system

Abstract

The system according to this embodiment aims to enable users to efficiently acquire information related to their hobbies and receive specific guidance. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an analysis unit. The reception unit receives user questions and concerns. The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos. The provision unit provides specific guidance based on the videos guided by the analysis unit. The analysis unit analyzes videos uploaded by users and points out areas for improvement and provides advice.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to efficiently obtain information related to hobbies and receive specific guidance.

[0005] The system according to the embodiment aims to enable a user to efficiently obtain information related to hobbies and receive specific guidance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an analysis unit. The reception unit receives user questions and concerns. The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos. The provision unit provides specific guidance based on the videos guided by the analysis unit. The analysis unit analyzes videos uploaded by users and points out areas for improvement and provides advice. [Effects of the Invention]

[0007] The system according to this embodiment allows users to efficiently acquire information related to their hobbies and receive specific guidance. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​platform for improving hobby-related problems, according to an embodiment of the present invention, is a system for resolving users' questions and concerns about their hobbies. This system allows users to input questions and concerns about hobbies they are interested in, and the AI ​​analyzes this information and guides them to relevant reference videos. Furthermore, the AI ​​digitizes professional techniques and experience to provide specific guidance. Additionally, when a user uploads a video of their work, the AI ​​analyzes the footage and points out areas for improvement and offers advice. This mechanism allows users to learn about their hobbies at their own pace and achieve satisfying results. It also aims to create a society where professional skills are not lost but passed on to future generations. As a result, the AI ​​platform for improving hobby-related problems can efficiently analyze users' questions and concerns about their hobbies and provide specific guidance and suggestions for improvement.

[0029] The AI ​​platform for improving hobby-related problems according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an analysis unit. The reception unit receives user questions and problems as input. User questions and problems include, but are not limited to, technical questions, life problems, and learning-related questions. The reception unit can, for example, analyze the questions and problems entered by the user using natural language processing technology. The reception unit can also classify the user's input using machine learning algorithms. The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos. The analysis unit can, for example, search for and guide users to relevant videos based on their questions and problems. The analysis unit can also suggest the most suitable videos considering the user's viewing history. The provision unit provides specific guidance based on the videos guided by the analysis unit. The provision unit can, for example, provide text-based guidance to the user based on the content of the videos. The provision unit can also provide specific technical guidance to the user using video tutorials. The analysis unit analyzes videos uploaded by the user and points out areas for improvement and provides advice. The analysis unit can, for example, analyze user-uploaded videos using image analysis technology and point out areas for technical improvement. The analysis unit can also evaluate user performance and suggest ways to improve it. As a result, the hobby-related problem-solving AI platform according to this embodiment can efficiently analyze user questions and concerns and provide specific guidance and suggestions for improvement.

[0030] The reception unit receives user questions and concerns. These questions and concerns may include, but are not limited to, technical questions, personal problems, or learning-related questions. The reception unit can analyze the user's input using natural language processing (NLP) technology. Specifically, it uses NLP to tokenize the user's input and perform grammatical and semantic analysis. This allows for an accurate understanding of the user's intent behind their questions and concerns. The reception unit can also classify the user's input using machine learning algorithms. For example, it can use classification algorithms such as support vector machines or random forests to categorize the user's questions and concerns. This enables the reception unit to respond appropriately to a wide range of user questions and concerns. Furthermore, the reception unit stores the user's input in a database, making it accessible to subsequent analysis and delivery units. This allows the reception unit to efficiently process user questions and concerns and improve the overall system performance.

[0031] The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos. For example, the analysis unit can search for and guide users to relevant videos based on their questions and concerns. Specifically, it extracts keywords related to the user's questions and concerns and searches for relevant videos in the video database. Keyword matching and content-based filtering technologies can be used for the search. The analysis unit can also suggest the most suitable videos by considering the user's viewing history. For example, it can use collaborative filtering technology to compare the viewing history of other users and recommend the most suitable videos. This allows the analysis unit to provide appropriate reference videos for users' questions and concerns, deepening their understanding. Furthermore, the analysis unit can analyze the content of the videos and suggest specific solutions to the user's questions and concerns. For example, it can analyze the video's subtitles and audio data, extract important points, and present them to the user. This allows the analysis unit to respond quickly and accurately to users' questions and concerns.

