system

Abstract

The system according to this embodiment aims to provide an appropriate learning plan for the desired occupation or role. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a search unit, and a customization unit. The reception unit receives input from the user regarding their desired occupation and role. The analysis unit analyzes the information received by the reception unit and extracts appropriate keywords and skill sets. The search unit searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. The customization unit customizes the learning plan based on the learning materials found by the search unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, formulating an appropriate learning plan for the target occupation or role is left to personal judgment, and there is a risk of wasting time on unnecessary learning.

[0005] The system according to the embodiment aims to provide an appropriate learning plan for the target occupation or role.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a customization unit. The reception unit receives input from the user regarding their desired occupation and role. The analysis unit analyzes the information received by the reception unit and extracts appropriate keywords and skill sets. The search unit searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. The customization unit customizes the learning plan based on the learning materials found by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an appropriate learning plan for the desired occupation or role. [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 manages communication between multiple 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 reception 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 reception 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 SignpostAgent system according to an embodiment of the present invention is a system that supports users in creating learning plans for their desired occupations and roles. When a user finds an occupation or role they want to pursue, they need to proceed with learning towards that goal, but how to proceed with learning is often left to the individual's judgment. This choice is important as it can shape one's life, but it is sometimes underestimated, and as a result, many people end up wasting time on ineffective learning. The SignpostAgent system supports learning plans and provides a roadmap for the kind of life one should lead. For example, the SignpostAgent system takes the user's desired occupation or role as input. Next, the generating AI analyzes the user's input and extracts appropriate keywords and skill sets. The generating AI then works in conjunction with learning platforms and book databases to search for learning materials suitable for the necessary skills. Furthermore, it customizes the learning plan based on the user's schedule and goals. For example, if the user inputs "I want to work in marketing," the generating AI extracts marketing-related skill sets and searches for appropriate learning materials. Next, it creates a specific learning plan based on the user's schedule and goals. This learning plan serves as a roadmap for the user to efficiently proceed with learning and reach their desired occupation or role. The SignpostAgent system not only supports users in developing learning plans for their desired careers and roles, but also supports their motivation for growth and contributes to a society where self-realization is encouraged. For example, if a user aims to become a cybersecurity expert, the generative AI extracts cybersecurity-related skill sets and searches for appropriate learning materials. Next, it creates a specific learning plan based on the user's schedule and goals. This learning plan serves as a guide for the user to efficiently progress in their learning and succeed as a cybersecurity expert. The SignpostAgent system utilizes generative AI to support users in developing learning plans for their desired careers and roles. The generative AI analyzes the user's input and extracts appropriate keywords and skill sets. The generative AI also connects with learning platforms and book databases to search for learning materials suitable for the necessary skills.Furthermore, the learning plan is customized based on the user's schedule and goals. This allows users to learn efficiently and reach their desired occupation or role. In this way, the SignpostAgent system can efficiently support users in developing their learning plans toward their desired occupation or role.

[0029] The SignpostAgent system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a customization unit. The reception unit receives input of the user's desired occupation or role. The user's desired occupation or role includes, but is not limited to, examples such as engineer, designer, and manager. The reception unit accepts, for example, the user entering their occupation or role into an input form. The reception unit can also accept the user's occupation or role input using voice input. Furthermore, the reception unit can assist with input by referring to the user's past occupation selection history. For example, the reception unit suggests relevant occupations or roles based on the occupations or roles the user has previously selected. The analysis unit uses generative AI to analyze the information received by the reception unit and extract appropriate keywords and skill sets. For example, the analysis unit uses generative AI to analyze the user's input and extract specialized terminology and necessary technical skills related to the occupation. The analysis unit can also use generative AI to identify the skill sets required for the occupation or role based on the user's input. Furthermore, the analysis unit can use generative AI to analyze the user's input and extract relevant keywords. For example, the generation AI analyzes the user's input, such as "I want to work in marketing," and extracts marketing-related skill sets. The search unit searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. The search unit can, for example, link with learning platforms and book databases to find materials suitable for the required skills. The search unit can also search for materials that meet the user's learning needs, such as online courses and workshops. Furthermore, the search unit can search for materials to customize the learning plan based on the user's schedule and goals. For example, if the user inputs "I want to work in marketing," the search unit will search for marketing-related materials. The customization unit customizes the learning plan based on the materials found by the search unit. The customization unit creates a specific learning plan based on the user's schedule and goals, for example. The customization unit can also adjust the learning plan according to the user's learning progress.Furthermore, the customization section can also customize the learning plan to support the user's desire for growth. For example, if the user enters "I want to work in marketing," the customization section will create a learning plan related to marketing. In this way, the SignpostAgent system according to the embodiment can efficiently support the user's learning plan toward the occupation or role they aspire to.

[0030] The reception desk accepts input from users regarding their desired occupation or role. This includes, but is not limited to, examples such as engineer, designer, and manager. The reception desk accepts user input of their occupation or role through an input form. It can also accept user input using voice input. Specifically, it uses speech recognition technology to convert what the user says into text and incorporates it into the system as occupation or role information. Furthermore, the reception desk can assist with input by referring to the user's past occupation selection history. For example, it can store the user's past occupation or role selections in a database and refer to that history during current input to suggest relevant occupations or roles. This allows users to select new occupations or roles while referring to past choices. The reception desk also has a function to analyze user input in real time and automatically correct input errors or unclear information. For example, if a user enters "engineer," the system will suggest specific occupations such as "software engineer" or "network engineer," helping the user make a more specific selection. This allows the reception desk to efficiently and accurately accept user input of their occupation or role.

