system

Abstract

The system according to the embodiment aims to provide answers to users' questions and support them in their career and study plans. [Solution] A system according to an embodiment includes a reception unit, a questioning unit, a storage unit, a support unit, and a growth unit. The reception unit receives questions from users. The questioning unit asks questions based on the questions received by the reception unit. The storage unit stores information obtained by the questioning unit. The support unit supports career and learning plans based on the information stored by the storage unit. The growth unit grows semi-permanently based on the information provided by the support 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 technologies simply answer users' questions, and there is room for improvement in terms of supporting users' career and study plans.

[0005] The system according to the embodiment aims to provide answers to users' questions and support them in their career and study plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a questioning unit, a storage unit, a support unit, and a growth unit. The reception unit receives questions from users. The questioning unit asks questions based on the questions received by the reception unit. The storage unit stores information obtained by the questioning unit. The support unit supports career and learning plans based on the information stored by the storage unit. The growth unit grows semi-permanently based on the information provided by the support unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide answers to user questions and support career and study plans. [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 ​​mentoring system according to an embodiment of the present invention uses an AI chatbot powered by generative AI to foster awareness and discovery in users and develop their problem-solving skills. Through dialogue with users, the AI ​​mentoring system accumulates and updates individual characteristics, personality, interests, skills, and other information. This supports the creation of career and learning plans and curricula without impairing autonomy. Furthermore, the AI ​​semi-permanently grows and accompanies the user. For example, when a user inputs a question, the generative AI responds to the question, guiding the user to think for themselves. For example, it asks questions such as, "What approaches can you think of to solve this problem?" This allows the user to develop their ability to think independently. Furthermore, through dialogue with the user, the AI ​​accumulates and updates individual characteristics, personality, interests, skills, and other information. For example, if a user is interested in a particular field, asking questions related to that field can pique the user's interest and increase their motivation to learn. The AI ​​also supports the creation of users' careers, learning plans, and curricula. For example, if a user is concerned about their future career, the AI ​​can suggest appropriate career paths based on the user's characteristics and interests. This allows users to find a career that suits them. Furthermore, the AI ​​will continue to grow and accompany the user semi-permanently. For example, if the user acquires a new skill, the AI ​​will store that information and use it in the next conversation. This allows the user to always continue learning based on the latest information. In this way, an AI chatbot using generative AI can bring users "awareness" and "discovery," develop problem-solving skills, and support the creation of career and learning plans, allowing it to grow semi-permanently along with the user. This allows the AI ​​mentor system to ask appropriate questions in response to users' questions, store information, support career and learning plans, and grow semi-permanently.

[0029] The AI ​​mentor system according to the embodiment includes a reception unit, a questioning unit, an accumulation unit, a support unit, and a growth unit. The reception unit receives a user's question. The user's question may be, for example, in text format, audio format, or on a specific topic, but is not limited to these examples. The reception unit may, for example, receive a text question via a chat window. The reception unit may also receive audio questions via a microphone. The reception unit may also filter and receive questions on a specific topic. For example, the reception unit may analyze text entered by the user using natural language processing technology to understand the content of the question. Audio questions may be converted into text using speech recognition technology and analyzed. Questions on a specific topic may be filtered based on pre-defined keywords. The questioning unit asks a question based on the question received by the reception unit. The question is asked based on, for example, the type of question, the timing of the question, the format of the question, etc., but is not limited to these examples. For example, the questioning unit may respond to the user's question with a specific question such as, "What approaches can be taken to solve this problem?" The questioning unit can also ask related questions based on the user's characteristics and interests. For example, if the user is interested in a particular field, the questioning unit can ask questions related to that field. The storage unit stores information obtained by the questioning unit. The stored information includes, but is not limited to, text data, numerical data, and image data. The storage unit can also store, for example, the user's characteristics and interests in a database. The storage unit can also store the user's behavioral history and dialogue history. The support unit supports the user's career and learning plans based on the information stored by the storage unit. Support is provided based on, for example, career selection, learning plans for skill development, and the like, but is not limited to, for example. For example, the support unit can suggest an appropriate career path based on the user's characteristics and interests. The support unit can also create a learning plan for the user and suggest a curriculum. The growth unit semi-permanently grows based on the information provided by the support unit.Growth can be achieved, for example, by storing information about a new skill acquired by the user and utilizing it in the next dialogue, but is not limited to such an example. For example, when a user acquires a new skill, the growth unit stores the information in a database and utilizes it in the next dialogue. As a result, the AI ​​mentor system according to the embodiment can ask appropriate questions in response to user questions, accumulate information, support career and learning plans, and achieve semi-permanent growth.

[0030] The questioning unit can ask a specific question such as, "What approaches can be taken to solve this problem?" Specific questions include, but are not limited to, approaches to solving a problem and specific example questions. For example, the questioning unit can ask a specific question such as, "What approaches can be taken to solve this problem?" in response to a question input by a user. The questioning unit can also ask related questions based on the user's characteristics and interests. For example, if the user is interested in a particular field, the questioning unit can ask questions related to that field. This can encourage the user's thinking by asking specific questions. Some or all of the above-described processing in the questioning unit can be performed using, or without, a generation AI. For example, the questioning unit can input a user's question into a generation AI, which can then generate an appropriate question.

[0031] The storage unit can store the user's characteristics and interests in a database. Examples of the stored information include, but are not limited to, questionnaire results and behavioral history. For example, the storage unit collects the user's characteristics and interests through questionnaires and stores them in a database. The storage unit can also analyze the user's behavioral history to identify and store the interests. For example, the storage unit can analyze pages the user has previously viewed or links the user has clicked to identify the interests. By storing the user's characteristics and interests, individualized support becomes possible. Some or all of the above-described processing in the storage unit may be performed using, or without, a generation AI. For example, the storage unit can input the user's behavioral history into a generation AI, which then identifies and stores the interests.

[0032] The support unit can propose career and study plans based on the accumulated data. Examples of career and study plans include, but are not limited to, career selection and study plans for skill development. The support unit can propose an appropriate career path based on the user's characteristics and interests. The support unit can also create a study plan for the user and propose a curriculum. For example, if the user is worried about their future career, the support unit can propose an appropriate career path based on the user's characteristics and interests. This makes it possible to propose an appropriate career or study plan based on the accumulated data. Some or all of the above-described processing in the support unit may be performed using, or without, a generation AI. For example, the support unit can input the accumulated data into a generation AI, which then proposes an appropriate career or study plan.

[0033] When a user acquires a new skill, the growth unit can store that information and use it in the next dialogue. New skills include, but are not limited to, technical skills, soft skills, etc. For example, when a user acquires a new technical skill, the growth unit can store that information in a database and use it in the next dialogue. Furthermore, when a user acquires a new soft skill, the growth unit can store that information and use it in the next dialogue. For example, when a user acquires a new programming language, the growth unit can store that information and use that skill in the next dialogue. This can support the user's growth by using the acquired new skill information in the next dialogue. Some or all of the above-described processing in the growth unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the growth unit can input the user's new skill acquisition information into the generation AI, which can use that information in the next dialogue.

