system

Abstract

The system according to this embodiment aims to enable users to receive prompt and appropriate support. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a learning unit. The reception unit receives questions from the user. The generation unit analyzes the questions received by the reception unit and generates appropriate answers. The provision unit provides the answers generated by the generation unit. The learning unit learns the user's usage patterns.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to receive quick and appropriate support when asking questions or troubleshooting regarding products or services.

[0005] The system according to the embodiment aims to enable a user to receive quick and appropriate support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a learning unit. The reception unit receives questions from the user. The generation unit analyzes the questions received by the reception unit and generates appropriate answers. The provision unit provides the answers generated by the generation unit. The learning unit learns the user's usage patterns. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to receive prompt and appropriate support. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 2也 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The mobile assist agent system according to an embodiment of the present invention is an AI assistant powered by generative AI integrated into a mobile phone. This mobile assist agent system allows users to input questions about products or services, which the generative AI analyzes and generates appropriate answers. These generated answers are then provided to the user. Furthermore, the generative AI learns the user's usage patterns and provides troubleshooting and guidance. This mechanism allows users to receive support 24 / 7, from anywhere. For example, a user inputs a question about a product or service. The user only needs to input the question in natural language. For example, they might input a question such as, "How do I use this product?" This information is input to the generative AI. Next, the generative AI analyzes the input question and generates an appropriate answer. The generative AI generates the optimal answer based on past data and a knowledge base. For example, in response to the question, "How do I use this product?", the generative AI provides detailed instructions on how to use the product. The generated answers are then provided to the user. The user can review the answers provided by the generative AI and obtain the necessary information. For example, they can use the product by following the instructions provided by the generative AI. Furthermore, the generative AI learns the user's usage patterns. This allows the system to understand what questions users have asked and what kind of support they have received in the past, enabling it to provide more personalized support. For example, if a user has previously asked a question about how to use a product, the generating AI will remember that information and use it for future support. This system allows users to receive support 24 / 7, from anywhere. This is expected to reduce user support wait times, increase service utilization rates, and improve problem-solving capabilities. As a result, the mobile assist agent system can efficiently receive, analyze, and answer user questions, and learn usage patterns.

[0029] The mobile assist agent system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a learning unit. The reception unit receives questions from the user. User questions include, for example, questions about products or services, but are not limited to such examples. The reception unit receives questions entered by the user in natural language, for example. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit can use speech recognition technology to convert the user's voice input into text. The generation unit uses a generation AI to analyze the questions received by the reception unit and generate appropriate answers. The generation unit generates optimal answers based on, for example, past data and a knowledge base. For example, the generation unit uses a text generation AI (e.g., LLM) to generate answers to user questions. The generation unit can also use a multimodal generation AI to generate answers to user questions. For example, the generation unit generates optimal answers to user questions by having the generation AI learn from past data. The provision unit provides the answers generated by the generation unit to the user. The delivery unit, for example, displays the answer generated by the generation AI to the user. The delivery unit can also support multiple delivery methods, such as providing answers in voice or text. For example, the delivery unit displays the answer on the user's device. The delivery unit can also provide the generated answer in voice using speech synthesis technology. The learning unit learns the user's usage patterns. For example, the learning unit learns what questions the user has asked in the past and what kind of support they have received. For example, the learning unit analyzes the user's operation history and question history and uses this information to provide support in the future. As a result, the mobile assist agent system according to this embodiment can efficiently receive, analyze, and provide answers to user questions and learn usage patterns.

[0030] The reception desk receives user inquiries. These inquiries may include, but are not limited to, questions about products and services. The reception desk accepts questions entered by users in natural language, for example. It can also support multiple input methods, such as voice input and text input. For example, the reception desk can use speech recognition technology to convert user voice input into text. Specifically, a deep learning-based speech recognition model is used. This model is trained to handle various accents and speaking styles by learning from a large amount of voice data. When a user enters a question by voice, the speech recognition model analyzes the voice and converts it into text data. In the case of text input, the user enters the question using a keyboard or touchscreen. The reception desk manages these input methods in an integrated manner to improve user convenience. Furthermore, the reception desk can analyze user input in real time and provide appropriate feedback. For example, it can help users complete their questions quickly by suggesting relevant options while they are typing. In addition, the reception desk securely manages user input and takes measures to protect privacy. This allows users to enter questions with confidence.

[0031] The generation unit uses a generation AI to analyze questions received by the reception unit and generate appropriate answers. For example, the generation unit generates optimal answers based on past data and a knowledge base. Specifically, the generation unit uses a text generation AI (e.g., LLM) to generate answers to user questions. LLM has the ability to understand and generate natural language by learning from large amounts of text data. When a user question is sent from the reception unit to the generation unit, LLM analyzes the question and generates the optimal answer. For example, if a user asks, "What are the features of the latest smartphones?", LLM refers to past data and a knowledge base to generate a detailed answer about the features of the latest smartphones. The generation unit can also generate answers to user questions using a multimodal generation AI. A multimodal generation AI has the ability to integrate and analyze multiple modals, such as images and audio, in addition to text. For example, if a user asks, "How do I use this product?", the multimodal generation AI can generate an answer combining text and images and provide it to the user. Furthermore, the generation unit ensures that the generation AI learns from past data to generate the optimal answer to the user's question. This allows the generation unit to provide quick and accurate answers to a wide range of user questions.

[0032] The providing unit provides the user with the answers generated by the generating unit. For example, the providing unit displays the answers generated by the generating AI to the user. Specifically, the providing unit displays the answers on the user's device. If the user is using a device such as a smartphone, tablet, or PC, the providing unit displays the generated answers on the screen of that device. The providing unit can also support multiple delivery methods, such as providing answers in voice or text. For example, the providing unit can provide the generated answers in voice using speech synthesis technology. Text-to-speech (TTS) technology is used as the speech synthesis technology. TTS technology has the ability to convert text data into natural-sounding speech. If the user prefers an audio answer, the providing unit converts the generated text answer into speech using TTS technology and provides it to the user. This allows the user to obtain answers using not only visual information but also auditory information. Furthermore, the providing unit can select the optimal delivery method depending on the user's device and environment. For example, if the user is driving, providing an audio answer is appropriate. On the other hand, if the user is working in a quiet place, providing a text answer is appropriate. In this way, the service provider can respond flexibly to the user's situation and needs, and provide the most appropriate answer.

[0033] The learning unit learns user usage patterns. For example, it learns what questions users have asked in the past and what kind of support they have received. Specifically, the learning unit analyzes the user's operation history and question history and uses this information to improve future support. For example, the learning unit understands the topics users have frequently asked in the past and the tendency for them to ask many questions at certain times of the day. This allows the learning unit to understand user needs and behavioral patterns and provide more personalized support. Furthermore, the learning unit collects user feedback and uses it to improve the system. For example, if a user rates the answers provided, the learning unit uses that rating to improve the accuracy of the output unit's responses. The learning unit can also monitor user usage in real time and adjust system settings and functions as needed. This allows the learning unit to always provide optimal support based on the latest information and improve user satisfaction. In addition, the learning unit takes measures to protect user privacy. For example, user data is anonymized and managed in a secure environment. This allows users to use the system with peace of mind.

