system

Abstract

The system according to the embodiment aims to provide appropriate support to users who are not good at operating smartphones. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit receives questions from users. The analysis unit analyzes the questions received by the reception unit and provides answers and operation methods. The provision unit provides specific operation methods based on the results of the analysis by the analysis unit. The monitoring unit monitors user operations in real time based on the operation methods provided by the provision unit, and provides additional advice as necessary.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to provide appropriate support to users who are not good at operating smartphones.

[0005] The system according to the embodiment aims to provide appropriate support to users who are not good at operating smartphones. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit receives questions from a user. The analysis unit analyzes the questions received by the reception unit and provides answers and operation methods. The provision unit provides specific operation methods based on the results of the analysis by the analysis unit. The monitoring unit monitors user operations in real time based on the operation methods provided by the provision unit and provides additional advice as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate support to users who are not good at operating smartphones. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The smartphone advisor system according to an embodiment of the present invention accepts user questions, analyzes them using AI, and provides appropriate answers and operational instructions. It also monitors and advises users in real time. The smartphone advisor system can instantly resolve user concerns about smartphone operation. For example, a user inputs a question about smartphone operation, such as "Tell me how to take a photo." This question is then input to an AI. The AI ​​then analyzes the input question, understands the content of the question, and provides appropriate answers and operational instructions. For example, in response to the question "Tell me how to take a photo," the AI ​​explains how to launch the camera app and how to use the shutter button. Furthermore, the AI ​​monitors user operations in real time and provides additional advice as needed. For example, if a user launches the camera app but cannot find the shutter button, the AI ​​will tell them where the shutter button is. This system allows even users who are not yet proficient in smartphone operation, such as the elderly, to easily master their smartphones. Users can ask a smartphone advisor at any time if they have questions, as if they were right in front of them. Furthermore, the AI ​​monitors operations in real time and provides advice as needed, allowing users to operate their smartphones with confidence. For example, if a user wants to know how to use a messaging app, the AI ​​will explain how to launch the messaging app and how to send a message. Also, if a user wants to know how to use the settings menu, the AI ​​will explain each item in the settings menu. In this way, users can instantly resolve any questions they have about using their smartphone. This allows the Smartphone Advisor system to instantly resolve any questions users have about using their smartphone, allowing them to operate their smartphone with confidence. For example, even users who are not good at using smartphones, such as the elderly, can easily master their smartphones. Users can ask the Smartphone Advisor at any time if they have any questions, as if they were sitting right in front of them. Furthermore, the AI ​​monitors operations in real time and provides advice as needed, allowing users to operate their smartphone with confidence.

[0029] The smartphone advisor system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit receives a question from a user. The question from the user may be in, for example, text format, audio format, or image format, but is not limited to these examples. The reception unit provides an interface for receiving text-format questions. The reception unit may also use voice recognition technology to receive audio questions. The reception unit may also use image recognition technology to receive image questions. For example, the reception unit provides an interface through which a user inputs a question regarding smartphone operation. The analysis unit uses AI to analyze the question received by the reception unit. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit understands the content of the question using text analysis technology. The analysis unit may also analyze audio questions using voice analysis technology. The analysis unit may also analyze image questions using image analysis technology. For example, the analysis unit analyzes a question input by a user and provides an appropriate answer or operation method. The providing unit provides specific operation instructions based on the results of the analysis by the analyzing unit. The provision may be in the form of, for example, a step-by-step guide or a video tutorial, but is not limited to these examples. For example, the providing unit may explain the operation instructions in text format. The providing unit may also explain the operation instructions in video format. The providing unit may also provide an interactive guide. For example, the providing unit may provide a guide for the user to learn how to launch a camera app and how to use a shutter button. The monitoring unit monitors the user's operations in real time based on the operation instructions provided by the providing unit and provides additional advice as needed. The monitoring may be performed by, for example, collecting a user's operation log or providing real-time feedback, but is not limited to these examples. For example, if the user cannot find the shutter button after launching the camera app, the monitoring unit may provide the location of the shutter button.Furthermore, if the user wants to know how to use a messaging app, the monitoring unit can explain how to launch the messaging app or how to send a message. Furthermore, if the user wants to know how to use a settings menu, the monitoring unit can explain each item in the settings menu. As a result, the smartphone advisor system according to the embodiment provides appropriate answers and operation methods to the user's questions, and by monitoring and advising in real time, even users who are not good at operating smartphones, such as the elderly, can easily master their smartphones.

[0030] The reception unit can analyze the user's past question history and select a question reception method. The reception unit analyzes the user's past question history using AI. The analysis of the question history is performed, for example, based on the frequency of past questions and trends in question content, but is not limited to such examples. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest question content that is frequently asked during a specific time period based on the user's past question history. This improves user convenience by providing an optimal question reception method based on the past question history. The analysis of the question history may be performed using AI or without AI. For example, the reception unit can input the user's past question history into a generation AI and cause the generation AI to select an optimal question reception method.

[0031] When receiving a question, the reception unit may filter the questions based on the user's current situation and areas of interest. The reception unit may use AI to identify the user's current situation and areas of interest. The current situation may be identified based on, for example, the user's current operation status and the application being used, but is not limited to these examples. For example, the reception unit may preferentially receive questions related to the app the user is currently using. The areas of interest may be identified based on, for example, past search history and social media activity, but is not limited to these examples. For example, the reception unit may filter and display related questions based on the user's areas of interest. The reception unit may also suggest appropriate questions depending on the user's current situation (e.g., while traveling). This allows for more appropriate support to be provided by preferentially receiving questions that correspond to the user's situation and areas of interest. The current situation and areas of interest may be identified using AI or without AI. For example, the reception unit may input data on the user's current operation status and areas of interest into the generation AI and have the generation AI perform filtering.

[0032] When receiving a question, the reception unit can select a reception means according to the user's input method. The reception unit uses AI to identify the user's input method. The input method can be identified based on, for example, voice input, text input, image input, etc., but is not limited to these examples. For example, when the user inputs a question by voice, the reception unit can accept the question using voice recognition technology. When the user inputs a question by text, the reception unit can also accept the question using text analysis technology. When the user inputs a question using an image, the reception unit can also accept the question using image recognition technology. This allows the user to smoothly enter a question by providing the optimal reception means according to the user's input method. The input method can be identified using, for example, AI or without AI. For example, the reception unit can input data of the user's input method into a generation AI and cause the generation AI to select the optimal reception means.

[0033] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. The reception unit uses AI to identify the user's geographical location information. The geographical location information can be identified based on, for example, GPS data or an IP address, but is not limited to these examples. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel. Furthermore, if the user is in a specific facility, the reception unit can prioritize receiving questions related to the facility. This allows for more appropriate support by prioritizing receiving questions based on the user's geographical location information. The identification of the geographical location information can be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location information data into a generation AI and cause the generation AI to select highly relevant questions.