[0034] The reception unit can analyze the user's past question history and select the optimal reception method. The optimal reception method includes, but is not limited to, a method for analyzing the past question history and a type of reception method. For example, the reception unit can prioritize reception of questions frequently asked by the user in the past. The reception unit can also find specific patterns from the user's past question history and suggest the optimal reception method. The reception unit can also prioritize reception of question formats (text, voice, etc.) used by the user in the past. For example, the reception unit accumulates the user's past question history in a database and analyzes it. Based on the analysis results, the reception unit prioritizes reception of questions frequently asked by the user in the past. A machine learning algorithm can also be used to find specific patterns. This allows the optimal reception method to be selected by analyzing the past question history. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI. For example, the reception unit can input the user's past question history into a generation AI, which can then suggest the optimal reception method.

[0035] When receiving a question, the reception unit may filter the questions based on the user's current project or field of interest. Examples of filtering include, but are not limited to, the type of project and a method for identifying the field of interest. For example, the reception unit may prioritize questions related to the project the user is currently working on. The reception unit may also filter and accept highly relevant questions based on the user's field of interest. Furthermore, the reception unit may select and accept appropriate questions based on the user's current learning topic. For example, the reception unit may accumulate and analyze the user's project information in a database. Based on the analysis results, related questions may be prioritized. Fields of interest may be identified using survey results or behavioral history. By filtering questions based on the current project or field of interest, highly relevant questions may be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's project information into a generation AI, which may then filter out relevant questions.

[0036] When receiving a question, the reception unit can prioritize relevant questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit can prioritize questions related to that area. The reception unit can also filter and accept relevant questions based on the user's current location. Furthermore, if the user is traveling, the reception unit can prioritize questions related to the user's travel destination. For example, the reception unit can accumulate and analyze the user's geographical location information in a database. Based on the analysis results, relevant questions can be prioritized. The user's current location can also be obtained in real time using a location information service. This allows for the priority acceptance of relevant questions by taking the geographical location information into account. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then filter relevant questions.

[0037] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. Social media activity includes, but is not limited to, analysis of post content and follower analysis. For example, the reception unit can prioritize receiving questions related to topics in which the user has expressed interest on social media. The reception unit can also analyze the user's interests from the user's social media activity and receive related questions. Furthermore, the reception unit can prioritize receiving questions related to accounts the user follows on social media. For example, the reception unit can accumulate the user's social media activity in a database and analyze it. Based on the analysis results, related questions can be prioritized. Natural language processing technology can be used to analyze the post content. This allows the analysis of social media activity to prioritize receiving related questions. Some or all of the above-described processing by the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity into a generation AI, which can then filter out related questions.

[0038] When issuing a question, the questioning unit can adjust the level of detail of the question based on the importance of the problem. The importance of the problem includes, but is not limited to, the scope of impact and the urgency of resolution. For example, the questioning unit issues detailed questions for problems of high importance. The questioning unit can also issue concise questions for problems of low importance. Furthermore, the questioning unit can issue questions with an appropriate level of detail depending on the importance of the problem. For example, the questioning unit accumulates and analyzes the scope of impact of the problem in a database. Based on the analysis results, the questioning unit issues detailed questions for problems of high importance. A machine learning algorithm can also be used to evaluate the urgency of resolution. This allows the questioning unit to issue an appropriate question by adjusting the level of detail of the question depending on the importance of the problem. Some or all of the above-described processing in the questioning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the questioning unit can input problem importance data into a generation AI, which can then generate a question with an appropriate level of detail.

[0039] When posing a question, the questioning unit can apply different questioning algorithms depending on the problem category. Problem categories include, but are not limited to, technical problems and business problems. For example, the questioning unit can pose questions that include technical details for technical problems. For interpersonal problems, the questioning unit can also pose questions that take emotions into consideration. For learning-related problems, the questioning unit can also pose questions about learning methods. For example, the questioning unit accumulates and analyzes problem categories in a database. Based on the analysis results, an appropriate questioning algorithm is applied. Natural language processing technology can also be used to classify problem categories. This allows for effective questioning by applying an appropriate questioning algorithm depending on the problem category. Some or all of the above-described processing in the questioning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the questioning unit can input problem category data into a generation AI, which then generates an appropriate questioning algorithm.

[0040] When issuing questions, the questioning unit can determine the priority of questions based on the time of question submission. The time of question submission includes, but is not limited to, the submission date and time, the submission frequency, etc. For example, the questioning unit prioritizes questions for urgent questions. The questioning unit can also promptly issue questions for questions whose submission deadline is approaching. Furthermore, the questioning unit can issue questions with appropriate priorities depending on the submission time. For example, the questioning unit accumulates and analyzes the time of question submission in a database. Based on the analysis results, the questioning unit prioritizes questions for urgent questions. A machine learning algorithm can also be used to evaluate the submission deadline. By doing so, the priority of questions can be determined based on the time of question submission, allowing questions to be issued in an appropriate order. Some or all of the above-described processing in the questioning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the questioning unit can input question submission time data into a generation AI, which can then generate questions with appropriate priorities.

[0041] The querying unit can adjust the order of questions based on the relevance of the questions when querying. Examples of question relevance include, but are not limited to, topic similarity and related keywords. For example, the querying unit prioritizes questions for highly relevant questions. The querying unit can also postpone questions for less relevant questions. Furthermore, the querying unit can query in an appropriate order depending on the relevance of the questions. For example, the querying unit accumulates and analyzes the relevance of questions in a database. Based on the analysis results, it prioritizes questions for highly relevant questions. Natural language processing technology can also be used to evaluate topic similarity. By adjusting the order of questions based on the relevance of questions, the questions can be displayed in an appropriate order. Some or all of the above-described processing in the querying unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the querying unit can input question relevance data into a generation AI, which can then generate questions in an appropriate order.

[0042] During storage, the storage unit can analyze the user's past dialogue history and select optimal information. The dialogue history includes, for example, past dialogue content and dialogue frequency, but is not limited to these examples. For example, the storage unit prioritizes storing information in which the user has shown interest in the past. The storage unit can also select and store highly relevant information from the user's past dialogue history. Furthermore, the storage unit can prioritize storing information formats (text, audio, etc.) previously used by the user. For example, the storage unit stores the user's past dialogue history in a database and analyzes it. Based on the analysis results, relevant information is preferentially stored. Natural language processing technology can be used to analyze the dialogue content. This allows optimal information to be selected and stored by analyzing the past dialogue history. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storage unit can input the user's past dialogue history into a generation AI, which can select optimal information.

[0043] The storage unit can filter information based on the user's current interests during storage. Examples of interests include, but are not limited to, survey results and behavioral history. The storage unit, for example, prioritizes storage of information related to topics in which the user is currently interested. The storage unit can also filter and store highly relevant information based on the user's current interests. Furthermore, the storage unit can select and store appropriate information based on the user's current study topic. For example, the storage unit can collect the user's interests through a survey and store them in a database. It can also analyze the behavioral history to identify and store the interests. By filtering information based on the user's current interests, highly relevant information can be preferentially stored. Some or all of the above-described processing in the storage unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the storage unit can input the user's interest data into a generation AI, which can then filter out relevant information.

[0044] The storage unit can prioritize storage of highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the storage unit prioritizes storage of information related to that area. The storage unit can also filter and store highly relevant information based on the user's current location. Furthermore, if the user is traveling, the storage unit can prioritize storage of information related to the user's travel destination. For example, the storage unit stores the user's geographical location information in a database and analyzes it. Based on the analysis results, relevant information is prioritized and stored. The user's current location can also be obtained in real time using a location information service. This allows highly relevant information to be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storage unit can input the user's geographical location information into a generation AI, which then filters relevant information.

