit predicts a career path that matches the student's aptitude and interests based on the data analyzed by the analysis unit. For example, the estimation unit uses AI to predict an appropriate occupation based on the student's hobbies, preferences, and interests. The estimation unit can also predict career paths that students are likely to be successful in based on their academic performance data. Furthermore, the estimation unit can predict career paths that students should avoid based on their past career counseling history. Step 4: The advice section provides specific advice based on the career path predicted by the prediction section. For example, when a student asks about their future career, the advice section provides detailed information about that career. The advice section can also provide advice on the necessary skills, qualifications, and career paths. Furthermore, the advice section can provide information on careers that may interest the student. Step 5: The proxy department provides career counseling on behalf of students based on the advice provided by the provider department. For example, the proxy department may conduct interview practice sessions and provide feedback on students' answers. The proxy department may also correct required documents to improve the quality of the documents submitted by students. Furthermore, when students ask about their future careers, the proxy department may provide detailed information about those careers.

[0065] (Example 2) The AI ​​career coaching system according to an embodiment of the present invention digitizes career counseling for students, reducing the burden on teachers while guiding them toward optimal career paths based on their future aspirations and values. This AI career coaching system can solve the challenges students face when deciding on their career paths and reduce the number of students who end up regretting their choices. For example, when deciding on their career path, students face limited opportunities to learn about society, occupations, and lifestyles; the limited knowledge and experience of their teachers and parents; and a lack of teachers, making it difficult to allocate sufficient time for career counseling. These challenges lead students to make career decisions based on limited information, which can lead to later regrets. The AI ​​career coach can solve these challenges and reduce the number of students who end up regretting their choices. Specifically, the AI ​​career coach interacts with students to understand their hobbies, preferences, interests, and grades, and then provides optimal career advice on behalf of their teachers. Furthermore, the AI ​​career coach leverages its multimodal capabilities to handle all career counseling needs, including job interview practice and document editing for job and entrance exams. For example, when a student asks about their future career, the AI ​​career coach provides detailed information about that job and advice on the necessary skills, qualifications, and career paths. Additionally, when practicing interviews, the AI ​​career coach conducts mock interviews and provides feedback on the student's answers. In this way, the AI ​​career coach helps students obtain sufficient information about their future careers and reduces regrets about their career choices. This allows the AI ​​career coach system to digitize career counseling for students, reducing the burden on teachers while guiding them to the optimal career path based on their future aspirations and values.

[0066] An AI career coaching system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, a provision unit, and an agent unit. The collection unit collects data on students' hobbies, preferences, interests, and grades. For example, the collection unit collects data on hobbies, preferences, and interests through questionnaires or online forms filled out by students. The collection unit can also obtain grade information from a school's grade database. The collection unit can also collect students' past career counseling histories. For example, the collection unit digitizes records of past career counseling sessions and stores them in a database. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze patterns of students' hobbies, preferences, and interests. The analysis unit can also analyze grade data to determine students' academic ability and favorite subjects. The analysis unit can also analyze past career counseling histories to determine students' career choice trends. The estimation unit estimates a career path that matches the student's aptitude and interests based on the data analyzed by the analysis unit. For example, the estimation unit uses AI to predict appropriate occupations based on a student's hobbies, preferences, and interests. The estimation unit can also predict career paths that students are likely to succeed in based on academic performance data. Furthermore, the estimation unit can predict career paths that students should avoid based on their past career counseling history. The provision unit provides specific advice based on the career paths predicted by the estimation unit. For example, when a student asks about their future career, the provision unit provides detailed information about that occupation. The provision unit can also provide advice on necessary skills, qualifications, and career paths. Furthermore, the provision unit can provide information on occupations that may interest the student. The proxy unit provides career counseling on behalf of the student based on the advice provided by the provision unit. For example, the proxy unit conducts interview practice and provides feedback on the student's answers. The proxy unit can also correct required documents to improve the quality of the documents submitted by the student. Furthermore, when a student asks about their future career, the proxy unit can provide detailed information about that occupation.As a result, the AI ​​career coaching system of the embodiment can collect and analyze data such as students' hobbies, preferences, interests, and grades, predict career paths that match their aptitudes and interests, provide specific advice, and act as career counseling agents.

[0067] The collection unit can collect data on students' hobbies, preferences, interests, grades, and past career counseling histories. The collection unit collects data on hobbies, preferences, and interests through, for example, questionnaires or online forms filled out by students. For example, the collection unit can collect information on students' hobbies, preferences, such as favorite sports, music, and reading. The collection unit can also collect information on students' academic fields and career fields of interest. For example, the collection unit can collect information on students' fields of interest, such as science, literature, and medicine. The collection unit can also obtain grade information from a school's grade database. For example, the collection unit can collect information on students' grade averages and subject-specific grades. The collection unit can also collect past career counseling histories. For example, the collection unit can digitize records of past career counseling sessions and store them in a database. By collecting data such as students' hobbies, preferences, interests, grades, and past career counseling histories, the collection unit can predict career paths based on more detailed information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input questionnaire data filled out by students into AI, which may then analyze the data and extract information on hobbies, preferences, and interests.

