system

Abstract

The system according to the embodiment aims to effectively support continuous skill improvement in a way that is suitable for each individual. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects information on interviews or daily lifestyle habits of users. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a support method for continuing skill development that is suited to the individual based on the analysis results obtained by the analysis unit. The support unit supports the user's continuing skill development based on the support method proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide effective methods for supporting individuals in continuing to improve their skills, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively support continuous skill improvement in a way that is suitable for each individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects information on interviews or daily lifestyle habits of users. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a support method for continuing skill development that is suited to the individual based on the analysis results obtained by the analysis unit. The support unit supports the user's continuing skill development based on the support method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively support continuous skill improvement in a way that is suitable for each individual. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A skill improvement support system according to an embodiment of the present invention collects information such as user interviews and daily lifestyle habits, analyzes it using AI, and provides personalized support for continued skill improvement. The skill improvement support system collects information such as user interviews and daily lifestyle habits, and then proposes a support method for continued skill improvement that is optimal for each individual. For example, the skill improvement support system collects detailed information such as the skills a user wants to acquire and their lifestyle habits. The skill improvement support system then analyzes the collected information using AI to analyze the user's behavioral patterns and learning style. For example, if a user has a morning habit, the system suggests studying in the morning. Furthermore, if a user prefers remote learning, the system suggests remote learning materials and services. Furthermore, the skill improvement support system supports the user's continued skill improvement based on the support method proposed by the AI. For example, the system periodically checks the user's progress and provides feedback to increase the user's motivation to continue learning. The system also provides appropriate learning materials and services to create an environment conducive to the user's continued learning. This allows the skill improvement support system to enhance persistence, which is the most important factor in acquiring skills and knowledge. This allows the skill improvement support system to efficiently support the user's continued skill improvement. For example, to motivate users to continue studying for exams, we regularly check their progress and provide feedback to support continued learning. We also provide appropriate learning materials and services to create an environment for users to reskill and support continued skill improvement.

[0029] A skill improvement support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a support unit. The collection unit collects information about a user's interviews or daily lifestyle habits. The information about the user's interviews or daily lifestyle habits includes, but is not limited to, the content of interview questions and details of the lifestyle habits. The collection unit collects specific information, for example, about the skills the user wants to acquire and the lifestyle habits the user has. The collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, information collection can be performed during a relaxed time. The analysis unit analyzes the information collected by the collection unit. The analysis is performed based on, for example, a data analysis method and accuracy of the analysis, but is not limited to, the example. The analysis unit analyzes the user's behavioral patterns and learning style based on the collected information. For example, the analysis unit analyzes the frequency of the user's behavior and learning method. The suggestion unit proposes a support method for continuing skill improvement that is suited to the individual based on the analysis results obtained by the analysis unit. The suggestion is performed based on, for example, a study plan and a method for maintaining motivation, but is not limited to, the example. If the user has a morning-type lifestyle, the suggestion unit suggests studying in the morning. Furthermore, if the user prefers remote learning, the suggestion unit can also suggest remote learning materials and services. The support unit supports the user's continued skill development based on the support method suggested by the suggestion unit. Support is provided, for example, based on the frequency of support and details of the support content, but is not limited to such examples. The support unit includes a confirmation unit that checks the user's progress. For example, the confirmation unit checks the progress based on the user's achievement level and a method for measuring progress. Furthermore, the support unit includes a feedback unit that provides feedback to the user. For example, the feedback unit provides feedback based on the format and frequency of the feedback. This allows the skill development support system according to the embodiment to efficiently support the user's continued skill development.

[0030] The support unit includes a confirmation unit that confirms the user's progress. The confirmation unit confirms the user's progress. The progress includes, for example, a degree of achievement and a method for measuring progress, but is not limited to these examples. The confirmation unit, for example, periodically checks the user's progress to confirm the user's degree of achievement. The confirmation unit can also use a method for measuring progress to measure the user's progress. For example, the confirmation unit can also use a method for measuring progress to measure the user's progress. This makes it possible to provide appropriate support by checking the user's progress. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input the user's progress into AI and have the AI ​​check the progress.

[0031] The support unit includes a feedback unit that provides feedback to the user. The feedback unit provides the user. The feedback includes, for example, but is not limited to, the form and frequency of the feedback. For example, the feedback unit periodically provides the feedback to the user. The feedback unit can also use a different form of feedback to provide the feedback to the user. For example, the feedback unit can also use a different form of feedback to provide the feedback to the user. This makes it possible to support continued learning by providing feedback to the user. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback to AI and cause the AI ​​to provide the feedback.

[0032] The collection unit can collect specific information about what skills the user wants to acquire and what lifestyle habits the user has. Specific information includes, for example, the type of skill and details of the lifestyle habits, but is not limited to these examples. The collection unit, for example, collects information about what skills the user wants to acquire. The collection unit can also collect information about what lifestyle habits the user has. For example, the collection unit can collect information about what lifestyle habits the user has. By collecting detailed information about the user, more appropriate support can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user information into AI and have the AI ​​collect the information.

[0033] The analysis unit can analyze the user's behavioral patterns and learning style based on the collected information. Examples of behavioral patterns include, but are not limited to, the frequency and type of behavior. Examples of learning styles include, but are not limited to, study methods and study times. The analysis unit, for example, analyzes the user's behavioral patterns based on the collected information. The analysis unit can also analyze the user's learning style based on the collected information. For example, the analysis unit can analyze the user's learning style based on the collected information. By analyzing the user's behavioral patterns and learning style, it is possible to propose an optimal support method for each individual. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​analyze the behavioral patterns and learning style.

[0034] The suggestion unit can suggest that the user study in the morning if the user has a morning-type lifestyle. Morning-type lifestyle habits include, but are not limited to, wake-up time and morning activities, for example. For example, the suggestion unit can suggest that the user study in the morning if the user has a morning-type lifestyle. The suggestion unit can also suggest that the user study in the morning if the user has a morning-type lifestyle. For example, the suggestion unit can suggest that the user study in the morning if the user has a morning-type lifestyle. This makes it possible to make study suggestions tailored to the user's lifestyle. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's lifestyle habits into AI and have the AI ​​execute the study suggestions.