[0031] The analysis unit uses generative AI to analyze information received by the reception unit and extract appropriate keywords and skill sets. For example, the analysis unit uses generative AI to analyze user input and extract specialized terminology and necessary technical skills related to the occupation. Specifically, it uses natural language processing technology to grammatically and semantically analyze user input and identify important keywords related to the occupation and role. For example, if a user inputs "I want to work in marketing," the generative AI will extract related keywords such as "marketing," "digital marketing," "SEO," and "content marketing." The analysis unit can also use generative AI to identify the skill sets necessary for the occupation and role based on the user input. For example, it may identify "data analysis," "advertising management," and "social media management" as skills necessary for a marketing position. Furthermore, the analysis unit can use generative AI to analyze user input and extract related keywords. For example, the generative AI analyzes the information "I want to work in marketing" entered by the user and extracts marketing-related skill sets. This allows the analysis unit to analyze user input in detail and accurately extract the information necessary for the occupation and role. Furthermore, the analysis unit can perform more accurate analysis by referring to the user's past input and learning history. For example, it can identify the skill set relevant to the user's current career choice based on what the user has learned in the past and the qualifications they have obtained. This allows the analysis unit to perform analysis tailored to the user's individual circumstances and provide more appropriate information.

[0032] The search unit searches for appropriate learning materials based on keywords and skill sets extracted by the analysis unit. For example, the search unit can connect with learning platforms and book databases to find materials suitable for the required skills. Specifically, it uses the API of online learning platforms to search for relevant courses and materials and provide them to the user. The search unit can also search for materials that meet the user's learning needs, such as online courses and workshops. For example, if a user enters "I want to work in marketing," the search unit will search for and suggest online courses and workshops related to marketing. Furthermore, the search unit can search for materials to customize a learning plan based on the user's schedule and goals. For example, if a user enters how many hours they can study per week, the search unit will suggest a learning plan tailored to that schedule. The search unit can also monitor the user's learning progress in real time and add materials or adjust the learning plan as needed. This allows the search unit to provide optimal learning materials tailored to the user's learning needs and support efficient learning. Additionally, the search unit can collect user feedback and continuously improve its search algorithm. For example, by having users leave ratings and comments on the provided materials, the search unit can use that feedback to improve the accuracy of its search results. This allows the search function to consistently provide users with the most suitable learning materials, thereby improving the quality of learning.

[0033] The customization unit customizes the learning plan based on the learning materials found by the search unit. For example, the customization unit creates a specific learning plan based on the user's schedule and goals. Specifically, it plans the daily learning content and progress targets in detail, taking into account the learning goals set by the user and the time available. The customization unit can also adjust the learning plan according to the user's learning progress. For example, if the user is ahead of schedule, it will suggest additional materials to move on to the next step, or conversely, if progress is behind, it will readjust the learning content to provide a manageable plan. Furthermore, the customization unit can customize the learning plan to support the user's motivation to grow. For example, if the user shows a strong interest in a particular skill, it will provide additional materials and assignments related to that skill to increase motivation. The customization unit can also collect user feedback and continuously improve the accuracy and effectiveness of the learning plan. For example, if the user leaves ratings and comments on the learning plan, the customization unit will revise the plan based on that feedback and provide a more effective learning plan. In this way, the customization unit can provide an optimal learning plan tailored to the user's individual needs and circumstances, supporting efficient learning. Furthermore, the customization function can also provide a step-by-step learning plan that takes into account the user's long-term career goals. For example, if a user enters "I want to work in marketing," the customization function will provide a plan to progress through the learning process, from basic to advanced marketing-related skills. This allows the customization function to support effective learning towards the user's career goals and promote their growth.

[0034] The reception desk can analyze the user's past occupation selection history and select the optimal input method. For example, the reception desk can suggest related occupations and roles based on the occupations and roles the user has selected in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past selection history. In this way, by analyzing the user's past occupation selection history, the optimal input method can be selected, promoting efficient input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past occupation selection history data into a generating AI and have the generating AI select the optimal input method.

[0035] The reception desk can filter the input of occupations and roles based on the user's current skill level and areas of interest. For example, the reception desk can suggest appropriate occupations and roles based on the user's current skill level. The reception desk can also prioritize displaying relevant occupations and roles based on the user's areas of interest. Furthermore, the reception desk can combine the user's skill level and areas of interest to suggest the most suitable occupations and roles. This allows for the suggestion of appropriate occupations and roles by filtering based on the user's current skill level and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's skill level data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0036] The reception desk can prioritize the input of highly relevant occupations and roles by considering the user's geographical location when the user enters their occupation and role. For example, the reception desk can suggest nearby occupations and roles based on the user's current location. It can also suggest region-specific occupations and roles based on the user's geographical location. Furthermore, the reception desk can suggest highly relevant occupations and roles by considering the user's travel history. In this way, by considering the user's geographical location, it is possible to prioritize the suggestion of highly relevant occupations and roles. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant occupations and roles.