[0045] During storage, the storage unit can analyze the user's social media activity and store related information. Social media activity includes, but is not limited to, analysis of posted content and follower analysis. For example, the storage unit prioritizes storage of information related to topics in which the user has shown interest on social media. The storage unit can also analyze the user's interests from the user's social media activity and store related information. Furthermore, the storage unit can prioritize storage of information related to accounts the user follows on social media. For example, the storage unit stores the user's social media activity in a database and analyzes it. Based on the analysis results, related information is preferentially stored. Natural language processing technology can be used to analyze the posted content. This allows for the analysis of social media activity to prioritize the storage of related information. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storage unit can input the user's social media activity into a generation AI, which then filters out related information.

[0046] When providing support, the support unit can analyze the user's past career and learning history to select the optimal support method. Examples of career and learning history include, but are not limited to, work history and learning history data. The support unit provides appropriate career support based on the user's past career history, for example. The support unit can also provide optimal learning support based on the user's past learning history. Furthermore, the support unit can select an appropriate support method based on the user's past experience. For example, the support unit accumulates and analyzes the user's past career history in a database. Based on the analysis results, a relevant support method is selected. A machine learning algorithm can be used to analyze the learning history. This allows the optimal support method to be selected by analyzing the past career and learning history. Some or all of the above-described processing in the support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the support unit can input the user's career and learning history data into a generation AI, which can then select the optimal support method.

[0047] The support unit can customize the support method based on the user's current living situation when providing support. Examples of living situations include, but are not limited to, the living environment and health condition. For example, when the user is busy, the support unit can provide a short, effective support method. Furthermore, when the user is relaxed, the support unit can also provide a detailed support method. Furthermore, the support unit can customize an appropriate support method according to the user's living situation. For example, the support unit accumulates and analyzes the user's living situation in a database. Based on the analysis results, a relevant support method is selected. The living environment and health condition can be evaluated using questionnaire results and behavioral history. This allows appropriate support to be provided by customizing the support method based on the current living situation. Some or all of the above-described processing in the support unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the support unit can input the user's living situation data into the generation AI, which then selects the optimal support method.

[0048] When providing assistance, the assistance unit can select the optimal assistance method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the assistance unit can provide assistance related to that area. The assistance unit can also provide highly relevant assistance based on the user's current location. Furthermore, if the user is traveling, the assistance unit can provide assistance related to the user's travel destination. For example, the assistance unit can accumulate the user's geographical location information in a database and analyze it. Based on the analysis results, the assistance unit can select a relevant assistance method. The user's current location can also be obtained in real time using a location information service. This allows the optimal assistance method to be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the assistance unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the assistance unit can input the user's geographical location information into the generation AI, which can then select a relevant assistance method.

[0049] When providing assistance, the assistance unit can analyze the user's social media activity and suggest assistance methods. Examples of social media activity include, but are not limited to, analyzing posted content and followers. The assistance unit can provide assistance related to topics the user has shown interest in on social media. The assistance unit can also analyze the user's interests from the user's social media activity and provide relevant assistance. Furthermore, the assistance unit can provide assistance related to accounts the user follows on social media. For example, the assistance unit can accumulate the user's social media activity in a database and analyze it. Based on the analysis results, relevant assistance methods are selected. Natural language processing technology can be used to analyze posted content. This allows relevant assistance to be provided by analyzing the social media activity. Some or all of the above-described processing in the assistance unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the assistance unit can input the user's social media activity into a generation AI, which can then select relevant assistance methods.

[0050] During growth, the growth unit can analyze the user's past skill acquisition history and select an optimal growth method. The skill acquisition history includes, but is not limited to, past learning content and skill evaluation results. The growth unit, for example, suggests an appropriate growth method based on the user's past skill acquisition history. The growth unit can also suggest an optimal growth method based on the user's past learning history. Furthermore, the growth unit can select an appropriate growth method based on the user's past experience. For example, the growth unit accumulates the user's past skill acquisition history in a database and analyzes it. Based on the analysis results, a relevant growth method is selected. A machine learning algorithm can be used to analyze the learning history. This allows the optimal growth method to be selected by analyzing the past skill acquisition history. Some or all of the above-described processing in the growth unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the growth unit can input the user's skill acquisition history data into the generation AI, which then selects the optimal growth method.

[0051] During growth, the growth unit can customize the growth method based on the user's current living situation. Examples of living situations include, but are not limited to, the living environment and health status. For example, if the user is busy, the growth unit can provide a short, effective growth method. Furthermore, if the user is relaxed, the growth unit can also provide a detailed growth method. Furthermore, the growth unit can customize an appropriate growth method according to the user's living situation. For example, the growth unit accumulates and analyzes the user's living situation in a database. Based on the analysis results, a relevant growth method is selected. Survey results and behavioral history can be used to evaluate the living environment and health status. This allows the growth method to be customized based on the current living situation, thereby providing an appropriate growth method. Some or all of the above-described processing in the growth unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the growth unit can input the user's living situation data into the generation AI, which can then select the optimal growth method.

[0052] During growth, the growth unit can select the optimal growth method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the growth unit can provide a growth method related to that area. The growth unit can also provide a highly relevant growth method based on the user's current location. Furthermore, if the user is traveling, the growth unit can provide a growth method related to the user's travel destination. For example, the growth unit accumulates the user's geographical location information in a database and analyzes it. Based on the analysis results, the relevant growth method is selected. The user's current location can also be obtained in real time using a location information service. This allows the optimal growth method to be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the growth unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the growth unit can input the user's geographical location information into the generation AI, which can then select a relevant growth method.

[0053] During growth, the growth unit can analyze the user's social media activity and suggest growth methods. Social media activity includes, but is not limited to, analysis of post content and follower analysis. For example, the growth unit can provide growth methods related to topics in which the user has shown interest on social media. The growth unit can also analyze the user's interests from the user's social media activity and provide relevant growth methods. Furthermore, the growth unit can provide growth methods related to accounts the user follows on social media. For example, the growth unit can accumulate the user's social media activity in a database and analyze it. Based on the analysis results, relevant growth methods are selected. Natural language processing technology can be used to analyze the post content. This allows the analysis of social media activity to provide relevant growth methods. Some or all of the above-mentioned processing in the growth unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the growth unit can input the user's social media activity into a generation AI, which can select relevant growth methods.

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

[0055] The AI ​​mentor system can further include a feedback unit. The feedback unit can collect feedback on the questions and assistance provided by the user and use the collected feedback to improve the system. For example, the feedback unit can collect, in the form of a questionnaire, how the user felt about the questions and whether the assistance was helpful. The feedback unit can also analyze the user's dialogue history and automatically evaluate how the user responded. Furthermore, the feedback unit can accumulate feedback provided by the user and reflect it in the next dialogue or assistance. This allows the AI ​​mentor system to be continuously improved based on user feedback and provide more effective questions and assistance.

[0056] The AI ​​mentor system can further include a notification unit. The notification unit can notify the user of important information and reminders. For example, the notification unit can notify the user of the start time of study based on the study schedule set by the user. The notification unit can also periodically notify the user of the progress of goals set by the user. Furthermore, the notification unit can notify the user of the latest information on areas in which the user is interested. This allows the user to receive important information without missing it, allowing them to effectively advance their study and career plans.