[0034] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit uses AI to identify the user's social media activity. The identification of social media activity is performed, for example, based on the content of posts and like history, but is not limited to such examples. For example, the reception unit preferentially receives questions related to content posted by the user on social media. The reception unit can also analyze the user's social media activity and suggest related questions. The reception unit can also suggest related questions based on the activity of the user's friends on social media. This allows for more relevant support to be provided by receiving questions based on the user's social media activity. The identification of social media activity may be performed, for example, using AI or without AI. For example, the reception unit can input data on the user's social media activity into the generation AI and cause the generation AI to select related questions.

[0035] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit uses AI to analyze the user's past feedback. The analysis of the past feedback is performed based on, for example, past ratings and comments, but is not limited to such examples. For example, the reception unit suggests an optimal question reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize an optimal question reception method. This improves user convenience by providing an optimal question reception method based on the user's past feedback. The analysis of the past feedback may be performed using AI or without AI. For example, the reception unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize an optimal question reception method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. The analysis unit uses AI to identify the importance of the question. The identification of the importance of the question is based on, for example, urgency or impact, but is not limited to these examples. For example, the analysis unit provides detailed analysis results for questions with high importance. The analysis unit can also provide concise analysis results for questions with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the question. This allows the user to appropriately obtain the information they need by providing analysis results according to the importance of the question. The identification of the importance of the question may be performed, for example, using AI or without using AI. For example, the analysis unit can input data on the importance of the question to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the question category. The analysis unit identifies the question category using AI. The question category can be identified based on, for example, technical questions or business questions, but is not limited to these examples. For example, the analysis unit applies a specialized analysis algorithm to technical questions. The analysis unit can also apply a simple analysis algorithm to general questions. The analysis unit can also select the optimal analysis algorithm depending on the question category. This improves analysis accuracy by applying the optimal analysis algorithm depending on the question category. The question category can be identified using, for example, AI or without AI. For example, the analysis unit can input question category data into the generation AI and cause the generation AI to select the optimal analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit uses AI to analyze the user's past analysis results. The analysis of the past analysis results is performed, for example, based on past analysis data and a history of analysis results, but is not limited to such examples. For example, the analysis unit proposes an optimal analysis method based on the user's past analysis results. The analysis unit can also preferentially apply a specific analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. This allows for more accurate analysis results to be provided by improving the analysis accuracy based on the past analysis results. The analysis of the past analysis results may be performed, for example, using AI or without AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time when the question was submitted. The analysis unit uses AI to identify the time when the question was submitted. The identification of the time when the question was submitted is performed based on, for example, the submission date and time or the submission frequency, but is not limited to such examples. For example, the analysis unit determines the analysis priority based on the time period when the question was submitted. The analysis unit can also dynamically adjust the analysis priority according to the time when the question was submitted. The analysis unit can also determine the optimal analysis order taking into account the time when the question was submitted. This allows the user to quickly obtain an answer by providing an analysis priority according to the time when the question was submitted. The identification of the time when the question was submitted may be performed using AI or without AI. For example, the analysis unit can input data on the time when the question was submitted to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. The analysis unit uses AI to identify the relevance of the questions. The identification of the relevance of the questions is performed, for example, based on the similarity of the question content or related topics, but is not limited to such examples. For example, the analysis unit prioritizes analysis of questions with high relevance. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the questions. The analysis unit can also determine an optimal analysis order taking into account the relevance of the questions. This allows the user to quickly obtain the information they need by providing an analysis order based on the relevance of the questions. The identification of the relevance of the questions may be performed, for example, using AI or without using AI. For example, the analysis unit can input data on the relevance of the questions to the generation AI and cause the generation AI to adjust the analysis order.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit uses AI to identify the user's level of expertise. Identifying the level of expertise is performed, for example, based on the content of past questions or the user's occupation, but is not limited to these examples. For example, if the user's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can provide analysis results that use simpler terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the user to obtain information that is easy to understand by providing analysis results that correspond to the user's level of expertise. Identifying the level of expertise may be performed, for example, using AI or without AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0042] The providing unit can adjust the level of detail of the provided information based on the importance of the operation method when providing the information. The providing unit uses AI to identify the importance of the operation method. The identification of the importance of the operation method is performed based on, for example, urgency or impact, but is not limited to such examples. For example, the providing unit provides a detailed explanation for an operation method with high importance. The providing unit can also provide a concise explanation for an operation method with low importance. The providing unit can also dynamically adjust the level of detail of the provided information according to the importance of the operation method. This allows the user to appropriately obtain the information they need by providing a level of detail according to the importance of the operation method. The identification of the importance of the operation method may be performed using AI or without AI. For example, the providing unit can input data on the importance of the operation method to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0043] The providing unit can apply different provision algorithms depending on the category of the operation method when providing the operation method. The providing unit identifies the category of the operation method using AI. The identification of the category of the operation method is performed based on, for example, a technical operation method or a business-related operation method, but is not limited to such examples. For example, the providing unit applies a specialized provision algorithm to a technical operation method. The providing unit can also apply a simple provision algorithm to a general operation method. The providing unit can also select an optimal provision algorithm depending on the category of the operation method. This improves the provision accuracy by applying the optimal provision algorithm depending on the category of the operation method. The identification of the category of the operation method may be performed using AI or without using AI. For example, the providing unit can input data on the category of the operation method to the generation AI and cause the generation AI to select an optimal provision algorithm.

[0044] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the operation. The providing unit uses AI to analyze the user's past provision results. The analysis of the past provision results is performed, for example, based on past provision data and a history of provision results, but is not limited to such examples. For example, the providing unit suggests an optimal provision method based on the user's past provision results. The providing unit can also preferentially apply a specific provision algorithm based on the user's past provision results. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. This allows for the provision of a more accurate operation method by improving the provision accuracy based on the past provision results. The analysis of the past provision results may be performed, for example, using AI or without AI. For example, the providing unit can input data of the user's past provision results into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0045] The providing unit can determine the priority of provision based on the time of submission of the operation method when the operation method is provided. The providing unit uses AI to identify the time of submission of the operation method. The identification of the submission time is performed based on, for example, the submission date and time or the frequency of submission, but is not limited to such examples. For example, the providing unit determines the priority of provision based on the time period in which the operation method is submitted. The providing unit can also dynamically adjust the priority of provision depending on the time of submission of the operation method. The providing unit can also determine the optimal provision order taking into account the time of submission of the operation method. This allows the user to receive a quick response by providing the priority of provision according to the time of submission of the operation method. The identification of the submission time may be performed using AI or without AI. For example, the providing unit can input data on the time of submission of the operation method to the generation AI and cause the generation AI to determine the priority of provision.