[0037] The reception desk can analyze the user's social media activity when they input their occupation and role, and input relevant occupations and roles. For example, the reception desk can analyze the user's interests on social media and suggest relevant occupations and roles. It can also suggest occupations and roles that the user's followers and friends are interested in. Furthermore, the reception desk can suggest the most suitable occupation and role based on the user's social media activity history. In this way, relevant occupations and roles can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant occupations and roles.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of occupations and roles during the analysis. For example, the analysis unit performs a detailed analysis for occupations and roles with high importance. It can also perform a concise analysis for occupations and roles with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to importance. By adjusting the level of detail of the analysis based on the importance of occupations and roles, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input occupation and role importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the occupation or role category during analysis. For example, for technical occupations, the analysis unit will focus on technical skill sets. For creative occupations, the analysis unit can focus on creativity and ideas. Furthermore, for management occupations, the analysis unit can focus on leadership and management abilities. By applying different analysis algorithms according to the occupation or role category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input occupation and role category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the submission timing of occupations and roles during the analysis process. For example, the analysis unit can prioritize the analysis of occupations and roles with approaching submission deadlines. It can also postpone the analysis of occupations and roles with later submission deadlines. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission timing. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the submission timing of occupations and roles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input occupation and role submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of occupations and roles during the analysis process. For example, the analysis unit can prioritize the analysis of occupations and roles with high relevance. It can also postpone the analysis of occupations and roles with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis according to relevance. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of occupations and roles. 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 occupation and role relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The search unit can improve search accuracy by considering the interrelationships between occupations and roles during the search process. For example, the search unit can group related occupations and roles and provide search results. Furthermore, the search unit can prioritize displaying highly relevant search results based on the interrelationships between occupations and roles. In addition, the search unit can adjust the order of search results by considering the interrelationships between occupations and roles. This allows for improved search accuracy by considering the interrelationships between occupations and roles. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input data on the interrelationships between occupations and roles into a generating AI and have the generating AI perform the search accuracy improvement.

[0043] The search unit can perform searches while considering the attribute information of the submitter of occupation and role. For example, the search unit can provide appropriate search results based on the submitter's skill level. The search unit can also prioritize displaying relevant search results based on the submitter's areas of interest. Furthermore, the search unit can provide optimal search results by considering the submitter's attribute information. This allows for the provision of more appropriate search results by considering the attribute information of the submitter of occupation and role. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input submitter attribute information data into a generating AI and have the generating AI perform the search.

[0044] The search unit can perform searches while considering the geographical distribution of occupations and roles. For example, the search unit can prioritize displaying nearby occupations and roles based on the user's current location. The search unit can also provide highly relevant search results by considering the geographical distribution of occupations and roles. Furthermore, the search unit can adjust the order of search results based on geographical distribution. This allows for the provision of more appropriate search results by considering the geographical distribution of occupations and roles. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of occupations and roles into a generating AI and have the generating AI perform the search.

[0045] The search unit can improve the accuracy of its searches by referring to related literature on occupations and roles during the search process. For example, the search unit provides detailed information on occupations and roles based on related literature. The search unit can also improve the accuracy of search results by referring to related literature. Furthermore, the search unit can adjust the order of search results based on related literature. This allows for improved search accuracy by referring to related literature on occupations and roles. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data into a generating AI and have the generating AI perform the search.

[0046] The customization unit can analyze the user's past learning behavior during customization to select the optimal customization method. For example, the customization unit can prioritize suggesting learning methods (videos, text, etc.) that the user has used in the past. The customization unit can also suggest an optimal learning pace based on the user's past learning history. Furthermore, the customization unit can select the optimal customization method based on the user's past learning achievements. In this way, by analyzing the user's past learning behavior, the optimal customization method can be selected, promoting efficient learning. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past learning behavior data into a generating AI and have the generating AI select the optimal customization method.

[0047] The customization unit can customize the means of customization based on the user's current lifestyle during the customization process. For example, if the user is busy, the customization unit can suggest a method that allows for learning in a short amount of time. If the user has ample time, the customization unit can also suggest a detailed learning plan. Furthermore, the customization unit can suggest the optimal learning time to match the user's lifestyle. This allows for the provision of a more appropriate learning plan by adjusting the means of customization based on the user's current lifestyle. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's lifestyle data into a generating AI and have the generating AI perform the adjustment of the means of customization.

[0048] The customization unit can select the optimal customization method by considering the user's geographical location information during the customization process. For example, the customization unit can suggest nearby learning resources based on the user's current location. It can also suggest region-specific learning resources based on the user's geographical location information. Furthermore, the customization unit can suggest optimal learning resources by considering the user's travel history. This allows for the selection of the optimal customization method by considering the user's geographical location information, thereby promoting efficient learning. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal customization method.

[0049] The customization unit can analyze the user's social media activity during the customization process and propose customization methods. For example, the customization unit can analyze the user's interests and preferences on social media and propose relevant learning resources. It can also propose learning resources that the user's followers and friends are interested in. Furthermore, the customization unit can propose optimal learning resources based on the user's social media activity history. In this way, relevant learning resources can be proposed by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of customization methods.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can provide trend information on relevant occupations and roles based on user input. For example, if a user enters "data scientist," the reception desk will display current job market trends and required skills for data scientists. Similarly, if a user enters "project manager," the reception desk can provide the latest industry trends and required skill sets for project managers. Furthermore, if a user enters "UI / UX designer," the reception desk can provide information on the latest trends and popular tools in UI / UX design. This allows users to obtain up-to-date information on their desired occupations and roles, enabling them to create more concrete learning plans.

[0052] The reception desk can analyze a user's past career selection history and re-suggest careers and roles that the user was interested in but did not choose in the past. For example, if a user was interested in "data analyst" in the past but did not choose it, the reception desk will suggest "data analyst" again. Similarly, if a user was interested in "product manager" but did not choose it, the reception desk can suggest "product manager" again. Furthermore, if a user was interested in "graphic designer" but did not choose it, the reception desk can suggest "graphic designer" again. This gives users an opportunity to reconsider careers and roles they were interested in in the past, allowing them to make more appropriate choices.