[0068] The analysis unit analyzes the collected data and can predict a career path that matches the student's aptitude and interests. The analysis unit, for example, uses AI to analyze the student's hobbies, preferences, and interest patterns. For example, the analysis unit can use AI to analyze the student's hobbies and preferences data to determine what activities the student is interested in. The analysis unit can also analyze grade data to determine the student's academic ability and favorite subjects. For example, the analysis unit can use AI to analyze the grade data to determine which subjects the student excels in. The analysis unit can also analyze past career counseling histories to determine the student's career choices. For example, the analysis unit can use AI to analyze past career counseling histories and analyze the career paths the student has chosen. This allows the analysis unit to analyze the collected data and predict a career path that matches the student's aptitude and interests, thereby providing more appropriate career advice. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, which can then analyze the data and predict career paths that match a person's aptitudes and interests.

[0069] The provision unit can provide specific advice based on the estimated career path. For example, when a student asks about their future career, the provision unit provides detailed information about that career. For example, the provision unit can use AI to refer to a database of careers and provide information such as the job description, required skills, and qualifications of the career. The provision unit can also provide advice on required skills, qualifications, and career paths. For example, the provision unit can use AI to advise the student on what skills and qualifications are needed based on the student's aptitude and interests. Furthermore, the provision unit can provide information on careers that may interest the student. For example, the provision unit can use AI to provide information on related careers based on the student's hobbies, preferences, and interests. As a result, the provision unit can provide specific advice based on the estimated career path, allowing the student to obtain guidelines for taking specific actions. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can input the student's question into AI, which then provides information on careers.

[0070] The proxy unit can perform tasks normally performed by teachers, such as interview practice and document correction. For example, the proxy unit may conduct interview practice and provide feedback on students' responses. For example, the proxy unit may use AI to conduct mock interviews, evaluate students' responses, and suggest areas for improvement. The proxy unit may also correct required documents to improve the quality of the documents submitted by students. For example, the proxy unit may use AI to check students' documents and correct grammar and expressions. Furthermore, when students ask about their future careers, the proxy unit may provide detailed information about those careers. For example, the proxy unit may use AI to reference a database of careers and provide information such as the job description, required skills, and qualifications. This reduces the burden on teachers by performing tasks normally performed by teachers, such as interview practice and document correction, thereby providing students with more support. Some or all of the above-described processing performed by the proxy unit may be performed using AI, or may be performed without AI. For example, the agency can input students' answers into the AI, which can then provide feedback.

[0071] When a student asks about their future career, the provision unit can provide detailed information about the career and advice on skills, qualifications, and career paths. For example, when a student asks about their future career, the provision unit provides detailed information about the career. For example, the provision unit can use AI to refer to a database of careers and provide information such as the job description, required skills, and qualifications of the career. The provision unit can also provide advice on the required skills, qualifications, and career paths. For example, the provision unit can use AI to advise the student on what skills and qualifications are needed based on the student's aptitude and interests. Furthermore, the provision unit can provide information on careers that may interest the student. For example, the provision unit can use AI to provide information on related careers based on the student's hobbies, preferences, and interests. As a result, when a student asks about their future career, the provision unit can provide detailed information about the career and advice on the required skills, qualifications, and career paths, making it easier for the student to formulate a specific career plan. Some or all of the above-described processing by the provision unit may be performed using AI, for example, or without AI. For example, the provider can input students' questions into the AI, which can then provide information about careers.

[0072] The proxy unit can conduct mock interviews and provide feedback to students on their answers. For example, the proxy unit can conduct mock interviews and provide feedback to students on their answers. For example, the proxy unit can have AI conduct mock interviews, evaluate students' answers, and point out areas for improvement. The proxy unit can also have AI provide real-time feedback to students when they practice interviews. For example, the proxy unit can have AI analyze students' answers and provide appropriate advice. Furthermore, the proxy unit can have AI generate mock interview scenarios when students practice interviews, allowing students to adapt to various situations. For example, the proxy unit can provide AI mock interview scenarios for different occupations or industries. This allows the proxy unit to conduct mock interviews and provide feedback to students on their answers, allowing students to more effectively prepare for interviews. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input students' answers into AI, which then provides feedback.

[0073] The collection unit can estimate a student's emotions and adjust the timing of data collection based on the estimated student's emotions. For example, if a student is feeling stressed, the collection unit delays data collection until the student is relaxed. For example, the collection unit can use AI to analyze the student's facial expressions and voice to detect signs of stress. The collection unit can also collect data when a student is concentrating, thereby obtaining more accurate information. For example, the collection unit can use AI to monitor the student's concentration level in real time and collect data at the optimal timing. Furthermore, if a student is tired, the collection unit can collect data after the student has rested, thereby collecting higher quality data. For example, the collection unit can use AI to analyze the student's biometric data and detect signs of fatigue. This allows the collection unit to adjust the timing of data collection based on the student's emotions, thereby collecting higher quality data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input student emotion data into AI, which can then adjust the timing of data collection.