[0046] The providing unit can adjust the order of providing the operation methods based on the relevance of the operation methods when providing the operation methods. The providing unit uses AI to identify the relevance of the operation methods. The identification of the relevance is performed, for example, based on the similarity of the operation content or related topics, but is not limited to such examples. For example, the providing unit prioritizes providing operation methods with high relevance. The providing unit can also dynamically adjust the order of providing the operation methods according to the relevance of the operation methods. The providing unit can also determine an optimal order of providing the operation methods taking into account the relevance of the operation methods. This allows the user to quickly obtain the information they need by providing the order of providing the operation methods according to the relevance of the operation methods. The identification of the relevance may be performed, for example, using AI or without using AI. For example, the providing unit can input data on the relevance of the operation methods to a generation AI and cause the generation AI to adjust the order of providing the operation methods.

[0047] The providing unit can adjust the use of technical terms provided according to the user's level of expertise when providing the information. The providing unit uses AI to identify the user's level of expertise. The identification of the level of expertise is performed, for example, based on the content of past questions or the user's occupation, but is not limited to such examples. For example, if the user's level of expertise is high, the providing unit can provide an operation method that makes extensive use of technical terms. Furthermore, if the user's level of expertise is low, the providing unit can also provide an operation method that uses simple terminology. The providing unit can also dynamically adjust the use of technical terms provided according to the user's level of expertise. This allows the user to obtain information that is easy to understand by providing an operation method that corresponds to the user's level of expertise. The identification of the level of expertise may be performed, for example, using AI or without AI. For example, the providing unit can input data on the user's level of expertise into the generating AI and cause the generating AI to adjust the use of technical terms.

[0048] During monitoring, the monitoring unit can analyze the user's past operation history and select an optimal monitoring method. The monitoring unit analyzes the user's past operation history using AI. The analysis of the past operation history is performed, for example, based on past operation logs and operation result histories, but is not limited to these examples. For example, the monitoring unit proposes an optimal monitoring method based on the user's past operation history. The monitoring unit can also preferentially apply a specific monitoring algorithm based on the user's past operation history. The monitoring unit can also analyze the user's past operation history and select an optimal monitoring method. This allows for more appropriate support of user operations by providing an optimal monitoring method based on the past operation history. The analysis of the past operation history may be performed, for example, using AI or without using AI. For example, the monitoring unit can input data of the user's past operation history into the generation AI and cause the generation AI to select an optimal monitoring method.

[0049] During monitoring, the monitoring unit can customize the monitoring means based on the user's current operation status. The monitoring unit uses AI to identify the user's current operation status. The current operation status can be identified based on, for example, the current operation content, the application being used, etc., but is not limited to these examples. For example, the monitoring unit provides the optimal monitoring means depending on the app the user is currently using. The monitoring unit can also dynamically adjust the monitoring means based on the user's current operation status. The monitoring unit can also select the optimal monitoring means taking into account the user's current operation status. This allows for more appropriate support of the user's operation by providing the monitoring means according to the current operation status. The current operation status can be identified using, for example, AI or without AI. For example, the monitoring unit can input data on the user's current operation status into the generation AI and cause the generation AI to customize the monitoring means.

[0050] During monitoring, the monitoring unit can improve the monitoring method by reflecting user feedback. The monitoring unit uses AI to analyze the user feedback. The analysis of the feedback is performed, for example, based on past ratings and comments, but is not limited to such examples. For example, the monitoring unit suggests an optimal monitoring method based on the user feedback. The monitoring unit can also preferentially apply a specific monitoring algorithm based on the user feedback. The monitoring unit can also analyze the user feedback and improve the monitoring method. In this way, more appropriate support can be provided by improving the monitoring method based on the user feedback. The analysis of the feedback may be performed, for example, using AI or without using AI. For example, the monitoring unit can input user feedback data into the generation AI and cause the generation AI to improve the monitoring method.

[0051] During monitoring, the monitoring unit can select an optimal monitoring method by taking into account the user's geographical location information. The monitoring unit uses AI to identify the user's geographical location information. The geographical location information can be identified based on, for example, GPS data or an IP address, but is not limited to these examples. For example, if the user is in a specific area, the monitoring unit can provide a monitoring method related to that area. Furthermore, if the user is traveling, the monitoring unit can provide a monitoring method related to the travel. Furthermore, if the user is in a specific facility, the monitoring unit can provide a monitoring method related to the facility. This allows for more appropriate support by providing a monitoring method based on the user's geographical location information. The identification of the geographical location information can be performed, for example, using AI or without using AI. For example, the monitoring unit can input the user's geographical location information data into a generation AI and cause the generation AI to select an optimal monitoring method.

[0052] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring measures. The monitoring unit uses AI to identify the user's social media activity. The identification of social media activity is performed, for example, based on the content of posts and like history, but is not limited to such examples. For example, the monitoring unit provides monitoring measures related to the content posted by the user on social media. The monitoring unit can also analyze the user's social media activity and suggest related monitoring measures. The monitoring unit can also suggest related monitoring measures based on the activity of the user's friends on social media. This allows for more relevant support to be provided by providing monitoring measures based on the user's social media activity. The identification of social media activity may be performed, for example, using AI or without AI. For example, the monitoring unit can input data on the user's social media activity into a generation AI and have the generation AI execute the suggested monitoring measures.

[0053] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit uses AI to analyze the user's past feedback. The analysis of the past feedback is performed based on, for example, past ratings and comments, but is not limited to such examples. For example, the monitoring unit suggests an optimal monitoring method based on the user's past feedback. The monitoring unit can also preferentially apply a specific monitoring algorithm based on the user's past feedback. The monitoring unit can also analyze the user's past feedback and customize the optimal monitoring method. This improves user convenience by providing the optimal monitoring method based on the user's past feedback. The analysis of the past feedback may be performed using, for example, AI or without AI. For example, the monitoring unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the monitoring method.

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

[0055] The smartphone advisor system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit monitors the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time, and can provide appropriate advice if an abnormality is detected. For example, if the user's heart rate suddenly increases, it can suggest breathing techniques to help them relax. If their blood pressure is high, it can also encourage them to consult a doctor. Furthermore, if their body temperature is high, it can advise them to take a rest. This allows the system to constantly monitor the user's health condition and provide appropriate support.

[0056] The smartphone advisor system can be equipped with a learning assessment unit that analyzes the user's past question history and evaluates the user's learning progress. The learning assessment unit analyzes what questions the user has asked in the past and evaluates the level of knowledge the user has acquired. For example, if the user repeatedly asks the same question, it can be determined that the user does not yet fully understand the content. Also, if the user asks a new question, it can be determined that the user is making progress in their learning. The learning assessment unit can suggest the next content to learn based on the user's learning progress. This can efficiently support the user's learning.