[0053] The reception desk can suggest relevant online communities and forums based on the user's current skill level and areas of interest when they enter their occupation or role. For example, if a user is interested in "data science," the reception desk will suggest online communities related to data science. If a user is interested in "project management," it can suggest forums related to project management. Furthermore, if a user is interested in "UI / UX design," it can suggest online communities related to UI / UX design. This allows users to interact with other professionals and people with similar interests and share information by participating in relevant communities and forums.

[0054] The reception desk can suggest region-specific career events and seminars based on the user's geographical location when they input their occupation and role. For example, if a user is interested in becoming a "data scientist," the reception desk will suggest data science seminars held nearby. If a user is interested in becoming a "project manager," it can suggest career events related to project management. Furthermore, if a user is interested in becoming a "UI / UX designer," it can suggest workshops on UI / UX design. This allows users to participate in region-specific career events and seminars, enabling them to learn about actual industry trends and build networks with experts.

[0055] The reception desk can analyze a user's social media activity when they input their occupation or role, and provide the latest news and articles related to that occupation or role. For example, if a user is interested in "data scientist," the reception desk will display the latest news and articles on data science. If a user is interested in "project manager," it can also provide the latest news and articles on project management. Furthermore, if a user is interested in "UI / UX designer," it can provide the latest news and articles on UI / UX design. This allows users to obtain up-to-date information on related occupations and roles and create more concrete learning plans.

[0056] The analysis unit can adjust the order in which analysis results are presented based on the importance of occupations and roles during the analysis process. For example, detailed analysis results are prioritized for occupations and roles of high importance. Conversely, concise analysis results can be displayed later for occupations and roles of low importance. Furthermore, the depth and scope of the analysis results can be adjusted according to their importance. By adjusting the order in which analysis results are presented based on the importance of occupations and roles, the system can prioritize providing users with more important information.

[0057] The analysis unit can apply different analysis algorithms depending on the occupational or role category during the analysis. For example, for technical positions, the analysis will focus on technical skill sets. For creative positions, the analysis can focus on creativity and ideas. Furthermore, for management positions, the analysis can focus on leadership and management abilities. By applying different analysis algorithms according to the occupational or role category, more appropriate analysis results can be provided.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives input from the user regarding their desired occupation or role. Users can enter occupations and roles such as engineer, designer, or manager into the input form. The reception desk can also accept input of occupations and roles using voice input, and can further assist with input by referring to the user's past occupation selection history. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit and extract appropriate keywords and skill sets. For example, the generation AI analyzes the user's input and extracts specialized terminology and necessary technical skills related to the occupation. It can also identify the skill sets required for the occupation and role based on the user's input and extract related keywords. Step 3: The search unit searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. For example, it can connect with learning platforms and book databases to find materials suitable for the required skills. It can also search for materials that meet the user's learning needs, such as online courses and workshops. Step 4: The customization section customizes the learning plan based on the learning materials found by the search section. For example, it can create a specific learning plan based on the user's schedule and goals, and adjust the learning plan according to the learning progress.

[0060] (Example of form 2) The SignpostAgent system according to an embodiment of the present invention is a system that supports users in creating learning plans for their desired occupations and roles. When a user finds an occupation or role they want to pursue, they need to proceed with learning towards that goal, but how to proceed with learning is often left to the individual's judgment. This choice is important as it can shape one's life, but it is sometimes underestimated, and as a result, many people end up wasting time on ineffective learning. The SignpostAgent system supports learning plans and provides a roadmap for the kind of life one should lead. For example, the SignpostAgent system takes the user's desired occupation or role as input. Next, the generating AI analyzes the user's input and extracts appropriate keywords and skill sets. The generating AI then works in conjunction with learning platforms and book databases to search for learning materials suitable for the necessary skills. Furthermore, it customizes the learning plan based on the user's schedule and goals. For example, if the user inputs "I want to work in marketing," the generating AI extracts marketing-related skill sets and searches for appropriate learning materials. Next, it creates a specific learning plan based on the user's schedule and goals. This learning plan serves as a roadmap for the user to efficiently proceed with learning and reach their desired occupation or role. The SignpostAgent system not only supports users in developing learning plans for their desired careers and roles, but also supports their motivation for growth and contributes to a society where self-realization is encouraged. For example, if a user aims to become a cybersecurity expert, the generative AI extracts cybersecurity-related skill sets and searches for appropriate learning materials. Next, it creates a specific learning plan based on the user's schedule and goals. This learning plan serves as a guide for the user to efficiently progress in their learning and succeed as a cybersecurity expert. The SignpostAgent system utilizes generative AI to support users in developing learning plans for their desired careers and roles. The generative AI analyzes the user's input and extracts appropriate keywords and skill sets. The generative AI also connects with learning platforms and book databases to search for learning materials suitable for the necessary skills.Furthermore, the learning plan is customized based on the user's schedule and goals. This allows users to learn efficiently and reach their desired occupation or role. In this way, the SignpostAgent system can efficiently support users in developing their learning plans toward their desired occupation or role.

