system

Abstract

The system according to this embodiment aims to analyze the user's personality and aptitudes and generate personalized career path suggestions. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a management unit, and a notification unit. The analysis unit analyzes the user's personality and aptitude. The proposal unit generates career path suggestions based on the analysis results obtained by the analysis unit. The management unit manages the user's learning progress. The notification unit proactively makes suggestions and reminders based on the progress status obtained by the management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that uniform route guidance and general career counseling services are provided, and route proposals based on the personalities and aptitudes of individual users are not sufficiently made.

[0005] The system according to the embodiment aims to analyze the personality and aptitude of a user and generate an individualized route proposal.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a management unit, and a notification unit. The analysis unit analyzes the user's personality and aptitude. The proposal unit generates career path suggestions based on the analysis results obtained by the analysis unit. The management unit manages the user's learning progress. The notification unit proactively makes suggestions and reminders based on the progress status obtained by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's personality and aptitudes and generate personalized career path suggestions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The career guidance and support system according to an embodiment of the present invention is a system for elementary, junior high, and high school students (especially truant students) and their guardians who are anxious about their future career paths and choices. This system uses an AI agent to analyze personality and aptitude in real time and autonomously supports career guidance and career planning. Unlike conventional, uniform career guidance and general career counseling services, the career guidance and support system has the function of continuously interacting with the user, managing progress, and proactively making suggestions and reminders as needed. For example, the career guidance and support system analyzes the user's personality and interests using natural language processing and generates career suggestions. Next, the career guidance and support system analyzes learning progress in real time and autonomously adjusts learning plans and career suggestions. Furthermore, the career guidance and support system takes in labor market data and higher education information in real time and proposes appropriate actions. For example, if a fifth-grade boy consults the career guidance and support system saying, "I want to go to junior high school, but I don't know what I should focus on," the system will suggest classes he can enjoy in junior high school and his future career based on his interests and strengths. It will also suggest specific goal setting and study plans, and send reminders while tracking his progress. If a second-year junior high school girl consults the system saying, "I want to become a childcare worker, but I don't know what to study," the system will explain the educational routes and necessary qualifications for becoming a childcare worker and provide information on childcare volunteer opportunities. It will also suggest high school options and provide information on open campuses. In this way, the career guidance and support system analyzes the user's personality and aptitude in real time and autonomously supports career guidance and career planning. By continuously interacting with the user, managing progress, and proactively making suggestions and reminders as needed, it provides individually optimized support. As a result, the career guidance and support system can analyze the user's personality and aptitude in real time and autonomously support career guidance and career planning.

[0029] The career guidance and career support system according to this embodiment comprises an analysis unit, a proposal unit, a management unit, and a notification unit. The analysis unit analyzes the user's personality and aptitude. The analysis unit, for example, collects user behavior data and dialogue content and analyzes it using natural language processing. For example, the analysis unit collects the user's click history and movement history and analyzes their personality and aptitude. The analysis unit can also collect dialogue content with the user and analyze it using natural language processing technology. For example, the analysis unit uses speech recognition technology to transcribe the user's statements into text and uses text analysis technology to analyze their personality and aptitude. The proposal unit generates career guidance based on the analysis results obtained by the analysis unit. The proposal unit, for example, takes in labor market data and educational information in real time and proposes appropriate actions. For example, the proposal unit collects job postings and salary data and proposes occupations suitable for the user. The proposal unit can also collect university curriculum and entrance examination information to support the selection of educational institutions. The management unit manages the user's learning progress. The management unit, for example, collects learning progress data in real time and makes suggestions and reminders based on the analysis results. For example, the management unit collects test results and study time and evaluates the degree of learning achievement and understanding. The management unit can also analyze the progress of the learning plan in real time and adjust the learning plan as needed. The notification unit proactively makes suggestions and reminders based on the progress obtained by the management unit. For example, the notification unit manages progress by continuously interacting with the user and proactively makes suggestions and reminders as needed. For example, the notification unit notifies the user of the progress of the learning plan and suggests necessary actions. The notification unit can also send reminders to the user to encourage them to execute the learning plan. As a result, the career guidance and career support system according to the embodiment can analyze the user's personality and aptitude in real time and autonomously support career guidance and career planning.

[0030] The analytics department analyzes users' personalities and aptitudes. Specifically, it collects user behavior data and dialogue content and analyzes it using natural language processing. For example, it collects the history of links clicked and pages viewed on websites and analyzes user interests and preferences based on this data. It also collects user movement history and analyzes the places they frequently visit to understand their lifestyle and preferences. Furthermore, it can collect the content of conversations with users and analyze it using natural language processing technology. For example, it uses speech recognition technology to transcribe user statements into text and uses text analysis technology to analyze personality and aptitudes. This allows for understanding the user's speech patterns and emotional changes, enabling more accurate personality analysis. The analytics department integrates this data to comprehensively evaluate users' personalities and aptitudes. For example, it analyzes what kind of actions users tend to take in different situations and evaluates characteristics such as stress tolerance and communication skills. It can also predict future behavioral patterns based on users' past behavior data and dialogue content. As a result, the analytics department can gain a detailed understanding of users' personalities and aptitudes and provide information that forms the basis for career guidance and career planning.

[0031] The Proposal Department generates career suggestions based on the analysis results obtained by the Analysis Department. Specifically, it incorporates labor market data and educational information in real time and proposes appropriate actions. For example, the Proposal Department collects job postings and salary data and suggests occupations suitable for the user. This uses algorithms to identify the optimal occupation and career path based on the user's skill set and interests. The Proposal Department can also collect university curriculum and entrance examination information to support the selection of educational institutions. For example, it suggests the optimal university and faculty based on the user's academic ability and interests, and provides advice on entrance examination preparation and necessary preparations. Furthermore, the Proposal Department can provide career suggestions tailored to the user's career goals and lifestyle. For example, if a user desires a career in a specific industry, it provides information on career paths and necessary skill sets in that industry and proposes a concrete action plan. The Proposal Department can also collect feedback on the user's career choices and continuously improve the accuracy and effectiveness of its suggestions. In this way, the Proposal Department can provide users with optimal career suggestions and support their career planning.