[0034] The mobile assist agent system includes a troubleshooting unit. The troubleshooting unit performs tasks such as correcting system errors and user errors. For example, if a system error occurs, the troubleshooting unit automatically detects and corrects it. The troubleshooting unit can also detect and correct user errors. For instance, if a user performs an incorrect operation, the troubleshooting unit provides guidance to correct that operation. This allows the troubleshooting unit to assist users in resolving problems.

[0035] The mobile assist agent system includes a guidance unit that provides guidance. The guidance unit, for example, explains operating procedures and provides instructions on how to use the system. For instance, the guidance unit explains the operating procedures for users when using a product or service. It can also provide instructions on how to use the system. For example, it provides instructions on how to use the system when a user uses it for the first time. In this way, the guidance unit can help the user understand the system.

[0036] The generation unit can generate optimal answers based on historical data and a knowledge base. For example, the generation unit can use historical data to generate the best answer to a user's question. For instance, the generation unit learns from past questions and their answers and generates the best answer for similar questions. Furthermore, the generation unit can also use a knowledge base to generate the best answer to a user's question. For example, the generation unit generates a detailed answer to a user's question based on a knowledge base. Thus, the generation unit can generate optimal answers by leveraging historical data and a knowledge base.

[0037] The service provider can provide users with answers generated by the generation AI. For example, the service provider can display the answers generated by the generation AI to the user. For example, the service provider can display the answers on the user's device. The service provider can also provide the generated answers in voice using speech synthesis technology. For example, the service provider can provide the answers generated by the generation AI to the user in voice. This allows the service provider to provide users with answers generated by the generation AI, enabling rapid support.

[0038] The learning unit can learn from the user's past questions and support history and use this information for future support. For example, the learning unit learns the content of the user's past questions and the results of the responses. For instance, it learns what kinds of questions the user has asked and what kind of support they have received in the past. The learning unit can also analyze the user's operation history and question history and use this information for future support. For example, the learning unit can personalize future support based on the user's operation history. In this way, the learning unit can improve future support by learning from the user's past questions and support history.

[0039] The reception desk can analyze a user's past question history and select the most suitable reception method. For example, the reception desk can automatically display as suggestions the type of question the user has frequently asked in the past. For instance, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest questions that the user might ask during specific time periods based on their past question history. For example, the reception desk can analyze the user's past question history to predict and suggest questions that might be asked during specific time periods. This allows the reception desk to select the most suitable reception method by analyzing the user's past question history.

[0040] The reception system can filter questions based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception system will prioritize receiving questions related to that situation. For example, the reception system will filter and display relevant questions based on the user's areas of interest. The reception system can also suggest appropriate question categories based on the user's current situation and areas of interest. For example, the reception system will suggest appropriate question categories based on the user's current situation and areas of interest. In this way, the reception system can prioritize receiving highly relevant questions by filtering them based on the user's current situation and areas of interest.

[0041] The reception desk can prioritize receiving questions that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to that region. For instance, the reception desk can filter and display relevant questions based on the user's geographical location. Furthermore, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. For example, if the user is traveling, the reception desk will prioritize receiving questions related to their travel destination. This allows the reception desk to provide more appropriate support by prioritizing highly relevant questions based on the user's geographical location.

[0042] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can prioritize questions related to the user's current interests based on their social media activity. For instance, the reception desk can filter and display relevant questions based on information the user has shared on social media. The reception desk can also analyze the user's social media activity and suggest appropriate question categories. For example, the reception desk can analyze the user's social media activity and suggest appropriate question categories. This allows the reception desk to prioritize accepting relevant questions by analyzing the user's social media activity.

[0043] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit will generate a detailed answer for a high-importance question. For example, the generation unit will generate a concise answer for a low-importance question. The generation unit can also dynamically adjust the level of detail in the answer according to the importance of the question. For example, the generation unit can dynamically adjust the level of detail in the answer according to the importance of the question. In this way, the generation unit can provide an appropriate answer by adjusting the level of detail in the answer according to the importance of the question.

[0044] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, it can apply a specialized generation algorithm to technical questions, or a general-purpose generation algorithm to general questions. Furthermore, the generation unit can select the most suitable generation algorithm based on the question category and generate the answer. This allows the generation unit to provide appropriate answers by applying the most suitable generation algorithm for each question category.

[0045] The generation unit can determine the priority of answers based on when the questions were submitted. For example, the generation unit can determine the priority of answers based on the time period in which the questions were submitted. For example, the generation unit can adjust the order of answers according to when the questions were submitted. The generation unit can also generate the optimal answer based on when the questions were submitted. For example, the generation unit can generate the optimal answer based on when the questions were submitted. This allows the generation unit to respond quickly by determining the priority of answers based on when the questions were submitted.

[0046] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can prioritize generating the most relevant answers based on the relevance of the questions. For example, the generation unit can dynamically adjust the order of answers according to the relevance of the questions. The generation unit can also evaluate the relevance of the questions and generate the optimal answer. For example, the generation unit evaluates the relevance of the questions and generates the optimal answer. In this way, the generation unit can provide appropriate answers by adjusting the order of answers based on the relevance of the questions.

[0047] The service provider can select the optimal service delivery method by referring to the user's past question history when providing answers. For example, the service provider can prioritize service delivery methods that the user has frequently used in the past. For example, the service provider can propose the optimal service delivery method based on the user's past question history. The service provider can also analyze the user's past question history and select the most effective service delivery method. For example, the service provider can analyze the user's past question history and select the most effective service delivery method. In this way, the service provider can select the optimal service delivery method by referring to the user's past question history.

[0048] The service provider can customize the method of providing answers based on the user's current situation. For example, if the user is on the move, the service provider will prioritize providing answers via voice. For example, if the user is in a desktop environment, the service provider will provide answers in detailed text format. The service provider can also select the most appropriate method of providing answers according to the user's current situation. For example, the service provider will select the most appropriate method of providing answers according to the user's current situation. This allows the service provider to provide appropriate support by customizing the method of providing answers according to the user's current situation.

[0049] The service provider can select the most appropriate delivery method when providing answers, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider will prioritize providing information related to that region. For example, the service provider will select the most appropriate delivery method based on the user's geographical location. Furthermore, if the user is traveling, the service provider can prioritize providing information related to their travel destination. For example, if the user is traveling, the service provider will prioritize providing information related to their travel destination. In this way, the service provider can provide appropriate support by selecting the most appropriate delivery method based on the user's geographical location.

[0050] The information provider can analyze the user's social media activity and propose a method of providing information when providing responses. For example, the information provider can prioritize providing information related to the user's current interests based on the user's social media activity. For example, the information provider can propose the most suitable method of providing information based on the information the user has shared on social media. The information provider can also analyze the user's social media activity and select an appropriate method of providing information. For example, the information provider can analyze the user's social media activity and select an appropriate method of providing information. In this way, the information provider can prioritize providing relevant information by analyzing the user's social media activity.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and adjust the parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data.