[0035] The suggestion unit can suggest remote learning materials and services if the user prefers remote learning. Remote learning includes, but is not limited to, online learning materials and remote learning tools. For example, the suggestion unit can suggest remote learning materials and services if the user prefers remote learning. The suggestion unit can also suggest remote learning materials and services if the user prefers remote learning. For example, the suggestion unit can suggest remote learning materials and services if the user prefers remote learning. This makes it possible to suggest materials and services that match the user's learning style. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's learning style into AI and have the AI ​​suggest materials and services.

[0036] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the collection unit prioritizes an interview format that the user has previously preferred. The collection unit can also collect information during a time period in which the user has previously shown a high response rate. For example, the collection unit can prioritize devices (smartphones, PCs, etc.) that the user has previously used when collecting information. This enables optimal information collection based on the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past behavioral history into a generation AI and have the generation AI select the optimal information collection method.

[0037] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting information related to a project the user is currently working on. The collection unit can also collect information on topics that interest the user. For example, the collection unit can collect information at an appropriate time in accordance with the user's daily rhythm. This makes it possible to collect information based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI and have the generation AI perform filtering of the information collection.

[0038] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can conduct a voice interview. Also, if the user prefers text input, the collection unit can provide a text-based questionnaire. For example, if the user prefers images, the collection unit can adopt a question format using images. This enables optimal information collection depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, collects learning resources related to the user's current location. The collection unit can also collect information about nearby learning events based on the user's geographical location. For example, the collection unit can also collect information corresponding to region-specific learning needs based on the user's location information. This makes it possible to collect highly relevant information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0040] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information on accounts the user follows on social media. The collection unit can also analyze the content of the user's posts and collect related learning resources. For example, the collection unit can collect information on topics of interest from the user's social media activities. This makes it possible to collect related information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activities into a generation AI and cause the generation AI to collect related information.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also prioritize preferred collection methods based on the user's past feedback. For example, the collection unit can adjust the content of the information to be collected by reflecting the user's feedback. This makes it possible to customize the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the collected information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a specific algorithm to information regarding learning styles. The analysis unit can also apply a different algorithm to information regarding behavioral patterns. For example, the analysis unit can apply yet another algorithm to information regarding lifestyle habits. This makes it possible to apply the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of information to the generation AI and cause the generation AI to apply different analysis algorithms.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis based on the user's past analysis results. The analysis unit can also improve accuracy by utilizing knowledge gained from past analysis results. For example, the analysis unit can compare the user's past analysis results and select the optimal analysis method. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, the analysis unit prioritizes the latest information in analysis. The analysis unit can also postpone information that was submitted earlier. For example, the analysis unit can adjust the analysis schedule according to the time of submission. This makes it possible to determine the priority of analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time of information submission to the generation AI and have the generation AI determine the priority of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. For example, the analysis unit can determine the order of analysis according to the relevance. This makes it possible to adjust the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the information to the generation AI and have the generation AI adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with high levels of expertise. The analysis unit can also provide analysis results in simpler language to a user with low levels of expertise. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the skill-up method. For example, the suggestion unit makes a detailed proposal for a skill-up method with a high importance. The suggestion unit can also make a concise proposal for a skill-up method with a low importance. For example, the suggestion unit can determine the priority of the proposal according to the importance. This makes it possible to adjust the level of detail of the proposal according to the importance of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the skill-up method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0049] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the skill-up method. For example, the suggestion unit applies a specific algorithm to suggestions related to learning styles. The suggestion unit can also apply a different algorithm to suggestions related to behavioral patterns. For example, the suggestion unit can apply yet another algorithm to suggestions related to lifestyle habits. This makes it possible to apply an optimal suggestion algorithm depending on the category of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the category of the skill-up method to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0050] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the current proposal based on the user's past proposal results. The suggestion unit can also improve accuracy by utilizing knowledge gained from the past proposal results. For example, the suggestion unit can compare the user's past proposal results and select the optimal suggestion method. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0051] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission date of the skill-up method. For example, the suggestion unit prioritizes the most recent skill-up method. The suggestion unit can also postpone a skill-up method that has been submitted earlier. For example, the suggestion unit can adjust the proposal schedule according to the submission date. This makes it possible to determine the priority of the proposal based on the submission date of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the submission date of the skill-up method to the generation AI and have the generation AI determine the priority of the proposal.

[0052] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the skill-up methods. For example, the proposal unit prioritizes the proposal of skill-up methods with high relevance. The proposal unit can also postpone the proposal of skill-up methods with low relevance. For example, the proposal unit can determine the order of proposals according to the relevance. This makes it possible to adjust the order of proposals based on the relevance of the skill-up methods. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the relevance of the skill-up methods to the generation AI and cause the generation AI to adjust the order of proposals.

[0053] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit may make a proposal that uses a lot of technical terminology for a user with high level of expertise. The suggestion unit may also make a proposal in simpler language for a user with low level of expertise. For example, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] When providing support, the support unit can analyze the user's past learning behavior and select the optimal support method. For example, the support unit prioritizes learning methods that the user has previously preferred. The support unit can also prioritize learning methods that the user has previously achieved good results with. For example, the support unit can select the optimal support method based on the user's past learning behavior. This makes it possible to select the optimal support method based on the user's past learning behavior. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's past learning behavior into a generation AI and have the generation AI select the optimal support method.

[0055] The support unit can customize the means of support based on the user's current living situation when providing support. For example, if the user is busy, the support unit can provide effective support in a short amount of time. The support unit can also provide detailed support when the user is relaxed. For example, the support unit can provide support at an appropriate time in accordance with the user's daily rhythm. This makes it possible to customize the means of support based on the user's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's current living situation into the generation AI and have the generation AI customize the means of support.

[0056] The support unit can improve the support method by reflecting the user's feedback when providing support. For example, the support unit improves the support method based on feedback provided by the user in the past. The support unit can also prioritize preferred support methods based on the user's past feedback. For example, the support unit can adjust the content of support by reflecting the user's feedback. This makes it possible to improve the support method based on the user's past feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's past feedback into a generation AI and have the generation AI improve the support method.