[0032] The management department manages users' learning progress. Specifically, it collects learning progress data in real time and provides suggestions and reminders based on the analysis results. For example, the management department collects users' test results and study time to evaluate their learning achievement and understanding. This includes collecting data to understand how much time users are spending on learning and what subjects or topics they have a strong interest in. The management department can also analyze the progress of learning plans in real time and adjust them as needed. For example, if a user is struggling with a particular subject, it can suggest increasing study time for that subject or provide additional learning resources. Furthermore, the management department continuously monitors users' learning habits and performance to support the achievement of long-term learning goals. For example, it evaluates how much progress users are making towards their set goals and provides the necessary support and resources. The management department can also suggest optimal learning methods and strategies to maximize learning effectiveness based on users' learning data. In this way, the management department can efficiently manage users' learning progress and support the achievement of learning goals.

[0033] The notification unit proactively makes suggestions and reminders based on the progress obtained by the management unit. Specifically, it manages progress while continuously interacting with the user, and proactively makes suggestions and reminders as needed. For example, the notification unit notifies the user of the progress of their learning plan and suggests necessary actions. This includes checking whether the user is progressing according to the learning plan and providing adjustments to the learning plan or additional resources as needed. The notification unit can also send reminders to the user to encourage them to follow their learning plan. For example, if the user is behind schedule to meet their set learning goals, it will send a reminder to encourage them to resume learning. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can analyze how users reacted to notifications and optimize the timing and content of notifications. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with quick and reliable instructions and support them in following their learning plan.

[0034] The analysis unit can collect user behavior data and dialogue content and analyze it using natural language processing. For example, the analysis unit can collect user click history and movement history and analyze personality and aptitude. For example, the analysis unit can collect user website browsing history and app usage history and analyze behavioral patterns. The analysis unit can also collect dialogue content with users and analyze it using natural language processing technology. For example, the analysis unit can use speech recognition technology to transcribe user statements into text and use text analysis technology to analyze personality and aptitude. Furthermore, the analysis unit can comprehensively analyze user behavior data and dialogue content to improve the accuracy of personality and aptitude analysis. This improves the accuracy of personality and aptitude analysis by analyzing user behavior data and dialogue content. Behavioral data includes, but is not limited to, click history and movement history. Dialogue content includes, but is not limited to, speech recognition and text analysis. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user behavior data and dialogue content into a generating AI and have the generating AI perform personality and aptitude analysis.

[0035] The suggestion department can take in labor market data and educational information in real time and propose appropriate actions. For example, the suggestion department can collect job postings and salary data and propose occupations suitable for the user. For example, the suggestion department can collect data from job posting sites on the internet and propose occupations based on the user's skills and interests. The suggestion department can also collect university curriculum and entrance examination information and support the selection of educational institutions. For example, the suggestion department can collect curriculum information from official university websites and propose educational institutions based on the user's interests and goals. Furthermore, the suggestion department can comprehensively analyze labor market data and educational information and propose appropriate actions. This makes it possible to propose appropriate career paths based on data taken in real time. Labor market data includes, but is not limited to, job postings and salary data. Educational information includes, but is not limited to, university curriculum and entrance examination information. Appropriate actions include, but are not limited to, suggestions for changing career paths or suggesting skill development. Some or all of the above processing in the suggestion department may be performed using, for example, AI, or not using AI. For example, the proposal department can input labor market data and educational information into a generating AI and have the AI ​​execute suggestions for appropriate actions.

[0036] The management department can collect learning progress data in real time and make suggestions and reminders based on the analysis results. For example, the management department can collect test results and study time and evaluate the degree of learning achievement and comprehension. For example, the management department can collect users' test results and evaluate their learning achievement. The management department can also collect users' study time and evaluate their learning progress. Furthermore, the management department can analyze the progress of the learning plan in real time and adjust the learning plan as needed. This makes it possible to make appropriate suggestions and reminders based on learning progress data collected in real time. Learning progress data includes, but is not limited to, test results and study time. Analysis results include, but is not limited to, the degree of learning achievement and comprehension. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input learning progress data into a generating AI and have the generating AI execute suggestions and reminders.

[0037] The notification unit can manage progress while continuously interacting with the user and proactively make suggestions and reminders as needed. For example, the notification unit can notify the user of the progress of their learning plan and suggest necessary actions. For example, the notification unit can notify the user of the progress of their learning plan and suggest what they should learn next. The notification unit can also send reminders to the user to encourage them to execute their learning plan. For example, the notification unit can send reminders to the user to encourage them to check the progress of their learning plan. Furthermore, the notification unit can provide individually optimized support by continuously interacting with the user to manage progress and proactively making suggestions and reminders as needed. This enables individually optimized support by continuously interacting with the user to manage progress. Continuous interaction includes, but is not limited to, the frequency of interaction and how the content of the interaction is customized. Proactive suggestions and reminders include, but are not limited to, the timing of notifications and how the content is customized. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the content of the conversation with the user into a generation AI, which can then perform proactive suggestions and reminders.