[0057] The AI ​​mentor system can further include an analysis unit. The analysis unit can analyze the user's dialogue history and behavioral history to understand the user's learning patterns and career trends. For example, the analysis unit can analyze what questions the user frequently asks and suggest an appropriate learning plan based on the results. The analysis unit can also analyze the user's behavioral history to understand what skills the user has acquired. Furthermore, the analysis unit can analyze the user's career trends and predict their future career path. This allows the user to understand the status of their learning and career and make effective plans.

[0058] The AI ​​mentor system may further include a recommendation unit. The recommendation unit may recommend appropriate learning resources and career information based on the user's characteristics and interests. For example, the recommendation unit may recommend online courses related to the user's fields of interest. The recommendation unit may also recommend learning materials according to the user's skill level. Furthermore, the recommendation unit may recommend appropriate career information and job information based on the user's career goals. This allows the user to efficiently find learning resources and career information that suit them.

[0059] The AI ​​mentor system can further include an evaluation unit. The evaluation unit can evaluate the user's learning outcomes and skill acquisition status and provide feedback. For example, the evaluation unit can evaluate the results of assignments and tests completed by the user and provide detailed feedback. The evaluation unit can also evaluate the user's skill acquisition status and suggest what content to study next. Furthermore, the evaluation unit can regularly evaluate the user's learning progress and provide advice to help achieve goals. This allows the user to understand their learning outcomes and create an effective learning plan.

[0060] The AI ​​mentor system may further include a customization unit. The customization unit may customize the system settings and display method based on the user's characteristics and interests. For example, the customization unit may customize the system display by setting the user's preferred colors and fonts. The customization unit may also adjust the display method of learning content according to the user's learning style. The customization unit may also customize the system's functions and settings based on the user's characteristics. This allows the user to proceed with their learning and career planning in an environment that suits them.

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

[0062] Step 1: The reception unit receives a user's question. The user's question may be in text format, voice format, or a question about a specific topic. For example, text format questions are received in a chat window, and voice format questions are received through a microphone. Questions about specific topics are filtered based on pre-defined keywords. The reception unit analyzes the text using natural language processing technology and converts the voice into text using speech recognition technology for analysis. Step 2: The questioning unit asks a question based on the question received by the reception unit. The question is asked based on the type of question, the timing of the question, the format of the question, etc. For example, the questioning unit asks a specific question in response to the user's question, and asks related questions based on the user's characteristics and interests. Step 3: The storage unit stores the information obtained by the questioning unit. The stored information includes text data, numerical data, image data, etc. The storage unit stores the user's characteristics, interests, behavioral history, and dialogue history in a database. Step 4: The support unit supports career and learning plans based on the information accumulated by the storage unit. Support is provided based on learning plans for career selection and skill development. For example, the support unit suggests appropriate career paths based on the user's characteristics and interests, creates learning plans, and suggests curricula. Step 5: The growth unit grows semi-permanently based on the information provided by the support unit. Growth occurs when the user acquires a new skill, by storing that information and utilizing it in the next interaction. For example, when the user acquires a new skill, the growth unit stores that information in a database and utilizes it in the next interaction.

[0063] (Example 2) The AI ​​mentoring system according to an embodiment of the present invention uses an AI chatbot powered by generative AI to foster awareness and discovery in users and develop their problem-solving skills. Through dialogue with users, the AI ​​mentoring system accumulates and updates individual characteristics, personality, interests, skills, and other information. This supports the creation of career and learning plans and curricula without impairing autonomy. Furthermore, the AI ​​semi-permanently grows and accompanies the user. For example, when a user inputs a question, the generative AI responds to the question, guiding the user to think for themselves. For example, it asks questions such as, "What approaches can you think of to solve this problem?" This allows the user to develop their ability to think independently. Furthermore, through dialogue with the user, the AI ​​accumulates and updates individual characteristics, personality, interests, skills, and other information. For example, if a user is interested in a particular field, asking questions related to that field can pique the user's interest and increase their motivation to learn. The AI ​​also supports the creation of users' careers, learning plans, and curricula. For example, if a user is concerned about their future career, the AI ​​can suggest appropriate career paths based on the user's characteristics and interests. This allows users to find a career that suits them. Furthermore, the AI ​​will continue to grow and accompany the user semi-permanently. For example, if the user acquires a new skill, the AI ​​will store that information and use it in the next conversation. This allows the user to always continue learning based on the latest information. In this way, an AI chatbot using generative AI can bring users "awareness" and "discovery," develop problem-solving skills, and support the creation of career and learning plans, allowing it to grow semi-permanently along with the user. This allows the AI ​​mentor system to ask appropriate questions in response to users' questions, store information, support career and learning plans, and grow semi-permanently.

[0064] The AI ​​mentor system according to the embodiment includes a reception unit, a questioning unit, an accumulation unit, a support unit, and a growth unit. The reception unit receives a user's question. The user's question may be, for example, in text format, audio format, or on a specific topic, but is not limited to these examples. The reception unit may, for example, receive a text question via a chat window. The reception unit may also receive audio questions via a microphone. The reception unit may also filter and receive questions on a specific topic. For example, the reception unit may analyze text entered by the user using natural language processing technology to understand the content of the question. Audio questions may be converted into text using speech recognition technology and analyzed. Questions on a specific topic may be filtered based on pre-defined keywords. The questioning unit asks a question based on the question received by the reception unit. The question is asked based on, for example, the type of question, the timing of the question, the format of the question, etc., but is not limited to these examples. For example, the questioning unit may respond to the user's question with a specific question such as, "What approaches can be taken to solve this problem?" The questioning unit can also ask related questions based on the user's characteristics and interests. For example, if the user is interested in a particular field, the questioning unit can ask questions related to that field. The storage unit stores information obtained by the questioning unit. The stored information includes, but is not limited to, text data, numerical data, and image data. The storage unit can also store, for example, the user's characteristics and interests in a database. The storage unit can also store the user's behavioral history and dialogue history. The support unit supports the user's career and learning plans based on the information stored by the storage unit. Support is provided based on, for example, career selection, learning plans for skill development, and the like, but is not limited to, for example. For example, the support unit can suggest an appropriate career path based on the user's characteristics and interests. The support unit can also create a learning plan for the user and suggest a curriculum. The growth unit semi-permanently grows based on the information provided by the support unit.Growth can be achieved, for example, by storing information about a new skill acquired by the user and utilizing it in the next dialogue, but is not limited to such an example. For example, when a user acquires a new skill, the growth unit stores the information in a database and utilizes it in the next dialogue. As a result, the AI ​​mentor system according to the embodiment can ask appropriate questions in response to user questions, accumulate information, support career and learning plans, and achieve semi-permanent growth.

[0065] The questioning unit can ask a specific question such as, "What approaches can be taken to solve this problem?" Specific questions include, but are not limited to, approaches to solving a problem and specific example questions. For example, the questioning unit can ask a specific question such as, "What approaches can be taken to solve this problem?" in response to a question input by a user. The questioning unit can also ask related questions based on the user's characteristics and interests. For example, if the user is interested in a particular field, the questioning unit can ask questions related to that field. This can encourage the user's thinking by asking specific questions. Some or all of the above-described processing in the questioning unit can be performed using, or without, a generation AI. For example, the questioning unit can input a user's question into a generation AI, which can then generate an appropriate question.