[0052] The learning unit can improve the accuracy of its learning by analyzing the user's past question history during training. For example, the learning unit selects training data based on the user's past question history. For example, the learning unit analyzes the user's past question history and adjusts the parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to the user's past question history. For example, the learning unit improves the accuracy of the learning algorithm by referring to the user's past question history. In this way, the learning unit can improve the accuracy of its learning by analyzing the user's past question history.

[0053] The learning unit can weight the training data based on when the questions were submitted during the training process. For example, the learning unit can weight the training data based on the time period in which the questions were submitted. For example, the learning unit can adjust the weighting of the training data according to when the questions were submitted. The learning unit can also weight the training data based on when the questions were submitted. For example, the learning unit can weight the training data based on when the questions were submitted. This allows the learning unit to improve the accuracy of the training by weighting the training data based on when the questions were submitted.

[0054] The learning unit can select training data while considering the user's geographical location during training. For example, if the user is in a specific region, the learning unit will prioritize learning data related to that region. For example, the learning unit will select training data based on the user's geographical location. The learning unit can also prioritize learning data related to the user's travel destination if the user is traveling. For example, if the user is traveling, the learning unit will prioritize learning data related to the user's travel destination. In this way, the learning unit can improve the accuracy of training by selecting training data based on the user's geographical location.

[0055] The troubleshooting unit can select the optimal method during troubleshooting by referring to the user's past trouble history. For example, the troubleshooting unit can select the optimal troubleshooting method based on troubles the user has experienced in the past. For example, the troubleshooting unit can analyze the user's past trouble history and propose the most effective troubleshooting method. The troubleshooting unit can also select a method that can be resolved quickly by referring to the user's past trouble history. For example, the troubleshooting unit can select a method that can be resolved quickly by referring to the user's past trouble history. In this way, the troubleshooting unit can select the optimal troubleshooting method by referring to the user's past trouble history.

[0056] The troubleshooting unit can customize its troubleshooting methods based on the user's current situation. For example, if the user is on the go, the troubleshooting unit will prioritize voice troubleshooting. For example, if the user is in a desktop environment, the troubleshooting unit will provide detailed text-based troubleshooting. The troubleshooting unit can also select the most appropriate troubleshooting method depending on the user's current situation. For example, the troubleshooting unit will select the most appropriate troubleshooting method depending on the user's current situation. This allows the troubleshooting unit to provide appropriate support by customizing troubleshooting methods according to the user's current situation.

[0057] The troubleshooting unit can select the optimal method during troubleshooting by considering the user's geographical location. For example, if the user is in a specific region, the troubleshooting unit will prioritize providing troubleshooting methods relevant to that region. For instance, the troubleshooting unit selects the optimal troubleshooting method based on the user's geographical location. Furthermore, if the user is traveling, the troubleshooting unit can prioritize providing troubleshooting methods relevant to their travel destination. For example, if the user is traveling, the troubleshooting unit will prioritize providing troubleshooting methods relevant to their travel destination. This allows the troubleshooting unit to provide appropriate support by selecting the optimal troubleshooting method based on the user's geographical location.

[0058] The troubleshooting unit can analyze a user's social media activity and propose solutions during troubleshooting. For example, the troubleshooting unit prioritizes providing troubleshooting methods relevant to the user's current concerns based on their social media activity. For instance, it proposes the optimal troubleshooting method based on information shared by the user on social media. Furthermore, the troubleshooting unit can analyze a user's social media activity and select appropriate troubleshooting methods. This allows the troubleshooting unit to propose relevant troubleshooting methods by analyzing the user's social media activity.

[0059] The guidance unit can select the optimal guidance method by referring to the user's past guidance history when providing guidance. For example, the guidance unit may prioritize guidance methods that the user has frequently used in the past. For example, the guidance unit may propose the optimal guidance method based on the user's past guidance history. The guidance unit can also analyze the user's past guidance history and select the most effective guidance method. For example, the guidance unit may analyze the user's past guidance history and select the most effective guidance method. In this way, the guidance unit can select the optimal guidance method by referring to the user's past guidance history.

[0060] The guidance unit can customize the means of providing guidance based on the user's current situation. For example, if the user is on the move, the guidance unit will prioritize providing voice guidance. For example, if the user is in a desktop environment, the guidance unit will provide detailed text guidance. The guidance unit can also select the most appropriate guidance method according to the user's current situation. For example, the guidance unit will select the most appropriate guidance method according to the user's current situation. In this way, the guidance unit can provide appropriate support by customizing the means of providing guidance according to the user's current situation.

[0061] The guidance unit can select the optimal method of providing guidance by considering the user's geographical location. For example, if the user is in a specific region, the guidance unit will prioritize providing guidance related to that region. For example, the guidance unit will select the optimal method of providing guidance based on the user's geographical location. Furthermore, if the user is traveling, the guidance unit can prioritize providing guidance related to their travel destination. For example, if the user is traveling, the guidance unit will prioritize providing guidance related to their travel destination. In this way, the guidance unit can provide appropriate support by selecting the optimal method of providing guidance based on the user's geographical location.

[0062] The guidance unit can analyze the user's social media activity and propose a means of providing guidance when delivering it. For example, the guidance unit can prioritize providing guidance related to the user's current interests based on their social media activity. For example, the guidance unit can propose the most suitable means of delivery based on information the user has shared on social media. The guidance unit can also analyze the user's social media activity and select an appropriate guidance method. For example, the guidance unit analyzes the user's social media activity and selects an appropriate guidance method. As a result, the guidance unit can prioritize providing relevant information by analyzing the user's social media activity.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The mobile assist agent system can also include a schedule management unit to manage the user's schedule. This unit can, for example, manage the user's calendar and schedule, and remind them of important appointments and tasks. For instance, if a user enters a meeting, the schedule management unit can remind them of the meeting's start time. Furthermore, the schedule management unit can provide optimal time management advice based on the user's schedule. For example, if a user has a busy schedule, the schedule management unit can suggest efficient time allocation. In this way, the mobile assist agent system can support the user's schedule management and achieve efficient time management.

[0065] The mobile assist agent system can also be equipped with a learning support unit to further assist the user's learning. This unit can, for example, provide appropriate learning resources based on what the user wants to learn. For instance, if a user wants to learn a new language, the unit can recommend learning materials and online courses in that language. The unit can also monitor the user's learning progress and provide appropriate feedback. For example, if a user is struggling with a particular task, the unit can provide explanations and hints for that task. This allows the mobile assist agent system to effectively support the user's learning.