[0057] The smartphone advisor system can include an information provider that provides relevant news and information based on the user's current situation and areas of interest. The information provider analyzes the user's current operating status, past search history, social media activity, and other factors to select news and information that may be of interest to the user. For example, if the user is traveling, tourist information and weather forecasts for the user's destination can be provided. Also, if the user frequently uses a particular app, the system can provide the latest information related to that app. This allows the system to provide information tailored to the user's interests and support a more fulfilling smartphone experience.

[0058] The smartphone advisor system can be equipped with an input assistance unit that provides optimal input assistance depending on the user's input method. The input assistance unit supports the user to smoothly input data when using voice input, text input, image input, etc. For example, in the case of voice input, voice recognition technology is used to accurately convert the data into text. In addition, in the case of text input, a predictive conversion function is provided to make input more efficient. Furthermore, in the case of image input, image recognition technology can be used to extract text from the image. This makes it possible to provide optimal support depending on the user's input method and reduce the effort required for input.

[0059] The smartphone advisor system can be equipped with a route guidance unit that provides optimal route guidance taking into account the user's geographical location information. The route guidance unit calculates the optimal route based on the user's current location and destination, and provides guidance in real time. For example, if the user is traveling on foot, it can suggest a pedestrian-only route. Also, if the user is traveling by car, it can suggest a route that avoids traffic congestion. Furthermore, if the user is using public transportation, it can also provide optimal transfer guidance. This can support the user so that they can reach their destination smoothly.

[0060] The smartphone advisor system can include a content provider that analyzes a user's social media activity and provides customized content based on the user's interests. The content provider analyzes the user's social media posts and like history to select content that is likely to interest the user. For example, if a user is interested in a particular genre of film, the content provider can provide the latest film information for that genre. Also, if a user is interested in a particular sport, the content provider can provide the latest news on that sport. This allows the system to provide content tailored to the user's interests and support a more fulfilling smartphone experience.

[0061] The smartphone advisor system may include a customized advice unit that reflects the user's past feedback and provides customized advice according to the user's preferences. The customized advice unit analyzes feedback provided by the user in the past and provides advice according to the user's preferences and needs. For example, if the user liked a particular piece of advice in the past, similar advice may be provided preferentially. Also, if the user avoided a particular piece of advice in the past, that advice may not be provided. This makes it possible to provide optimal advice according to the user's preferences and improve user satisfaction.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives a question from a user. The question from the user may be in text format, voice format, image format, or the like. For example, the reception unit may provide an interface for receiving text-format questions and may use voice recognition technology for receiving voice-format questions or image recognition technology for receiving image-format questions. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis is performed using natural language processing technology and machine learning algorithms. For example, the analysis unit can understand the content of the question using text analysis technology, and can also analyze questions in voice or image format using voice analysis technology and image analysis technology. The analysis unit provides an appropriate answer or operation method. Step 3: The providing unit provides specific operating instructions based on the results of the analysis by the analyzing unit. The providing unit may provide step-by-step guides, video tutorials, etc. For example, the providing unit may explain the operating instructions in text or video format, or may provide an interactive guide. Step 4: The monitoring unit monitors the user's operations in real time based on the operation method provided by the providing unit, and provides additional advice as needed. Monitoring is performed by collecting user operation logs and providing real-time feedback. For example, after the user launches a camera app, the monitoring unit may provide advice such as instructing the user on the location of the shutter button.

[0064] (Example 2) The smartphone advisor system according to an embodiment of the present invention accepts user questions, analyzes them using AI, and provides appropriate answers and operational instructions. It also monitors and advises users in real time. The smartphone advisor system can instantly resolve user concerns about smartphone operation. For example, a user inputs a question about smartphone operation, such as "Tell me how to take a photo." This question is then input to an AI. The AI ​​then analyzes the input question, understands the content of the question, and provides appropriate answers and operational instructions. For example, in response to the question "Tell me how to take a photo," the AI ​​explains how to launch the camera app and how to use the shutter button. Furthermore, the AI ​​monitors user operations in real time and provides additional advice as needed. For example, if a user launches the camera app but cannot find the shutter button, the AI ​​will tell them where the shutter button is. This system allows even users who are not yet proficient in smartphone operation, such as the elderly, to easily master their smartphones. Users can ask a smartphone advisor at any time if they have questions, as if they were right in front of them. Furthermore, the AI ​​monitors operations in real time and provides advice as needed, allowing users to operate their smartphones with confidence. For example, if a user wants to know how to use a messaging app, the AI ​​will explain how to launch the messaging app and how to send a message. Also, if a user wants to know how to use the settings menu, the AI ​​will explain each item in the settings menu. In this way, users can instantly resolve any questions they have about using their smartphone. This allows the Smartphone Advisor system to instantly resolve any questions users have about using their smartphone, allowing them to operate their smartphone with confidence. For example, even users who are not good at using smartphones, such as the elderly, can easily master their smartphones. Users can ask the Smartphone Advisor at any time if they have any questions, as if they were sitting right in front of them. Furthermore, the AI ​​monitors operations in real time and provides advice as needed, allowing users to operate their smartphone with confidence.

[0065] The smartphone advisor system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a monitoring unit. The reception unit receives a question from a user. The question from the user may be in, for example, text format, audio format, or image format, but is not limited to these examples. The reception unit provides an interface for receiving text-format questions. The reception unit may also use voice recognition technology to receive audio questions. The reception unit may also use image recognition technology to receive image questions. For example, the reception unit provides an interface through which a user inputs a question regarding smartphone operation. The analysis unit uses AI to analyze the question received by the reception unit. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit understands the content of the question using text analysis technology. The analysis unit may also analyze audio questions using voice analysis technology. The analysis unit may also analyze image questions using image analysis technology. For example, the analysis unit analyzes a question input by a user and provides an appropriate answer or operation method. The providing unit provides specific operation instructions based on the results of the analysis by the analyzing unit. The provision may be in the form of, for example, a step-by-step guide or a video tutorial, but is not limited to these examples. For example, the providing unit may explain the operation instructions in text format. The providing unit may also explain the operation instructions in video format. The providing unit may also provide an interactive guide. For example, the providing unit may provide a guide for the user to learn how to launch a camera app and how to use a shutter button. The monitoring unit monitors the user's operations in real time based on the operation instructions provided by the providing unit and provides additional advice as needed. The monitoring may be performed by, for example, collecting a user's operation log or providing real-time feedback, but is not limited to these examples. For example, if the user cannot find the shutter button after launching the camera app, the monitoring unit may provide the location of the shutter button.Furthermore, if the user wants to know how to use a messaging app, the monitoring unit can explain how to launch the messaging app or how to send a message. Furthermore, if the user wants to know how to use a settings menu, the monitoring unit can explain each item in the settings menu. As a result, the smartphone advisor system according to the embodiment provides appropriate answers and operation methods to the user's questions, and by monitoring and advising in real time, even users who are not good at operating smartphones, such as the elderly, can easily master their smartphones.