[0032] The service provider will provide specific guidance based on videos presented by the analysis department. For example, the service provider can provide text-based guidance to users based on the video content. Specifically, they can summarize the video content and explain important points and procedures to users in text. The service provider can also provide specific technical guidance to users using video tutorials. For example, they can demonstrate the techniques and methods shown in the video, providing guidance in a visually easy-to-understand manner. In this way, the service provider can provide specific and practical guidance to address users' questions and concerns, supporting their skill improvement. Furthermore, the service provider can collect user feedback and improve the content of the guidance. For example, they can collect feedback on how users felt about the guidance provided, which parts were difficult to understand, and incorporate this into future guidance. In this way, the service provider can provide more effective guidance to users and improve overall system satisfaction.

[0033] The analytics department analyzes user-uploaded videos and provides feedback and advice. For example, it can analyze user-uploaded videos using image analysis technology to identify areas for technical improvement. Specifically, it can use image recognition technology to analyze actions and procedures within the video and evaluate accuracy and efficiency. The analytics department can also evaluate user performance and suggest ways to improve it. For example, it can play the user's actions in slow motion and point out areas for improvement in subtle movements. Furthermore, the analytics department can compare user performance with other users and provide benchmarks. This allows the analytics department to support user skill improvement and provide specific areas for improvement. In addition, the analytics department can monitor user progress and provide regular feedback. For example, it can evaluate the progress a user has made within a certain period and suggest the next steps. This allows the analytics department to support the user's continuous growth and maximize the overall effectiveness of the system.

[0034] The reception desk can analyze the user's past question history and suggest the optimal input format. For example, the reception desk can automatically display as suggestions questions and concerns that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and concerns that the user will use at specific times of day based on the user's past question history. This streamlines the user's input process by suggesting the optimal input format based on past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past question history data into a generating AI and have the generating AI suggest the optimal input format.

[0035] The reception system can filter input based on the user's current interests and concerns when they enter questions or concerns. For example, the reception system can prioritize displaying relevant questions and concerns based on keywords the user has recently searched for. It can also suggest relevant questions and concerns based on topics in online communities the user participates in. Furthermore, the reception system can filter relevant questions and concerns based on the content of videos the user has recently watched. By filtering input based on the user's interests and concerns, it prioritizes displaying highly relevant questions and concerns. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's search history data into a generating AI and have the generating AI perform the filtering of relevant questions and concerns.

[0036] The reception desk can prioritize retrieving highly relevant questions by considering the user's geographical location when they input questions or concerns. For example, if the user is in a specific region, the reception desk will prioritize displaying questions and concerns related to that region. Furthermore, if the user is traveling, the reception desk can suggest questions and concerns related to their travel destination. Additionally, if the user is at home, the reception desk can filter questions and concerns based on information about their surroundings. This allows the reception desk to provide region-specific information by prioritizing the retrieval of highly relevant questions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI retrieve relevant questions and concerns.

[0037] The reception desk can analyze a user's social media activity when they input questions or concerns and retrieve relevant questions. For example, the reception desk can suggest relevant questions or concerns based on what the user has recently posted. It can also display relevant questions or concerns based on the topics of accounts the user follows. Furthermore, the reception desk can filter relevant questions or concerns based on the activities of groups the user participates in. In this way, by analyzing the user's social media activity, it suggests questions and concerns that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI retrieve relevant questions or concerns.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions and concerns during the analysis. For example, the analysis unit performs a detailed analysis for questions and concerns of high importance. It can also perform a concise analysis for questions and concerns of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the questions and concerns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of questions and concerns into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the question or problem during analysis. For example, the analysis unit can apply a specialized technical analysis algorithm to technical questions. It can also apply a specialized arts analysis algorithm to artistic problems. Furthermore, it can apply a specialized health analysis algorithm to health-related questions. By applying an analysis algorithm appropriate to the category of the question or problem, the analysis unit can provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question and problem category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0040] The analysis unit can determine the priority of analysis based on when questions and concerns were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions and concerns. It can also postpone the analysis of older questions and concerns. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission date. This ensures that the latest information is analyzed first by determining the analysis priority based on the submission date of questions and concerns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date data of questions and concerns into a generating AI and have the generating AI determine the analysis priority.

[0041] The analysis unit can adjust the order of analysis results based on the relevance of questions and concerns during analysis. For example, the analysis unit can prioritize the analysis of highly relevant questions and concerns. It can also postpone the analysis of less relevant questions and concerns. Furthermore, the analysis unit can dynamically adjust the order of analysis results according to their relevance. This allows the system to prioritize the provision of highly relevant information by adjusting the order of analysis results based on the relevance of questions and concerns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of questions and concerns into a generating AI and have the generating AI perform the adjustment of the order of analysis results.