[0061] The SignpostAgent system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a customization unit. The reception unit receives input of the user's desired occupation or role. The user's desired occupation or role includes, but is not limited to, examples such as engineer, designer, and manager. The reception unit accepts, for example, the user entering their occupation or role into an input form. The reception unit can also accept the user's occupation or role input using voice input. Furthermore, the reception unit can assist with input by referring to the user's past occupation selection history. For example, the reception unit suggests relevant occupations or roles based on the occupations or roles the user has previously selected. The analysis unit uses generative AI to analyze the information received by the reception unit and extract appropriate keywords and skill sets. For example, the analysis unit uses generative AI to analyze the user's input and extract specialized terminology and necessary technical skills related to the occupation. The analysis unit can also use generative AI to identify the skill sets required for the occupation or role based on the user's input. Furthermore, the analysis unit can use generative AI to analyze the user's input and extract relevant keywords. For example, the generation AI analyzes the user's input, such as "I want to work in marketing," and extracts marketing-related skill sets. The search unit searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. The search unit can, for example, link with learning platforms and book databases to find materials suitable for the required skills. The search unit can also search for materials that meet the user's learning needs, such as online courses and workshops. Furthermore, the search unit can search for materials to customize the learning plan based on the user's schedule and goals. For example, if the user inputs "I want to work in marketing," the search unit will search for marketing-related materials. The customization unit customizes the learning plan based on the materials found by the search unit. The customization unit creates a specific learning plan based on the user's schedule and goals, for example. The customization unit can also adjust the learning plan according to the user's learning progress.Furthermore, the customization section can also customize the learning plan to support the user's desire for growth. For example, if the user enters "I want to work in marketing," the customization section will create a learning plan related to marketing. In this way, the SignpostAgent system according to the embodiment can efficiently support the user's learning plan toward the occupation or role they aspire to.

[0062] The reception desk accepts input from users regarding their desired occupation or role. This includes, but is not limited to, examples such as engineer, designer, and manager. The reception desk accepts user input of their occupation or role through an input form. It can also accept user input using voice input. Specifically, it uses speech recognition technology to convert what the user says into text and incorporates it into the system as occupation or role information. Furthermore, the reception desk can assist with input by referring to the user's past occupation selection history. For example, it can store the user's past occupation or role selections in a database and refer to that history during current input to suggest relevant occupations or roles. This allows users to select new occupations or roles while referring to past choices. The reception desk also has a function to analyze user input in real time and automatically correct input errors or unclear information. For example, if a user enters "engineer," the system will suggest specific occupations such as "software engineer" or "network engineer," helping the user make a more specific selection. This allows the reception desk to efficiently and accurately accept user input of their occupation or role.

[0063] The analysis unit uses generative AI to analyze information received by the reception unit and extract appropriate keywords and skill sets. For example, the analysis unit uses generative AI to analyze user input and extract specialized terminology and necessary technical skills related to the occupation. Specifically, it uses natural language processing technology to grammatically and semantically analyze user input and identify important keywords related to the occupation and role. For example, if a user inputs "I want to work in marketing," the generative AI will extract related keywords such as "marketing," "digital marketing," "SEO," and "content marketing." The analysis unit can also use generative AI to identify the skill sets necessary for the occupation and role based on the user input. For example, it may identify "data analysis," "advertising management," and "social media management" as skills necessary for a marketing position. Furthermore, the analysis unit can use generative AI to analyze user input and extract related keywords. For example, the generative AI analyzes the information "I want to work in marketing" entered by the user and extracts marketing-related skill sets. This allows the analysis unit to analyze user input in detail and accurately extract the information necessary for the occupation and role. Furthermore, the analysis unit can perform more accurate analysis by referring to the user's past input and learning history. For example, it can identify the skill set relevant to the user's current career choice based on what the user has learned in the past and the qualifications they have obtained. This allows the analysis unit to perform analysis tailored to the user's individual circumstances and provide more appropriate information.

[0064] The search unit searches for appropriate learning materials based on keywords and skill sets extracted by the analysis unit. For example, the search unit can connect with learning platforms and book databases to find materials suitable for the required skills. Specifically, it uses the API of online learning platforms to search for relevant courses and materials and provide them to the user. The search unit can also search for materials that meet the user's learning needs, such as online courses and workshops. For example, if a user enters "I want to work in marketing," the search unit will search for and suggest online courses and workshops related to marketing. Furthermore, the search unit can search for materials to customize a learning plan based on the user's schedule and goals. For example, if a user enters how many hours they can study per week, the search unit will suggest a learning plan tailored to that schedule. The search unit can also monitor the user's learning progress in real time and add materials or adjust the learning plan as needed. This allows the search unit to provide optimal learning materials tailored to the user's learning needs and support efficient learning. Additionally, the search unit can collect user feedback and continuously improve its search algorithm. For example, by having users leave ratings and comments on the provided materials, the search unit can use that feedback to improve the accuracy of its search results. This allows the search function to consistently provide users with the most suitable learning materials, thereby improving the quality of learning.

[0065] The customization unit customizes the learning plan based on the learning materials found by the search unit. For example, the customization unit creates a specific learning plan based on the user's schedule and goals. Specifically, it plans the daily learning content and progress targets in detail, taking into account the learning goals set by the user and the time available. The customization unit can also adjust the learning plan according to the user's learning progress. For example, if the user is ahead of schedule, it will suggest additional materials to move on to the next step, or conversely, if progress is behind, it will readjust the learning content to provide a manageable plan. Furthermore, the customization unit can customize the learning plan to support the user's motivation to grow. For example, if the user shows a strong interest in a particular skill, it will provide additional materials and assignments related to that skill to increase motivation. The customization unit can also collect user feedback and continuously improve the accuracy and effectiveness of the learning plan. For example, if the user leaves ratings and comments on the learning plan, the customization unit will revise the plan based on that feedback and provide a more effective learning plan. In this way, the customization unit can provide an optimal learning plan tailored to the user's individual needs and circumstances, supporting efficient learning. Furthermore, the customization function can also provide a step-by-step learning plan that takes into account the user's long-term career goals. For example, if a user enters "I want to work in marketing," the customization function will provide a plan to progress through the learning process, from basic to advanced marketing-related skills. This allows the customization function to support effective learning towards the user's career goals and promote their growth.