[0066] The smartphone advisor system includes a reception unit that estimates a user's emotions and adjusts a question reception method based on the estimated user emotions. The reception unit estimates the user's emotions using AI. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, and other technologies, but is not limited to these examples. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. The reception unit can also analyze the user's text input and estimate the emotion using text analysis technology. The reception unit then adjusts the question reception method based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can provide an interface that accepts questions in a gentle tone. If the user is relaxed, the reception unit can provide detailed question options and suggest a customizable question reception method. If the user is in a hurry, the reception unit can prioritize a simple question input method and quickly accept questions. By providing a question reception method that corresponds to the user's emotions, the reception unit can more comfortably input questions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The reception unit can analyze the user's past question history and select a question reception method. The reception unit analyzes the user's past question history using AI. The analysis of the question history is performed, for example, based on the frequency of past questions and trends in question content, but is not limited to such examples. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest question content that is frequently asked during a specific time period based on the user's past question history. This improves user convenience by providing an optimal question reception method based on the past question history. The analysis of the question history may be performed using AI or without AI. For example, the reception unit can input the user's past question history into a generation AI and cause the generation AI to select an optimal question reception method.

[0068] When receiving a question, the reception unit may filter the questions based on the user's current situation and areas of interest. The reception unit may use AI to identify the user's current situation and areas of interest. The current situation may be identified based on, for example, the user's current operation status and the application being used, but is not limited to these examples. For example, the reception unit may preferentially receive questions related to the app the user is currently using. The areas of interest may be identified based on, for example, past search history and social media activity, but is not limited to these examples. For example, the reception unit may filter and display related questions based on the user's areas of interest. The reception unit may also suggest appropriate questions depending on the user's current situation (e.g., while traveling). This allows for more appropriate support to be provided by preferentially receiving questions that correspond to the user's situation and areas of interest. The current situation and areas of interest may be identified using AI or without AI. For example, the reception unit may input data on the user's current operation status and areas of interest into the generation AI and have the generation AI perform filtering.

[0069] When receiving a question, the reception unit can select a reception means according to the user's input method. The reception unit uses AI to identify the user's input method. The input method can be identified based on, for example, voice input, text input, image input, etc., but is not limited to these examples. For example, when the user inputs a question by voice, the reception unit can accept the question using voice recognition technology. When the user inputs a question by text, the reception unit can also accept the question using text analysis technology. When the user inputs a question using an image, the reception unit can also accept the question using image recognition technology. This allows the user to smoothly enter a question by providing the optimal reception means according to the user's input method. The input method can be identified using, for example, AI or without AI. For example, the reception unit can input data of the user's input method into a generation AI and cause the generation AI to select the optimal reception means.

[0070] The reception unit can estimate the user's emotions and prioritize questions to be received based on the estimated user emotions. The reception unit estimates the user's emotions using AI. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. The reception unit can also analyze the user's text input and estimate the emotion using text analysis technology. Next, the reception unit prioritizes questions to be received based on the estimated user emotions. For example, if the user is stressed, it can prioritize urgent questions. If the user is relaxed, it can prioritize detailed questions. If the user is in a hurry, it can prioritize concise questions. By prioritizing questions according to the user's emotions, urgent questions can be quickly addressed. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0071] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. The reception unit uses AI to identify the user's geographical location information. The geographical location information can be identified based on, for example, GPS data or an IP address, but is not limited to these examples. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel. Furthermore, if the user is in a specific facility, the reception unit can prioritize receiving questions related to the facility. This allows for more appropriate support by prioritizing receiving questions based on the user's geographical location information. The identification of the geographical location information can be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location information data into a generation AI and cause the generation AI to select highly relevant questions.

[0072] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit uses AI to identify the user's social media activity. The identification of social media activity is performed, for example, based on the content of posts and like history, but is not limited to such examples. For example, the reception unit preferentially receives questions related to content posted by the user on social media. The reception unit can also analyze the user's social media activity and suggest related questions. The reception unit can also suggest related questions based on the activity of the user's friends on social media. This allows for more relevant support to be provided by receiving questions based on the user's social media activity. The identification of social media activity may be performed, for example, using AI or without AI. For example, the reception unit can input data on the user's social media activity into the generation AI and cause the generation AI to select related questions.

[0073] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit uses AI to analyze the user's past feedback. The analysis of the past feedback is performed based on, for example, past ratings and comments, but is not limited to such examples. For example, the reception unit suggests an optimal question reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize an optimal question reception method. This improves user convenience by providing an optimal question reception method based on the user's past feedback. The analysis of the past feedback may be performed using AI or without AI. For example, the reception unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize an optimal question reception method.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit uses AI to estimate the user's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also analyze the user's text input and estimate the emotion using text analysis technology. The analysis unit then adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis result can be provided in a gentle tone. If the user is relaxed, the analysis result can be provided in a detailed form. If the user is in a hurry, the analysis result can be provided in a concise form. This allows the analysis result to be provided according to the user's emotions, making it easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. The analysis unit uses AI to identify the importance of the question. The identification of the importance of the question is based on, for example, urgency or impact, but is not limited to these examples. For example, the analysis unit provides detailed analysis results for questions with high importance. The analysis unit can also provide concise analysis results for questions with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the question. This allows the user to appropriately obtain the information they need by providing analysis results according to the importance of the question. The identification of the importance of the question may be performed, for example, using AI or without using AI. For example, the analysis unit can input data on the importance of the question to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the question category. The analysis unit identifies the question category using AI. The question category can be identified based on, for example, technical questions or business questions, but is not limited to these examples. For example, the analysis unit applies a specialized analysis algorithm to technical questions. The analysis unit can also apply a simple analysis algorithm to general questions. The analysis unit can also select the optimal analysis algorithm depending on the question category. This improves analysis accuracy by applying the optimal analysis algorithm depending on the question category. The question category can be identified using, for example, AI or without AI. For example, the analysis unit can input question category data into the generation AI and cause the generation AI to select the optimal analysis algorithm.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit uses AI to analyze the user's past analysis results. The analysis of the past analysis results is performed, for example, based on past analysis data and a history of analysis results, but is not limited to such examples. For example, the analysis unit proposes an optimal analysis method based on the user's past analysis results. The analysis unit can also preferentially apply a specific analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. This allows for more accurate analysis results to be provided by improving the analysis accuracy based on the past analysis results. The analysis of the past analysis results may be performed, for example, using AI or without AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit uses AI to estimate the user's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also analyze the user's text input and estimate the emotion using text analysis technology. The analysis unit then adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. By providing the length of the analysis result according to the user's emotions, the user can appropriately obtain the information they need. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] During analysis, the analysis unit can determine the analysis priority based on the time when the question was submitted. The analysis unit uses AI to identify the time when the question was submitted. The identification of the time when the question was submitted is performed based on, for example, the submission date and time or the submission frequency, but is not limited to such examples. For example, the analysis unit determines the analysis priority based on the time period when the question was submitted. The analysis unit can also dynamically adjust the analysis priority according to the time when the question was submitted. The analysis unit can also determine the optimal analysis order taking into account the time when the question was submitted. This allows the user to quickly obtain an answer by providing an analysis priority according to the time when the question was submitted. The identification of the time when the question was submitted may be performed using AI or without AI. For example, the analysis unit can input data on the time when the question was submitted to the generation AI and have the generation AI determine the analysis priority.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the questions. The analysis unit uses AI to identify the relevance of the questions. The identification of the relevance of the questions is performed, for example, based on the similarity of the question content or related topics, but is not limited to such examples. For example, the analysis unit prioritizes analysis of questions with high relevance. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the questions. The analysis unit can also determine an optimal analysis order taking into account the relevance of the questions. This allows the user to quickly obtain the information they need by providing an analysis order based on the relevance of the questions. The identification of the relevance of the questions may be performed, for example, using AI or without using AI. For example, the analysis unit can input data on the relevance of the questions to the generation AI and cause the generation AI to adjust the analysis order.