[0042] The service provider can adjust the level of detail in the instruction based on the importance of the reference videos at the time of delivery. For example, the service provider can provide detailed instruction for high-importance reference videos. It can also provide concise instruction for low-importance reference videos. Furthermore, the service provider can determine the priority of instruction according to importance. This enables efficient instruction by adjusting the level of detail in the instruction based on the importance of the reference videos. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance data of the reference videos into a generating AI and have the generating AI perform the adjustment of the level of detail in the instruction.

[0043] The service provider can apply different instruction algorithms depending on the category of the reference video at the time of delivery. For example, the service provider can apply a specialized technical instruction algorithm to technical reference videos. It can also apply a specialized arts instruction algorithm to artistic reference videos. Furthermore, it can apply a specialized health instruction algorithm to health-related reference videos. This allows for highly accurate instruction by applying instruction algorithms appropriate to the category of the reference video. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category data of the reference video into a generating AI and have the generating AI execute the application of the instruction algorithm.

[0044] The provisioning department can determine the priority of instruction based on the submission date of the reference videos at the time of provision. For example, the provisioning department will prioritize instruction on recently submitted reference videos. It can also postpone instruction on older reference videos. Furthermore, the provisioning department can dynamically adjust the instructional priority according to the submission date. This ensures that the latest information is prioritized by determining the instructional priority based on the submission date of the reference videos. Some or all of the above processing in the provisioning department may be performed using AI, for example, or not using AI. For example, the provisioning department can input reference video submission date data into a generating AI and have the generating AI perform the determination of instructional priority.

[0045] The service provider can adjust the order of instruction based on the relevance of the reference videos at the time of delivery. For example, the service provider will prioritize instruction on highly relevant reference videos. The service provider can also postpone instruction on less relevant reference videos. Furthermore, the service provider can dynamically adjust the order of instruction according to relevance. This allows the service provider to prioritize the provision of highly relevant information by adjusting the order of instruction based on the relevance of the reference videos. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance data of the reference videos into a generating AI and have the generating AI perform the adjustment of the order of instruction.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the uploaded videos. For example, the analysis unit can perform a detailed analysis on high-importance videos, and a concise analysis on low-importance videos. Furthermore, the analysis unit can determine the priority of the analysis according to importance. This enables efficient analysis by adjusting the level of detail based on the importance of the uploaded videos. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the importance data of the uploaded videos into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the uploaded video during analysis. For example, the analysis unit can apply a specialized technical analysis algorithm to technical videos. It can also apply a specialized artistic analysis algorithm to artistic videos. Furthermore, it can apply a specialized health analysis algorithm to health-related videos. By applying an analysis algorithm appropriate to the category of the uploaded video, the analysis unit can provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the uploaded video into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the submission date of uploaded videos. For example, the analysis unit may prioritize the analysis of recently submitted videos. It can also postpone the analysis of older videos. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission date. This ensures that the most up-to-date information is analyzed first by determining the analysis priority based on the submission date of uploaded videos. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the submission date data of uploaded videos into a generating AI and have the generating AI determine the analysis priority.

[0049] The analysis unit can adjust the order of analysis results based on the relevance of the uploaded videos during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant videos. It can also postpone the analysis of less relevant videos. Furthermore, the analysis unit can dynamically adjust the order of analysis results according to relevance. This allows for the priority provision of highly relevant information by adjusting the order of analysis results based on the relevance of the uploaded videos. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the uploaded videos into a generating AI and have the generating AI perform the adjustment of the order of analysis results.

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

[0051] The reception desk can analyze a user's past activity history related to their hobbies and suggest the most suitable way to input questions and concerns. For example, if a user has frequently entered questions about a particular hobby in the past, it will automatically display templates related to that hobby. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and concerns that a user might use at a specific time of day based on their past activity history. This streamlines the user's input process by suggesting the most suitable input method based on their past activity history.

[0052] The analytics unit can collect trend information related to users' hobbies and reflect it in the analysis results. For example, it can prioritize suggesting relevant videos and information based on recent trends. The analytics unit can also provide analysis results that incorporate the latest technologies and methods related to users' hobbies. Furthermore, the analytics unit can analyze the activity status of communities related to users' hobbies and provide relevant information. In this way, by providing analysis results that reflect trend information, it can provide users with the latest information.