[0066] The mobile assist agent system can also be equipped with a fitness support unit to further assist the user's fitness activities. The fitness support unit, for example, provides an appropriate fitness plan based on the user's exercise history and goals. For instance, if the user is aiming to lose weight, the fitness support unit can suggest an exercise plan that promotes calorie burning. Furthermore, the fitness support unit can monitor the user's exercise data in real time and provide appropriate feedback. For example, if the user's heart rate becomes too high during exercise, the fitness support unit can advise them to take a break. This allows the mobile assist agent system to effectively support the user's fitness activities.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk receives user inquiries. User inquiries may include, but are not limited to, questions about products and services. The reception desk accepts inquiries entered by users in natural language. It can also support multiple input methods, such as voice input and text input. For example, speech recognition technology can be used to convert the user's voice input into text. Step 2: The generation unit uses generation AI to analyze the questions received by the reception unit and generate appropriate answers. The generation unit generates the optimal answers based on past data and knowledge bases. For example, it uses text generation AI (e.g., LLM) or multimodal generation AI to generate answers to user questions. Step 3: The providing unit provides the user with the answer generated by the generating unit. The providing unit displays the answer generated by the generating AI to the user. It can also support multiple delivery methods, such as providing the answer in voice or text. For example, the answer can be displayed on the user's device, or the answer generated using speech synthesis technology can be provided in voice. Step 4: The learning unit learns the user's usage patterns. The learning unit learns what questions the user has asked in the past and what kind of support they have received. For example, it analyzes the user's operation history and question history and uses this information to improve future support.

[0069] (Example of form 2) The mobile assist agent system according to an embodiment of the present invention is an AI assistant powered by generative AI integrated into a mobile phone. This mobile assist agent system allows users to input questions about products or services, which the generative AI analyzes and generates appropriate answers. These generated answers are then provided to the user. Furthermore, the generative AI learns the user's usage patterns and provides troubleshooting and guidance. This mechanism allows users to receive support 24 / 7, from anywhere. For example, a user inputs a question about a product or service. The user only needs to input the question in natural language. For example, they might input a question such as, "How do I use this product?" This information is input to the generative AI. Next, the generative AI analyzes the input question and generates an appropriate answer. The generative AI generates the optimal answer based on past data and a knowledge base. For example, in response to the question, "How do I use this product?", the generative AI provides detailed instructions on how to use the product. The generated answers are then provided to the user. The user can review the answers provided by the generative AI and obtain the necessary information. For example, they can use the product by following the instructions provided by the generative AI. Furthermore, the generative AI learns the user's usage patterns. This allows the system to understand what questions users have asked and what kind of support they have received in the past, enabling it to provide more personalized support. For example, if a user has previously asked a question about how to use a product, the generating AI will remember that information and use it for future support. This system allows users to receive support 24 / 7, from anywhere. This is expected to reduce user support wait times, increase service utilization rates, and improve problem-solving capabilities. As a result, the mobile assist agent system can efficiently receive, analyze, and answer user questions, and learn usage patterns.

[0070] The mobile assist agent system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a learning unit. The reception unit receives questions from the user. User questions include, for example, questions about products or services, but are not limited to such examples. The reception unit receives questions entered by the user in natural language, for example. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit can use speech recognition technology to convert the user's voice input into text. The generation unit uses a generation AI to analyze the questions received by the reception unit and generate appropriate answers. The generation unit generates optimal answers based on, for example, past data and a knowledge base. For example, the generation unit uses a text generation AI (e.g., LLM) to generate answers to user questions. The generation unit can also use a multimodal generation AI to generate answers to user questions. For example, the generation unit generates optimal answers to user questions by having the generation AI learn from past data. The provision unit provides the answers generated by the generation unit to the user. The delivery unit, for example, displays the answer generated by the generation AI to the user. The delivery unit can also support multiple delivery methods, such as providing answers in voice or text. For example, the delivery unit displays the answer on the user's device. The delivery unit can also provide the generated answer in voice using speech synthesis technology. The learning unit learns the user's usage patterns. For example, the learning unit learns what questions the user has asked in the past and what kind of support they have received. For example, the learning unit analyzes the user's operation history and question history and uses this information to provide support in the future. As a result, the mobile assist agent system according to this embodiment can efficiently receive, analyze, and provide answers to user questions and learn usage patterns.

[0071] The reception desk receives user inquiries. These inquiries may include, but are not limited to, questions about products and services. The reception desk accepts questions entered by users in natural language, for example. It can also support multiple input methods, such as voice input and text input. For example, the reception desk can use speech recognition technology to convert user voice input into text. Specifically, a deep learning-based speech recognition model is used. This model is trained to handle various accents and speaking styles by learning from a large amount of voice data. When a user enters a question by voice, the speech recognition model analyzes the voice and converts it into text data. In the case of text input, the user enters the question using a keyboard or touchscreen. The reception desk manages these input methods in an integrated manner to improve user convenience. Furthermore, the reception desk can analyze user input in real time and provide appropriate feedback. For example, it can help users complete their questions quickly by suggesting relevant options while they are typing. In addition, the reception desk securely manages user input and takes measures to protect privacy. This allows users to enter questions with confidence.

[0072] The generation unit uses a generation AI to analyze questions received by the reception unit and generate appropriate answers. For example, the generation unit generates optimal answers based on past data and a knowledge base. Specifically, the generation unit uses a text generation AI (e.g., LLM) to generate answers to user questions. LLM has the ability to understand and generate natural language by learning from large amounts of text data. When a user question is sent from the reception unit to the generation unit, LLM analyzes the question and generates the optimal answer. For example, if a user asks, "What are the features of the latest smartphones?", LLM refers to past data and a knowledge base to generate a detailed answer about the features of the latest smartphones. The generation unit can also generate answers to user questions using a multimodal generation AI. A multimodal generation AI has the ability to integrate and analyze multiple modals, such as images and audio, in addition to text. For example, if a user asks, "How do I use this product?", the multimodal generation AI can generate an answer combining text and images and provide it to the user. Furthermore, the generation unit ensures that the generation AI learns from past data to generate the optimal answer to the user's question. This allows the generation unit to provide quick and accurate answers to a wide range of user questions.

[0073] The providing unit provides the user with the answers generated by the generating unit. For example, the providing unit displays the answers generated by the generating AI to the user. Specifically, the providing unit displays the answers on the user's device. If the user is using a device such as a smartphone, tablet, or PC, the providing unit displays the generated answers on the screen of that device. The providing unit can also support multiple delivery methods, such as providing answers in voice or text. For example, the providing unit can provide the generated answers in voice using speech synthesis technology. Text-to-speech (TTS) technology is used as the speech synthesis technology. TTS technology has the ability to convert text data into natural-sounding speech. If the user prefers an audio answer, the providing unit converts the generated text answer into speech using TTS technology and provides it to the user. This allows the user to obtain answers using not only visual information but also auditory information. Furthermore, the providing unit can select the optimal delivery method depending on the user's device and environment. For example, if the user is driving, providing an audio answer is appropriate. On the other hand, if the user is working in a quiet place, providing a text answer is appropriate. In this way, the service provider can respond flexibly to the user's situation and needs, and provide the most appropriate answer.