[0038] The analysis unit can analyze users' past behavioral data and select the optimal analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on the choices and answers users have made in the past. The analysis unit can also extract specific patterns from users' past behavioral data and select an analysis algorithm based on those patterns. The analysis unit can also cluster users' past behavioral data and select the optimal analysis algorithm for each cluster. For example, the analysis unit clusters users' past behavioral data and selects the optimal analysis algorithm for each cluster. By selecting the optimal analysis algorithm based on past behavioral data, the accuracy of the analysis is improved. Past behavioral data includes, but is not limited to, past click history and past movement history. The optimal analysis algorithm includes, but is not limited to, selecting an algorithm that suits the characteristics of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0039] The analysis unit can perform filtering based on the user's current lifestyle and areas of interest during analysis. For example, the analysis unit can perform appropriate filtering by considering the user's current lifestyle (e.g., school performance, home environment). The analysis unit can also prioritize the analysis of relevant information based on the user's areas of interest (e.g., hobbies, subjects of interest). The analysis unit can also perform optimal filtering by considering the user's daily rhythm (e.g., wake-up time, bedtime). This allows for more appropriate analysis by filtering based on the user's lifestyle and areas of interest. Current lifestyle includes, but is not limited to, lifestyle habits and daily behavioral patterns. Areas of interest include, but are not limited to, hobbies and topics of interest. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's current living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The proposal department can adjust the level of detail in a proposal based on the importance of the career path. For example, if the importance of the career path is high, the proposal department will provide a proposal that includes detailed information. For example, if the importance of the career path is high, the proposal department will provide a proposal that includes detailed information. The proposal department can also provide a proposal that includes a moderate amount of information if the importance of the career path is moderate. For example, if the importance of the career path is moderate, the proposal department will provide a proposal that includes a moderate amount of information. The proposal department can also provide a proposal that includes concise information if the importance of the career path is low. For example, if the importance of the career path is low, the proposal department will provide a proposal that includes concise information. By adjusting the level of detail in a proposal based on the importance of the career path, it becomes possible to provide more appropriate proposals. The importance of the career path includes, for example, the importance of future goals and career paths, but is not limited to these examples. Some or all of the above-described processes in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section can input career importance data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0041] The proposal function can apply different proposal algorithms depending on the career path category when making a proposal. For example, in the case of an academic career, the proposal function can apply a proposal algorithm based on academic data. For example, in the case of an academic career, the proposal function can apply a proposal algorithm based on academic data. The proposal function can also apply a proposal algorithm based on technical data in the case of a technical career. For example, in the case of an artistic career, the proposal function can apply a proposal algorithm based on artistic data. For example, in the case of an artistic career, the proposal function can apply a proposal algorithm based on artistic data. By applying different proposal algorithms depending on the career path category, more appropriate proposals can be made. Career paths include, but are not limited to, occupational categories and academic fields. Different proposal algorithms include, but are not limited to, the selection of an algorithm according to the category. Some or all of the above processing in the proposal function may be performed using, for example, AI, or not using AI. For example, the proposal unit can input career path and career category data into the generation AI and have the generation AI apply different proposal algorithms.

[0042] The management department can select the optimal management method by referring to the user's past learning history when managing learning progress. For example, the management department can analyze the user's past learning history and select the optimal management method. For example, the management department can analyze the user's past learning history and select the optimal management method. The management department can also extract specific patterns from the user's past learning history and select a management method based on them. For example, the management department can extract specific patterns from the user's past learning history and select a management method based on them. The management department can also cluster the user's past learning history and select the optimal management method for each cluster. For example, the management department clusters the user's past learning history and selects the optimal management method for each cluster. This improves management accuracy by selecting the optimal management method based on past learning history. Past learning history includes, but is not limited to, past test results and learning time. Optimal management methods include, but are not limited to, selecting a management method according to history. Some or all of the above-described processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's past learning history data into a generating AI and have the generating AI select the optimal management method.

[0043] The management department can customize the management methods based on the user's current living situation when managing learning progress. For example, the management department can customize appropriate management methods by considering the user's current living situation (school performance, home environment, etc.). For example, the management department can customize appropriate management methods by considering the user's current living situation (school performance, home environment, etc.). The management department can also customize optimal management methods by considering the user's daily rhythm (wake-up time, bedtime, etc.). For example, the management department can customize optimal management methods by considering the user's daily rhythm (wake-up time, bedtime, etc.). The management department can also customize the learning progress management methods based on the user's current living situation. For example, the management department can customize the learning progress management methods based on the user's current living situation. This allows for more appropriate management by customizing the management methods based on the current living situation. Current living situation includes, but is not limited to, lifestyle habits and daily behavior patterns. Customizing management methods includes, but is not limited to, changing management methods according to living situation. Some or all of the above-described processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's current living situation data into a generating AI and have the generating AI customize the management means.

[0044] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can analyze the user's past notification history and select the optimal notification method. For example, the notification unit can analyze the user's past notification history and select the optimal notification method. The notification unit can also extract specific patterns from the user's past notification history and select a notification method based on them. For example, the notification unit can extract specific patterns from the user's past notification history and select a notification method based on them. The notification unit can also cluster the user's past notification history and select the optimal notification method for each cluster. For example, the notification unit clusters the user's past notification history and selects the optimal notification method for each cluster. This improves notification accuracy by selecting the optimal notification method based on past notification history. Past notification history includes, but is not limited to, past notification content and notification timing. The optimal notification method includes, but is not limited to, selecting a notification method according to the history. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without using AI. For example, the notification unit can input the user's past notification history data into a generating AI, which then selects the optimal notification method.

[0045] The notification unit can customize notification methods based on the user's current living situation when a notification is sent. For example, the notification unit can customize appropriate notification methods by considering the user's current living situation (e.g., school performance, home environment). For example, the notification unit can customize appropriate notification methods by considering the user's current living situation (e.g., school performance, home environment). The notification unit can also customize optimal notification methods by considering the user's daily rhythm (e.g., wake-up time, bedtime). For example, the notification unit can customize optimal notification methods by considering the user's daily rhythm (e.g., wake-up time, bedtime). The notification unit can also customize notification methods based on the user's current living situation. For example, the notification unit can customize notification methods based on the user's current living situation. This makes it possible to provide more appropriate notifications by customizing notification methods based on the current living situation. Current living situation includes, but is not limited to, lifestyle habits and daily behavior patterns. Customizing notification methods includes, but is not limited to, changing notification methods according to living situation. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current living situation data into a generating AI and have the generating AI customize the notification method.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The management system can include features to visually display users' learning progress. For example, it can use graphs and charts to visually display learning progress. It can also use color-coding to display learning achievement levels, allowing users to grasp their progress at a glance. Furthermore, using animations in accordance with learning progress can increase user motivation. This makes managing learning progress more intuitive and effective.

[0048] The proposal department can refer to the user's past proposal history and select the optimal proposal method. For example, it can increase the success rate of proposals by reapplying proposal methods that have been successful in the past. It can also improve the effectiveness of proposals by avoiding proposal methods that have failed in the past. Furthermore, it can analyze past proposal history and select proposal methods that match the user's preferences. As a result, the accuracy of proposals is improved by selecting the optimal proposal method based on past proposal history.

[0049] The notification system can adjust the frequency of notifications based on the user's current lifestyle. For example, it can reduce the frequency of notifications when the user is busy and increase it when they have more free time. It can also adjust the timing of notifications to match the user's daily rhythm, ensuring they are received effectively. Furthermore, it can customize the content of notifications according to the user's lifestyle, enabling more appropriate notifications. This allows for optimal notifications tailored to the user's circumstances.