[0066] The reception desk can estimate the user's emotions and adjust the timing of inputting occupation and role based on the estimated emotions. For example, if the user is stressed, the reception desk can prompt the user to input their occupation and role at a time when they can relax. It can also prompt the user to input their occupation and role when they are focused. Furthermore, if the user is tired, the reception desk can prompt them to input their occupation and role after a break. By adjusting the timing of occupation and role input according to the user's emotions, input can be prompted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0067] The reception desk can analyze the user's past occupation selection history and select the optimal input method. For example, the reception desk can suggest related occupations and roles based on the occupations and roles the user has selected in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past selection history. In this way, by analyzing the user's past occupation selection history, the optimal input method can be selected, promoting efficient input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past occupation selection history data into a generating AI and have the generating AI select the optimal input method.

[0068] The reception desk can filter the input of occupations and roles based on the user's current skill level and areas of interest. For example, the reception desk can suggest appropriate occupations and roles based on the user's current skill level. The reception desk can also prioritize displaying relevant occupations and roles based on the user's areas of interest. Furthermore, the reception desk can combine the user's skill level and areas of interest to suggest the most suitable occupations and roles. This allows for the suggestion of appropriate occupations and roles by filtering based on the user's current skill level and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's skill level data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0069] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of occupations and roles to be entered. For example, if the user is excited, the reception desk may prioritize suggesting challenging occupations and roles. Similarly, if the user is relaxed, it may prioritize suggesting stable occupations and roles. Furthermore, if the user is feeling anxious, it may prioritize suggesting occupations and roles with ample support. This allows for the suggestion of more appropriate occupations and roles by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0070] The reception desk can prioritize the input of highly relevant occupations and roles by considering the user's geographical location when the user enters their occupation and role. For example, the reception desk can suggest nearby occupations and roles based on the user's current location. It can also suggest region-specific occupations and roles based on the user's geographical location. Furthermore, the reception desk can suggest highly relevant occupations and roles by considering the user's travel history. In this way, by considering the user's geographical location, it is possible to prioritize the suggestion of highly relevant occupations and roles. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant occupations and roles.

[0071] The reception desk can analyze the user's social media activity when they input their occupation and role, and input relevant occupations and roles. For example, the reception desk can analyze the user's interests on social media and suggest relevant occupations and roles. It can also suggest occupations and roles that the user's followers and friends are interested in. Furthermore, the reception desk can suggest the most suitable occupation and role based on the user's social media activity history. In this way, relevant occupations and roles can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant occupations and roles.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, 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 presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of occupations and roles during the analysis. For example, the analysis unit performs a detailed analysis for occupations and roles with high importance. It can also perform a concise analysis for occupations and roles with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to importance. By adjusting the level of detail of the analysis based on the importance of occupations and roles, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input occupation and role importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the occupation or role category during analysis. For example, for technical occupations, the analysis unit will focus on technical skill sets. For creative occupations, the analysis unit can focus on creativity and ideas. Furthermore, for management occupations, the analysis unit can focus on leadership and management abilities. By applying different analysis algorithms according to the occupation or role category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input occupation and role category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing 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 length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the submission timing of occupations and roles during the analysis process. For example, the analysis unit can prioritize the analysis of occupations and roles with approaching submission deadlines. It can also postpone the analysis of occupations and roles with later submission deadlines. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission timing. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the submission timing of occupations and roles. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input occupation and role submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of occupations and roles during the analysis process. For example, the analysis unit can prioritize the analysis of occupations and roles with high relevance. It can also postpone the analysis of occupations and roles with low relevance. Furthermore, the analysis unit can dynamically adjust the order of analysis according to relevance. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of occupations and roles. 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 occupation and role relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0078] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is relaxed, the search unit can provide a wide range of search results. If the user is in a hurry, the search unit can also provide concise search results. Furthermore, if the user is excited, the search unit can provide visually appealing search results. In this way, by adjusting the search criteria according to the user's emotions, more appropriate search results can be provided. 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. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust the search criteria.

[0079] The search unit can improve search accuracy by considering the interrelationships between occupations and roles during the search process. For example, the search unit can group related occupations and roles and provide search results. Furthermore, the search unit can prioritize displaying highly relevant search results based on the interrelationships between occupations and roles. In addition, the search unit can adjust the order of search results by considering the interrelationships between occupations and roles. This allows for improved search accuracy by considering the interrelationships between occupations and roles. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input data on the interrelationships between occupations and roles into a generating AI and have the generating AI perform the search accuracy improvement.

[0080] The search unit can perform searches while considering the attribute information of the submitter of occupation and role. For example, the search unit can provide appropriate search results based on the submitter's skill level. The search unit can also prioritize displaying relevant search results based on the submitter's areas of interest. Furthermore, the search unit can provide optimal search results by considering the submitter's attribute information. This allows for the provision of more appropriate search results by considering the attribute information of the submitter of occupation and role. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input submitter attribute information data into a generating AI and have the generating AI perform the search.

[0081] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is relaxed, the search unit can provide a wide range of search results. If the user is in a hurry, the search unit can prioritize displaying concise search results. Furthermore, if the user is excited, the search unit can prioritize displaying visually appealing search results. By adjusting the display order of search results according to the user's emotions, more appropriate search results can be provided. 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. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust the display order of search results.

[0082] The search unit can perform searches while considering the geographical distribution of occupations and roles. For example, the search unit can prioritize displaying nearby occupations and roles based on the user's current location. The search unit can also provide highly relevant search results by considering the geographical distribution of occupations and roles. Furthermore, the search unit can adjust the order of search results based on geographical distribution. This allows for the provision of more appropriate search results by considering the geographical distribution of occupations and roles. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of occupations and roles into a generating AI and have the generating AI perform the search.