[0074] The learning unit learns user usage patterns. For example, it learns what questions users have asked in the past and what kind of support they have received. Specifically, the learning unit analyzes the user's operation history and question history and uses this information to improve future support. For example, the learning unit understands the topics users have frequently asked in the past and the tendency for them to ask many questions at certain times of the day. This allows the learning unit to understand user needs and behavioral patterns and provide more personalized support. Furthermore, the learning unit collects user feedback and uses it to improve the system. For example, if a user rates the answers provided, the learning unit uses that rating to improve the accuracy of the output unit's responses. The learning unit can also monitor user usage in real time and adjust system settings and functions as needed. This allows the learning unit to always provide optimal support based on the latest information and improve user satisfaction. In addition, the learning unit takes measures to protect user privacy. For example, user data is anonymized and managed in a secure environment. This allows users to use the system with peace of mind.

[0075] The mobile assist agent system includes a troubleshooting unit. The troubleshooting unit performs tasks such as correcting system errors and user errors. For example, if a system error occurs, the troubleshooting unit automatically detects and corrects it. The troubleshooting unit can also detect and correct user errors. For instance, if a user performs an incorrect operation, the troubleshooting unit provides guidance to correct that operation. This allows the troubleshooting unit to assist users in resolving problems.

[0076] The mobile assist agent system includes a guidance unit that provides guidance. The guidance unit, for example, explains operating procedures and provides instructions on how to use the system. For instance, the guidance unit explains the operating procedures for users when using a product or service. It can also provide instructions on how to use the system. For example, it provides instructions on how to use the system when a user uses it for the first time. In this way, the guidance unit can help the user understand the system.

[0077] The generation unit can generate optimal answers based on historical data and a knowledge base. For example, the generation unit can use historical data to generate the best answer to a user's question. For instance, the generation unit learns from past questions and their answers and generates the best answer for similar questions. Furthermore, the generation unit can also use a knowledge base to generate the best answer to a user's question. For example, the generation unit generates a detailed answer to a user's question based on a knowledge base. Thus, the generation unit can generate optimal answers by leveraging historical data and a knowledge base.

[0078] The service provider can provide users with answers generated by the generation AI. For example, the service provider can display the answers generated by the generation AI to the user. For example, the service provider can display the answers on the user's device. The service provider can also provide the generated answers in voice using speech synthesis technology. For example, the service provider can provide the answers generated by the generation AI to the user in voice. This allows the service provider to provide users with answers generated by the generation AI, enabling rapid support.

[0079] The learning unit can learn from the user's past questions and support history and use this information for future support. For example, the learning unit learns the content of the user's past questions and the results of the responses. For instance, it learns what kinds of questions the user has asked and what kind of support they have received in the past. The learning unit can also analyze the user's operation history and question history and use this information for future support. For example, the learning unit can personalize future support based on the user's operation history. In this way, the learning unit can improve future support by learning from the user's past questions and support history.

[0080] The reception system can estimate the user's emotions and adjust how questions are answered based on those emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception system can provide detailed input options and suggest customizable input methods. The reception system can also prioritize voice input if the user is in a hurry, allowing them to enter questions quickly. This allows the reception system to provide more appropriate support by adjusting how questions are answered according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk can analyze a user's past question history and select the most suitable reception method. For example, the reception desk can automatically display as suggestions the type of question the user has frequently asked in the past. For instance, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest questions that the user might ask during specific time periods based on their past question history. For example, the reception desk can analyze the user's past question history to predict and suggest questions that might be asked during specific time periods. This allows the reception desk to select the most suitable reception method by analyzing the user's past question history.

[0082] The reception system can filter questions based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception system will prioritize receiving questions related to that situation. For example, the reception system will filter and display relevant questions based on the user's areas of interest. The reception system can also suggest appropriate question categories based on the user's current situation and areas of interest. For example, the reception system will suggest appropriate question categories based on the user's current situation and areas of interest. In this way, the reception system can prioritize receiving highly relevant questions by filtering them based on the user's current situation and areas of interest.

[0083] The reception desk can estimate the user's emotions and determine the priority of questions to accept based on the estimated emotions. For example, if the user has an urgent question, the reception desk will prioritize that question. For example, if the user is relaxed, the reception desk will accept questions with normal priority. The reception desk can also raise the priority of questions to respond quickly if the user is stressed. For example, if the reception desk is stressed, the reception desk will raise the priority of questions to respond quickly. This allows the reception desk to respond quickly by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception desk can prioritize receiving questions that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to that region. For instance, the reception desk can filter and display relevant questions based on the user's geographical location. Furthermore, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. For example, if the user is traveling, the reception desk will prioritize receiving questions related to their travel destination. This allows the reception desk to provide more appropriate support by prioritizing highly relevant questions based on the user's geographical location.

[0085] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can prioritize questions related to the user's current interests based on their social media activity. For instance, the reception desk can filter and display relevant questions based on information the user has shared on social media. The reception desk can also analyze the user's social media activity and suggest appropriate question categories. For example, the reception desk can analyze the user's social media activity and suggest appropriate question categories. This allows the reception desk to prioritize accepting relevant questions by analyzing the user's social media activity.

[0086] The generation unit can estimate the user's emotions and adjust the way it expresses its response based on those emotions. For example, if the user is stressed, the generation unit will generate a concise and clear response. For example, if the user is relaxed, the generation unit will generate a response that includes detailed explanations. The generation unit can also generate a concise and easy-to-understand response if the user is in a hurry. For example, if the user is in a hurry, the generation unit will generate a concise and easy-to-understand response. In this way, the generation unit can provide more appropriate responses by adjusting the way it expresses its response according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit will generate a detailed answer for a high-importance question. For example, the generation unit will generate a concise answer for a low-importance question. The generation unit can also dynamically adjust the level of detail in the answer according to the importance of the question. For example, the generation unit can dynamically adjust the level of detail in the answer according to the importance of the question. In this way, the generation unit can provide an appropriate answer by adjusting the level of detail in the answer according to the importance of the question.

[0088] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, it can apply a specialized generation algorithm to technical questions, or a general-purpose generation algorithm to general questions. Furthermore, the generation unit can select the most suitable generation algorithm based on the question category and generate the answer. This allows the generation unit to provide appropriate answers by applying the most suitable generation algorithm for each question category.

[0089] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise response. For example, if the user is relaxed, the generation unit will generate a longer response that includes detailed explanations. The generation unit can also generate a response with visually stimulating effects if the user is excited. For example, if the user is excited, the generation unit will generate a response with visually stimulating effects. In this way, the generation unit can provide an appropriate response by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The generation unit can determine the priority of answers based on when the questions were submitted. For example, the generation unit can determine the priority of answers based on the time period in which the questions were submitted. For example, the generation unit can adjust the order of answers according to when the questions were submitted. The generation unit can also generate the optimal answer based on when the questions were submitted. For example, the generation unit can generate the optimal answer based on when the questions were submitted. This allows the generation unit to respond quickly by determining the priority of answers based on when the questions were submitted.

[0091] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can prioritize generating the most relevant answers based on the relevance of the questions. For example, the generation unit can dynamically adjust the order of answers according to the relevance of the questions. The generation unit can also evaluate the relevance of the questions and generate the optimal answer. For example, the generation unit evaluates the relevance of the questions and generates the optimal answer. In this way, the generation unit can provide appropriate answers by adjusting the order of answers based on the relevance of the questions.