[0074] The collection unit can analyze the student's past career counseling history and select the optimal data collection method. For example, the collection unit selects the student's preferred data collection method (such as a questionnaire or interview) from the past career counseling history. For example, the collection unit can use AI to analyze the past career counseling history and identify the student's preferred data collection method. The collection unit can also collect data during the student's most relaxed time based on the past career counseling history. For example, the collection unit can use AI to analyze the past career counseling history and identify the student's most relaxed time. The collection unit can also analyze the past career counseling history and prioritize the method in which the student provided the most information. For example, the collection unit can use AI to analyze the past career counseling history and identify the data collection method in which the student provided the most information. This allows the collection unit to analyze the student's past career counseling history and select the optimal data collection method, enabling more effective data collection. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection department can input past career counseling history into the AI, which can then select the most appropriate data collection method.

[0075] When collecting data, the collection unit can filter the data based on the student's current learning situation and living environment. For example, the collection unit can collect data during times that do not affect the student's academic work, taking into account the student's current learning situation. For example, the collection unit can use AI to analyze the student's learning schedule and identify the optimal timing for data collection. The collection unit can also select an appropriate data collection method by taking into account the student's living environment (home environment, commuting environment, etc.). For example, the collection unit can use AI to analyze the student's living environment data and identify the optimal data collection method. Furthermore, the collection unit can adjust the type and amount of data to be collected based on the student's learning situation and living environment. For example, the collection unit can use AI to analyze the student's learning situation and living environment data and identify the optimal range of data collection. This allows the collection unit to collect more relevant data by filtering the data based on the student's current learning situation and living environment. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection department can input data on students' learning status and living environment into the AI, which can then filter the data.

[0076] The collection unit can estimate a student's emotions and prioritize data to be collected based on the estimated student's emotions. For example, if a student is excited, the collection unit prioritizes collecting data related to their interests. For example, the collection unit can use AI to analyze the student's facial expressions and voice to detect signs of excitement. The collection unit can also prioritize collecting data related to grades if the student is relaxed. For example, the collection unit can use AI to monitor the student's concentration level in real time and collect grade data at the optimal time. Furthermore, if a student is feeling stressed, the collection unit can prioritize collecting data related to their hobbies and preferences. For example, the collection unit can use AI to analyze the student's biometric data to detect signs of stress. This allows the collection unit to prioritize collecting more important data by prioritizing data to be collected based on the student's emotions. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the student's emotional data into AI, which then prioritizes the data.

[0077] When collecting data, the collection unit can prioritize collecting relevant data by taking into account the student's geographic location information. For example, if the student lives in an urban area, the collection unit can prioritize collecting data related to urban occupations. For example, the collection unit can use AI to analyze the student's geographic location information and identify data related to urban occupations. Furthermore, if the student lives in a rural area, the collection unit can prioritize collecting data related to agricultural-related occupations. For example, the collection unit can use AI to analyze the student's geographic location information and identify data related to agricultural-related occupations. Furthermore, if the student lives overseas, the collection unit can prioritize collecting data related to occupations in that country. For example, the collection unit can use AI to analyze the student's geographic location information and identify data related to occupations in that country. This allows the collection unit to prioritize collecting relevant data by taking into account the student's geographic location information, thereby providing more appropriate career advice. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's geographic location information into AI, which can identify relevant data.

[0078] During data collection, the collection unit can analyze students' social media activities and collect related data. For example, the collection unit can collect data on interests from students' social media activities. For example, the collection unit can use AI to analyze students' social media posts and identify topics the students are interested in. The collection unit can also analyze students' social media activities and collect data related to their future careers. For example, the collection unit can use AI to analyze students' social media activities and identify what careers the students are interested in. Furthermore, the collection unit can collect data on hobbies and preferences based on students' social media activities. For example, the collection unit can use AI to analyze students' social media posts and identify what hobbies the students have. This allows the collection unit to analyze students' social media activities and collect related data, thereby predicting career paths based on more detailed information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input students' social media data into AI, which then collects related data.

[0079] The analysis unit can estimate a student's emotions and adjust the analysis method based on the estimated student's emotions. For example, if a student is relaxed, the analysis unit can perform a detailed analysis to provide deep insights. For example, the analysis unit can use AI to analyze a student's facial expressions and voice to detect signs of relaxation. If a student is in a hurry, the analysis unit can perform a concise analysis to provide key information. For example, the analysis unit can use AI to analyze a student's behavioral data to detect signs of hurry. Furthermore, if a student is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit can use AI to analyze a student's emotional data to detect signs of excitement. This allows the analysis unit to adjust the analysis method based on the student's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student emotional data into AI, which can then adjust the analysis method.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit can use AI to evaluate the importance of data and perform a detailed analysis on the data of high importance. The analysis unit can also perform a brief analysis on data of low importance. For example, the analysis unit can use AI to evaluate the importance of data and perform a brief analysis on the data of low importance. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit can use AI to evaluate the importance of data and perform an analysis with a moderate level of detail on data of medium importance. As a result, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data, thereby performing a detailed analysis on more important data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which can evaluate the importance of the data and adjust the level of detail of the analysis.