[0050] The management system can include a function to compare users' learning progress with that of other users. For example, displaying the progress of other users with the same goals can increase user motivation. It can also provide learning inspiration by showcasing the success stories of other users. Furthermore, encouraging competition among users can boost motivation. This allows for more effective management of learning progress through comparison with other users.

[0051] The proposal department can customize the content of its suggestions based on the user's current lifestyle. For example, during busy periods, it can suggest short-term goals to increase their feasibility. Conversely, during periods when the user has more free time, it can suggest long-term goals to support their future career planning. Furthermore, by adjusting the content of the suggestions to match the user's lifestyle, it can provide more effective suggestions. This enables the provision of optimal suggestions tailored to the user's circumstances.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The analysis unit analyzes the user's personality and aptitudes. For example, it collects user behavior data and dialogue content and analyzes it using natural language processing. Specifically, it collects click history and movement history and analyzes personality and aptitudes. It can also use speech recognition technology to transcribe user speech into text and use text analysis technology to analyze personality and aptitudes. Step 2: The proposal department generates career suggestions based on the analysis results obtained by the analysis department. For example, it incorporates labor market data and educational information in real time and proposes appropriate actions. Specifically, it collects job postings and salary data and suggests occupations suitable for the user. It can also collect university curriculum and entrance examination information to support the selection of educational institutions. Step 3: The management department manages the user's learning progress. For example, they collect learning progress data in real time and make suggestions and reminders based on the analysis results. Specifically, they collect test results and study time and evaluate the degree of learning achievement and understanding. They can also analyze the progress of the learning plan in real time and adjust the learning plan as needed. Step 4: The notification unit proactively makes suggestions and reminders based on the progress obtained by the management unit. For example, it manages progress while continuously interacting with the user and proactively makes suggestions and reminders as needed. Specifically, it notifies the user of the progress of their learning plan and suggests necessary actions. It can also send reminders to encourage the user to carry out their learning plan.

[0054] (Example of form 2) The career guidance and support system according to an embodiment of the present invention is a system for elementary, junior high, and high school students (especially truant students) and their guardians who are anxious about their future career paths and choices. This system uses an AI agent to analyze personality and aptitude in real time and autonomously supports career guidance and career planning. Unlike conventional, uniform career guidance and general career counseling services, the career guidance and support system has the function of continuously interacting with the user, managing progress, and proactively making suggestions and reminders as needed. For example, the career guidance and support system analyzes the user's personality and interests using natural language processing and generates career suggestions. Next, the career guidance and support system analyzes learning progress in real time and autonomously adjusts learning plans and career suggestions. Furthermore, the career guidance and support system takes in labor market data and higher education information in real time and proposes appropriate actions. For example, if a fifth-grade boy consults the career guidance and support system saying, "I want to go to junior high school, but I don't know what I should focus on," the system will suggest classes he can enjoy in junior high school and his future career based on his interests and strengths. It will also suggest specific goal setting and study plans, and send reminders while tracking his progress. If a second-year junior high school girl consults the system saying, "I want to become a childcare worker, but I don't know what to study," the system will explain the educational routes and necessary qualifications for becoming a childcare worker and provide information on childcare volunteer opportunities. It will also suggest high school options and provide information on open campuses. In this way, the career guidance and support system analyzes the user's personality and aptitude in real time and autonomously supports career guidance and career planning. By continuously interacting with the user, managing progress, and proactively making suggestions and reminders as needed, it provides individually optimized support. As a result, the career guidance and support system can analyze the user's personality and aptitude in real time and autonomously support career guidance and career planning.

[0055] The career guidance and career support system according to this embodiment comprises an analysis unit, a proposal unit, a management unit, and a notification unit. The analysis unit analyzes the user's personality and aptitude. The analysis unit, for example, collects user behavior data and dialogue content and analyzes it using natural language processing. For example, the analysis unit collects the user's click history and movement history and analyzes their personality and aptitude. The analysis unit can also collect dialogue content with the user and analyze it using natural language processing technology. For example, the analysis unit uses speech recognition technology to transcribe the user's statements into text and uses text analysis technology to analyze their personality and aptitude. The proposal unit generates career guidance based on the analysis results obtained by the analysis unit. The proposal unit, for example, takes in labor market data and educational information in real time and proposes appropriate actions. For example, the proposal unit collects job postings and salary data and proposes occupations suitable for the user. The proposal unit can also collect university curriculum and entrance examination information to support the selection of educational institutions. The management unit manages the user's learning progress. The management unit, for example, collects learning progress data in real time and makes suggestions and reminders based on the analysis results. For example, the management unit collects test results and study time and evaluates the degree of learning achievement and understanding. The management unit can also analyze the progress of the learning plan in real time and adjust the learning plan as needed. The notification unit proactively makes suggestions and reminders based on the progress obtained by the management unit. For example, the notification unit manages progress by continuously interacting with the user and proactively makes suggestions and reminders as needed. For example, the notification unit notifies the user of the progress of the learning plan and suggests necessary actions. The notification unit can also send reminders to the user to encourage them to execute the learning plan. As a result, the career guidance and career support system according to the embodiment can analyze the user's personality and aptitude in real time and autonomously support career guidance and career planning.

[0056] The analytics department analyzes users' personalities and aptitudes. Specifically, it collects user behavior data and dialogue content and analyzes it using natural language processing. For example, it collects the history of links clicked and pages viewed on websites and analyzes user interests and preferences based on this data. It also collects user movement history and analyzes the places they frequently visit to understand their lifestyle and preferences. Furthermore, it can collect the content of conversations with users and analyze it using natural language processing technology. For example, it uses speech recognition technology to transcribe user statements into text and uses text analysis technology to analyze personality and aptitudes. This allows for understanding the user's speech patterns and emotional changes, enabling more accurate personality analysis. The analytics department integrates this data to comprehensively evaluate users' personalities and aptitudes. For example, it analyzes what kind of actions users tend to take in different situations and evaluates characteristics such as stress tolerance and communication skills. It can also predict future behavioral patterns based on users' past behavior data and dialogue content. As a result, the analytics department can gain a detailed understanding of users' personalities and aptitudes and provide information that forms the basis for career guidance and career planning.