[0053] The service provider can include functions to support users in setting goals related to their hobbies and to evaluate their progress. For example, it can provide specific guidance based on the goals set by the user. The service provider can also periodically evaluate the user's progress and provide feedback according to their level of achievement. Furthermore, the service provider can provide step-by-step guidance for users to achieve their goals. This helps maintain user motivation and supports effective learning through goal setting and progress evaluation.

[0054] The analytics department can assess users' skill levels related to their hobbies and provide appropriate advice. For example, it can analyze videos uploaded by users and point out areas for improvement based on their skill level. The analytics department can also suggest reference videos of appropriate difficulty based on the user's skill level. Furthermore, the analytics department can track the user's skill level improvement and provide feedback based on their progress. This supports user growth by providing advice tailored to their skill level.

[0055] The reception desk can prioritize retrieving highly relevant questions by considering the user's geographical location. For example, if a user is in a specific region, it will prioritize displaying questions and concerns related to that region. Furthermore, if a user is traveling, the reception desk can suggest questions and concerns related to their travel destination. Additionally, if a user is at home, the reception desk can filter questions and concerns based on information about their surroundings. This allows the system to provide region-specific information by prioritizing highly relevant questions based on the user's geographical location.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk inputs the user's questions and concerns. These include technical questions, life problems, and learning-related questions. The reception desk can analyze and classify the user's input using natural language processing technology and machine learning algorithms. Step 2: The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos. The analysis unit searches for and guides users to relevant videos based on their questions and concerns. It can also suggest the most suitable videos by considering the user's viewing history. Step 3: The service provider provides specific guidance based on the video provided by the analysis department. The service provider can also provide text-based guidance based on the video content. They can also provide specific technical guidance using video tutorials. Step 4: The analysis team analyzes the user's uploaded videos and points out areas for improvement and provides advice. The analysis team uses image analysis technology to analyze the user's videos and point out areas for technical improvement. They can also evaluate the user's performance and suggest ways to improve it.

[0058] (Example of form 2) The AI ​​platform for improving hobby-related problems, according to an embodiment of the present invention, is a system for resolving users' questions and concerns about their hobbies. This system allows users to input questions and concerns about hobbies they are interested in, and the AI ​​analyzes this information and guides them to relevant reference videos. Furthermore, the AI ​​digitizes professional techniques and experience to provide specific guidance. Additionally, when a user uploads a video of their work, the AI ​​analyzes the footage and points out areas for improvement and offers advice. This mechanism allows users to learn about their hobbies at their own pace and achieve satisfying results. It also aims to create a society where professional skills are not lost but passed on to future generations. As a result, the AI ​​platform for improving hobby-related problems can efficiently analyze users' questions and concerns about their hobbies and provide specific guidance and suggestions for improvement.

[0059] The AI ​​platform for improving hobby-related problems according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and an analysis unit. The reception unit receives user questions and problems as input. User questions and problems include, but are not limited to, technical questions, life problems, and learning-related questions. The reception unit can, for example, analyze the questions and problems entered by the user using natural language processing technology. The reception unit can also classify the user's input using machine learning algorithms. The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos. The analysis unit can, for example, search for and guide users to relevant videos based on their questions and problems. The analysis unit can also suggest the most suitable videos considering the user's viewing history. The provision unit provides specific guidance based on the videos guided by the analysis unit. The provision unit can, for example, provide text-based guidance to the user based on the content of the videos. The provision unit can also provide specific technical guidance to the user using video tutorials. The analysis unit analyzes videos uploaded by the user and points out areas for improvement and provides advice. The analysis unit can, for example, analyze user-uploaded videos using image analysis technology and point out areas for technical improvement. The analysis unit can also evaluate user performance and suggest ways to improve it. As a result, the hobby-related problem-solving AI platform according to this embodiment can efficiently analyze user questions and concerns and provide specific guidance and suggestions for improvement.

[0060] The reception unit receives user questions and concerns. These questions and concerns may include, but are not limited to, technical questions, personal problems, or learning-related questions. The reception unit can analyze the user's input using natural language processing (NLP) technology. Specifically, it uses NLP to tokenize the user's input and perform grammatical and semantic analysis. This allows for an accurate understanding of the user's intent behind their questions and concerns. The reception unit can also classify the user's input using machine learning algorithms. For example, it can use classification algorithms such as support vector machines or random forests to categorize the user's questions and concerns. This enables the reception unit to respond appropriately to a wide range of user questions and concerns. Furthermore, the reception unit stores the user's input in a database, making it accessible to subsequent analysis and delivery units. This allows the reception unit to efficiently process user questions and concerns and improve the overall system performance.