[0081] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a statistical analysis algorithm to grade data. For example, the analysis unit can have AI analyze the grade data and analyze the data using statistical methods. The analysis unit can also apply a clustering algorithm to hobby and preference data. For example, the analysis unit can have AI analyze the hobby and preference data and classify the data using clustering methods. The analysis unit can also apply a natural language processing algorithm to interest data. For example, the analysis unit can have AI analyze the interest data and analyze the data using natural language processing technology. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category into AI, and the AI ​​can apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate a student's emotions and determine analysis priorities based on the estimated student's emotions. For example, if a student is excited, the analysis unit prioritizes analyzing data related to their interests. For example, the analysis unit can use AI to analyze the student's facial expressions and voice to detect signs of excitement. Furthermore, if a student is relaxed, the analysis unit can prioritize analyzing data related to their grades. For example, the analysis unit can use AI to monitor the student's concentration level in real time and analyze grade data at the optimal timing. Furthermore, if a student is feeling stressed, the analysis unit can prioritize analyzing data related to their hobbies and preferences. For example, the analysis unit can use AI to analyze the student's biometric data to detect signs of stress. This allows the analysis unit to prioritize analysis of more important data by determining analysis priorities based on the student's emotions. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the student's emotional data into AI, which then determines the analysis priorities.

[0083] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of recently collected data. For example, the analysis unit can use AI to evaluate the time when the data was collected and prioritize analysis of recently collected data. The analysis unit can also prioritize analysis of data collected immediately before an important event. For example, the analysis unit can use AI to evaluate the time when the data was collected and prioritize analysis of data collected immediately before an important event. The analysis unit can also prioritize analysis of data collected over a long period of time. For example, the analysis unit can use AI to evaluate the time when the data was collected and prioritize analysis of data collected over a long period of time. This allows the analysis unit to determine the analysis priority based on the time when the data was collected, thereby providing more timely analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the analysis priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit can have AI evaluate the relevance of the data and prioritize analysis of data with high relevance. The analysis unit can also analyze data with moderate relevance next. For example, the analysis unit can have AI evaluate the relevance of the data and analyze data with moderate relevance next. Furthermore, the analysis unit can analyze data with low relevance last. For example, the analysis unit can evaluate the relevance of the data and analyze data with low relevance last. In this way, the analysis unit can prioritize analysis of more highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, and the AI ​​can adjust the order of analysis.

[0085] The estimation unit can estimate the student's emotions and adjust the career path estimation method based on the estimated student's emotions. For example, if the student is relaxed, the estimation unit estimates a detailed career path. For example, the estimation unit can use AI to analyze the student's facial expressions and voice to detect signs of relaxation. Furthermore, if the student is in a hurry, the estimation unit can estimate a concise career path. For example, the estimation unit can use AI to analyze the student's behavioral data to detect signs of hurry. Furthermore, if the student is excited, the estimation unit can estimate a visually appealing career path. For example, the estimation unit can use AI to analyze the student's emotional data to detect signs of excitement. This allows the estimation unit to estimate a more appropriate career path by adjusting the career path estimation method based on the student's emotions. Some or all of the above-described processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the student's emotional data into AI, which then adjusts the career path estimation method.

[0086] During estimation, the estimation unit can analyze the student's past career selection history to estimate an optimal career path. The estimation unit, for example, estimates a career path that the student is likely to be interested in based on the student's past career selection history. For example, the estimation unit can use AI to analyze the student's past career selection history and identify which career path the student is likely to be interested in. The estimation unit can also analyze the student's past career selection history to estimate a career path that the student is likely to be successful in. For example, the estimation unit can use AI to analyze the student's past career selection history and identify which career path the student is likely to be successful in. Furthermore, the estimation unit can also estimate a career path the student should avoid based on the student's past career selection history. For example, the estimation unit can use AI to analyze the student's past career selection history and identify which career path the student should avoid. This allows the estimation unit to analyze the student's past career selection history to estimate an optimal career path, thereby providing more appropriate career advice. Some or all of the above-described processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input past career choice history into the AI, which can then estimate the optimal career path.

[0087] The estimation unit can customize the career path based on the student's current learning situation and living environment during estimation. The estimation unit, for example, takes into account the student's current learning situation to estimate an appropriate career path. For example, the estimation unit can use AI to analyze the student's learning data and identify the student's learning situation. The estimation unit can also estimate a realistic career path by taking into account the student's living environment (home environment, commuting environment, etc.). For example, the estimation unit can use AI to analyze the student's living environment data and identify the student's living environment. The estimation unit can also customize career path options based on the student's learning situation and living environment. For example, the estimation unit can use AI to analyze the student's learning situation and living environment data and identify the optimal career path for the student. This allows the estimation unit to provide more realistic career advice by customizing the career path based on the student's current learning situation and living environment. Some or all of the above-described processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input data on students' learning status and living environment into the AI, allowing the AI ​​to customize their career path.

[0088] The estimation unit can estimate a student's emotions and prioritize career paths based on the estimated student's emotions. For example, if a student is excited, the estimation unit can prioritize career paths based on their interests. For example, the estimation unit can use AI to analyze the student's facial expressions and voice to detect signs of excitement. Furthermore, if a student is relaxed, the estimation unit can prioritize career paths based on their grades. For example, the estimation unit can use AI to monitor the student's concentration level in real time and analyze grade data at the optimal timing. Furthermore, if a student is stressed, the estimation unit can prioritize career paths based on their hobbies and preferences. For example, the estimation unit can use AI to analyze the student's biometric data to detect signs of stress. This allows the estimation unit to prioritize career paths based on the student's emotions, thereby prioritizing more important career paths. Some or all of the above-described processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the student's emotional data into AI, which then prioritizes career paths.