[0081] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit uses AI to identify the user's level of expertise. Identifying the level of expertise is performed, for example, based on the content of past questions or the user's occupation, but is not limited to these examples. For example, if the user's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can provide analysis results that use simpler terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the user to obtain information that is easy to understand by providing analysis results that correspond to the user's level of expertise. Identifying the level of expertise may be performed, for example, using AI or without AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0082] The providing unit can estimate the user's emotions and adjust the expression of the operation instructions to be provided based on the estimated user's emotions. The providing unit estimates the user's emotions using AI. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. The providing unit can also analyze the user's text input and estimate the emotion using text analysis technology. Next, the providing unit adjusts the expression of the operation instructions to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, the operation instructions can be provided in a gentle tone. If the user is relaxed, detailed operation instructions can be provided. If the user is in a hurry, concise operation instructions can be provided. This allows the user to obtain operation instructions that are easier to understand by providing expressions of operation instructions that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The providing unit can adjust the level of detail of the provided information based on the importance of the operation method when providing the information. The providing unit uses AI to identify the importance of the operation method. The identification of the importance of the operation method is performed based on, for example, urgency or impact, but is not limited to such examples. For example, the providing unit provides a detailed explanation for an operation method with high importance. The providing unit can also provide a concise explanation for an operation method with low importance. The providing unit can also dynamically adjust the level of detail of the provided information according to the importance of the operation method. This allows the user to appropriately obtain the information they need by providing a level of detail according to the importance of the operation method. The identification of the importance of the operation method may be performed using AI or without AI. For example, the providing unit can input data on the importance of the operation method to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0084] The providing unit can apply different provision algorithms depending on the category of the operation method when providing the operation method. The providing unit identifies the category of the operation method using AI. The identification of the category of the operation method is performed based on, for example, a technical operation method or a business-related operation method, but is not limited to such examples. For example, the providing unit applies a specialized provision algorithm to a technical operation method. The providing unit can also apply a simple provision algorithm to a general operation method. The providing unit can also select an optimal provision algorithm depending on the category of the operation method. This improves the provision accuracy by applying the optimal provision algorithm depending on the category of the operation method. The identification of the category of the operation method may be performed using AI or without using AI. For example, the providing unit can input data on the category of the operation method to the generation AI and cause the generation AI to select an optimal provision algorithm.

[0085] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the operation. The providing unit uses AI to analyze the user's past provision results. The analysis of the past provision results is performed, for example, based on past provision data and a history of provision results, but is not limited to such examples. For example, the providing unit suggests an optimal provision method based on the user's past provision results. The providing unit can also preferentially apply a specific provision algorithm based on the user's past provision results. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. This allows for the provision of a more accurate operation method by improving the provision accuracy based on the past provision results. The analysis of the past provision results may be performed, for example, using AI or without AI. For example, the providing unit can input data of the user's past provision results into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0086] The providing unit can estimate the user's emotions and adjust the length of the operation instructions to be provided based on the estimated user's emotions. The providing unit uses AI to estimate the user's emotions. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. The providing unit can also analyze the user's text input and estimate the emotion using text analysis technology. Next, the providing unit adjusts the length of the operation instructions to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, concise operation instructions. If the user is relaxed, the providing unit can provide detailed operation instructions. If the user is excited, the providing unit can provide operation instructions with visually stimulating effects. This allows the user to appropriately obtain the information they need by providing the length of the operation instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The providing unit can determine the priority of provision based on the time of submission of the operation method when the operation method is provided. The providing unit uses AI to identify the time of submission of the operation method. The identification of the submission time is performed based on, for example, the submission date and time or the frequency of submission, but is not limited to such examples. For example, the providing unit determines the priority of provision based on the time period in which the operation method is submitted. The providing unit can also dynamically adjust the priority of provision depending on the time of submission of the operation method. The providing unit can also determine the optimal provision order taking into account the time of submission of the operation method. This allows the user to receive a quick response by providing the priority of provision according to the time of submission of the operation method. The identification of the submission time may be performed using AI or without AI. For example, the providing unit can input data on the time of submission of the operation method to the generation AI and cause the generation AI to determine the priority of provision.

[0088] The providing unit can adjust the order of providing the operation methods based on the relevance of the operation methods when providing the operation methods. The providing unit uses AI to identify the relevance of the operation methods. The identification of the relevance is performed, for example, based on the similarity of the operation content or related topics, but is not limited to such examples. For example, the providing unit prioritizes providing operation methods with high relevance. The providing unit can also dynamically adjust the order of providing the operation methods according to the relevance of the operation methods. The providing unit can also determine an optimal order of providing the operation methods taking into account the relevance of the operation methods. This allows the user to quickly obtain the information they need by providing the order of providing the operation methods according to the relevance of the operation methods. The identification of the relevance may be performed, for example, using AI or without using AI. For example, the providing unit can input data on the relevance of the operation methods to a generation AI and cause the generation AI to adjust the order of providing the operation methods.