[0057] When providing support, the support unit can select the optimal support method by taking into account the user's geographical location information. For example, the support unit provides learning resources related to the user's current location. The support unit can also provide information about nearby learning events based on the user's geographical location. For example, the support unit can provide support that meets local learning needs based on the user's location information. This makes it possible to select the optimal support method based on the user's geographical location information. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal support method.

[0058] When providing support, the support unit can analyze the user's social media activity and suggest means of support. For example, the support unit provides support based on information about accounts the user follows on social media. The support unit can also analyze the content posted by the user and provide related learning resources. For example, the support unit can provide support related to topics of interest to the user based on the user's social media activity. This makes it possible to suggest means of support based on the user's social media activity. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's social media activity into a generation AI and have the generation AI suggest means of support.

[0059] When providing support, the support unit can customize the support method by reflecting the user's past feedback. For example, the support unit improves the support method based on feedback provided by the user in the past. The support unit can also prioritize preferred support methods based on the user's past feedback. For example, the support unit can adjust the content of support by reflecting the user's feedback. This makes it possible to customize the support method based on the user's past feedback. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's past feedback into a generation AI and have the generation AI customize the support method.

[0060] When checking progress, the confirmation unit can select the optimal confirmation method by referring to the user's past learning history. For example, the confirmation unit prioritizes a confirmation method that the user has used favorably in the past. The confirmation unit can also prioritize a confirmation method in which the user has achieved good results in the past. For example, the confirmation unit can select the optimal confirmation method based on the user's past learning history. This makes it possible to optimally check progress based on the user's past learning history. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the user's past learning history into the generation AI and have the generation AI select the optimal confirmation method.

[0061] The confirmation unit can customize the confirmation means based on the user's current learning situation when checking progress. For example, if the user is busy, the confirmation unit can perform a short but effective check. The confirmation unit can also perform a detailed check when the user is relaxed. For example, the confirmation unit can perform the check at an appropriate time in accordance with the user's daily rhythm. This makes it possible to customize the confirmation means based on the user's current learning situation. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the user's current learning situation into the generation AI and have the generation AI customize the confirmation means.

[0062] The confirmation unit can select the optimal confirmation method by taking into account the user's geographical location information when checking progress. For example, the confirmation unit provides learning resources related to the user's current location. The confirmation unit can also provide nearby learning event information based on the user's geographical location. For example, the confirmation unit can provide progress confirmation that meets local learning needs based on the user's location information. This enables optimal progress confirmation based on the user's geographical location information. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal confirmation method.

[0063] When checking progress, the confirmation unit can analyze the user's social media activity and suggest a means of confirmation. For example, the confirmation unit checks progress based on information about accounts the user follows on social media. The confirmation unit can also analyze the content of the user's posts and provide related learning resources. For example, the confirmation unit can check progress on topics of interest based on the user's social media activity. This makes it possible to suggest a means of confirmation based on the user's social media activity. Some or all of the above-mentioned processing in the confirmation unit may be performed using, or without, AI, for example. For example, the confirmation unit can input the user's social media activity into a generation AI and have the generation AI execute a suggestion of a means of confirmation.

[0064] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past learning results. For example, the feedback unit prioritizes a feedback method that the user has used favorably in the past. The feedback unit can also prioritize a feedback method that has produced good results for the user in the past. For example, the feedback unit can provide optimal feedback based on the user's past learning results. This enables optimal feedback based on the user's past learning results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past learning results into the generation AI and cause the generation AI to provide optimal feedback.

[0065] The feedback unit can customize the feedback means based on the user's current learning situation when providing feedback. For example, when the user is busy, the feedback unit can provide effective feedback in a short amount of time. The feedback unit can also provide detailed feedback when the user is relaxed. For example, the feedback unit can provide feedback at an appropriate timing in accordance with the user's daily rhythm. This makes it possible to customize the feedback means based on the user's current learning situation. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's current learning situation into the generation AI and cause the generation AI to customize the feedback means.

[0066] The feedback unit can select the optimal feedback method by taking into account the user's geographical location information when providing feedback. For example, the feedback unit can provide learning resources related to the user's current location. The feedback unit can also provide nearby learning event information based on the user's geographical location. For example, the feedback unit can provide feedback that meets local learning needs based on the user's location information. This makes it possible to select the optimal feedback method based on the user's geographical location information. Some or all of the above-described processing in the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal feedback method.

[0067] When providing feedback, the feedback unit can analyze the user's social media activity and suggest a means of feedback. For example, the feedback unit provides feedback based on information about accounts the user follows on social media. The feedback unit can also analyze the content of the user's posts and provide related learning resources. For example, the feedback unit can provide feedback on topics of interest based on the user's social media activity. This makes it possible to suggest a means of feedback based on the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the user's social media activity into a generation AI and cause the generation AI to suggest a means of feedback.

[0068] The feedback unit can customize the feedback method by reflecting the user's past feedback when providing feedback. For example, the feedback unit can improve the feedback method based on feedback provided by the user in the past. The feedback unit can also prioritize preferred feedback means based on the user's past feedback. For example, the feedback unit can reflect the user's feedback and adjust the content of the feedback. This makes it possible to customize the feedback method based on the user's past feedback. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the user's past feedback into a generation AI and cause the generation AI to customize the feedback method.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the skill-up method. For example, a detailed proposal can be made for a skill-up method with high importance. Also, a concise proposal can be made for a skill-up method with low importance. For example, the suggestion unit can determine the priority of the proposal based on the importance. This makes it possible to adjust the level of detail of the proposal based on the importance of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the skill-up method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0071] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the skill-up method. For example, a specific algorithm can be applied to suggestions related to learning styles. The suggestion unit can also apply a different algorithm to suggestions related to behavioral patterns. For example, the suggestion unit can apply yet another algorithm to suggestions related to lifestyle habits. This makes it possible to apply the optimal suggestion algorithm depending on the category of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the category of the skill-up method to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0072] When providing support, the support unit can analyze the user's past learning behavior and select the optimal support method. For example, the support unit can prioritize learning methods that the user has used favorably in the past. The support unit can also prioritize learning methods that the user has used with high success in the past. For example, the support unit can select the optimal support method based on the user's past learning behavior. This makes it possible to select the optimal support method based on the user's past learning behavior. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can input the user's past learning behavior into the generation AI and have the generation AI select the optimal support method.