[0066] The storage unit can store the user's characteristics and interests in a database. Examples of the stored information include, but are not limited to, questionnaire results and behavioral history. For example, the storage unit collects the user's characteristics and interests through questionnaires and stores them in a database. The storage unit can also analyze the user's behavioral history to identify and store the interests. For example, the storage unit can analyze pages the user has previously viewed or links the user has clicked to identify the interests. By storing the user's characteristics and interests, individualized support becomes possible. Some or all of the above-described processing in the storage unit may be performed using, or without, a generation AI. For example, the storage unit can input the user's behavioral history into a generation AI, which then identifies and stores the interests.

[0067] The support unit can propose career and study plans based on the accumulated data. Examples of career and study plans include, but are not limited to, career selection and study plans for skill development. The support unit can propose an appropriate career path based on the user's characteristics and interests. The support unit can also create a study plan for the user and propose a curriculum. For example, if the user is worried about their future career, the support unit can propose an appropriate career path based on the user's characteristics and interests. This makes it possible to propose an appropriate career or study plan based on the accumulated data. Some or all of the above-described processing in the support unit may be performed using, or without, a generation AI. For example, the support unit can input the accumulated data into a generation AI, which then proposes an appropriate career or study plan.

[0068] When a user acquires a new skill, the growth unit can store that information and use it in the next dialogue. New skills include, but are not limited to, technical skills, soft skills, etc. For example, when a user acquires a new technical skill, the growth unit can store that information in a database and use it in the next dialogue. Furthermore, when a user acquires a new soft skill, the growth unit can store that information and use it in the next dialogue. For example, when a user acquires a new programming language, the growth unit can store that information and use that skill in the next dialogue. This can support the user's growth by using the acquired new skill information in the next dialogue. Some or all of the above-described processing in the growth unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the growth unit can input the user's new skill acquisition information into the generation AI, which can use that information in the next dialogue.

[0069] The reception unit can estimate the user's emotions and adjust the timing of question acceptance based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the reception unit can temporarily delay the acceptance of questions to provide the user with time to relax. Furthermore, if the user is relaxed, the reception unit can immediately accept questions to promote smooth dialogue. Furthermore, if the user is in a hurry, the reception unit can quickly accept questions and begin responding immediately. For example, the reception unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. It can also use voice analysis technology to analyze the tone and speed of the user's voice to estimate emotions. This allows the timing of question acceptance to be adjusted according to the user's emotions, thereby allowing questions to be accepted at an appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI, which may then estimate the user's emotions and adjust the timing of receiving questions.

[0070] The reception unit can analyze the user's past question history and select the optimal reception method. The optimal reception method includes, but is not limited to, a method for analyzing the past question history and a type of reception method. For example, the reception unit can prioritize reception of questions frequently asked by the user in the past. The reception unit can also find specific patterns from the user's past question history and suggest the optimal reception method. The reception unit can also prioritize reception of question formats (text, voice, etc.) used by the user in the past. For example, the reception unit accumulates the user's past question history in a database and analyzes it. Based on the analysis results, the reception unit prioritizes reception of questions frequently asked by the user in the past. A machine learning algorithm can also be used to find specific patterns. This allows the optimal reception method to be selected by analyzing the past question history. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI. For example, the reception unit can input the user's past question history into a generation AI, which can then suggest the optimal reception method.

[0071] When receiving a question, the reception unit may filter the questions based on the user's current project or field of interest. Examples of filtering include, but are not limited to, the type of project and a method for identifying the field of interest. For example, the reception unit may prioritize questions related to the project the user is currently working on. The reception unit may also filter and accept highly relevant questions based on the user's field of interest. Furthermore, the reception unit may select and accept appropriate questions based on the user's current learning topic. For example, the reception unit may accumulate and analyze the user's project information in a database. Based on the analysis results, related questions may be prioritized. Fields of interest may be identified using survey results or behavioral history. By filtering questions based on the current project or field of interest, highly relevant questions may be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's project information into a generation AI, which may then filter out relevant questions.

[0072] The reception unit can estimate the user's emotions and prioritize questions to be received based on the estimated user emotions. Emotion estimation can include, but is not limited to, facial expression recognition and voice analysis. For example, when the user is stressed, the reception unit can prioritize simple questions. Furthermore, when the user is relaxed, the reception unit can prioritize complex questions. Furthermore, when the user is in a hurry, the reception unit can prioritize urgent questions. For example, the reception unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. Voice analysis technology can also be used to analyze the tone and speed of the user's voice to estimate emotions. By prioritizing questions based on the user's emotions, appropriate questions can be prioritized. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI, which may then infer the user's emotions and determine the priority of the questions.

[0073] When receiving a question, the reception unit can prioritize relevant questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the reception unit can prioritize questions related to that area. The reception unit can also filter and accept relevant questions based on the user's current location. Furthermore, if the user is traveling, the reception unit can prioritize questions related to the user's travel destination. For example, the reception unit can accumulate and analyze the user's geographical location information in a database. Based on the analysis results, relevant questions can be prioritized. The user's current location can also be obtained in real time using a location information service. This allows for the priority acceptance of relevant questions by taking the geographical location information into account. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then filter relevant questions.

[0074] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. Social media activity includes, but is not limited to, analysis of post content and follower analysis. For example, the reception unit can prioritize receiving questions related to topics in which the user has expressed interest on social media. The reception unit can also analyze the user's interests from the user's social media activity and receive related questions. Furthermore, the reception unit can prioritize receiving questions related to accounts the user follows on social media. For example, the reception unit can accumulate the user's social media activity in a database and analyze it. Based on the analysis results, related questions can be prioritized. Natural language processing technology can be used to analyze the post content. This allows the analysis of social media activity to prioritize receiving related questions. Some or all of the above-described processing by the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity into a generation AI, which can then filter out related questions.

[0075] The questioning unit can estimate the user's emotions and adjust the way the question is expressed based on the estimated user's emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is nervous, the questioning unit can ask the question in a gentle tone. Furthermore, if the user is relaxed, the questioning unit can ask the question in a friendly tone. Furthermore, if the user is in a hurry, the questioning unit can ask a concise and quick question. For example, the questioning unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. It can also estimate the emotion by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the appropriate question to be asked by adjusting the way the question is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the questioning unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the questioning unit may input the user's facial expression data into the generation AI, which may infer the user's emotions and adjust the way the question is expressed.

[0076] When issuing a question, the questioning unit can adjust the level of detail of the question based on the importance of the problem. The importance of the problem includes, but is not limited to, the scope of impact and the urgency of resolution. For example, the questioning unit issues detailed questions for problems of high importance. The questioning unit can also issue concise questions for problems of low importance. Furthermore, the questioning unit can issue questions with an appropriate level of detail depending on the importance of the problem. For example, the questioning unit accumulates and analyzes the scope of impact of the problem in a database. Based on the analysis results, the questioning unit issues detailed questions for problems of high importance. A machine learning algorithm can also be used to evaluate the urgency of resolution. This allows the questioning unit to issue an appropriate question by adjusting the level of detail of the question depending on the importance of the problem. Some or all of the above-described processing in the questioning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the questioning unit can input problem importance data into a generation AI, which can then generate a question with an appropriate level of detail.

[0077] When posing a question, the questioning unit can apply different questioning algorithms depending on the problem category. Problem categories include, but are not limited to, technical problems and business problems. For example, the questioning unit can pose questions that include technical details for technical problems. For interpersonal problems, the questioning unit can also pose questions that take emotions into consideration. For learning-related problems, the questioning unit can also pose questions about learning methods. For example, the questioning unit accumulates and analyzes problem categories in a database. Based on the analysis results, an appropriate questioning algorithm is applied. Natural language processing technology can also be used to classify problem categories. This allows for effective questioning by applying an appropriate questioning algorithm depending on the problem category. Some or all of the above-described processing in the questioning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the questioning unit can input problem category data into a generation AI, which then generates an appropriate questioning algorithm.