[0089] The providing unit can adjust the use of technical terms provided according to the user's level of expertise when providing the information. The providing unit uses AI to identify the user's level of expertise. The identification of the level of expertise is performed, for example, based on the content of past questions or the user's occupation, but is not limited to such examples. For example, if the user's level of expertise is high, the providing unit can provide an operation method that makes extensive use of technical terms. Furthermore, if the user's level of expertise is low, the providing unit can also provide an operation method that uses simple terminology. The providing unit can also dynamically adjust the use of technical terms provided according to the user's level of expertise. This allows the user to obtain information that is easy to understand by providing an operation method that corresponds to the user's level of expertise. The identification of the level of expertise may be performed, for example, using AI or without AI. For example, the providing unit can input data on the user's level of expertise into the generating AI and cause the generating AI to adjust the use of technical terms.

[0090] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The monitoring unit estimates the user's emotions using AI. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the monitoring unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The monitoring unit can also record the user's voice and estimate the emotion using voice analysis technology. The monitoring unit can also analyze the user's text input and estimate the emotion using text analysis technology. Next, the monitoring unit adjusts the monitoring method based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit can provide monitoring results in a gentle tone. If the user is relaxed, the monitoring unit can provide detailed monitoring results. If the user is in a hurry, the monitoring unit can provide concise monitoring results. This allows the user to operate the device with greater peace of mind by providing a monitoring method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] During monitoring, the monitoring unit can analyze the user's past operation history and select an optimal monitoring method. The monitoring unit analyzes the user's past operation history using AI. The analysis of the past operation history is performed, for example, based on past operation logs and operation result histories, but is not limited to these examples. For example, the monitoring unit proposes an optimal monitoring method based on the user's past operation history. The monitoring unit can also preferentially apply a specific monitoring algorithm based on the user's past operation history. The monitoring unit can also analyze the user's past operation history and select an optimal monitoring method. This allows for more appropriate support of user operations by providing an optimal monitoring method based on the past operation history. The analysis of the past operation history may be performed, for example, using AI or without using AI. For example, the monitoring unit can input data of the user's past operation history into the generation AI and cause the generation AI to select an optimal monitoring method.

[0092] During monitoring, the monitoring unit can customize the monitoring means based on the user's current operation status. The monitoring unit uses AI to identify the user's current operation status. The current operation status can be identified based on, for example, the current operation content, the application being used, etc., but is not limited to these examples. For example, the monitoring unit provides the optimal monitoring means depending on the app the user is currently using. The monitoring unit can also dynamically adjust the monitoring means based on the user's current operation status. The monitoring unit can also select the optimal monitoring means taking into account the user's current operation status. This allows for more appropriate support of the user's operation by providing the monitoring means according to the current operation status. The current operation status can be identified using, for example, AI or without AI. For example, the monitoring unit can input data on the user's current operation status into the generation AI and cause the generation AI to customize the monitoring means.

[0093] During monitoring, the monitoring unit can improve the monitoring method by reflecting user feedback. The monitoring unit uses AI to analyze the user feedback. The analysis of the feedback is performed, for example, based on past ratings and comments, but is not limited to such examples. For example, the monitoring unit suggests an optimal monitoring method based on the user feedback. The monitoring unit can also preferentially apply a specific monitoring algorithm based on the user feedback. The monitoring unit can also analyze the user feedback and improve the monitoring method. In this way, more appropriate support can be provided by improving the monitoring method based on the user feedback. The analysis of the feedback may be performed, for example, using AI or without using AI. For example, the monitoring unit can input user feedback data into the generation AI and cause the generation AI to improve the monitoring method.

[0094] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. The monitoring unit estimates the user's emotions using AI. Emotion estimation can be performed using technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis. For example, the monitoring unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The monitoring unit can also record the user's voice and estimate the emotion using voice analysis technology. The monitoring unit can also analyze the user's text input and estimate the emotion using text analysis technology. Next, the monitoring unit determines monitoring priorities based on the estimated user emotions. For example, if the user is stressed, priority can be given to monitoring with high urgency. If the user is relaxed, priority can be given to detailed monitoring. If the user is in a hurry, priority can be given to brief monitoring. This allows for quick response to urgent operations by providing monitoring priorities according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] During monitoring, the monitoring unit can select an optimal monitoring method by taking into account the user's geographical location information. The monitoring unit uses AI to identify the user's geographical location information. The geographical location information can be identified based on, for example, GPS data or an IP address, but is not limited to these examples. For example, if the user is in a specific area, the monitoring unit can provide a monitoring method related to that area. Furthermore, if the user is traveling, the monitoring unit can provide a monitoring method related to the travel. Furthermore, if the user is in a specific facility, the monitoring unit can provide a monitoring method related to the facility. This allows for more appropriate support by providing a monitoring method based on the user's geographical location information. The identification of the geographical location information can be performed, for example, using AI or without using AI. For example, the monitoring unit can input the user's geographical location information data into a generation AI and cause the generation AI to select an optimal monitoring method.

[0096] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring measures. The monitoring unit uses AI to identify the user's social media activity. The identification of social media activity is performed, for example, based on the content of posts and like history, but is not limited to such examples. For example, the monitoring unit provides monitoring measures related to the content posted by the user on social media. The monitoring unit can also analyze the user's social media activity and suggest related monitoring measures. The monitoring unit can also suggest related monitoring measures based on the activity of the user's friends on social media. This allows for more relevant support to be provided by providing monitoring measures based on the user's social media activity. The identification of social media activity may be performed, for example, using AI or without AI. For example, the monitoring unit can input data on the user's social media activity into a generation AI and have the generation AI execute the suggested monitoring measures.

[0097] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit uses AI to analyze the user's past feedback. The analysis of the past feedback is performed based on, for example, past ratings and comments, but is not limited to such examples. For example, the monitoring unit suggests an optimal monitoring method based on the user's past feedback. The monitoring unit can also preferentially apply a specific monitoring algorithm based on the user's past feedback. The monitoring unit can also analyze the user's past feedback and customize the optimal monitoring method. This improves user convenience by providing the optimal monitoring method based on the user's past feedback. The analysis of the past feedback may be performed using, for example, AI or without AI. For example, the monitoring unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the monitoring method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and monitoring unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a question from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question using AI. The provision unit is realized by the control unit 46A of the smart device 14 and provides an operation method based on the analysis result. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's operation in real time and provides additional advice as necessary. Furthermore, the reception unit estimates the user's emotion using AI and adjusts the method of receiving the question based on the estimated emotion. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and monitoring unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a question from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question using AI. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides an operation method based on the analysis result. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's operation in real time and provides additional advice as necessary. Furthermore, the reception unit estimates the user's emotion using AI and adjusts the method of receiving the question based on the estimated emotion. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives questions from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using AI. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides an operation method based on the analysis results. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's operations in real time and provides additional advice as necessary. Furthermore, the reception unit estimates the user's emotions using AI and adjusts the method of receiving the questions based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives questions from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using AI. The provision unit is realized by the control unit 46A of the robot 414 and provides an operation method based on the analysis results. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the user's operation in real time and provides additional advice as necessary. Furthermore, the reception unit estimates the user's emotions using AI and adjusts the method of receiving the questions based on the estimated emotions.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The smartphone advisor system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit monitors the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time, and can provide appropriate advice if an abnormality is detected. For example, if the user's heart rate suddenly increases, it can suggest breathing techniques to help them relax. If their blood pressure is high, it can also encourage them to consult a doctor. Furthermore, if their body temperature is high, it can advise them to take a rest. This allows the system to constantly monitor the user's health condition and provide appropriate support.