[0061] The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos. For example, the analysis unit can search for and guide users to relevant videos based on their questions and concerns. Specifically, it extracts keywords related to the user's questions and concerns and searches for relevant videos in the video database. Keyword matching and content-based filtering technologies can be used for the search. The analysis unit can also suggest the most suitable videos by considering the user's viewing history. For example, it can use collaborative filtering technology to compare the viewing history of other users and recommend the most suitable videos. This allows the analysis unit to provide appropriate reference videos for users' questions and concerns, deepening their understanding. Furthermore, the analysis unit can analyze the content of the videos and suggest specific solutions to the user's questions and concerns. For example, it can analyze the video's subtitles and audio data, extract important points, and present them to the user. This allows the analysis unit to respond quickly and accurately to users' questions and concerns.

[0062] The service provider will provide specific guidance based on videos presented by the analysis department. For example, the service provider can provide text-based guidance to users based on the video content. Specifically, they can summarize the video content and explain important points and procedures to users in text. The service provider can also provide specific technical guidance to users using video tutorials. For example, they can demonstrate the techniques and methods shown in the video, providing guidance in a visually easy-to-understand manner. In this way, the service provider can provide specific and practical guidance to address users' questions and concerns, supporting their skill improvement. Furthermore, the service provider can collect user feedback and improve the content of the guidance. For example, they can collect feedback on how users felt about the guidance provided, which parts were difficult to understand, and incorporate this into future guidance. In this way, the service provider can provide more effective guidance to users and improve overall system satisfaction.

[0063] The analytics department analyzes user-uploaded videos and provides feedback and advice. For example, it can analyze user-uploaded videos using image analysis technology to identify areas for technical improvement. Specifically, it can use image recognition technology to analyze actions and procedures within the video and evaluate accuracy and efficiency. The analytics department can also evaluate user performance and suggest ways to improve it. For example, it can play the user's actions in slow motion and point out areas for improvement in subtle movements. Furthermore, the analytics department can compare user performance with other users and provide benchmarks. This allows the analytics department to support user skill improvement and provide specific areas for improvement. In addition, the analytics department can monitor user progress and provide regular feedback. For example, it can evaluate the progress a user has made within a certain period and suggest the next steps. This allows the analytics department to support the user's continuous growth and maximize the overall effectiveness of the system.

[0064] The reception desk can estimate the user's emotions and adjust the input method for questions and concerns based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of questions and concerns. This reduces user stress and improves input efficiency by providing an input method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The reception desk can analyze the user's past question history and suggest the optimal input format. For example, the reception desk can automatically display as suggestions questions and concerns that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and concerns that the user will use at specific times of day based on the user's past question history. This streamlines the user's input process by suggesting the optimal input format based on past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past question history data into a generating AI and have the generating AI suggest the optimal input format.

[0066] The reception system can filter input based on the user's current interests and concerns when they enter questions or concerns. For example, the reception system can prioritize displaying relevant questions and concerns based on keywords the user has recently searched for. It can also suggest relevant questions and concerns based on topics in online communities the user participates in. Furthermore, the reception system can filter relevant questions and concerns based on the content of videos the user has recently watched. By filtering input based on the user's interests and concerns, it prioritizes displaying highly relevant questions and concerns. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's search history data into a generating AI and have the generating AI perform the filtering of relevant questions and concerns.

[0067] The reception desk can estimate the user's emotions and prioritize the questions and concerns entered based on the estimated emotions. For example, if the user is feeling anxious, the reception desk will prioritize those questions and concerns. Conversely, if the user is agitated, the reception desk can postpone those questions and concerns. Furthermore, if the user is relaxed, the reception desk can process them with normal priority. This allows for the rapid resolution of important issues by prioritizing questions and concerns according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0068] The reception desk can prioritize retrieving highly relevant questions by considering the user's geographical location when they input questions or concerns. For example, if the user is in a specific region, the reception desk will prioritize displaying questions and concerns related to that region. Furthermore, if the user is traveling, the reception desk can suggest questions and concerns related to their travel destination. Additionally, if the user is at home, the reception desk can filter questions and concerns based on information about their surroundings. This allows the reception desk to provide region-specific information by prioritizing the retrieval of highly relevant questions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI retrieve relevant questions and concerns.