[0078] The questioning unit can estimate the user's emotions and adjust the length of the questions based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is in a hurry, the questioning unit can ask short, to-the-point questions. Furthermore, if the user is relaxed, the questioning unit can ask longer questions with detailed explanations. Furthermore, if the user is excited, the questioning unit can ask questions with visually stimulating effects. For example, the questioning unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate the emotion. This allows the length of the questions to be adjusted according to the user's emotions, thereby enabling appropriate questions to be asked. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the questioning unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the questioning unit may input the user's facial expression data into the generation AI, which may then estimate the emotion and adjust the length of the question.

[0079] When issuing questions, the questioning unit can determine the priority of questions based on the time of question submission. The time of question submission includes, but is not limited to, the submission date and time, the submission frequency, etc. For example, the questioning unit prioritizes questions for urgent questions. The questioning unit can also promptly issue questions for questions whose submission deadline is approaching. Furthermore, the questioning unit can issue questions with appropriate priorities depending on the submission time. For example, the questioning unit accumulates and analyzes the time of question submission in a database. Based on the analysis results, the questioning unit prioritizes questions for urgent questions. A machine learning algorithm can also be used to evaluate the submission deadline. By doing so, the priority of questions can be determined based on the time of question submission, allowing questions to be issued in an appropriate order. Some or all of the above-described processing in the questioning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the questioning unit can input question submission time data into a generation AI, which can then generate questions with appropriate priorities.

[0080] The querying unit can adjust the order of questions based on the relevance of the questions when querying. Examples of question relevance include, but are not limited to, topic similarity and related keywords. For example, the querying unit prioritizes questions for highly relevant questions. The querying unit can also postpone questions for less relevant questions. Furthermore, the querying unit can query in an appropriate order depending on the relevance of the questions. For example, the querying unit accumulates and analyzes the relevance of questions in a database. Based on the analysis results, it prioritizes questions for highly relevant questions. Natural language processing technology can also be used to evaluate topic similarity. By adjusting the order of questions based on the relevance of questions, the questions can be displayed in an appropriate order. Some or all of the above-described processing in the querying unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the querying unit can input question relevance data into a generation AI, which can then generate questions in an appropriate order.

[0081] The storage unit can estimate the user's emotions and determine the priority of information to be stored based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the storage unit can prioritize storing information that helps the user relax. Furthermore, if the user is relaxed, the storage unit can prioritize storing information that allows for quick responses. For example, the storage unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate the user's emotions. This allows the user to prioritize information according to the user's emotions, thereby prioritizing the storage of appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or without the generation AI. For example, the storage unit can input the user's facial expression data into the generation AI, which can then infer emotions and determine the priority of the information.

[0082] During storage, the storage unit can analyze the user's past dialogue history and select optimal information. The dialogue history includes, for example, past dialogue content and dialogue frequency, but is not limited to these examples. For example, the storage unit prioritizes storing information in which the user has shown interest in the past. The storage unit can also select and store highly relevant information from the user's past dialogue history. Furthermore, the storage unit can prioritize storing information formats (text, audio, etc.) previously used by the user. For example, the storage unit stores the user's past dialogue history in a database and analyzes it. Based on the analysis results, relevant information is preferentially stored. Natural language processing technology can be used to analyze the dialogue content. This allows optimal information to be selected and stored by analyzing the past dialogue history. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storage unit can input the user's past dialogue history into a generation AI, which can select optimal information.

[0083] The storage unit can filter information based on the user's current interests during storage. Examples of interests include, but are not limited to, survey results and behavioral history. The storage unit, for example, prioritizes storage of information related to topics in which the user is currently interested. The storage unit can also filter and store highly relevant information based on the user's current interests. Furthermore, the storage unit can select and store appropriate information based on the user's current study topic. For example, the storage unit can collect the user's interests through a survey and store them in a database. It can also analyze the behavioral history to identify and store the interests. By filtering information based on the user's current interests, highly relevant information can be preferentially stored. Some or all of the above-described processing in the storage unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the storage unit can input the user's interest data into a generation AI, which can then filter out relevant information.

[0084] The storage unit can estimate the user's emotions and adjust the display method of the stored information based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, when the user is nervous, the storage unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the storage unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the storage unit can provide a display method that focuses on the main points. For example, the storage unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate the emotion. This allows the display method of information to be adjusted according to the user's emotions, thereby providing an appropriate display method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or without the generation AI. For example, the storage unit can input the user's facial expression data into the generation AI, which can then infer the emotion and adjust how the information is displayed.

[0085] The storage unit can prioritize storage of highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the storage unit prioritizes storage of information related to that area. The storage unit can also filter and store highly relevant information based on the user's current location. Furthermore, if the user is traveling, the storage unit can prioritize storage of information related to the user's travel destination. For example, the storage unit stores the user's geographical location information in a database and analyzes it. Based on the analysis results, relevant information is prioritized and stored. The user's current location can also be obtained in real time using a location information service. This allows highly relevant information to be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storage unit can input the user's geographical location information into a generation AI, which then filters relevant information.

[0086] During storage, the storage unit can analyze the user's social media activity and store related information. Social media activity includes, but is not limited to, analysis of posted content and follower analysis. For example, the storage unit prioritizes storage of information related to topics in which the user has shown interest on social media. The storage unit can also analyze the user's interests from the user's social media activity and store related information. Furthermore, the storage unit can prioritize storage of information related to accounts the user follows on social media. For example, the storage unit stores the user's social media activity in a database and analyzes it. Based on the analysis results, related information is preferentially stored. Natural language processing technology can be used to analyze the posted content. This allows for the analysis of social media activity to prioritize the storage of related information. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the storage unit can input the user's social media activity into a generation AI, which then filters out related information.

[0087] The support unit can estimate the user's emotions and adjust the method of assistance based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is nervous, the support unit can provide assistance in a gentle tone. Furthermore, if the user is relaxed, the support unit can provide assistance in a friendly tone. Furthermore, if the user is in a hurry, the support unit can provide concise and quick assistance. For example, the support unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate the user's emotions. This allows the support method to be adjusted according to the user's emotions, thereby providing appropriate assistance. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit can be performed using, for example, the generation AI, or without the generation AI. For example, the support unit can input the user's facial expression data into the generation AI, which can then estimate the user's emotions and adjust the method of support.

[0088] When providing support, the support unit can analyze the user's past career and learning history to select the optimal support method. Examples of career and learning history include, but are not limited to, work history and learning history data. The support unit provides appropriate career support based on the user's past career history, for example. The support unit can also provide optimal learning support based on the user's past learning history. Furthermore, the support unit can select an appropriate support method based on the user's past experience. For example, the support unit accumulates and analyzes the user's past career history in a database. Based on the analysis results, a relevant support method is selected. A machine learning algorithm can be used to analyze the learning history. This allows the optimal support method to be selected by analyzing the past career and learning history. Some or all of the above-described processing in the support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the support unit can input the user's career and learning history data into a generation AI, which can then select the optimal support method.