[0073] The support unit can customize the means of support based on the user's current living situation when providing support. For example, if the user is busy, it can provide effective support in a short amount of time. The support unit can also provide detailed support when the user is relaxed. For example, the support unit can provide support at an appropriate time in accordance with the user's daily rhythm. This makes it possible to customize the means of support based on the user's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's current living situation into the generation AI and have the generation AI customize the means of support.

[0074] When checking progress, the confirmation unit can select the optimal confirmation method by referring to the user's past learning history. For example, the confirmation unit can prioritize a confirmation method that the user has used favorably in the past. The confirmation unit can also prioritize a confirmation method that the user has used to achieve good results in the past. For example, the confirmation unit can select the optimal confirmation method based on the user's past learning history. This makes it possible to optimally check progress based on the user's past learning history. Some or all of the above-mentioned processing in the confirmation unit can be performed using, for example, AI, or can be performed without using AI. For example, the confirmation unit can input the user's past learning history into the generation AI and have the generation AI select the optimal confirmation method.

[0075] The feedback unit can customize the feedback method by reflecting the user's past feedback when providing feedback. For example, the feedback unit can improve the feedback method based on feedback provided by the user in the past. The feedback unit can also prioritize preferred feedback means based on the user's past feedback. For example, the feedback unit can also reflect the user's feedback and adjust the content of the feedback. This makes it possible to customize the feedback method based on the user's past feedback. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's past feedback into a generation AI and have the generation AI customize the feedback method.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The collection unit collects information about the user's interviews or daily lifestyle habits. The collected information includes the content of interview questions and details of lifestyle habits. The collection unit collects specific information about what skills the user wants to acquire and what lifestyle habits the user has. The collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling stressed, information collection will be performed during times when the user is relaxed. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed based on the data analysis method and accuracy of the analysis. The analysis unit analyzes the user's behavioral patterns and learning style based on the collected information. For example, it analyzes the frequency of the user's behavior and learning methods. Step 3: The suggestion unit proposes a method of supporting continued skill development that is suited to the individual based on the analysis results obtained by the analysis unit. Suggestions are made based on the user's study plan and methods for maintaining motivation. For example, if the user has a morning habit, it can suggest studying in the morning. Also, if the user prefers remote learning, it can suggest remote learning materials and services. Step 4: The support unit supports the user in continuing to improve their skills based on the support methods proposed by the proposal unit. The support is provided based on the frequency of support and the details of the support content. The support unit includes a confirmation unit that checks the user's progress, and checks the progress based on the user's achievement level and progress measurement method. The support unit also includes a feedback unit that provides feedback to the user, and provides the feedback based on the feedback format and frequency.

[0078] (Example 2) A skill improvement support system according to an embodiment of the present invention collects information such as user interviews and daily lifestyle habits, analyzes it using AI, and provides personalized support for continued skill improvement. The skill improvement support system collects information such as user interviews and daily lifestyle habits, and then proposes a support method for continued skill improvement that is optimal for each individual. For example, the skill improvement support system collects detailed information such as the skills a user wants to acquire and their lifestyle habits. The skill improvement support system then analyzes the collected information using AI to analyze the user's behavioral patterns and learning style. For example, if a user has a morning habit, the system suggests studying in the morning. Furthermore, if a user prefers remote learning, the system suggests remote learning materials and services. Furthermore, the skill improvement support system supports the user's continued skill improvement based on the support method proposed by the AI. For example, the system periodically checks the user's progress and provides feedback to increase the user's motivation to continue learning. The system also provides appropriate learning materials and services to create an environment conducive to the user's continued learning. This allows the skill improvement support system to enhance persistence, which is the most important factor in acquiring skills and knowledge. This allows the skill improvement support system to efficiently support the user's continued skill improvement. For example, to motivate users to continue studying for exams, we regularly check their progress and provide feedback to support continued learning. We also provide appropriate learning materials and services to create an environment for users to reskill and support continued skill improvement.

[0079] A skill improvement support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a support unit. The collection unit collects information about a user's interviews or daily lifestyle habits. The information about the user's interviews or daily lifestyle habits includes, but is not limited to, the content of interview questions and details of the lifestyle habits. The collection unit collects specific information, for example, about the skills the user wants to acquire and the lifestyle habits the user has. The collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, information collection can be performed during a relaxed time. The analysis unit analyzes the information collected by the collection unit. The analysis is performed based on, for example, a data analysis method and accuracy of the analysis, but is not limited to, the example. The analysis unit analyzes the user's behavioral patterns and learning style based on the collected information. For example, the analysis unit analyzes the frequency of the user's behavior and learning method. The suggestion unit proposes a support method for continuing skill improvement that is suited to the individual based on the analysis results obtained by the analysis unit. The suggestion is performed based on, for example, a study plan and a method for maintaining motivation, but is not limited to, the example. If the user has a morning-type lifestyle, the suggestion unit suggests studying in the morning. Furthermore, if the user prefers remote learning, the suggestion unit can also suggest remote learning materials and services. The support unit supports the user's continued skill development based on the support method suggested by the suggestion unit. Support is provided, for example, based on the frequency of support and details of the support content, but is not limited to such examples. The support unit includes a confirmation unit that checks the user's progress. For example, the confirmation unit checks the progress based on the user's achievement level and a method for measuring progress. Furthermore, the support unit includes a feedback unit that provides feedback to the user. For example, the feedback unit provides feedback based on the format and frequency of the feedback. This allows the skill development support system according to the embodiment to efficiently support the user's continued skill development.

[0080] The support unit includes a confirmation unit that confirms the user's progress. The confirmation unit confirms the user's progress. The progress includes, for example, a degree of achievement and a method for measuring progress, but is not limited to these examples. The confirmation unit, for example, periodically checks the user's progress to confirm the user's degree of achievement. The confirmation unit can also use a method for measuring progress to measure the user's progress. For example, the confirmation unit can also use a method for measuring progress to measure the user's progress. This makes it possible to provide appropriate support by checking the user's progress. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input the user's progress into AI and have the AI ​​check the progress.