[0057] The Proposal Department generates career suggestions based on the analysis results obtained by the Analysis Department. Specifically, it incorporates labor market data and educational information in real time and proposes appropriate actions. For example, the Proposal Department collects job postings and salary data and suggests occupations suitable for the user. This uses algorithms to identify the optimal occupation and career path based on the user's skill set and interests. The Proposal Department can also collect university curriculum and entrance examination information to support the selection of educational institutions. For example, it suggests the optimal university and faculty based on the user's academic ability and interests, and provides advice on entrance examination preparation and necessary preparations. Furthermore, the Proposal Department can provide career suggestions tailored to the user's career goals and lifestyle. For example, if a user desires a career in a specific industry, it provides information on career paths and necessary skill sets in that industry and proposes a concrete action plan. The Proposal Department can also collect feedback on the user's career choices and continuously improve the accuracy and effectiveness of its suggestions. In this way, the Proposal Department can provide users with optimal career suggestions and support their career planning.

[0058] The management department manages users' learning progress. Specifically, it collects learning progress data in real time and provides suggestions and reminders based on the analysis results. For example, the management department collects users' test results and study time to evaluate their learning achievement and understanding. This includes collecting data to understand how much time users are spending on learning and what subjects or topics they have a strong interest in. The management department can also analyze the progress of learning plans in real time and adjust them as needed. For example, if a user is struggling with a particular subject, it can suggest increasing study time for that subject or provide additional learning resources. Furthermore, the management department continuously monitors users' learning habits and performance to support the achievement of long-term learning goals. For example, it evaluates how much progress users are making towards their set goals and provides the necessary support and resources. The management department can also suggest optimal learning methods and strategies to maximize learning effectiveness based on users' learning data. In this way, the management department can efficiently manage users' learning progress and support the achievement of learning goals.

[0059] The notification unit proactively makes suggestions and reminders based on the progress obtained by the management unit. Specifically, it manages progress while continuously interacting with the user, and proactively makes suggestions and reminders as needed. For example, the notification unit notifies the user of the progress of their learning plan and suggests necessary actions. This includes checking whether the user is progressing according to the learning plan and providing adjustments to the learning plan or additional resources as needed. The notification unit can also send reminders to the user to encourage them to follow their learning plan. For example, if the user is behind schedule to meet their set learning goals, it will send a reminder to encourage them to resume learning. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can analyze how users reacted to notifications and optimize the timing and content of notifications. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with quick and reliable instructions and support them in following their learning plan.

[0060] The analysis unit can collect user behavior data and dialogue content and analyze it using natural language processing. For example, the analysis unit can collect user click history and movement history and analyze personality and aptitude. For example, the analysis unit can collect user website browsing history and app usage history and analyze behavioral patterns. The analysis unit can also collect dialogue content with users and analyze it using natural language processing technology. For example, the analysis unit can use speech recognition technology to transcribe user statements into text and use text analysis technology to analyze personality and aptitude. Furthermore, the analysis unit can comprehensively analyze user behavior data and dialogue content to improve the accuracy of personality and aptitude analysis. This improves the accuracy of personality and aptitude analysis by analyzing user behavior data and dialogue content. Behavioral data includes, but is not limited to, click history and movement history. Dialogue content includes, but is not limited to, speech recognition and text analysis. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user behavior data and dialogue content into a generating AI and have the generating AI perform personality and aptitude analysis.

[0061] The suggestion department can take in labor market data and educational information in real time and propose appropriate actions. For example, the suggestion department can collect job postings and salary data and propose occupations suitable for the user. For example, the suggestion department can collect data from job posting sites on the internet and propose occupations based on the user's skills and interests. The suggestion department can also collect university curriculum and entrance examination information and support the selection of educational institutions. For example, the suggestion department can collect curriculum information from official university websites and propose educational institutions based on the user's interests and goals. Furthermore, the suggestion department can comprehensively analyze labor market data and educational information and propose appropriate actions. This makes it possible to propose appropriate career paths based on data taken in real time. Labor market data includes, but is not limited to, job postings and salary data. Educational information includes, but is not limited to, university curriculum and entrance examination information. Appropriate actions include, but are not limited to, suggestions for changing career paths or suggesting skill development. Some or all of the above processing in the suggestion department may be performed using, for example, AI, or not using AI. For example, the proposal department can input labor market data and educational information into a generating AI and have the AI ​​execute suggestions for appropriate actions.

[0062] The management department can collect learning progress data in real time and make suggestions and reminders based on the analysis results. For example, the management department can collect test results and study time and evaluate the degree of learning achievement and comprehension. For example, the management department can collect users' test results and evaluate their learning achievement. The management department can also collect users' study time and evaluate their learning progress. Furthermore, the management department can analyze the progress of the learning plan in real time and adjust the learning plan as needed. This makes it possible to make appropriate suggestions and reminders based on learning progress data collected in real time. Learning progress data includes, but is not limited to, test results and study time. Analysis results include, but is not limited to, the degree of learning achievement and comprehension. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input learning progress data into a generating AI and have the generating AI execute suggestions and reminders.

[0063] The notification unit can manage progress while continuously interacting with the user and proactively make suggestions and reminders as needed. For example, the notification unit can notify the user of the progress of their learning plan and suggest necessary actions. For example, the notification unit can notify the user of the progress of their learning plan and suggest what they should learn next. The notification unit can also send reminders to the user to encourage them to execute their learning plan. For example, the notification unit can send reminders to the user to encourage them to check the progress of their learning plan. Furthermore, the notification unit can provide individually optimized support by continuously interacting with the user to manage progress and proactively making suggestions and reminders as needed. This enables individually optimized support by continuously interacting with the user to manage progress. Continuous interaction includes, but is not limited to, the frequency of interaction and how the content of the interaction is customized. Proactive suggestions and reminders include, but are not limited to, the timing of notifications and how the content is customized. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the content of the conversation with the user into a generation AI, which can then perform proactive suggestions and reminders.