[0089] The support unit can customize the support method based on the user's current living situation when providing support. Examples of living situations include, but are not limited to, the living environment and health condition. For example, when the user is busy, the support unit can provide a short, effective support method. Furthermore, when the user is relaxed, the support unit can also provide a detailed support method. Furthermore, the support unit can customize an appropriate support method according to the user's living situation. For example, the support unit accumulates and analyzes the user's living situation in a database. Based on the analysis results, a relevant support method is selected. The living environment and health condition can be evaluated using questionnaire results and behavioral history. This allows appropriate support to be provided by customizing the support method based on the current living situation. Some or all of the above-described processing in the support unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the support unit can input the user's living situation data into the generation AI, which then selects the optimal support method.

[0090] The support unit can estimate the user's emotions and prioritize support based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the support unit can prioritize providing support that helps the user relax. Furthermore, if the user is relaxed, the support unit can prioritize providing support that is useful for learning. Furthermore, if the user is in a hurry, the support unit can prioritize providing support that can be quickly responded to. For example, the support unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. It can also estimate the user's emotions by analyzing the tone and speed of the user's voice using voice analysis technology. This allows the user to prioritize support based on the user's emotions, thereby providing appropriate support preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the support unit may input the user's facial expression data into the generation AI, which may then estimate the user's emotions and determine the priority of support.

[0091] When providing assistance, the assistance unit can select the optimal assistance method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the assistance unit can provide assistance related to that area. The assistance unit can also provide highly relevant assistance based on the user's current location. Furthermore, if the user is traveling, the assistance unit can provide assistance related to the user's travel destination. For example, the assistance unit can accumulate the user's geographical location information in a database and analyze it. Based on the analysis results, the assistance unit can select a relevant assistance method. The user's current location can also be obtained in real time using a location information service. This allows the optimal assistance method to be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the assistance unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the assistance unit can input the user's geographical location information into the generation AI, which can then select a relevant assistance method.

[0092] When providing assistance, the assistance unit can analyze the user's social media activity and suggest assistance methods. Examples of social media activity include, but are not limited to, analyzing posted content and followers. The assistance unit can provide assistance related to topics the user has shown interest in on social media. The assistance unit can also analyze the user's interests from the user's social media activity and provide relevant assistance. Furthermore, the assistance unit can provide assistance related to accounts the user follows on social media. For example, the assistance unit can accumulate the user's social media activity in a database and analyze it. Based on the analysis results, relevant assistance methods are selected. Natural language processing technology can be used to analyze posted content. This allows relevant assistance to be provided by analyzing the social media activity. Some or all of the above-described processing in the assistance unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the assistance unit can input the user's social media activity into a generation AI, which can then select relevant assistance methods.

[0093] The growth unit can estimate the user's emotions and adjust the growth method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is nervous, the growth unit can suggest a growth method in a gentle tone. Furthermore, if the user is relaxed, the growth unit can suggest a growth method in a friendly tone. Furthermore, if the user is in a hurry, the growth unit can suggest a concise and quick growth method. For example, the growth unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate the user's emotions. This allows the user to adjust the growth method according to the user's emotions and provide an appropriate growth method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the growth unit can be performed using, for example, the generation AI, or without the generation AI. For example, the growth unit can input the user's facial expression data into the generation AI, which can then infer the emotion and adjust the growth method.

[0094] During growth, the growth unit can analyze the user's past skill acquisition history and select an optimal growth method. The skill acquisition history includes, but is not limited to, past learning content and skill evaluation results. The growth unit, for example, suggests an appropriate growth method based on the user's past skill acquisition history. The growth unit can also suggest an optimal growth method based on the user's past learning history. Furthermore, the growth unit can select an appropriate growth method based on the user's past experience. For example, the growth unit accumulates the user's past skill acquisition history in a database and analyzes it. Based on the analysis results, a relevant growth method is selected. A machine learning algorithm can be used to analyze the learning history. This allows the optimal growth method to be selected by analyzing the past skill acquisition history. Some or all of the above-described processing in the growth unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the growth unit can input the user's skill acquisition history data into the generation AI, which then selects the optimal growth method.

[0095] During growth, the growth unit can customize the growth method based on the user's current living situation. Examples of living situations include, but are not limited to, the living environment and health status. For example, if the user is busy, the growth unit can provide a short, effective growth method. Furthermore, if the user is relaxed, the growth unit can also provide a detailed growth method. Furthermore, the growth unit can customize an appropriate growth method according to the user's living situation. For example, the growth unit accumulates and analyzes the user's living situation in a database. Based on the analysis results, a relevant growth method is selected. Survey results and behavioral history can be used to evaluate the living environment and health status. This allows the growth method to be customized based on the current living situation, thereby providing an appropriate growth method. Some or all of the above-described processing in the growth unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the growth unit can input the user's living situation data into the generation AI, which can then select the optimal growth method.

[0096] The growth unit can estimate the user's emotions and determine growth priorities based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the growth unit can prioritize providing a growth method that helps the user relax. Furthermore, if the user is relaxed, the growth unit can prioritize providing a growth method that helps the user learn. Furthermore, if the user is in a hurry, the growth unit can prioritize providing a growth method that allows for quick responses. For example, the growth unit can capture the user's facial expression with a camera and estimate the user's emotions using facial expression recognition technology. Voice analysis technology can also be used to analyze the tone and speed of the user's voice to estimate emotions. This allows the user to prioritize growth based on the user's emotions, thereby providing appropriate growth methods preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the growth unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the growth unit may input the user's facial expression data into the generation AI, which may infer the emotions and determine the growth priority.

[0097] During growth, the growth unit can select the optimal growth method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the growth unit can provide a growth method related to that area. The growth unit can also provide a highly relevant growth method based on the user's current location. Furthermore, if the user is traveling, the growth unit can provide a growth method related to the user's travel destination. For example, the growth unit accumulates the user's geographical location information in a database and analyzes it. Based on the analysis results, the relevant growth method is selected. The user's current location can also be obtained in real time using a location information service. This allows the optimal growth method to be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the growth unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the growth unit can input the user's geographical location information into the generation AI, which can then select a relevant growth method.

[0098] During growth, the growth unit can analyze the user's social media activity and suggest growth methods. Social media activity includes, but is not limited to, analysis of post content and follower analysis. For example, the growth unit can provide growth methods related to topics in which the user has shown interest on social media. The growth unit can also analyze the user's interests from the user's social media activity and provide relevant growth methods. Furthermore, the growth unit can provide growth methods related to accounts the user follows on social media. For example, the growth unit can accumulate the user's social media activity in a database and analyze it. Based on the analysis results, relevant growth methods are selected. Natural language processing technology can be used to analyze the post content. This allows the analysis of social media activity to provide relevant growth methods. Some or all of the above-mentioned processing in the growth unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the growth unit can input the user's social media activity into a generation AI, which can select relevant growth methods. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, questioning unit, accumulation unit, support unit, and growth unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a user's question in text or voice format. The questioning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and asks an appropriate question in response to the user's question. The accumulation unit is realized, for example, as a processing unit that accumulates information in the database 24 of the data processing device 12. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports career and learning plans based on the accumulated information. The growth unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accumulates information related to the user's growth and uses it for the next dialogue. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, questioning unit, accumulation unit, support unit, and growth unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a user's question in text or voice format. The questioning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and asks an appropriate question in response to the user's question. The accumulation unit is realized, for example, as a processing unit that accumulates information in the database 24 of the data processing device 12. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports career and learning plans based on the accumulated information. The growth unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accumulates information accompanying the user's growth and uses it for the next dialogue. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, questioning unit, accumulation unit, support unit, and growth unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives a user's question in text format or voice format. The questioning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues an appropriate question in response to the user's question. The accumulation unit is realized, for example, as a processing unit that accumulates information in the database 24 of the data processing device 12. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports career and learning plans based on the accumulated information. The growth unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accumulates information accompanying the user's growth and uses it for the next dialogue. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, questioning unit, accumulation unit, support unit, and growth unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a user's question in text format or voice format. The questioning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and asks an appropriate question in response to the user's question. The accumulation unit is realized, for example, as a processing unit that accumulates information in the database 24 of the data processing device 12. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports career and learning plans based on the accumulated information. The growth unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accumulates information accompanying the user's growth and uses it for the next dialogue.