[0089] The estimation unit can estimate an optimal career path by taking into account the student's geographical location information. For example, if the student lives in an urban area, the estimation unit estimates a career path related to urban occupations. For example, the estimation unit can use AI to analyze the student's geographical location information and identify a career path related to urban occupations. Furthermore, if the student lives in a rural area, the estimation unit can estimate a career path related to agricultural-related occupations. For example, the estimation unit can use AI to analyze the student's geographical location information and identify a career path related to agricultural-related occupations. Furthermore, if the student lives overseas, the estimation unit can estimate a career path related to occupations in that country. For example, the estimation unit can use AI to analyze the student's geographical location information and identify a career path related to occupations in that country. This allows the estimation unit to estimate an optimal career path by taking the student's geographical location information into account, thereby providing more realistic career advice. Some or all of the above-described processing in the estimation unit may be performed using AI, for example, or may be performed without using AI. For example, the estimation unit can input the student's geographical location information into AI, which can then estimate an optimal career path.

[0090] During estimation, the estimation unit can analyze the student's social media activity to estimate a career path. The estimation unit, for example, estimates a career path based on the student's interests from the student's social media activity. For example, the estimation unit can use AI to analyze the student's social media posts and identify topics the student is interested in. The estimation unit can also analyze the student's social media activity to estimate a career path related to the student's future career. For example, the estimation unit can use AI to analyze the student's social media activity and identify what occupation the student is interested in. Furthermore, the estimation unit can estimate a career path based on the student's hobbies and preferences based on the student's social media activity. For example, the estimation unit can use AI to analyze the student's social media posts and identify what hobbies the student has. This allows the estimation unit to estimate a career path based on more detailed information by analyzing the student's social media activity. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without AI. For example, the estimation unit can input the student's social media data into AI, which then estimates the student's career path.

[0091] The providing unit can estimate the student's emotions and adjust the way the advice is expressed based on the estimated student's emotions. For example, if the student is nervous, the providing unit can provide advice in a calm manner. For example, the providing unit can use AI to analyze the student's facial expressions and voice to detect signs of nervousness. Furthermore, if the student is relaxed, the providing unit can provide advice in a manner that includes detailed information. For example, the providing unit can use AI to monitor the student's concentration level in real time and provide detailed advice at the optimal timing. Furthermore, if the student is in a hurry, the providing unit can provide advice in a concise and to-the-point manner. For example, the providing unit can use AI to analyze the student's behavioral data and detect signs of hurry. This allows the providing unit to adjust the way the advice is expressed based on the student's emotions, thereby providing more appropriate advice. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the student's emotional data into AI, which can then adjust the way the advice is expressed.

[0092] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the career path. The providing unit, for example, provides detailed advice for a highly important career path. For example, the providing unit can use AI to evaluate the importance of a career path and provide detailed advice for a highly important career path. The providing unit can also provide concise advice for a low-importance career path. For example, the providing unit can use AI to evaluate the importance of a career path and provide concise advice for a low-importance career path. The providing unit can also provide advice with an appropriate level of detail for a medium-importance career path. For example, the providing unit can use AI to evaluate the importance of a career path and provide advice with an appropriate level of detail for a medium-importance career path. As a result, the providing unit can adjust the level of detail of the advice based on the importance of the career path, thereby providing detailed advice for a more important career path. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provider can input the importance of a career path into the AI, which can then adjust the level of detail in its advice.

[0093] When providing advice, the providing unit can apply different advice algorithms depending on the career path category. For example, the providing unit can provide advice on technical skills and qualifications to technical occupations. For example, the providing unit can use AI to analyze data related to technical occupations and provide advice on technical skills and qualifications. The providing unit can also provide advice on portfolio creation and creative skills to creative occupations. For example, the providing unit can use AI to analyze data related to creative occupations and provide advice on portfolio creation and creative skills. The providing unit can also provide advice on leadership and management skills to management occupations. For example, the providing unit can use AI to analyze data related to management occupations and provide advice on leadership and management skills. This allows the providing unit to provide more appropriate advice by applying different advice algorithms depending on the career path category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the career path category into AI, which then applies an appropriate advice algorithm.

[0094] The providing unit can estimate the student's emotions and adjust the length of the advice based on the estimated student's emotions. For example, if the student is nervous, the providing unit can provide short, to-the-point advice. For example, the providing unit can use AI to analyze the student's facial expressions and voice to detect signs of nervousness. Furthermore, if the student is relaxed, the providing unit can provide longer advice with detailed explanations. For example, the providing unit can use AI to monitor the student's concentration level in real time and provide detailed advice at the optimal time. Furthermore, if the student is in a hurry, the providing unit can provide quick, concise advice. For example, the providing unit can use AI to analyze the student's behavioral data and detect signs of hurry. This allows the providing unit to adjust the length of the advice based on the student's emotions, thereby providing more appropriate advice. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the student's emotional data into AI, which can then adjust the length of the advice.