[0083] The search unit can improve the accuracy of its searches by referring to related literature on occupations and roles during the search process. For example, the search unit provides detailed information on occupations and roles based on related literature. The search unit can also improve the accuracy of search results by referring to related literature. Furthermore, the search unit can adjust the order of search results based on related literature. This allows for improved search accuracy by referring to related literature on occupations and roles. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data into a generating AI and have the generating AI perform the search.

[0084] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated emotions. For example, if the user is relaxed, the customization unit can provide detailed customization options. If the user is in a hurry, it can also provide concise customization options. Furthermore, if the user is excited, it can provide visually appealing customization options. This allows for more appropriate customization by adjusting the customization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI adjust the customization method.

[0085] The customization unit can analyze the user's past learning behavior during customization to select the optimal customization method. For example, the customization unit can prioritize suggesting learning methods (videos, text, etc.) that the user has used in the past. The customization unit can also suggest an optimal learning pace based on the user's past learning history. Furthermore, the customization unit can select the optimal customization method based on the user's past learning achievements. In this way, by analyzing the user's past learning behavior, the optimal customization method can be selected, promoting efficient learning. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past learning behavior data into a generating AI and have the generating AI select the optimal customization method.

[0086] The customization unit can customize the means of customization based on the user's current lifestyle during the customization process. For example, if the user is busy, the customization unit can suggest a method that allows for learning in a short amount of time. If the user has ample time, the customization unit can also suggest a detailed learning plan. Furthermore, the customization unit can suggest the optimal learning time to match the user's lifestyle. This allows for the provision of a more appropriate learning plan by adjusting the means of customization based on the user's current lifestyle. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's lifestyle data into a generating AI and have the generating AI perform the adjustment of the means of customization.

[0087] The customization unit can estimate the user's emotions and determine customization priorities based on those emotions. For example, if the user is relaxed, the customization unit may prioritize detailed customization options. If the user is in a hurry, it may prioritize concise customization options. Furthermore, if the user is excited, it may prioritize visually appealing customization options. This allows for more appropriate customization by prioritizing customization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI determine the customization priorities.

[0088] The customization unit can select the optimal customization method by considering the user's geographical location information during the customization process. For example, the customization unit can suggest nearby learning resources based on the user's current location. It can also suggest region-specific learning resources based on the user's geographical location information. Furthermore, the customization unit can suggest optimal learning resources by considering the user's travel history. This allows for the selection of the optimal customization method by considering the user's geographical location information, thereby promoting efficient learning. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal customization method.

[0089] The customization unit can analyze the user's social media activity during the customization process and propose customization methods. For example, the customization unit can analyze the user's interests and preferences on social media and propose relevant learning resources. It can also propose learning resources that the user's followers and friends are interested in. Furthermore, the customization unit can propose optimal learning resources based on the user's social media activity history. In this way, relevant learning resources can be proposed by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of customization methods.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The reception desk can provide trend information on relevant occupations and roles based on user input. For example, if a user enters "data scientist," the reception desk will display current job market trends and required skills for data scientists. Similarly, if a user enters "project manager," the reception desk can provide the latest industry trends and required skill sets for project managers. Furthermore, if a user enters "UI / UX designer," the reception desk can provide information on the latest trends and popular tools in UI / UX design. This allows users to obtain up-to-date information on their desired occupations and roles, enabling them to create more concrete learning plans.

[0092] The reception desk can estimate the user's emotions and, based on those estimates, provide feedback on the input regarding occupation and role. For example, if the user is feeling anxious, the reception desk can display an encouraging message regarding the user's choices. If the user is excited, the reception desk can also provide positive feedback that affirms those emotions. Furthermore, if the user is undecided, the reception desk can provide advice to narrow down the options. This allows users to receive appropriate feedback tailored to their emotions, enabling them to choose occupations and roles with greater confidence.

[0093] The reception desk can analyze a user's past career selection history and re-suggest careers and roles that the user was interested in but did not choose in the past. For example, if a user was interested in "data analyst" in the past but did not choose it, the reception desk will suggest "data analyst" again. Similarly, if a user was interested in "product manager" but did not choose it, the reception desk can suggest "product manager" again. Furthermore, if a user was interested in "graphic designer" but did not choose it, the reception desk can suggest "graphic designer" again. This gives users an opportunity to reconsider careers and roles they were interested in in the past, allowing them to make more appropriate choices.

[0094] The reception desk can suggest relevant online communities and forums based on the user's current skill level and areas of interest when they enter their occupation or role. For example, if a user is interested in "data science," the reception desk will suggest online communities related to data science. If a user is interested in "project management," it can suggest forums related to project management. Furthermore, if a user is interested in "UI / UX design," it can suggest online communities related to UI / UX design. This allows users to interact with other professionals and people with similar interests and share information by participating in relevant communities and forums.

[0095] The reception desk can estimate the user's emotions and, based on that estimation, provide advice regarding the input content for occupation and role. For example, if the user is feeling stressed, the reception desk can provide advice on how to relax. If the user is concentrating, the reception desk can also provide advice on how to make the most of that concentration. Furthermore, if the user is tired, the reception desk can advise them to take a break. This allows users to receive appropriate advice tailored to their emotions, enabling them to choose occupations and roles more effectively.

[0096] The reception desk can suggest region-specific career events and seminars based on the user's geographical location when they input their occupation and role. For example, if a user is interested in becoming a "data scientist," the reception desk will suggest data science seminars held nearby. If a user is interested in becoming a "project manager," it can suggest career events related to project management. Furthermore, if a user is interested in becoming a "UI / UX designer," it can suggest workshops on UI / UX design. This allows users to participate in region-specific career events and seminars, enabling them to learn about actual industry trends and build networks with experts.