[0081] The support unit includes a feedback unit that provides feedback to the user. The feedback unit provides the user. The feedback includes, for example, but is not limited to, the form and frequency of the feedback. For example, the feedback unit periodically provides the feedback to the user. The feedback unit can also use a different form of feedback to provide the feedback to the user. For example, the feedback unit can also use a different form of feedback to provide the feedback to the user. This makes it possible to support continued learning by providing feedback to the user. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback to AI and cause the AI ​​to provide the feedback.

[0082] The collection unit can collect specific information about what skills the user wants to acquire and what lifestyle habits the user has. Specific information includes, for example, the type of skill and details of the lifestyle habits, but is not limited to these examples. The collection unit, for example, collects information about what skills the user wants to acquire. The collection unit can also collect information about what lifestyle habits the user has. For example, the collection unit can collect information about what lifestyle habits the user has. By collecting detailed information about the user, more appropriate support can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user information into AI and have the AI ​​collect the information.

[0083] The analysis unit can analyze the user's behavioral patterns and learning style based on the collected information. Examples of behavioral patterns include, but are not limited to, the frequency and type of behavior. Examples of learning styles include, but are not limited to, study methods and study times. The analysis unit, for example, analyzes the user's behavioral patterns based on the collected information. The analysis unit can also analyze the user's learning style based on the collected information. For example, the analysis unit can analyze the user's learning style based on the collected information. By analyzing the user's behavioral patterns and learning style, it is possible to propose an optimal support method for each individual. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​analyze the behavioral patterns and learning style.

[0084] The suggestion unit can suggest that the user study in the morning if the user has a morning-type lifestyle. Morning-type lifestyle habits include, but are not limited to, wake-up time and morning activities, for example. For example, the suggestion unit can suggest that the user study in the morning if the user has a morning-type lifestyle. The suggestion unit can also suggest that the user study in the morning if the user has a morning-type lifestyle. For example, the suggestion unit can suggest that the user study in the morning if the user has a morning-type lifestyle. This makes it possible to make study suggestions tailored to the user's lifestyle. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's lifestyle habits into AI and have the AI ​​execute the study suggestions.

[0085] The suggestion unit can suggest remote learning materials and services if the user prefers remote learning. Remote learning includes, but is not limited to, online learning materials and remote learning tools. For example, the suggestion unit can suggest remote learning materials and services if the user prefers remote learning. The suggestion unit can also suggest remote learning materials and services if the user prefers remote learning. For example, the suggestion unit can suggest remote learning materials and services if the user prefers remote learning. This makes it possible to suggest materials and services that match the user's learning style. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's learning style into AI and have the AI ​​suggest materials and services.

[0086] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can collect information during a relaxed time. Furthermore, if the user is concentrating, the collection unit can also conduct a detailed interview at that time. For example, if the user is tired, the collection unit can ask only simple questions and collect detailed information at a later date. This enables information collection at an appropriate time according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of information collection.

[0087] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the collection unit prioritizes an interview format that the user has previously preferred. The collection unit can also collect information during a time period in which the user has previously shown a high response rate. For example, the collection unit can prioritize devices (smartphones, PCs, etc.) that the user has previously used when collecting information. This enables optimal information collection based on the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past behavioral history into a generation AI and have the generation AI select the optimal information collection method.

[0088] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting information related to a project the user is currently working on. The collection unit can also collect information on topics that interest the user. For example, the collection unit can collect information at an appropriate time in accordance with the user's daily rhythm. This makes it possible to collect information based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation and areas of interest into the generation AI and have the generation AI perform filtering of the information collection.

[0089] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can conduct a voice interview. Also, if the user prefers text input, the collection unit can provide a text-based questionnaire. For example, if the user prefers images, the collection unit can adopt a question format using images. This enables optimal information collection depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0090] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information related to relaxation. Furthermore, if the user is excited, the collection unit can also prioritize collecting information related to topics of interest. For example, if the user is tired, the collection unit can prioritize collecting simple and important information. This enables efficient information collection by determining the priority of information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0091] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, collects learning resources related to the user's current location. The collection unit can also collect information about nearby learning events based on the user's geographical location. For example, the collection unit can also collect information corresponding to region-specific learning needs based on the user's location information. This makes it possible to collect highly relevant information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0092] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information on accounts the user follows on social media. The collection unit can also analyze the content of the user's posts and collect related learning resources. For example, the collection unit can collect information on topics of interest from the user's social media activities. This makes it possible to collect related information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activities into a generation AI and cause the generation AI to collect related information.

[0093] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also prioritize preferred collection methods based on the user's past feedback. For example, the collection unit can adjust the content of the information to be collected by reflecting the user's feedback. This makes it possible to customize the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and to-the-point analysis results when the user is stressed. For example, the analysis unit can provide visually appealing analysis results when the user is excited. This enables the presentation method of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0095] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the collected information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0096] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a specific algorithm to information regarding learning styles. The analysis unit can also apply a different algorithm to information regarding behavioral patterns. For example, the analysis unit can apply yet another algorithm to information regarding lifestyle habits. This makes it possible to apply the optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of information to the generation AI and cause the generation AI to apply different analysis algorithms.

[0097] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis based on the user's past analysis results. The analysis unit can also improve accuracy by utilizing knowledge gained from past analysis results. For example, the analysis unit can compare the user's past analysis results and select the optimal analysis method. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can perform a short and to-the-point analysis. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, if the user is excited, the analysis unit can perform a visually appealing analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0099] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, the analysis unit prioritizes the latest information in analysis. The analysis unit can also postpone information that was submitted earlier. For example, the analysis unit can adjust the analysis schedule according to the time of submission. This makes it possible to determine the priority of analysis based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time of information submission to the generation AI and have the generation AI determine the priority of analysis.

[0100] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. For example, the analysis unit can determine the order of analysis according to the relevance. This makes it possible to adjust the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the information to the generation AI and have the generation AI adjust the order of analysis.