[0069] The reception desk can analyze a user's social media activity when they input questions or concerns and retrieve relevant questions. For example, the reception desk can suggest relevant questions or concerns based on what the user has recently posted. It can also display relevant questions or concerns based on the topics of accounts the user follows. Furthermore, the reception desk can filter relevant questions or concerns based on the activities of groups the user participates in. In this way, by analyzing the user's social media activity, it suggests questions and concerns that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI retrieve relevant questions or concerns.

[0070] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is feeling anxious, the analysis unit can provide analysis results in a way that provides reassurance. This deepens the user's understanding by providing analysis results in a way that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions and concerns during the analysis. For example, the analysis unit performs a detailed analysis for questions and concerns of high importance. It can also perform a concise analysis for questions and concerns of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the questions and concerns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of questions and concerns into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of the question or problem during analysis. For example, the analysis unit can apply a specialized technical analysis algorithm to technical questions. It can also apply a specialized arts analysis algorithm to artistic problems. Furthermore, it can apply a specialized health analysis algorithm to health-related questions. By applying an analysis algorithm appropriate to the category of the question or problem, the analysis unit can provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question and problem category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis results based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. It can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is feeling anxious, the analysis unit can provide a reassuring analysis result. By providing analysis results of a length appropriate to the user's emotions, the system provides information that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can determine the priority of analysis based on when questions and concerns were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions and concerns. It can also postpone the analysis of older questions and concerns. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission date. This ensures that the latest information is analyzed first by determining the analysis priority based on the submission date of questions and concerns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date data of questions and concerns into a generating AI and have the generating AI determine the analysis priority.

[0075] The analysis unit can adjust the order of analysis results based on the relevance of questions and concerns during analysis. For example, the analysis unit can prioritize the analysis of highly relevant questions and concerns. It can also postpone the analysis of less relevant questions and concerns. Furthermore, the analysis unit can dynamically adjust the order of analysis results according to their relevance. This allows the system to prioritize the provision of highly relevant information by adjusting the order of analysis results based on the relevance of questions and concerns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of questions and concerns into a generating AI and have the generating AI perform the adjustment of the order of analysis results.

[0076] The service provider can estimate the user's emotions and adjust the way the instruction is presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed instruction. If the user is in a hurry, the service provider can also provide concise instruction that gets straight to the point. Furthermore, if the user is feeling anxious, the service provider can provide instruction in a way that provides reassurance. In this way, by providing instruction in a way that is appropriate to the user's emotions, the service provider can deepen the user's understanding. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0077] The service provider can adjust the level of detail in the instruction based on the importance of the reference videos at the time of delivery. For example, the service provider can provide detailed instruction for high-importance reference videos. It can also provide concise instruction for low-importance reference videos. Furthermore, the service provider can determine the priority of instruction according to importance. This enables efficient instruction by adjusting the level of detail in the instruction based on the importance of the reference videos. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance data of the reference videos into a generating AI and have the generating AI perform the adjustment of the level of detail in the instruction.

[0078] The service provider can apply different instruction algorithms depending on the category of the reference video at the time of delivery. For example, the service provider can apply a specialized technical instruction algorithm to technical reference videos. It can also apply a specialized arts instruction algorithm to artistic reference videos. Furthermore, it can apply a specialized health instruction algorithm to health-related reference videos. This allows for highly accurate instruction by applying instruction algorithms appropriate to the category of the reference video. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category data of the reference video into a generating AI and have the generating AI execute the application of the instruction algorithm.

[0079] The service provider can estimate the user's emotions and adjust the length of the instruction based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise instruction. If the user is relaxed, the service provider can also provide detailed instruction. Furthermore, if the user is feeling anxious, the service provider can provide reassuring instruction. By providing instruction of a length that matches the user's emotions, the service provider can provide information that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0080] The provisioning department can determine the priority of instruction based on the submission date of the reference videos at the time of provision. For example, the provisioning department will prioritize instruction on recently submitted reference videos. It can also postpone instruction on older reference videos. Furthermore, the provisioning department can dynamically adjust the instructional priority according to the submission date. This ensures that the latest information is prioritized by determining the instructional priority based on the submission date of the reference videos. Some or all of the above processing in the provisioning department may be performed using AI, for example, or not using AI. For example, the provisioning department can input reference video submission date data into a generating AI and have the generating AI perform the determination of instructional priority.