[0097] The reception desk can analyze a user's social media activity when they input their occupation or role, and provide the latest news and articles related to that occupation or role. For example, if a user is interested in "data scientist," the reception desk will display the latest news and articles on data science. If a user is interested in "project manager," it can also provide the latest news and articles on project management. Furthermore, if a user is interested in "UI / UX designer," it can provide the latest news and articles on UI / UX design. This allows users to obtain up-to-date information on related occupations and roles and create more concrete learning plans.

[0098] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation method of the analysis results according to the user's emotions, more appropriate analysis results can be provided.

[0099] The analysis unit can adjust the order in which analysis results are presented based on the importance of occupations and roles during the analysis process. For example, detailed analysis results are prioritized for occupations and roles of high importance. Conversely, concise analysis results can be displayed later for occupations and roles of low importance. Furthermore, the depth and scope of the analysis results can be adjusted according to their importance. By adjusting the order in which analysis results are presented based on the importance of occupations and roles, the system can prioritize providing users with more important information.

[0100] The analysis unit can apply different analysis algorithms depending on the occupational or role category during the analysis. For example, for technical positions, the analysis will focus on technical skill sets. For creative positions, the analysis can focus on creativity and ideas. Furthermore, for management positions, the analysis can focus on leadership and management abilities. By applying different analysis algorithms according to the occupational or role category, more appropriate analysis results can be provided.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives input from the user regarding their desired occupation or role. Users can enter occupations and roles such as engineer, designer, or manager into the input form. The reception desk can also accept input of occupations and roles using voice input, and can further assist with input by referring to the user's past occupation selection history. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit and extract appropriate keywords and skill sets. For example, the generation AI analyzes the user's input and extracts specialized terminology and necessary technical skills related to the occupation. It can also identify the skill sets required for the occupation and role based on the user's input and extract related keywords. Step 3: The search unit searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. For example, it can connect with learning platforms and book databases to find materials suitable for the required skills. It can also search for materials that meet the user's learning needs, such as online courses and workshops. Step 4: The customization section customizes the learning plan based on the learning materials found by the search section. For example, it can create a specific learning plan based on the user's schedule and goals, and adjust the learning plan according to the learning progress.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of the user's desired occupation or role. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's input using generating AI to extract appropriate keywords and skill sets. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. The customization unit is implemented by the control unit 46A of the smart device 14 and customizes the learning plan based on the learning materials retrieved by the search unit. The reception unit can, for example, estimate the user's emotions and adjust the timing of inputting occupations and roles based on the estimated user emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of the user's desired occupation or role. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's input using generating AI to extract appropriate keywords and skill sets. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. The customization unit is implemented by the control unit 46A of the smart glasses 214 and customizes the learning plan based on the learning materials retrieved by the search unit. The reception unit can, for example, estimate the user's emotions and adjust the timing of occupation and role input based on the estimated user emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input of the user's desired occupation or role. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's input using generating AI to extract appropriate keywords and skill sets. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. The customization unit is implemented by the control unit 46A of the headset terminal 314 and customizes the learning plan based on the learning materials retrieved by the search unit. The reception unit can, for example, estimate the user's emotions and adjust the timing of occupation and role input based on the estimated user emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and customization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input of the user's desired occupation or role. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's input using generating AI to extract appropriate keywords and skill sets. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for appropriate learning materials based on the keywords and skill sets extracted by the analysis unit. The customization unit is implemented by the control unit 46A of the robot 414 and customizes the learning plan based on the learning materials retrieved by the search unit. The reception unit can, for example, estimate the user's emotions and adjust the timing of inputting occupations and roles based on the estimated user emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A reception desk that accepts input from users regarding their desired occupation and role, An analysis unit analyzes the information received by the reception unit and extracts appropriate keywords and skill sets. A search unit searches for appropriate teaching materials based on the keywords and skill sets extracted by the aforementioned analysis unit, The system includes a customization unit that customizes the learning plan based on the learning materials retrieved by the search unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of occupation and role input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past career choice history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter their occupation or role, the system filters the results based on their current skill level and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of occupations and roles to input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter their occupation or role, the system prioritizes selecting the most relevant occupation or role based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter their occupation or role, the system analyzes their social media activity and inputs relevant occupations and roles. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of occupations and roles. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the occupation or role category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when occupation and role information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relationships between occupations and roles. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, consider the interrelationships between occupations and roles to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the system takes into account the attribute information of the person submitting the occupation and role. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, consider the geographical distribution of occupations and roles. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, When searching, refer to relevant literature related to occupation and role to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned customization unit is During customization, the system analyzes the user's past learning behavior to select the optimal customization method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned customization unit is During customization, the customization methods are tailored based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization unit is During customization, we analyze the user's social media activity and suggest customization options. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 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. A reception desk that accepts input from users regarding their desired occupation and role, An analysis unit analyzes the information received by the reception unit and extracts appropriate keywords and skill sets. A search unit searches for appropriate teaching materials based on the keywords and skill sets extracted by the aforementioned analysis unit, The system includes a customization unit that customizes the learning plan based on the learning materials retrieved by the search unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of occupation and role input based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past career choice history and select the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When users enter their occupation or role, the system filters the results based on their current skill level and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of occupations and roles to input based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter their occupation or role, the system prioritizes selecting the most relevant occupation or role based on their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter their occupation or role, the system analyzes their social media activity and inputs relevant occupations and roles. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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