[0101] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with high levels of expertise. The analysis unit can also provide analysis results in simpler language to a user with low levels of expertise. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0102] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, when the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. For example, when the user is excited, the suggestion unit can provide visually appealing suggestions. This enables the way suggestions are expressed to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0103] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the skill-up method. For example, the suggestion unit makes a detailed proposal for a skill-up method with a high importance. The suggestion unit can also make a concise proposal for a skill-up method with a low importance. For example, the suggestion unit can determine the priority of the proposal according to the importance. This makes it possible to adjust the level of detail of the proposal according to the importance of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the skill-up method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0104] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the skill-up method. For example, the suggestion unit applies a specific algorithm to suggestions related to learning styles. The suggestion unit can also apply a different algorithm to suggestions related to behavioral patterns. For example, the suggestion unit can apply yet another algorithm to suggestions related to lifestyle habits. This makes it possible to apply an optimal suggestion algorithm depending on the category of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the category of the skill-up method to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0105] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the current proposal based on the user's past proposal results. The suggestion unit can also improve accuracy by utilizing knowledge gained from the past proposal results. For example, the suggestion unit can compare the user's past proposal results and select the optimal suggestion method. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0106] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, if the user is excited, the suggestion unit can provide visually appealing suggestions. This enables the length of the suggestions to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0107] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission date of the skill-up method. For example, the suggestion unit prioritizes the most recent skill-up method. The suggestion unit can also postpone a skill-up method that has been submitted earlier. For example, the suggestion unit can adjust the proposal schedule according to the submission date. This makes it possible to determine the priority of the proposal based on the submission date of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the submission date of the skill-up method to the generation AI and have the generation AI determine the priority of the proposal.

[0108] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the skill-up methods. For example, the proposal unit prioritizes the proposal of skill-up methods with high relevance. The proposal unit can also postpone the proposal of skill-up methods with low relevance. For example, the proposal unit can determine the order of proposals according to the relevance. This makes it possible to adjust the order of proposals based on the relevance of the skill-up methods. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the relevance of the skill-up methods to the generation AI and cause the generation AI to adjust the order of proposals.

[0109] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit may make a proposal that uses a lot of technical terminology for a user with high level of expertise. The suggestion unit may also make a proposal in simpler language for a user with low level of expertise. For example, the suggestion unit may adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0110] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, the support unit can provide detailed support when the user is relaxed. The support unit can also provide concise and to-the-point support when the user is stressed. For example, the support unit can provide visually appealing support when the user is excited. This makes it possible to adjust the support method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI adjust the support method.

[0111] When providing support, the support unit can analyze the user's past learning behavior and select the optimal support method. For example, the support unit prioritizes learning methods that the user has previously preferred. The support unit can also prioritize learning methods that the user has previously achieved good results with. For example, the support unit can select the optimal support method based on the user's past learning behavior. This makes it possible to select the optimal support method based on the user's past learning behavior. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's past learning behavior into a generation AI and have the generation AI select the optimal support method.

[0112] The support unit can customize the means of support based on the user's current living situation when providing support. For example, if the user is busy, the support unit can provide effective support in a short amount of time. The support unit can also provide detailed support when the user is relaxed. For example, the support unit can provide support at an appropriate time in accordance with the user's daily rhythm. This makes it possible to customize the means of support based on the user's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's current living situation into the generation AI and have the generation AI customize the means of support.

[0113] The support unit can improve the support method by reflecting the user's feedback when providing support. For example, the support unit improves the support method based on feedback provided by the user in the past. The support unit can also prioritize preferred support methods based on the user's past feedback. For example, the support unit can adjust the content of support by reflecting the user's feedback. This makes it possible to improve the support method based on the user's past feedback. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's past feedback into a generation AI and have the generation AI improve the support method.

[0114] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, if the user is feeling stressed, the support unit can prioritize support related to relaxation. Furthermore, if the user is excited, the support unit can prioritize support related to topics of interest. For example, if the user is tired, the support unit can prioritize simple but important support. This enables the priority of support to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of support.

[0115] When providing support, the support unit can select the optimal support method by taking into account the user's geographical location information. For example, the support unit provides learning resources related to the user's current location. The support unit can also provide information about nearby learning events based on the user's geographical location. For example, the support unit can provide support that meets local learning needs based on the user's location information. This makes it possible to select the optimal support method based on the user's geographical location information. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal support method.

[0116] When providing support, the support unit can analyze the user's social media activity and suggest means of support. For example, the support unit provides support based on information about accounts the user follows on social media. The support unit can also analyze the content posted by the user and provide related learning resources. For example, the support unit can provide support related to topics of interest to the user based on the user's social media activity. This makes it possible to suggest means of support based on the user's social media activity. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's social media activity into a generation AI and have the generation AI suggest means of support.

[0117] When providing support, the support unit can customize the support method by reflecting the user's past feedback. For example, the support unit improves the support method based on feedback provided by the user in the past. The support unit can also prioritize preferred support methods based on the user's past feedback. For example, the support unit can adjust the content of support by reflecting the user's feedback. This makes it possible to customize the support method based on the user's past feedback. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's past feedback into a generation AI and have the generation AI customize the support method.

[0118] The confirmation unit can estimate the user's emotions and adjust the timing of progress checks based on the estimated user emotions. For example, when the user is relaxed, the confirmation unit can provide detailed progress checks. Furthermore, when the user is stressed, the confirmation unit can provide concise and to-the-point progress checks. For example, when the user is excited, the confirmation unit can provide visually appealing progress checks. This enables the timing of progress checks to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit can be performed using, for example, an AI, or without an AI. For example, the confirmation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of progress checks.

[0119] When checking progress, the confirmation unit can select the optimal confirmation method by referring to the user's past learning history. For example, the confirmation unit prioritizes a confirmation method that the user has used favorably in the past. The confirmation unit can also prioritize a confirmation method in which the user has achieved good results in the past. For example, the confirmation unit can select the optimal confirmation method based on the user's past learning history. This makes it possible to optimally check progress based on the user's past learning history. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the user's past learning history into the generation AI and have the generation AI select the optimal confirmation method.