[0092] The service provider can estimate the user's emotions and adjust the way it delivers responses based on those emotions. For example, if the user is stressed, the service provider might select a simple and highly visible delivery method. For example, if the user is relaxed, the service provider might select a delivery method that includes detailed information. Furthermore, if the user is in a hurry, the service provider might select a concise delivery method that can be quickly understood. This allows the service provider to provide appropriate support by adjusting the delivery method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The service provider can select the optimal service delivery method by referring to the user's past question history when providing answers. For example, the service provider can prioritize service delivery methods that the user has frequently used in the past. For example, the service provider can propose the optimal service delivery method based on the user's past question history. The service provider can also analyze the user's past question history and select the most effective service delivery method. For example, the service provider can analyze the user's past question history and select the most effective service delivery method. In this way, the service provider can select the optimal service delivery method by referring to the user's past question history.

[0094] The service provider can customize the method of providing answers based on the user's current situation. For example, if the user is on the move, the service provider will prioritize providing answers via voice. For example, if the user is in a desktop environment, the service provider will provide answers in detailed text format. The service provider can also select the most appropriate method of providing answers according to the user's current situation. For example, the service provider will select the most appropriate method of providing answers according to the user's current situation. This allows the service provider to provide appropriate support by customizing the method of providing answers according to the user's current situation.

[0095] The service provider can estimate the user's emotions and determine the order in which answers are provided based on the estimated emotions. For example, if the user is asking an urgent question, the service provider will prioritize providing that answer. For example, if the user is relaxed, the service provider will provide answers in the normal order. The service provider can also adjust the order in which answers are provided to respond quickly if the user is stressed. For example, if the service provider is stressed, the service provider will adjust the order in which answers are provided to respond quickly. This allows the service provider to respond quickly by determining the order in which answers are provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The service provider can select the most appropriate delivery method when providing answers, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider will prioritize providing information related to that region. For example, the service provider will select the most appropriate delivery method based on the user's geographical location. Furthermore, if the user is traveling, the service provider can prioritize providing information related to their travel destination. For example, if the user is traveling, the service provider will prioritize providing information related to their travel destination. In this way, the service provider can provide appropriate support by selecting the most appropriate delivery method based on the user's geographical location.

[0097] The information provider can analyze the user's social media activity and propose a method of providing information when providing responses. For example, the information provider can prioritize providing information related to the user's current interests based on the user's social media activity. For example, the information provider can propose the most suitable method of providing information based on the information the user has shared on social media. The information provider can also analyze the user's social media activity and select an appropriate method of providing information. For example, the information provider can analyze the user's social media activity and select an appropriate method of providing information. In this way, the information provider can prioritize providing relevant information by analyzing the user's social media activity.

[0098] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize learning data related to that emotion. For example, if the user is relaxed, the learning unit will use normal training data. The learning unit can also prioritize learning data related to the user's excitement. For example, if the user is excited, the learning unit will prioritize learning data related to that emotion. In this way, the learning unit can improve the accuracy of learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and adjust the parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data.

[0100] The learning unit can improve the accuracy of its learning by analyzing the user's past question history during training. For example, the learning unit selects training data based on the user's past question history. For example, the learning unit analyzes the user's past question history and adjusts the parameters of the learning algorithm. The learning unit can also improve the accuracy of the learning algorithm by referring to the user's past question history. For example, the learning unit improves the accuracy of the learning algorithm by referring to the user's past question history. In this way, the learning unit can improve the accuracy of its learning by analyzing the user's past question history.

[0101] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit will reduce the learning frequency. For example, if the user is relaxed, the learning unit will maintain the normal learning frequency. The learning unit can also increase the learning frequency if the user is excited. For example, if the user is excited, the learning unit will increase the learning frequency. In this way, the learning unit can improve the efficiency of learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The learning unit can weight the training data based on when the questions were submitted during the training process. For example, the learning unit can weight the training data based on the time period in which the questions were submitted. For example, the learning unit can adjust the weighting of the training data according to when the questions were submitted. The learning unit can also weight the training data based on when the questions were submitted. For example, the learning unit can weight the training data based on when the questions were submitted. This allows the learning unit to improve the accuracy of the training by weighting the training data based on when the questions were submitted.

[0103] The learning unit can select training data while considering the user's geographical location during training. For example, if the user is in a specific region, the learning unit will prioritize learning data related to that region. For example, the learning unit will select training data based on the user's geographical location. The learning unit can also prioritize learning data related to the user's travel destination if the user is traveling. For example, if the user is traveling, the learning unit will prioritize learning data related to the user's travel destination. In this way, the learning unit can improve the accuracy of training by selecting training data based on the user's geographical location.

[0104] The troubleshooting unit can estimate the user's emotions and adjust the troubleshooting method based on the estimated emotions. For example, if the user is stressed, the troubleshooting unit can provide a simple and quick troubleshooting method. For example, if the user is relaxed, the troubleshooting unit can provide a troubleshooting method that includes detailed steps. The troubleshooting unit can also provide a troubleshooting method that can be resolved quickly if the user is in a hurry. For example, if the troubleshooting unit is in a hurry, the troubleshooting unit can provide a troubleshooting method that can be resolved quickly. In this way, the troubleshooting unit can quickly resolve problems by adjusting the troubleshooting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The troubleshooting unit can select the optimal method during troubleshooting by referring to the user's past trouble history. For example, the troubleshooting unit can select the optimal troubleshooting method based on troubles the user has experienced in the past. For example, the troubleshooting unit can analyze the user's past trouble history and propose the most effective troubleshooting method. The troubleshooting unit can also select a method that can be resolved quickly by referring to the user's past trouble history. For example, the troubleshooting unit can select a method that can be resolved quickly by referring to the user's past trouble history. In this way, the troubleshooting unit can select the optimal troubleshooting method by referring to the user's past trouble history.

[0106] The troubleshooting unit can customize its troubleshooting methods based on the user's current situation. For example, if the user is on the go, the troubleshooting unit will prioritize voice troubleshooting. For example, if the user is in a desktop environment, the troubleshooting unit will provide detailed text-based troubleshooting. The troubleshooting unit can also select the most appropriate troubleshooting method depending on the user's current situation. For example, the troubleshooting unit will select the most appropriate troubleshooting method depending on the user's current situation. This allows the troubleshooting unit to provide appropriate support by customizing troubleshooting methods according to the user's current situation.