[0081] The service provider can adjust the order of instruction based on the relevance of the reference videos at the time of delivery. For example, the service provider will prioritize instruction on highly relevant reference videos. The service provider can also postpone instruction on less relevant reference videos. Furthermore, the service provider can dynamically adjust the order of instruction according to relevance. This allows the service provider to prioritize the provision of highly relevant information by adjusting the order of instruction based on the relevance of the reference videos. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance data of the reference videos into a generating AI and have the generating AI perform the adjustment of the order of instruction.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is feeling anxious, the analysis unit can provide analysis results in a way that provides reassurance. This deepens the user's understanding by providing analysis results in a way that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the uploaded videos. For example, the analysis unit can perform a detailed analysis on high-importance videos, and a concise analysis on low-importance videos. Furthermore, the analysis unit can determine the priority of the analysis according to importance. This enables efficient analysis by adjusting the level of detail based on the importance of the uploaded videos. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the importance data of the uploaded videos into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the uploaded video during analysis. For example, the analysis unit can apply a specialized technical analysis algorithm to technical videos. It can also apply a specialized artistic analysis algorithm to artistic videos. Furthermore, it can apply a specialized health analysis algorithm to health-related videos. By applying an analysis algorithm appropriate to the category of the uploaded video, the analysis unit can provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the uploaded video into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis results based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is feeling anxious, the analysis unit can provide a reassuring analysis result. By providing analysis results of a length that matches the user's emotions, the system provides information that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can determine the priority of analysis based on the submission date of uploaded videos. For example, the analysis unit may prioritize the analysis of recently submitted videos. It can also postpone the analysis of older videos. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission date. This ensures that the most up-to-date information is analyzed first by determining the analysis priority based on the submission date of uploaded videos. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the submission date data of uploaded videos into a generating AI and have the generating AI determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis results based on the relevance of the uploaded videos during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant videos. It can also postpone the analysis of less relevant videos. Furthermore, the analysis unit can dynamically adjust the order of analysis results according to relevance. This allows for the priority provision of highly relevant information by adjusting the order of analysis results based on the relevance of the uploaded videos. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the uploaded videos into a generating AI and have the generating AI perform the adjustment of the order of analysis results.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The reception desk can analyze a user's past activity history related to their hobbies and suggest the most suitable way to input questions and concerns. For example, if a user has frequently entered questions about a particular hobby in the past, it will automatically display templates related to that hobby. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and concerns that a user might use at a specific time of day based on their past activity history. This streamlines the user's input process by suggesting the most suitable input method based on their past activity history.

[0090] The analytics unit can collect trend information related to users' hobbies and reflect it in the analysis results. For example, it can prioritize suggesting relevant videos and information based on recent trends. The analytics unit can also provide analysis results that incorporate the latest technologies and methods related to users' hobbies. Furthermore, the analytics unit can analyze the activity status of communities related to users' hobbies and provide relevant information. In this way, by providing analysis results that reflect trend information, it can provide users with the latest information.

[0091] The service provider can include functions to support users in setting goals related to their hobbies and to evaluate their progress. For example, it can provide specific guidance based on the goals set by the user. The service provider can also periodically evaluate the user's progress and provide feedback according to their level of achievement. Furthermore, the service provider can provide step-by-step guidance for users to achieve their goals. This helps maintain user motivation and supports effective learning through goal setting and progress evaluation.

[0092] The analytics department can assess users' skill levels related to their hobbies and provide appropriate advice. For example, it can analyze videos uploaded by users and point out areas for improvement based on their skill level. The analytics department can also suggest reference videos of appropriate difficulty based on the user's skill level. Furthermore, the analytics department can track the user's skill level improvement and provide feedback based on their progress. This supports user growth by providing advice tailored to their skill level.

[0093] The reception system can estimate the user's emotions and adjust the input method for questions and concerns based on those estimates. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick input of questions and concerns. By providing input methods that are tailored to the user's emotions, it reduces user stress and improves input efficiency.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on those estimated emotions. For example, if the user is relaxed, it will provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is feeling anxious, the analysis unit can present the results in a way that provides reassurance. This allows for a deeper understanding of the user by providing analysis results that are tailored to their emotions.

[0095] The system can estimate the user's emotions and adjust the way it presents the instruction based on those emotions. For example, if the user is relaxed, it will provide detailed instruction. If the user is in a hurry, it can provide concise instruction that gets straight to the point. Furthermore, if the user is feeling anxious, it can provide instruction in a way that provides reassurance. By providing instruction tailored to the user's emotions, the system deepens the user's understanding.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis results based on those estimates. For example, if the user is in a hurry, it can provide a short, concise analysis. If the user is relaxed, it can provide a detailed analysis. Furthermore, if the user is feeling anxious, it can provide a reassuring analysis. By providing analysis results of a length appropriate to the user's emotions, the system can deliver information that meets the user's needs.