[0095] When providing advice, the providing unit can determine the priority of advice based on the submission time of the career path. The providing unit, for example, can prioritize advice for career paths with high urgency. For example, the providing unit can use AI to evaluate the submission time of the career path and prioritize advice for career paths with high urgency. The providing unit can also quickly provide advice for career paths with an approaching submission deadline. For example, the providing unit can use AI to evaluate the submission time of the career path and quickly provide advice for career paths with an upcoming submission deadline. Furthermore, the providing unit can also provide detailed advice for career paths with a distant submission deadline. For example, the providing unit can use AI to evaluate the submission time of the career path and provide detailed advice for career paths with a distant submission deadline. This allows the providing unit to determine the priority of advice based on the submission time of the career path, thereby providing more timely advice. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the submission time of the career path into AI, and the AI ​​can determine the priority of advice.

[0096] When providing advice, the providing unit can adjust the order of advice based on the relevance of the career paths. For example, the providing unit can prioritize providing advice for career paths with high relevance. For example, the providing unit can use AI to evaluate the relevance of career paths and prioritize providing advice for career paths with high relevance. The providing unit can also provide advice next for career paths with medium relevance. For example, the providing unit can use AI to evaluate the relevance of career paths and provide advice next for career paths with medium relevance. Furthermore, the providing unit can also provide advice last for career paths with low relevance. For example, the providing unit can use AI to evaluate the relevance of career paths and provide advice last for career paths with low relevance. In this way, the providing unit can prioritize providing advice for more relevant career paths by adjusting the order of advice based on the relevance of the career paths. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of career paths to AI, and the AI ​​can adjust the order of advice.

[0097] The proxy unit can estimate a student's emotions and adjust its proxy work method based on the estimated student's emotions. For example, if a student is nervous, the proxy unit can perform the proxy work in a calm manner. For example, the proxy unit can use AI to analyze the student's facial expressions and voice to detect signs of nervousness. Furthermore, if a student is relaxed, the proxy unit can perform the proxy work in a manner that includes detailed explanations. For example, the proxy unit can use AI to monitor the student's concentration level in real time and provide detailed explanations at the optimal time. Furthermore, if a student is in a hurry, the proxy unit can perform the proxy work in a quick and concise manner. For example, the proxy unit can use AI to analyze the student's behavioral data and detect signs of hurry. This allows the proxy unit to provide more appropriate proxy work by adjusting its proxy work method based on the student's emotions. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input the student's emotional data into AI, which can then adjust its proxy work method.

[0098] During the proxy service, the proxy unit can analyze the student's past career counseling history and select the optimal proxy method. For example, the proxy unit selects the proxy method preferred by the student based on the past career counseling history. For example, the proxy unit can use AI to analyze the past career counseling history and identify the proxy method preferred by the student. The proxy unit can also analyze the past career counseling history and perform the proxy service using the method that makes the student most relaxed. For example, the proxy unit can use AI to analyze the past career counseling history and identify the method that makes the student most relaxed. Furthermore, the proxy unit can select the method that allows the student to provide the most information based on the past career counseling history. For example, the proxy unit can use AI to analyze the past career counseling history and identify the proxy method that allows the student to provide the most information. This allows the proxy unit to analyze the student's past career counseling history and select the optimal proxy method, thereby providing more effective proxy service. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or without AI. For example, the agency can input past career counseling history into the AI, which can then select the most appropriate agency method.

[0099] The proxy unit can customize the proxy work based on the student's current learning situation and living environment when performing proxy work. For example, the proxy unit performs proxy work in a manner that does not affect the student's academic performance, taking into account the student's current learning situation. For example, the proxy unit can use AI to analyze the student's learning data and identify the student's learning situation. The proxy unit can also select an appropriate proxy method by taking into account the student's living environment (e.g., home environment, commuting environment, etc.). For example, the proxy unit can use AI to analyze the student's living environment data and identify the student's living environment. Furthermore, the proxy unit can adjust the content and method of the proxy work based on the student's learning situation and living environment. For example, the proxy unit can use AI to analyze the student's learning situation and living environment data and identify the proxy method that is optimal for the student. This allows the proxy unit to customize the proxy work based on the student's current learning situation and living environment, thereby providing more appropriate proxy work. Some or all of the above-mentioned processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy department can input data on students' learning status and living environment into the AI, which can then customize the proxy work.

[0100] The proxy unit can estimate a student's emotions and prioritize proxy tasks based on the estimated student emotions. For example, if a student is excited, the proxy unit can prioritize proxy tasks based on the student's interests. For example, the proxy unit can use AI to analyze the student's facial expressions and voice to detect signs of excitement. Furthermore, if a student is relaxed, the proxy unit can prioritize proxy tasks based on grades. For example, the proxy unit can use AI to monitor the student's concentration level in real time and analyze grade data at the optimal time. Furthermore, if a student is stressed, the proxy unit can prioritize proxy tasks based on the student's hobbies and preferences. For example, the proxy unit can use AI to analyze the student's biometric data to detect signs of stress. This allows the proxy unit to prioritize proxy tasks based on the student's emotions, allowing the proxy unit to prioritize more important proxy tasks. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or without AI. For example, the agency can input students' emotional data into AI, which can then determine the priorities of the agency's work.