[0120] The confirmation unit can customize the confirmation means based on the user's current learning situation when checking progress. For example, if the user is busy, the confirmation unit can perform a short but effective check. The confirmation unit can also perform a detailed check when the user is relaxed. For example, the confirmation unit can perform the check at an appropriate time in accordance with the user's daily rhythm. This makes it possible to customize the confirmation means based on the user's current learning situation. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the user's current learning situation into the generation AI and have the generation AI customize the confirmation means.

[0121] The confirmation unit can estimate the user's emotions and determine the priority of progress checks based on the estimated user emotions. For example, if the user is feeling stressed, the confirmation unit can prioritize progress checks related to relaxation. Furthermore, if the user is excited, the confirmation unit can also prioritize progress checks related to topics of interest. For example, if the user is tired, the confirmation unit can prioritize simple but important progress checks. This enables the priority of progress checks to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the confirmation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of progress checks.

[0122] The confirmation unit can select the optimal confirmation method by taking into account the user's geographical location information when checking progress. For example, the confirmation unit provides learning resources related to the user's current location. The confirmation unit can also provide nearby learning event information based on the user's geographical location. For example, the confirmation unit can provide progress confirmation that meets local learning needs based on the user's location information. This enables optimal progress confirmation based on the user's geographical location information. Some or all of the above-described processing in the confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the confirmation unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal confirmation method.

[0123] When checking progress, the confirmation unit can analyze the user's social media activity and suggest a means of confirmation. For example, the confirmation unit checks progress based on information about accounts the user follows on social media. The confirmation unit can also analyze the content of the user's posts and provide related learning resources. For example, the confirmation unit can check progress on topics of interest based on the user's social media activity. This makes it possible to suggest a means of confirmation based on the user's social media activity. Some or all of the above-mentioned processing in the confirmation unit may be performed using, or without, AI, for example. For example, the confirmation unit can input the user's social media activity into a generation AI and have the generation AI execute a suggestion of a means of confirmation.

[0124] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, the feedback unit can provide detailed feedback when the user is relaxed. The feedback unit can also provide concise and to-the-point feedback when the user is stressed. For example, the feedback unit can provide visually appealing feedback when the user is excited. This enables the feedback expression method to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback expression method.

[0125] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past learning results. For example, the feedback unit prioritizes a feedback method that the user has used favorably in the past. The feedback unit can also prioritize a feedback method that has produced good results for the user in the past. For example, the feedback unit can provide optimal feedback based on the user's past learning results. This enables optimal feedback based on the user's past learning results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past learning results into the generation AI and cause the generation AI to provide optimal feedback.

[0126] The feedback unit can customize the feedback means based on the user's current learning situation when providing feedback. For example, when the user is busy, the feedback unit can provide effective feedback in a short amount of time. The feedback unit can also provide detailed feedback when the user is relaxed. For example, the feedback unit can provide feedback at an appropriate timing in accordance with the user's daily rhythm. This makes it possible to customize the feedback means based on the user's current learning situation. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's current learning situation into the generation AI and cause the generation AI to customize the feedback means.

[0127] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can prioritize feedback related to relaxation. Furthermore, if the user is excited, the feedback unit can also prioritize feedback related to topics of interest. For example, if the user is tired, the feedback unit can prioritize simple but important feedback. This enables the priority of feedback to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of feedback.

[0128] The feedback unit can select the optimal feedback method by taking into account the user's geographical location information when providing feedback. For example, the feedback unit can provide learning resources related to the user's current location. The feedback unit can also provide nearby learning event information based on the user's geographical location. For example, the feedback unit can provide feedback that meets local learning needs based on the user's location information. This makes it possible to select the optimal feedback method based on the user's geographical location information. Some or all of the above-described processing in the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal feedback method.

[0129] When providing feedback, the feedback unit can analyze the user's social media activity and suggest a means of feedback. For example, the feedback unit provides feedback based on information about accounts the user follows on social media. The feedback unit can also analyze the content of the user's posts and provide related learning resources. For example, the feedback unit can provide feedback on topics of interest based on the user's social media activity. This makes it possible to suggest a means of feedback based on the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the user's social media activity into a generation AI and cause the generation AI to suggest a means of feedback.

[0130] The feedback unit can customize the feedback method by reflecting the user's past feedback when providing feedback. For example, the feedback unit can improve the feedback method based on feedback provided by the user in the past. The feedback unit can also prioritize preferred feedback means based on the user's past feedback. For example, the feedback unit can reflect the user's feedback and adjust the content of the feedback. This makes it possible to customize the feedback method based on the user's past feedback. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the user's past feedback into a generation AI and cause the generation AI to customize the feedback method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and support unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on the user's interviews and lifestyle habits using the camera 42 and microphone 38B of the smart device 14, and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a support method for continuing skill improvement that is suited to the individual based on the analysis results. The support unit is realized by the control unit 46A of the smart device 14 and supports the user in continuing skill improvement based on the suggested support method. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and support unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information on the user's interviews and lifestyle habits using the camera 42 and microphone 238 of the smart glasses 214, and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a support method for continuing skill improvement that is suited to the individual based on the analysis results. The support unit is realized by the control unit 46A of the smart glasses 214 and supports the user in continuing skill improvement based on the suggested support method. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and support unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information on the user's interviews and lifestyle habits using the camera 42 and microphone 238 of the headset-type terminal 314, and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a support method for continuing skill improvement that is suited to the individual based on the analysis results. The support unit is realized by the control unit 46A of the headset-type terminal 314 and supports the user in continuing skill improvement based on the suggested support method. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on the user's interviews and lifestyle habits using the camera 42 and microphone 238 of the robot 414, and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a support method for continuing skill improvement that is suited to the individual based on the analysis results. The support unit is realized by the control unit 46A of the robot 414 and supports the user in continuing skill improvement based on the suggested support method.

[0131] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0132] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, detailed suggestions can be provided. Also, if the user is stressed, concise and to-the-point suggestions can be provided. For example, if the user is excited, visually appealing suggestions can be provided. This enables the way suggestions are expressed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0133] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, if the user is relaxed, detailed support can be provided. Also, if the user is stressed, concise and to-the-point support can be provided. For example, if the user is excited, visually appealing support can be provided. This makes it possible to adjust the support method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the support unit can input the user's emotion data into the generation AI and have the generation AI adjust the support method.