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

[0100] The AI ​​mentor system can further include a feedback unit. The feedback unit can collect feedback on the questions and assistance provided by the user and use the collected feedback to improve the system. For example, the feedback unit can collect, in the form of a questionnaire, how the user felt about the questions and whether the assistance was helpful. The feedback unit can also analyze the user's dialogue history and automatically evaluate how the user responded. Furthermore, the feedback unit can accumulate feedback provided by the user and reflect it in the next dialogue or assistance. This allows the AI ​​mentor system to be continuously improved based on user feedback and provide more effective questions and assistance.

[0101] The questioning unit can estimate the user's emotions and adjust the content of the questions based on the estimated user's emotions. For example, if the user is feeling stressed, the questioning unit can ask questions that will help the user relax. Also, if the user is excited, the questioning unit can use that excitement to ask positive questions. Furthermore, if the user is feeling down, the questioning unit can ask questions that include encouraging words. In this way, by asking appropriate questions according to the user's emotions, the user's thinking can be more effectively promoted.

[0102] The storage unit can estimate the user's emotions and determine the priority of information to be stored based on the estimated user's emotions. For example, if the user is feeling stressed, information that helps the user relax can be stored preferentially. Also, if the user is relaxed, information that is useful for learning can be stored preferentially. Furthermore, if the user is in a hurry, information that allows for a quick response can be stored preferentially. In this way, by determining the priority of information according to the user's emotions, appropriate information can be stored preferentially.

[0103] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, if the user is nervous, the support unit can provide support in a gentle tone. If the user is relaxed, the support unit can provide support in a friendly tone. Furthermore, if the user is in a hurry, the support unit can provide concise and quick support. In this way, appropriate support can be provided by adjusting the support method according to the user's emotions.

[0104] The growth unit can estimate the user's emotions and adjust the growth method based on the estimated user's emotions. For example, if the user is nervous, the growth method can be suggested in a gentle tone. If the user is relaxed, the growth method can be suggested in a friendly tone. Furthermore, if the user is in a hurry, a simple and quick growth method can be suggested. In this way, the growth method can be adjusted according to the user's emotions, thereby providing an appropriate growth method.

[0105] The AI ​​mentor system can further include a notification unit. The notification unit can notify the user of important information and reminders. For example, the notification unit can notify the user of the start time of study based on the study schedule set by the user. The notification unit can also periodically notify the user of the progress of goals set by the user. Furthermore, the notification unit can notify the user of the latest information on areas in which the user is interested. This allows the user to receive important information without missing it, allowing them to effectively advance their study and career plans.

[0106] The AI ​​mentor system can further include an analysis unit. The analysis unit can analyze the user's dialogue history and behavioral history to understand the user's learning patterns and career trends. For example, the analysis unit can analyze what questions the user frequently asks and suggest an appropriate learning plan based on the results. The analysis unit can also analyze the user's behavioral history to understand what skills the user has acquired. Furthermore, the analysis unit can analyze the user's career trends and predict their future career path. This allows the user to understand the status of their learning and career and make effective plans.

[0107] The AI ​​mentor system may further include a recommendation unit. The recommendation unit may recommend appropriate learning resources and career information based on the user's characteristics and interests. For example, the recommendation unit may recommend online courses related to the user's fields of interest. The recommendation unit may also recommend learning materials according to the user's skill level. Furthermore, the recommendation unit may recommend appropriate career information and job information based on the user's career goals. This allows the user to efficiently find learning resources and career information that suit them.

[0108] The AI ​​mentor system can further include an evaluation unit. The evaluation unit can evaluate the user's learning outcomes and skill acquisition status and provide feedback. For example, the evaluation unit can evaluate the results of assignments and tests completed by the user and provide detailed feedback. The evaluation unit can also evaluate the user's skill acquisition status and suggest what content to study next. Furthermore, the evaluation unit can regularly evaluate the user's learning progress and provide advice to help achieve goals. This allows the user to understand their learning outcomes and create an effective learning plan.

[0109] The AI ​​mentor system may further include a customization unit. The customization unit may customize the system settings and display method based on the user's characteristics and interests. For example, the customization unit may customize the system display by setting the user's preferred colors and fonts. The customization unit may also adjust the display method of learning content according to the user's learning style. The customization unit may also customize the system's functions and settings based on the user's characteristics. This allows the user to proceed with their learning and career planning in an environment that suits them.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The reception unit receives a user's question. The user's question may be in text format, voice format, or a question about a specific topic. For example, text format questions are received in a chat window, and voice format questions are received through a microphone. Questions about specific topics are filtered based on pre-defined keywords. The reception unit analyzes the text using natural language processing technology and converts the voice into text using speech recognition technology for analysis. Step 2: The questioning unit asks a question based on the question received by the reception unit. The question is asked based on the type of question, the timing of the question, the format of the question, etc. For example, the questioning unit asks a specific question in response to the user's question, and asks related questions based on the user's characteristics and interests. Step 3: The storage unit stores the information obtained by the questioning unit. The stored information includes text data, numerical data, image data, etc. The storage unit stores the user's characteristics, interests, behavioral history, and dialogue history in a database. Step 4: The support unit supports career and learning plans based on the information accumulated by the storage unit. Support is provided based on learning plans for career selection and skill development. For example, the support unit suggests appropriate career paths based on the user's characteristics and interests, creates learning plans, and suggests curricula. Step 5: The growth unit grows semi-permanently based on the information provided by the support unit. Growth occurs when the user acquires a new skill, by storing that information and utilizing it in the next interaction. For example, when the user acquires a new skill, the growth unit stores that information in a database and utilizes it in the next interaction.

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

[0113] 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.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0118] The data processing device 12 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

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

[0129] 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.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0162] 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.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] 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.

[0176] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] [Explanation of symbols]

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

Claims

1. a reception unit that receives questions from users; a questioning unit that asks a question based on the question received by the receiving unit; a storage unit that stores the information obtained by the query unit; a support unit that supports career and learning plans based on the information accumulated by the accumulation unit; a growing section that grows semi-permanently based on the information provided by the support section. A system characterized by:

2. The storage unit is Accumulate user characteristics and interests in a database 2. The system of claim 1.

3. The support unit Proposing career and learning plans based on accumulated data 2. The system of claim 1.

4. The growth portion is When a user acquires a new skill, that information is stored and used in the next interaction.

2. The system of claim 1.

5. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past question history and select the optimal reception method 2. The system of claim 1.

7. The reception unit Filter questions based on your current projects and interests 2. The system of claim 1.

Images