[0064] The analysis unit can estimate the user's emotions and adjust the personality and aptitude analysis method based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can ask questions that provide reassurance and analyze their personality and aptitude in a relaxed state. The analysis unit can also ask questions that help the user calm down if they are excited and perform an accurate personality and aptitude analysis. The analysis unit can also ask simple questions to reduce the burden on the user while analyzing their personality and aptitude if they are tired. By adjusting the analysis method according to the user's emotions, a more accurate personality and aptitude analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Adjusting the analysis method may include, but is not limited to, selecting an algorithm based on emotions. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis method for personality and aptitude.

[0065] The analysis unit can analyze users' past behavioral data and select the optimal analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on the choices and answers users have made in the past. The analysis unit can also extract specific patterns from users' past behavioral data and select an analysis algorithm based on those patterns. The analysis unit can also cluster users' past behavioral data and select the optimal analysis algorithm for each cluster. For example, the analysis unit clusters users' past behavioral data and selects the optimal analysis algorithm for each cluster. By selecting the optimal analysis algorithm based on past behavioral data, the accuracy of the analysis is improved. Past behavioral data includes, but is not limited to, past click history and past movement history. The optimal analysis algorithm includes, but is not limited to, selecting an algorithm that suits the characteristics of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0066] The analysis unit can perform filtering based on the user's current lifestyle and areas of interest during analysis. For example, the analysis unit can perform appropriate filtering by considering the user's current lifestyle (e.g., school performance, home environment). The analysis unit can also prioritize the analysis of relevant information based on the user's areas of interest (e.g., hobbies, subjects of interest). The analysis unit can also perform optimal filtering by considering the user's daily rhythm (e.g., wake-up time, bedtime). This allows for more appropriate analysis by filtering based on the user's lifestyle and areas of interest. Current lifestyle includes, but is not limited to, lifestyle habits and daily behavioral patterns. Areas of interest include, but are not limited to, hobbies and topics of interest. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's current living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0067] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion function can present suggestions in a way that provides reassurance. Similarly, if the user is excited, the suggestion function can present suggestions in a way that calms them down. Furthermore, if the user is tired, the suggestion function can present suggestions in a simple and easy-to-understand way. By adjusting the presentation of suggestions according to the user's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjusting the presentation of suggestions includes, but is not limited to, changing the expression according to the emotion. Some or all of the above-described processes in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section can input user emotion data into a generating AI and have the generating AI adjust the way the proposal is expressed.

[0068] The proposal department can adjust the level of detail in a proposal based on the importance of the career path. For example, if the importance of the career path is high, the proposal department will provide a proposal that includes detailed information. For example, if the importance of the career path is high, the proposal department will provide a proposal that includes detailed information. The proposal department can also provide a proposal that includes a moderate amount of information if the importance of the career path is moderate. For example, if the importance of the career path is moderate, the proposal department will provide a proposal that includes a moderate amount of information. The proposal department can also provide a proposal that includes concise information if the importance of the career path is low. For example, if the importance of the career path is low, the proposal department will provide a proposal that includes concise information. By adjusting the level of detail in a proposal based on the importance of the career path, it becomes possible to provide more appropriate proposals. The importance of the career path includes, for example, the importance of future goals and career paths, but is not limited to these examples. Some or all of the above-described processes in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section can input career importance data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0069] The proposal function can apply different proposal algorithms depending on the career path category when making a proposal. For example, in the case of an academic career, the proposal function can apply a proposal algorithm based on academic data. For example, in the case of an academic career, the proposal function can apply a proposal algorithm based on academic data. The proposal function can also apply a proposal algorithm based on technical data in the case of a technical career. For example, in the case of an artistic career, the proposal function can apply a proposal algorithm based on artistic data. For example, in the case of an artistic career, the proposal function can apply a proposal algorithm based on artistic data. By applying different proposal algorithms depending on the career path category, more appropriate proposals can be made. Career paths include, but are not limited to, occupational categories and academic fields. Different proposal algorithms include, but are not limited to, the selection of an algorithm according to the category. Some or all of the above processing in the proposal function may be performed using, for example, AI, or not using AI. For example, the proposal unit can input career path and career category data into the generation AI and have the generation AI apply different proposal algorithms.

[0070] The management department can estimate the user's emotions and adjust the learning progress management method based on the estimated user emotions. For example, if the user is feeling anxious, the management department can adopt a management method that provides a sense of security. For example, if the user is feeling anxious, the management department can adopt a management method that provides a sense of security. The management department can also adopt a management method that calms the user if they are excited. For example, if the user is excited, the management department can adopt a management method that calms the user if they are excited. The management department can also adopt a management method that reduces the burden on the user if they are tired. For example, if the user is tired, the management department can adopt a management method that reduces the burden on the user. By adjusting the learning progress management method according to the user's emotions, more appropriate management becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjusting the learning progress management method includes, but is not limited to, changing the management method according to emotions. Some or all of the above-mentioned processes in the management department may be performed using AI, for example, or without AI. For example, the management department may input user emotion data into a generating AI and have the generating AI adjust the method for managing learning progress.

[0071] The management department can select the optimal management method by referring to the user's past learning history when managing learning progress. For example, the management department can analyze the user's past learning history and select the optimal management method. For example, the management department can analyze the user's past learning history and select the optimal management method. The management department can also extract specific patterns from the user's past learning history and select a management method based on them. For example, the management department can extract specific patterns from the user's past learning history and select a management method based on them. The management department can also cluster the user's past learning history and select the optimal management method for each cluster. For example, the management department clusters the user's past learning history and selects the optimal management method for each cluster. This improves management accuracy by selecting the optimal management method based on past learning history. Past learning history includes, but is not limited to, past test results and learning time. Optimal management methods include, but are not limited to, selecting a management method according to history. Some or all of the above-described processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's past learning history data into a generating AI and have the generating AI select the optimal management method.

[0072] The management department can customize the management methods based on the user's current living situation when managing learning progress. For example, the management department can customize appropriate management methods by considering the user's current living situation (school performance, home environment, etc.). For example, the management department can customize appropriate management methods by considering the user's current living situation (school performance, home environment, etc.). The management department can also customize optimal management methods by considering the user's daily rhythm (wake-up time, bedtime, etc.). For example, the management department can customize optimal management methods by considering the user's daily rhythm (wake-up time, bedtime, etc.). The management department can also customize the learning progress management methods based on the user's current living situation. For example, the management department can customize the learning progress management methods based on the user's current living situation. This allows for more appropriate management by customizing the management methods based on the current living situation. Current living situation includes, but is not limited to, lifestyle habits and daily behavior patterns. Customizing management methods includes, but is not limited to, changing management methods according to living situation. Some or all of the above-described processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's current living situation data into a generating AI and have the generating AI customize the management means.