[0101] When performing a proxy service, the proxy unit can select the optimal proxy method by taking into account the student's geographic location information. For example, if the student lives in an urban area, the proxy unit can perform proxy services related to urban occupations. For example, the proxy unit can use AI to analyze the student's geographic location information and identify proxy services related to urban occupations. Furthermore, if the student lives in a rural area, the proxy unit can perform proxy services related to agriculture-related occupations. For example, the proxy unit can use AI to analyze the student's geographic location information and identify proxy services related to agriculture-related occupations. Furthermore, if the student lives overseas, the proxy unit can perform proxy services related to occupations in that country. For example, the proxy unit can use AI to analyze the student's geographic location information and identify proxy services related to occupations in that country. This allows the proxy unit to select the optimal proxy method by taking into account the student's geographic location information, thereby providing more realistic proxy services. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or may be performed without using AI. For example, the proxy department can input the student's geographical location information into the AI, which can then select the most appropriate proxy method.

[0102] When performing a task, the proxy unit can analyze the student's social media activity and suggest a task based on the student's interests. For example, the proxy unit can use AI to analyze the student's social media posts and identify topics the student is interested in. The proxy unit can also analyze the student's social media activity and suggest a task related to the student's future career. For example, the proxy unit can use AI to analyze the student's social media activity and identify the type of career the student is interested in. Furthermore, the proxy unit can suggest a task based on the student's hobbies and preferences based on the student's social media activity. For example, the proxy unit can use AI to analyze the student's social media posts and identify the student's hobbies. This allows the proxy unit to analyze the student's social media activity and suggest a task based on more detailed information. Some or all of the above-described processing in the proxy unit may be performed using AI, for example, or without AI. For example, the agency can input students' social media data into AI, which can then suggest substitute tasks. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, provision unit, and proxy unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect data on a student's hobbies, preferences, interests, grades, and past career counseling history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts a career path that matches the student's aptitude and interests based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides specific advice based on the predicted career path. The proxy unit is realized, for example, by the control unit 46A of the smart device 14 and performs career counseling on behalf of the student. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, provision unit, and proxy unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect data on a student's hobbies, preferences, interests, grades, and past career counseling history. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The estimation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, predicts a career path that matches the student's aptitude and interests based on the analyzed data. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides specific advice based on the predicted career path. The proxy unit, realized, for example, by the control unit 46A of the smart glasses 214, performs career counseling on behalf of the student. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, provision unit, and proxy unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect data on the student's hobbies, preferences, interests, grades, and past career counseling history. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and predicts a career path that matches the student's aptitude and interests based on the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides specific advice based on the predicted career path. The proxy unit is implemented, for example, by the control unit 46A of the headset terminal 314 and performs career counseling on behalf of the student. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, provision unit, and proxy unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect data on the student's hobbies, preferences, interests, grades, and past career counseling history. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts a career path that matches the student's aptitude and interests based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides specific advice based on the predicted career path. The proxy unit is realized, for example, by the control unit 46A of the robot 414 and performs career counseling on behalf of the student.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The AI ​​career coaching system can further include a feedback unit. The feedback unit can collect feedback from students regarding the advice and proxy services they receive and use the information to improve the system as a whole. For example, the feedback unit can conduct a survey after students receive advice and collect the results. The feedback unit can also provide a form for students to fill out after taking a mock interview and analyze the content of the form. Furthermore, the feedback unit can introduce an evaluation system to evaluate students' satisfaction with the results of the corrections to their submitted documents. This allows the feedback unit to collect students' opinions and feedback and use the information to improve the system, thereby providing more effective career support.

[0105] The collection unit can estimate the student's emotions and adjust the timing of data collection based on the estimated student's emotions. For example, if the student is feeling stressed, the collection unit can delay data collection until the student is relaxed. Also, if the student is concentrating, the collection unit can collect data at that timing to obtain more accurate information. Furthermore, if the student is tired, the collection unit can collect data after the student has rested, thereby collecting higher quality data. In this way, the collection unit can collect higher quality data by adjusting the timing of data collection based on the student's emotions.

[0106] The analysis unit can analyze the collected data and predict a career path that matches the student's aptitude and interests. Furthermore, the analysis unit can predict the student's emotions and adjust the analysis method based on the predicted student emotions. For example, if the student is relaxed, a detailed analysis can be performed to provide deep insights. If the student is in a hurry, a concise analysis can be performed to provide information that focuses on the main points. Furthermore, if the student is excited, a visually appealing analysis result can be provided. This allows the analysis unit to provide more appropriate analysis results by adjusting the analysis method based on the student's emotions.

[0107] The advice providing unit can provide specific advice based on the estimated career path. Furthermore, the advice providing unit can estimate the student's emotions and adjust the way the advice is expressed based on the estimated student's emotions. For example, if the student is nervous, the advice can be provided in a calm manner. Furthermore, if the student is relaxed, the advice can be provided in a manner that includes detailed information. Furthermore, if the student is in a hurry, the advice can be provided in a concise and to-the-point manner. In this way, the advice providing unit can provide more appropriate advice by adjusting the way the advice is expressed based on the student's emotions.

[0108] The Substitute Department can substitute for tasks that teachers would normally perform, such as practicing for interviews and correcting necessary documents. Furthermore, the Substitute Department can estimate students' emotions and adjust the method of substitute work based on the estimated student emotions. For example, if a student is nervous, the Substitute Department can perform the substitute work in a calm manner. Alternatively, if a student is relaxed, the Substitute Department can perform the substitute work in a manner that includes detailed explanations. Furthermore, if a student is in a hurry, the Substitute Department can perform the substitute work in a quick and concise manner. This allows the Substitute Department to provide more appropriate substitute work by adjusting the method of substitute work based on the student's emotions.