[0107] The troubleshooting unit can estimate the user's emotions and determine troubleshooting priorities based on those emotions. For example, if the user has an urgent problem, the troubleshooting unit will prioritize resolving that problem. For example, if the user is relaxed, the troubleshooting unit will troubleshoot with normal priorities. The troubleshooting unit can also raise the troubleshooting priority to respond quickly if the user is stressed. For example, if the user is stressed, the troubleshooting unit will raise the troubleshooting priority to respond quickly. This allows the troubleshooting unit to respond quickly by determining troubleshooting priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The troubleshooting unit can select the optimal method during troubleshooting by considering the user's geographical location. For example, if the user is in a specific region, the troubleshooting unit will prioritize providing troubleshooting methods relevant to that region. For instance, the troubleshooting unit selects the optimal troubleshooting method based on the user's geographical location. Furthermore, if the user is traveling, the troubleshooting unit can prioritize providing troubleshooting methods relevant to their travel destination. For example, if the user is traveling, the troubleshooting unit will prioritize providing troubleshooting methods relevant to their travel destination. This allows the troubleshooting unit to provide appropriate support by selecting the optimal troubleshooting method based on the user's geographical location.

[0109] The troubleshooting unit can analyze a user's social media activity and propose solutions during troubleshooting. For example, the troubleshooting unit prioritizes providing troubleshooting methods relevant to the user's current concerns based on their social media activity. For instance, it proposes the optimal troubleshooting method based on information shared by the user on social media. Furthermore, the troubleshooting unit can analyze a user's social media activity and select appropriate troubleshooting methods. This allows the troubleshooting unit to propose relevant troubleshooting methods by analyzing the user's social media activity.

[0110] The guidance unit can estimate the user's emotions and adjust the guidance delivery method based on the estimated emotions. For example, if the user is stressed, the guidance unit will select a simple and highly visible guidance method. For example, if the user is relaxed, the guidance unit will select a guidance method that includes detailed information. The guidance unit can also select a concise guidance method that can be quickly understood if the user is in a hurry. For example, if the guidance unit is in a hurry, the guidance unit will select a concise guidance method that can be quickly understood. In this way, the guidance unit can provide appropriate support by adjusting the guidance delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The guidance unit can select the optimal guidance method by referring to the user's past guidance history when providing guidance. For example, the guidance unit may prioritize guidance methods that the user has frequently used in the past. For example, the guidance unit may propose the optimal guidance method based on the user's past guidance history. The guidance unit can also analyze the user's past guidance history and select the most effective guidance method. For example, the guidance unit may analyze the user's past guidance history and select the most effective guidance method. In this way, the guidance unit can select the optimal guidance method by referring to the user's past guidance history.

[0112] The guidance unit can customize the means of providing guidance based on the user's current situation. For example, if the user is on the move, the guidance unit will prioritize providing voice guidance. For example, if the user is in a desktop environment, the guidance unit will provide detailed text guidance. The guidance unit can also select the most appropriate guidance method according to the user's current situation. For example, the guidance unit will select the most appropriate guidance method according to the user's current situation. In this way, the guidance unit can provide appropriate support by customizing the means of providing guidance according to the user's current situation.

[0113] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated emotions. For example, if the user needs urgent guidance, the guidance unit will provide that guidance with priority. For example, if the user is relaxed, the guidance unit will provide guidance with normal priority. The guidance unit can also raise the priority of guidance to respond quickly if the user is stressed. For example, if the guidance unit is stressed, the guidance unit will raise the priority of guidance to respond quickly. In this way, the guidance unit can respond quickly by determining the priority of guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The guidance unit can select the optimal method of providing guidance by considering the user's geographical location. For example, if the user is in a specific region, the guidance unit will prioritize providing guidance related to that region. For example, the guidance unit will select the optimal method of providing guidance based on the user's geographical location. Furthermore, if the user is traveling, the guidance unit can prioritize providing guidance related to their travel destination. For example, if the user is traveling, the guidance unit will prioritize providing guidance related to their travel destination. In this way, the guidance unit can provide appropriate support by selecting the optimal method of providing guidance based on the user's geographical location.

[0115] The guidance unit can analyze the user's social media activity and propose a means of providing guidance when delivering it. For example, the guidance unit can prioritize providing guidance related to the user's current interests based on their social media activity. For example, the guidance unit can propose the most suitable means of delivery based on information the user has shared on social media. The guidance unit can also analyze the user's social media activity and select an appropriate guidance method. For example, the guidance unit analyzes the user's social media activity and selects an appropriate guidance method. As a result, the guidance unit can prioritize providing relevant information by analyzing the user's social media activity.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The mobile assist agent system can also be equipped with a health monitoring unit that monitors the user's health status. The health monitoring unit measures vital signs such as the user's heart rate, blood pressure, and body temperature, and analyzes this data. For example, a user's heart rate may increase if they are stressed. Based on this data, the health monitoring unit can monitor the user's health status in real time and issue alerts as needed. Furthermore, the health monitoring unit can provide appropriate advice and guidance based on the user's health status. For example, if the user is tired, it can advise them to rest. This allows the mobile assist agent system to monitor the user's health status and provide appropriate support.

[0118] The mobile assist agent system can also include a schedule management unit to manage the user's schedule. This unit can, for example, manage the user's calendar and schedule, and remind them of important appointments and tasks. For instance, if a user enters a meeting, the schedule management unit can remind them of the meeting's start time. Furthermore, the schedule management unit can provide optimal time management advice based on the user's schedule. For example, if a user has a busy schedule, the schedule management unit can suggest efficient time allocation. In this way, the mobile assist agent system can support the user's schedule management and achieve efficient time management.

[0119] The mobile assist agent system can also include a content recommendation unit that recommends content based on the user's hobbies and interests. For example, the content recommendation unit analyzes the user's past browsing and search history to recommend content that matches the user's interests. If the user is interested in movies, the content recommendation unit can provide the latest movie information and reviews. Furthermore, the content recommendation unit can recommend appropriate content based on the user's current mood and situation. For example, if the user wants to relax, it can recommend relaxing music or videos. This allows the mobile assist agent system to provide personalized content based on the user's hobbies and interests.

[0120] The mobile assist agent system can also be equipped with a learning support unit to further assist the user's learning. This unit can, for example, provide appropriate learning resources based on what the user wants to learn. For instance, if a user wants to learn a new language, the unit can recommend learning materials and online courses in that language. The unit can also monitor the user's learning progress and provide appropriate feedback. For example, if a user is struggling with a particular task, the unit can provide explanations and hints for that task. This allows the mobile assist agent system to effectively support the user's learning.

[0121] The mobile assist agent system can also be equipped with a fitness support unit to further assist the user's fitness activities. The fitness support unit, for example, provides an appropriate fitness plan based on the user's exercise history and goals. For instance, if the user is aiming to lose weight, the fitness support unit can suggest an exercise plan that promotes calorie burning. Furthermore, the fitness support unit can monitor the user's exercise data in real time and provide appropriate feedback. For example, if the user's heart rate becomes too high during exercise, the fitness support unit can advise them to take a break. This allows the mobile assist agent system to effectively support the user's fitness activities.

[0122] The mobile assist agent system can further estimate the user's emotions and adjust the fitness plan based on those emotions. For example, if the user is feeling stressed, the fitness support system can suggest relaxing yoga or stretching. If the user is feeling energetic, the fitness support system can suggest high-intensity interval training (HIIT). Furthermore, if the user is tired, the fitness support system can suggest light walking or recovery exercises. In this way, the mobile assist agent system can provide the optimal fitness plan according to the user's emotions.