[0097] The service provider can estimate the user's emotions and adjust the length of the instruction based on those estimates. For example, if the user is in a hurry, it can provide short, concise instruction. If the user is relaxed, it can provide detailed instruction. Furthermore, if the user is feeling anxious, it can provide reassuring instruction. By providing instruction lengths tailored to the user's emotions, the service provider delivers information that meets the user's needs.

[0098] The reception desk can prioritize retrieving highly relevant questions by considering the user's geographical location. For example, if a user is in a specific region, it will prioritize displaying questions and concerns related to that region. Furthermore, if a user is traveling, the reception desk can suggest questions and concerns related to their travel destination. Additionally, if a user is at home, the reception desk can filter questions and concerns based on information about their surroundings. This allows the system to provide region-specific information by prioritizing highly relevant questions based on the user's geographical location.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception desk inputs the user's questions and concerns. These include technical questions, life problems, and learning-related questions. The reception desk can analyze and classify the user's input using natural language processing technology and machine learning algorithms. Step 2: The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos. The analysis unit searches for and guides users to relevant videos based on their questions and concerns. It can also suggest the most suitable videos by considering the user's viewing history. Step 3: The service provider provides specific guidance based on the video provided by the analysis department. The service provider can also provide text-based guidance based on the video content. They can also provide specific technical guidance using video tutorials. Step 4: The analysis team analyzes the user's uploaded videos and points out areas for improvement and provides advice. The analysis team uses image analysis technology to analyze the user's videos and point out areas for technical improvement. They can also evaluate the user's performance and suggest ways to improve it.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and data processing unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user can input questions and concerns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and guides the user to relevant reference videos. The provision unit is implemented by the output device 40 of the smart device 14, which provides specific guidance. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the video uploaded by the user and points out areas for improvement and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which can input the user's questions and concerns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and guides the user to relevant reference videos. The provision unit is implemented by the speaker 240 of the smart glasses 214, which provides specific guidance. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the video uploaded by the user and points out areas for improvement and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and analysis unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input questions and concerns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and guides the user to relevant reference videos. The provision unit is implemented by the speaker 240 of the headset terminal 314, providing specific guidance. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes videos uploaded by the user and points out areas for improvement and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which can input user questions and concerns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and guides users to relevant reference videos. The provision unit is implemented by the speaker 240 of the robot 414, which provides specific guidance. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes videos uploaded by users and points out areas for improvement and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A reception area where users can input their questions and concerns, The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos, A provision unit provides specific guidance based on the video provided by the analysis unit, It includes an analysis unit that analyzes user-uploaded videos and points out areas for improvement and provides advice. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the way questions and concerns are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past question history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter questions or concerns, the input content is filtered based on their current interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes the entered questions and concerns based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users input questions or concerns, the system prioritizes retrieving highly relevant questions based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users input questions or concerns, the system analyzes their social media activity to retrieve relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the questions and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the question or problem. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the questions and concerns were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of the analysis results is adjusted based on the relevance of the questions and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way the instruction is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing the instruction, we will adjust the level of detail based on the importance of the reference videos. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing the content, different instructional algorithms are applied depending on the category of the reference video. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the instruction based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the materials, we will prioritize instruction based on when the reference videos were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the materials, we will adjust the order of instruction based on the relevance of the reference videos. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the uploaded videos. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the uploaded video. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During the analysis, the priority of the analysis is determined based on when the uploaded videos were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is During analysis, the order of the analysis results will be adjusted based on the relevance of the uploaded videos. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users can input their questions and concerns, The analysis unit analyzes the information entered by the reception unit and guides users to relevant reference videos, A provision unit provides specific guidance based on the video provided by the analysis unit, It includes an analysis unit that analyzes user-uploaded videos and points out areas for improvement and provides advice. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the way questions and concerns are entered based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past question history and suggests the optimal input format. The system according to feature 1.

4. The aforementioned reception unit is When users enter questions or concerns, the input content is filtered based on their current interests and concerns. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes the entered questions and concerns based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users input questions or concerns, the system prioritizes retrieving highly relevant questions based on their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users input questions or concerns, the system analyzes their social media activity to retrieve relevant questions. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the questions and concerns. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the question or problem. The system according to feature 1.

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