[0100] The smartphone advisor system can include a stress assessment unit that estimates the user's emotions and evaluates the user's stress level based on the estimated emotions. The stress assessment unit analyzes the user's facial expressions, voice, text input, etc., and quantifies the stress level. For example, if the user frequently shows anxious expressions, the stress level can be determined to be high. The stress level can also be determined to be high if the voice tone becomes lower or the text input becomes shorter. If the stress level is determined to be high, advice on relaxation or activities to reduce stress can be suggested. This can reduce the user's stress and support more comfortable smartphone operation.

[0101] The smartphone advisor system can be equipped with a learning assessment unit that analyzes the user's past question history and evaluates the user's learning progress. The learning assessment unit analyzes what questions the user has asked in the past and evaluates the level of knowledge the user has acquired. For example, if the user repeatedly asks the same question, it can be determined that the user does not yet fully understand the content. Also, if the user asks a new question, it can be determined that the user is making progress in their learning. The learning assessment unit can suggest the next content to learn based on the user's learning progress. This can efficiently support the user's learning.

[0102] The smartphone advisor system can include an information provider that provides relevant news and information based on the user's current situation and areas of interest. The information provider analyzes the user's current operating status, past search history, social media activity, and other factors to select news and information that may be of interest to the user. For example, if the user is traveling, tourist information and weather forecasts for the user's destination can be provided. Also, if the user frequently uses a particular app, the system can provide the latest information related to that app. This allows the system to provide information tailored to the user's interests and support a more fulfilling smartphone experience.

[0103] The smartphone advisor system can be equipped with an input assistance unit that provides optimal input assistance depending on the user's input method. The input assistance unit supports the user to smoothly input data when using voice input, text input, image input, etc. For example, in the case of voice input, voice recognition technology is used to accurately convert the data into text. In addition, in the case of text input, a predictive conversion function is provided to make input more efficient. Furthermore, in the case of image input, image recognition technology can be used to extract text from the image. This makes it possible to provide optimal support depending on the user's input method and reduce the effort required for input.

[0104] The smartphone advisor system may include a motivation improvement unit that estimates the user's emotions and improves the user's motivation based on the estimated emotions. The motivation improvement unit analyzes the user's facial expressions, voice, text input, etc. to evaluate the user's motivation level. For example, if the user is tired, it may display an encouraging message. Also, if the user achieves a goal, it may display a message of praise. Furthermore, if the user is working on a challenging task, it may display a message of encouragement. This improves the user's motivation and allows them to use their smartphone more actively.

[0105] The smartphone advisor system can be equipped with a route guidance unit that provides optimal route guidance taking into account the user's geographical location information. The route guidance unit calculates the optimal route based on the user's current location and destination, and provides guidance in real time. For example, if the user is traveling on foot, it can suggest a pedestrian-only route. Also, if the user is traveling by car, it can suggest a route that avoids traffic congestion. Furthermore, if the user is using public transportation, it can also provide optimal transfer guidance. This can support the user so that they can reach their destination smoothly.

[0106] The smartphone advisor system can include a content provider that analyzes a user's social media activity and provides customized content based on the user's interests. The content provider analyzes the user's social media posts and like history to select content that is likely to interest the user. For example, if a user is interested in a particular genre of film, the content provider can provide the latest film information for that genre. Also, if a user is interested in a particular sport, the content provider can provide the latest news on that sport. This allows the system to provide content tailored to the user's interests and support a more fulfilling smartphone experience.

[0107] The smartphone advisor system may include a customized advice unit that reflects the user's past feedback and provides customized advice according to the user's preferences. The customized advice unit analyzes feedback provided by the user in the past and provides advice according to the user's preferences and needs. For example, if the user liked a particular piece of advice in the past, similar advice may be provided preferentially. Also, if the user avoided a particular piece of advice in the past, that advice may not be provided. This makes it possible to provide optimal advice according to the user's preferences and improve user satisfaction.

[0108] The smartphone advisor system can include a relaxation promotion unit that estimates the user's emotions and promotes relaxation for the user based on the estimated emotions. The relaxation promotion unit analyzes the user's facial expressions, voice, text input, and so on, and suggests content and activities that will help the user relax. For example, if the user is feeling stressed, it can provide relaxing music or a meditation guide. If the user is tired, it can also suggest relaxing videos or apps. Furthermore, if the user feels like relaxing, it can suggest relaxing environmental settings or aromas. This helps the user relax and provides a more comfortable smartphone experience.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit receives a question from a user. The question from the user may be in text format, voice format, image format, or the like. For example, the reception unit may provide an interface for receiving text-format questions and may use voice recognition technology for receiving voice-format questions or image recognition technology for receiving image-format questions. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis is performed using natural language processing technology and machine learning algorithms. For example, the analysis unit can understand the content of the question using text analysis technology, and can also analyze questions in voice or image format using voice analysis technology and image analysis technology. The analysis unit provides an appropriate answer or operation method. Step 3: The providing unit provides specific operating instructions based on the results of the analysis by the analyzing unit. The providing unit may provide step-by-step guides, video tutorials, etc. For example, the providing unit may explain the operating instructions in text or video format, or may provide an interactive guide. Step 4: The monitoring unit monitors the user's operations in real time based on the operation method provided by the providing unit, and provides additional advice as needed. Monitoring is performed by collecting user operation logs and providing real-time feedback. For example, after the user launches a camera app, the monitoring unit may provide advice such as instructing the user on the location of the shutter button.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit and provides an answer and an operation method; a providing unit that provides a specific operation method based on the analysis result by the analyzing unit; a monitoring unit that monitors the user's operation in real time based on the operation method provided by the providing unit and provides additional advice as necessary. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the way questions are accepted based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past question history and determine how to accept questions 2. The system of claim 1.

4. The reception unit When questions are submitted, they are filtered based on the user's current situation and interests.

2. The system of claim 1.

5. The reception unit When accepting a question, select the acceptance method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the questions to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting questions, the system takes into account the user's geographic location information to prioritize relevant questions.

2. The system of claim 1.

8. The reception unit When a question is received, the social media activity of the user is analyzed and related questions are received.

2. The system of claim 1.

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