[0123] The mobile assist agent system can further estimate the user's emotions and adjust its learning support methods based on those emotions. For example, if the user is feeling stressed, the learning support unit can suggest a relaxing learning environment. If the user wants to improve their concentration, the learning support unit can provide music or ambient sounds to enhance focus. Furthermore, if the user is losing motivation, the learning support unit can suggest encouraging messages or steps to achieve their goals. In this way, the mobile assist agent system can provide optimal learning support according to the user's emotions.

[0124] The mobile assist agent system can further estimate the user's emotions and adjust its content recommendation methods based on those emotions. For example, if the user wants to relax, the content recommendation system can recommend relaxing music or videos. If the user is feeling energetic, the system can recommend action movies or exercise videos. Furthermore, if the user is feeling sad, the system can recommend mood-boosting comedy movies or content containing positive messages. In this way, the mobile assist agent system can provide the most suitable content according to the user's emotions.

[0125] The mobile assistance agent system can further estimate the user's emotions and adjust its troubleshooting methods based on those emotions. For example, if the user is stressed, the troubleshooting unit can provide a simple and quick solution. If the user is relaxed, the troubleshooting unit can provide a solution with detailed steps. Furthermore, if the user is in a hurry, the troubleshooting unit can prioritize providing a solution that can be resolved quickly. In this way, the mobile assistance agent system can provide the optimal troubleshooting method according to the user's emotions.

[0126] The mobile assistance agent system can further estimate the user's emotions and adjust the guidance provided based on those emotions. For example, if the user is stressed, the guidance unit can select a simple and easy-to-understand guidance method. If the user is relaxed, the guidance unit can select a guidance method that includes detailed information. Furthermore, if the user is in a hurry, the guidance unit can select a concise guidance method that can be quickly understood. In this way, the mobile assistance agent system can provide optimal guidance according to the user's emotions.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk receives user inquiries. User inquiries may include, but are not limited to, questions about products and services. The reception desk accepts inquiries entered by users in natural language. It can also support multiple input methods, such as voice input and text input. For example, speech recognition technology can be used to convert the user's voice input into text. Step 2: The generation unit uses generation AI to analyze the questions received by the reception unit and generate appropriate answers. The generation unit generates the optimal answers based on past data and knowledge bases. For example, it uses text generation AI (e.g., LLM) or multimodal generation AI to generate answers to user questions. Step 3: The providing unit provides the user with the answer generated by the generating unit. The providing unit displays the answer generated by the generating AI to the user. It can also support multiple delivery methods, such as providing the answer in voice or text. For example, the answer can be displayed on the user's device, or the answer generated using speech synthesis technology can be provided in voice. Step 4: The learning unit learns the user's usage patterns. The learning unit learns what questions the user has asked in the past and what kind of support they have received. For example, it analyzes the user's operation history and question history and uses this information to improve future support.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, learning unit, troubleshooting unit, and guidance unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user questions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the question using generation AI and generates an appropriate answer. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated answer to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's usage patterns. The troubleshooting unit is implemented by the specific processing unit 290 of the data processing unit 12 and corrects system errors and operational mistakes. The guidance unit is implemented by the control unit 46A of the smart device 14 and guides the user through operating procedures and usage methods. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, learning unit, troubleshooting unit, and guidance unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user questions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the question using generation AI and generates an appropriate answer. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated answer to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's usage patterns. The troubleshooting unit is implemented by the specific processing unit 290 of the data processing unit 12 and corrects system errors and operational mistakes. The guidance unit is implemented by the control unit 46A of the smart glasses 214 and guides the user through operating procedures and usage methods. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, learning unit, troubleshooting unit, and guidance unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user questions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the question using generation AI and generates an appropriate answer. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated answer to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's usage patterns. The troubleshooting unit is implemented by the specific processing unit 290 of the data processing unit 12 and corrects system errors and operational mistakes. The guidance unit is implemented by the control unit 46A of the headset terminal 314 and guides the user through operating procedures and usage methods. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, learning unit, troubleshooting unit, and guidance unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user questions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the question using a generation AI to generate an appropriate answer. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated answer to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's usage patterns. The troubleshooting unit is implemented by the specific processing unit 290 of the data processing unit 12 and corrects system errors and operational mistakes. The guidance unit is implemented by the control unit 46A of the robot 414 and guides the user through operating procedures and usage methods. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A reception desk that handles user inquiries, A generation unit analyzes the questions received by the reception unit and generates appropriate answers, A providing unit that provides the answer generated by the generation unit, It comprises a learning unit that learns the user's usage patterns. A system characterized by the following features. (Note 2) It includes a troubleshooting unit for troubleshooting. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a guidance unit that provides guidance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on past data and knowledge bases, generate the optimal answer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The AI ​​generates the answers and provides them to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, Learn from the user's past questions and support history, and use this information to improve future support. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to ask based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating answers, different generation algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing an answer, the system will refer to the user's past question history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing responses, customize the method of delivery based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which responses are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing responses, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing responses, we analyze the user's social media activity and suggest methods for providing the responses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the system analyzes the user's past question history to improve the accuracy of the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the training data is weighted based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, During training, the training data is selected taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The troubleshooting unit described above, It estimates the user's emotions and adjusts troubleshooting methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The troubleshooting unit described above, During troubleshooting, the system selects the optimal solution by referring to the user's past troubleshooting history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The troubleshooting unit described above, During troubleshooting, customize the approach based on the user's current situation. The system described in Appendix 2, characterized by the features described herein. (Note 34) The troubleshooting unit described above, It estimates the user's emotions and prioritizes troubleshooting based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The troubleshooting unit described above, When troubleshooting, the optimal method is selected considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The troubleshooting unit described above, During troubleshooting, we analyze the user's social media activity and suggest solutions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned guidance unit, It estimates the user's emotions and adjusts how guidance is provided based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned guidance unit, When providing guidance, the system will refer to the user's past guidance history to select the most suitable delivery method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned guidance unit, When providing guidance, customize the means of delivery based on the user's current situation. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned guidance unit, It estimates the user's emotions and determines the priority of guidance based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned guidance unit, When providing guidance, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned guidance unit, When providing guidance, we analyze users' social media activity and suggest methods for providing it. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles user inquiries, A generation unit analyzes the questions received by the reception unit and generates appropriate answers, A providing unit that provides the answer generated by the generation unit, It comprises a learning unit that learns the user's usage patterns. A system characterized by the following features.

2. It includes a troubleshooting unit for troubleshooting. The system according to feature 1.

3. It includes a guidance unit that provides guidance. The system according to feature 1.

4. The generating unit is Based on past data and knowledge bases, generate the optimal answer. The system according to feature 1.

5. The aforementioned supply unit is, The AI ​​generates answers that are provided to the user. The system according to feature 1.

6. The aforementioned learning unit, Learn from the user's past questions and support history, and use this information to improve future support. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

9. The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to ask based on those estimated emotions. The system according to feature 1.

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