[0134] The confirmation unit can estimate the user's emotions and adjust the timing of progress checks based on the estimated user emotions. For example, if the user is relaxed, a detailed progress check can be performed. Alternatively, if the user is stressed, a concise and to-the-point progress check can be performed. For example, if the user is excited, a visually appealing progress check can be performed. This enables the timing of progress checks to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the confirmation unit can be performed using, for example, an AI, or without an AI. For example, the confirmation unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of progress checks.

[0135] The feedback unit can estimate the user's emotions and adjust the way the feedback is expressed based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. Alternatively, if the user is stressed, concise and to-the-point feedback can be provided. For example, if the user is excited, visually appealing feedback can be provided. This enables the way the feedback is expressed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the feedback is expressed.

[0136] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the skill-up method. For example, a detailed proposal can be made for a skill-up method with high importance. Also, a concise proposal can be made for a skill-up method with low importance. For example, the suggestion unit can determine the priority of the proposal based on the importance. This makes it possible to adjust the level of detail of the proposal based on the importance of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the importance of the skill-up method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0137] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the skill-up method. For example, a specific algorithm can be applied to suggestions related to learning styles. The suggestion unit can also apply a different algorithm to suggestions related to behavioral patterns. For example, the suggestion unit can apply yet another algorithm to suggestions related to lifestyle habits. This makes it possible to apply the optimal suggestion algorithm depending on the category of the skill-up method. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the category of the skill-up method to the generation AI and cause the generation AI to apply different suggestion algorithms.

[0138] When providing support, the support unit can analyze the user's past learning behavior and select the optimal support method. For example, the support unit can prioritize learning methods that the user has used favorably in the past. The support unit can also prioritize learning methods that the user has used with high success in the past. For example, the support unit can select the optimal support method based on the user's past learning behavior. This makes it possible to select the optimal support method based on the user's past learning behavior. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can input the user's past learning behavior into the generation AI and have the generation AI select the optimal support method.

[0139] The support unit can customize the means of support based on the user's current living situation when providing support. For example, if the user is busy, it can provide effective support in a short amount of time. The support unit can also provide detailed support when the user is relaxed. For example, the support unit can provide support at an appropriate time in accordance with the user's daily rhythm. This makes it possible to customize the means of support based on the user's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's current living situation into the generation AI and have the generation AI customize the means of support.

[0140] When checking progress, the confirmation unit can select the optimal confirmation method by referring to the user's past learning history. For example, the confirmation unit can prioritize a confirmation method that the user has used favorably in the past. The confirmation unit can also prioritize a confirmation method that the user has used to achieve good results in the past. For example, the confirmation unit can select the optimal confirmation method based on the user's past learning history. This makes it possible to optimally check progress based on the user's past learning history. Some or all of the above-mentioned processing in the confirmation unit can be performed using, for example, AI, or can be performed without using AI. For example, the confirmation unit can input the user's past learning history into the generation AI and have the generation AI select the optimal confirmation method.

[0141] The feedback unit can customize the feedback method by reflecting the user's past feedback when providing feedback. For example, the feedback unit can improve the feedback method based on feedback provided by the user in the past. The feedback unit can also prioritize preferred feedback means based on the user's past feedback. For example, the feedback unit can also reflect the user's feedback and adjust the content of the feedback. This makes it possible to customize the feedback method based on the user's past feedback. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's past feedback into a generation AI and have the generation AI customize the feedback method.

[0142] The processing flow of the second embodiment will be briefly explained below.

[0143] Step 1: The collection unit collects information about the user's interviews or daily lifestyle habits. The collected information includes the content of interview questions and details of lifestyle habits. The collection unit collects specific information about what skills the user wants to acquire and what lifestyle habits the user has. The collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling stressed, information collection will be performed during times when the user is relaxed. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed based on the data analysis method and accuracy of the analysis. The analysis unit analyzes the user's behavioral patterns and learning style based on the collected information. For example, it analyzes the frequency of the user's behavior and learning methods. Step 3: The suggestion unit proposes a method of supporting continued skill development that is suited to the individual based on the analysis results obtained by the analysis unit. Suggestions are made based on the user's study plan and methods for maintaining motivation. For example, if the user has a morning habit, it can suggest studying in the morning. Also, if the user prefers remote learning, it can suggest remote learning materials and services. Step 4: The support unit supports the user in continuing to improve their skills based on the support methods proposed by the proposal unit. The support is provided based on the frequency of support and the details of the support content. The support unit includes a confirmation unit that checks the user's progress, and checks the progress based on the user's achievement level and progress measurement method. The support unit also includes a feedback unit that provides feedback to the user, and provides the feedback based on the feedback format and frequency.

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0148] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0149] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0165] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0171] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0181] 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.

[0182] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0183] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0184] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0185] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0186] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0187] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0188] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0189] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0190] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0191] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0192] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0193] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0195] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0196] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0197] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0198] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0199] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0200] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0201] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0202] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0203] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0204] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0205] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0206] 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.

[0207] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0208] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0209] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0210] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0211] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0212] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0213] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0214] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0215] [Explanation of symbols]

[0216] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting interview or daily life habit information of the user; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that proposes a support method for continuing skill improvement suitable for an individual based on the analysis result obtained by the analysis unit; a support unit that supports the user in continuing to improve their skills based on the support method proposed by the proposal unit. A system characterized by:

2. The support portion is Equipped with a confirmation section to check the user's progress 2. The system of claim 1.

3. The support portion is A feedback unit is provided to provide feedback to the user.

2. The system of claim 1.

4. The collecting unit Collect specific information about the skills the user wants to acquire and their lifestyle habits 2. The system of claim 1.

5. The analysis unit Analyze user behavior patterns and learning styles based on collected information 2. The system of claim 1.

6. The proposal unit If the user has a morning habit, suggest studying in the morning hours.

2. The system of claim 1.

7. The proposal unit If the user prefers remote learning, suggest remote learning materials and services 2. The system of claim 1.

8. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

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