[0073] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is feeling anxious, the notification unit may adopt a notification method that provides reassurance. For example, if the user is feeling anxious, the notification unit may adopt a notification method that provides reassurance. For example, if the user is feeling excited, the notification unit may adopt a notification method that helps the user calm down. For example, if the user is feeling excited, the notification unit may adopt a notification method that helps the user calm down. For example, if the user is tired, the notification unit may adopt a notification method that reduces the burden on the user. For example, if the user is tired, the notification unit may adopt a notification method that reduces the burden on the user. By adjusting the notification method according to the user's emotions, more appropriate notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Adjusting the notification method includes, for example, changing the notification method according to emotions, but is not limited to such examples. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the generating AI adjust the notification method.

[0074] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can analyze the user's past notification history and select the optimal notification method. For example, the notification unit can analyze the user's past notification history and select the optimal notification method. The notification unit can also extract specific patterns from the user's past notification history and select a notification method based on them. For example, the notification unit can extract specific patterns from the user's past notification history and select a notification method based on them. The notification unit can also cluster the user's past notification history and select the optimal notification method for each cluster. For example, the notification unit clusters the user's past notification history and selects the optimal notification method for each cluster. This improves notification accuracy by selecting the optimal notification method based on past notification history. Past notification history includes, but is not limited to, past notification content and notification timing. The optimal notification method includes, but is not limited to, selecting a notification method according to the history. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without using AI. For example, the notification unit can input the user's past notification history data into a generating AI, which then selects the optimal notification method.

[0075] The notification unit can customize notification methods based on the user's current living situation when a notification is sent. For example, the notification unit can customize appropriate notification methods by considering the user's current living situation (e.g., school performance, home environment). For example, the notification unit can customize appropriate notification methods by considering the user's current living situation (e.g., school performance, home environment). The notification unit can also customize optimal notification methods by considering the user's daily rhythm (e.g., wake-up time, bedtime). For example, the notification unit can customize optimal notification methods by considering the user's daily rhythm (e.g., wake-up time, bedtime). The notification unit can also customize notification methods based on the user's current living situation. For example, the notification unit can customize notification methods based on the user's current living situation. This makes it possible to provide more appropriate notifications by customizing notification methods based on the current living situation. Current living situation includes, but is not limited to, lifestyle habits and daily behavior patterns. Customizing notification methods includes, but is not limited to, changing notification methods according to living situation. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current living situation data into a generating AI and have the generating AI customize the notification method.

[0076] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will prioritize notifications that provide reassurance. For example, if the user is feeling anxious, the notification unit will prioritize notifications that provide reassurance. For example, if the user is feeling excited, the notification unit will prioritize notifications that help the user calm down. For example, if the user is feeling excited, the notification unit will prioritize notifications that help the user calm down. For example, if the user is feeling tired, the notification unit will prioritize notifications that help the user reduce their burden. For example, if the user is feeling tired, the notification unit will prioritize notifications that help the user reduce their burden. In this way, more appropriate notifications can be made by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Determining the priority of notifications includes, for example, setting priorities according to emotions, but is not limited to such examples. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the generating AI determine the priority of notifications.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those estimates. For example, if the user is feeling stressed, the suggestion function will make suggestions during a time when the user can relax. It can also make suggestions at the right time if the user is concentrating. Furthermore, if the user is tired, suggesting suggestions after they have rested will allow for more effective suggestions. This enables the system to deliver suggestions at the optimal time, tailored to the user's emotions.

[0079] The management system can include features to visually display users' learning progress. For example, it can use graphs and charts to visually display learning progress. It can also use color-coding to display learning achievement levels, allowing users to grasp their progress at a glance. Furthermore, using animations in accordance with learning progress can increase user motivation. This makes managing learning progress more intuitive and effective.

[0080] The notification unit can estimate the user's emotions and customize the content of notifications based on those emotions. For example, if the user is feeling anxious, it can send a notification that provides reassurance. If the user is excited, it can send a notification that calms them down. Furthermore, if the user is tired, it can send a concise and easy-to-understand notification to reduce their burden. This allows the system to provide optimal notification content tailored to the user's emotions.

[0081] The proposal department can refer to the user's past proposal history and select the optimal proposal method. For example, it can increase the success rate of proposals by reapplying proposal methods that have been successful in the past. It can also improve the effectiveness of proposals by avoiding proposal methods that have failed in the past. Furthermore, it can analyze past proposal history and select proposal methods that match the user's preferences. As a result, the accuracy of proposals is improved by selecting the optimal proposal method based on past proposal history.

[0082] The management department can estimate the user's emotions and adjust the learning plan based on those estimates. For example, if a user is feeling stressed, the learning plan can be eased to reduce the burden. Conversely, if a user is highly motivated, the learning plan can be strengthened to promote efficient learning. Furthermore, if a user is tired, a learning plan incorporating rest can be suggested to maximize the effectiveness of the learning. This allows for the provision of an optimal learning plan tailored to the user's emotions.

[0083] The notification system can adjust the frequency of notifications based on the user's current lifestyle. For example, it can reduce the frequency of notifications when the user is busy and increase it when they have more free time. It can also adjust the timing of notifications to match the user's daily rhythm, ensuring they are received effectively. Furthermore, it can customize the content of notifications according to the user's lifestyle, enabling more appropriate notifications. This allows for optimal notifications tailored to the user's circumstances.

[0084] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is feeling anxious, it can prioritize suggestions that provide reassurance. Similarly, if the user is excited, it can prioritize suggestions that help them calm down. Furthermore, if the user is tired, prioritizing suggestions that reduce their burden can lead to more effective suggestions. This allows for the determination of the optimal suggestion priority based on the user's emotions.

[0085] The management system can include a function to compare users' learning progress with that of other users. For example, displaying the progress of other users with the same goals can increase user motivation. It can also provide learning inspiration by showcasing the success stories of other users. Furthermore, encouraging competition among users can boost motivation. This allows for more effective management of learning progress through comparison with other users.