[0109] The AI ​​career coaching system can further include a networking department. The networking department can help students network with experts and peers related to the occupations and fields in which they are interested. For example, the networking department can provide information about industry events and seminars that students can attend. The networking department can also provide a platform for students to book mentoring sessions with experts. Furthermore, the networking department can operate an online community where students can connect with other students who share the same interests. In this way, the networking department can provide students with an opportunity not only to obtain career-related information but also to actually connect with industry experts and peers.

[0110] The AI ​​career coaching system can further include a motivation unit. The motivation unit can provide support to increase students' motivation for learning and career goals. For example, the motivation unit can track the progress of students toward goals set by them and provide rewards according to the degree of achievement. The motivation unit can also send encouraging messages to students when they face difficulties. Furthermore, the motivation unit can send congratulatory messages to students when they succeed, helping them feel a sense of accomplishment. In this way, the motivation unit can provide more effective career support by increasing students' motivation for learning and career goals.

[0111] The AI ​​career coaching system may further include a resource unit. The resource unit may provide resources for students to obtain career-related information. For example, the resource unit may operate an online library that students can access and provide career-related books, papers, articles, etc. The resource unit may also provide information on online courses and training programs that students can use. Furthermore, the resource unit may operate a forum where students can post career-related questions and receive answers from experts. This allows the resource unit to provide more effective career support by providing a variety of resources for students to obtain career-related information.

[0112] The AI ​​career coaching system can further include an internship department. The internship department can provide internship opportunities for students to gain real-world work experience. For example, the internship department can provide a list of internships that students can apply for and support the application process. The internship department can also provide training programs for students to learn the skills and knowledge they need during their internship. Furthermore, the internship department can provide feedback to students after they complete their internship and advice on how to proceed to the next step. This allows the internship department to provide more realistic career support by allowing students to gain real-world work experience.

[0113] The AI ​​career coach system can further include a career events department. The career events department can plan and run career-related events that students can participate in. For example, the career events department can hold career fairs and company information sessions that students can participate in. The career events department can also plan panel discussions and workshops where students can interact directly with industry experts. Furthermore, the career events department can hold social events for students to network. In this way, the career events department can provide students with an opportunity not only to obtain career-related information but also to actually connect with industry experts and peers.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The collection unit collects data on students' hobbies, preferences, interests, and grades. For example, the collection unit collects data on hobbies, preferences, and interests through questionnaires or online forms filled out by students. The collection unit can also obtain grade information from the school's grade database. Furthermore, the collection unit can collect students' past career counseling histories. For example, the collection unit digitizes records of past career counseling sessions and stores them in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze patterns of students' hobbies, preferences, and interests. The analysis unit can also analyze grade data to understand students' academic ability and favorite subjects. Furthermore, the analysis unit can analyze past career counseling history to understand students' career choice trends. Step 3: The estimation unit predicts a career path that matches the student's aptitude and interests based on the data analyzed by the analysis unit. For example, the estimation unit uses AI to predict an appropriate occupation based on the student's hobbies, preferences, and interests. The estimation unit can also predict career paths that students are likely to be successful in based on their academic performance data. Furthermore, the estimation unit can predict career paths that students should avoid based on their past career counseling history. Step 4: The advice section provides specific advice based on the career path predicted by the prediction section. For example, when a student asks about their future career, the advice section provides detailed information about that career. The advice section can also provide advice on the necessary skills, qualifications, and career paths. Furthermore, the advice section can provide information on careers that may interest the student. Step 5: The proxy department provides career counseling on behalf of students based on the advice provided by the provider department. For example, the proxy department may conduct interview practice sessions and provide feedback on students' answers. The proxy department may also correct required documents to improve the quality of the documents submitted by students. Furthermore, when students ask about their future careers, the proxy department may provide detailed information about those careers.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[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 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.

[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 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.

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

[0167] 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.

[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] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects data on students' hobbies, preferences, interests, and grades; an analysis unit that analyzes the data collected by the collection unit; an estimation unit that estimates an appropriate career path that matches the aptitude and interests of the student based on the data analyzed by the analysis unit; a providing unit that provides specific advice based on the career path estimated by the estimation unit; an agent unit that provides career counseling on behalf of the client based on the advice provided by the provider unit; A system characterized by:

2. The collecting unit Collect data on students' hobbies, preferences, interests, grades, and career counseling history 2. The system of claim 1.

3. The analysis unit Analyze the collected data to predict career paths that match students' aptitudes and interests 2. The system of claim 1.

4. The providing unit Providing specific advice based on predicted career paths 2. The system of claim 1.

5. The agent unit: Acting on behalf of teachers, such as practicing for interviews and correcting necessary documents 2. The system of claim 1.

6. The providing unit When students ask about their future careers, we provide detailed information about those jobs and advise on skills, qualifications and career paths.

2. The system of claim 1.

7. The agent unit: Conduct mock interviews and provide feedback on students' responses 2. The system of claim 1.

8. The collecting unit Estimate student emotions and adjust the timing of data collection based on the estimated student emotions.

2. The system of claim 1.

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