[0086] The notification unit can estimate the user's emotions and adjust the notification format based on those emotions. For example, if the user is feeling anxious, the notification can be delivered in a calming format to provide reassurance. If the user is excited, the notification can be delivered in a calming format to help them relax. Furthermore, if the user is tired, the notification can be delivered in a concise and easy-to-understand format to reduce their burden. This allows the system to provide the optimal notification format tailored to the user's emotions.

[0087] The proposal department can customize the content of its suggestions based on the user's current lifestyle. For example, during busy periods, it can suggest short-term goals to increase their feasibility. Conversely, during periods when the user has more free time, it can suggest long-term goals to support their future career planning. Furthermore, by adjusting the content of the suggestions to match the user's lifestyle, it can provide more effective suggestions. This enables the provision of optimal suggestions tailored to the user's circumstances.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The analysis unit analyzes the user's personality and aptitudes. For example, it collects user behavior data and dialogue content and analyzes it using natural language processing. Specifically, it collects click history and movement history and analyzes personality and aptitudes. It can also use speech recognition technology to transcribe user speech into text and use text analysis technology to analyze personality and aptitudes. Step 2: The proposal department generates career suggestions based on the analysis results obtained by the analysis department. For example, it incorporates labor market data and educational information in real time and proposes appropriate actions. Specifically, it collects job postings and salary data and suggests occupations suitable for the user. It can also collect university curriculum and entrance examination information to support the selection of educational institutions. Step 3: The management department manages the user's learning progress. For example, they collect learning progress data in real time and make suggestions and reminders based on the analysis results. Specifically, they collect test results and study time and evaluate the degree of learning achievement and understanding. They can also analyze the progress of the learning plan in real time and adjust the learning plan as needed. Step 4: The notification unit proactively makes suggestions and reminders based on the progress obtained by the management unit. For example, it manages progress while continuously interacting with the user and proactively makes suggestions and reminders as needed. Specifically, it notifies the user of the progress of their learning plan and suggests necessary actions. It can also send reminders to encourage the user to carry out their learning plan.

[0090] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0091] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0092] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0093] Each of the multiple elements described above, including the analysis unit, proposal unit, management unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit collects user behavior data and dialogue content using the camera 42 and microphone 38B of the smart device 14, and analyzes personality and aptitude using the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and takes in labor market data and educational information in real time and proposes appropriate actions. The management unit is implemented in the specific processing unit 46A of the smart device 14, for example, and collects learning progress data in real time and makes suggestions and reminders based on the analysis results. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies the user of the progress of the learning plan and proposes necessary actions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] As shown in Figure 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.

[0096] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0100] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0101] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0102] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0103] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0104] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0106] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0108] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements described above, including the analysis unit, proposal unit, management unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit collects user behavior data and dialogue content using the camera 42 and microphone 238 of the smart glasses 214, and analyzes personality and aptitude using the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and takes in labor market data and educational information in real time and proposes appropriate actions. The management unit is implemented in the specific processing unit 46A of the smart glasses 214, for example, and collects learning progress data in real time and makes suggestions and reminders based on the analysis results. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies the user of the progress of the learning plan and proposes necessary actions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the analysis unit, proposal unit, management unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit collects user behavior data and dialogue content using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A analyzes the user's personality and aptitude. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and takes in labor market data and educational information in real time and proposes appropriate actions. The management unit is implemented in the specific processing unit 46A of the headset terminal 314, for example, and collects learning progress data in real time and makes suggestions and reminders based on the analysis results. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies the user of the progress of their learning plan and proposes necessary actions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] As shown in Figure 7, the 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.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0134] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the analysis unit, proposal unit, management unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit collects user behavior data and dialogue content using the camera 42 and microphone 238 of the robot 414, and the control unit 46A analyzes the user's personality and aptitude. The proposal unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, and takes in labor market data and educational information, and proposes appropriate actions. The management unit is implemented in real time by the control unit 46A of the robot 414, and collects learning progress data in real time, and makes suggestions and reminders based on the analysis results. The notification unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, and notifies the user of the progress of the learning plan and proposes necessary actions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0145] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0146] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0147] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0151] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0152] 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.

[0153] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0154] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0155] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0156] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0158] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0159] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0160] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0161] (Note 1) The analysis department analyzes the user's personality and aptitude, A proposal unit that generates career path suggestions based on the analysis results obtained by the aforementioned analysis unit, The management department manages the learning progress of users, The system includes a notification unit that proactively makes suggestions and reminders based on the progress obtained by the management unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is We collect user behavior data and dialogue content, and analyze it using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, It incorporates labor market data and educational information in real time and proposes appropriate actions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, The system collects learning progress data in real time and provides suggestions and reminders based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, We manage progress by continuously interacting with users and proactively make suggestions and reminders as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is It estimates the user's emotions and adjusts the personality and aptitude analysis methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is Analyze the user's past behavioral data and select the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is During analysis, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the career path and other factors. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the career path or category. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned management department, It estimates the user's emotions and adjusts the learning progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned management department, When managing learning progress, the system selects the optimal management method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, When managing learning progress, customize the management method based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the user's personality and aptitude, A proposal unit that generates career path suggestions based on the analysis results obtained by the aforementioned analysis unit, The management department manages the learning progress of users, The system includes a notification unit that proactively makes suggestions and reminders based on the progress obtained by the management unit. A system characterized by the following features.

2. The aforementioned analysis unit is We collect user behavior data and dialogue content, and analyze it using natural language processing. The system according to feature 1.

3. The aforementioned proposal section is, It incorporates labor market data and educational information in real time and proposes appropriate actions. The system according to feature 1.

4. The aforementioned management department, The system collects learning progress data in real time and provides suggestions and reminders based on the analysis results. The system according to feature 1.

5. The aforementioned notification unit, We manage progress by continuously interacting with users and proactively make suggestions and reminders as needed. The system according to feature 1.

6. The aforementioned analysis unit is It estimates the user's emotions and adjusts the personality and aptitude analysis methods based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit is Analyze the user's past behavioral data and select the optimal analysis algorithm. The system according to feature 1.

8. The aforementioned analysis unit is During analysis, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system according to feature 1.

10. The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the career path and other factors. The system according to feature 1.

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