system

Abstract

The system according to this embodiment aims to provide balanced answers and different perspectives to user questions. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives questions from users. The collection unit collects relevant information from a database where current or past news information is stored, based on the questions received by the reception unit. The generation unit analyzes the information collected by the collection unit and generates answers from multiple different perspectives. The provision unit provides explanations of background information related to the answers, along with the answers generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to provide a balanced answer or different perspectives to a user's question.

[0005] The system according to the embodiment aims to provide a balanced answer or different perspectives to a user's question.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives questions from users. The collection unit collects relevant information from a database containing current or past news information based on the questions received by the reception unit. The generation unit analyzes the information collected by the collection unit and generates answers from multiple different perspectives. The provision unit provides explanations of background information related to the answers, along with the answers generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide balanced answers and different perspectives to user questions. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​assistant question-answering system for news and current events according to an embodiment of the present invention is a system that collects and analyzes relevant information in response to a user's question and provides answers from different perspectives. The AI ​​assistant question-answering system for news and current events starts when the user inputs a question. For example, the user might input a question such as, "Please tell me about the current economic situation." This question is received by the reception unit. Next, based on the question received by the reception unit, the collection unit collects relevant information from a database where current or past news information is stored. For example, it collects information from news sites, academic papers, and official government databases. The collected information is analyzed by the generation unit, and answers from multiple different perspectives are generated. For example, answers from different perspectives such as the perspective of an economic expert, the government, and the citizens are generated. The generated answers are provided to the user by the provision unit. Along with the generated answers, the provision unit provides explanations of background information related to the answers. For example, it explains the historical background of the economic situation and the process leading up to the current situation. Furthermore, the provision unit can provide explanations of background information and historical context related to current events. For example, it explains past events related to the current economic situation and comparisons with the economic situation of other countries. Furthermore, the generation unit can evaluate and compare the collected information based on factors such as reliability, timeliness, and source. This improves the reliability of the information provided to the user. In addition, the reception unit can estimate the user's emotions and adjust the timing of question submission based on the estimated emotions. For example, if the user is feeling anxious, the timing of question submission may be delayed. The generation unit can also determine the perspective based on the user's areas of interest at the time of question submission. For example, if the user is interested in economics, an answer from an economic perspective will be generated. Furthermore, the generation unit can estimate the user's emotions and generate an answer from a perspective related to the estimated emotions of the user. For example, if the user is feeling anxious, an answer from a perspective that alleviates anxiety will be generated.This allows the AI ​​assistant question-answering system for news and current events to collect and analyze relevant information and provide answers from different perspectives in response to user questions.

[0029] The AI ​​assistant question-answering system for news and current events according to this embodiment comprises a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives questions from the user. User questions include, but are not limited to, text format, voice format, etc. The reception unit can, for example, receive questions in text format. The reception unit can also receive questions in voice format. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of question acceptance based on the estimated user emotions. For example, if the user is feeling anxious, the acceptance of the question can be delayed. The collection unit collects relevant information from a database where current or past news information is stored, based on the questions received by the reception unit. The collection unit can, for example, collect information from news websites. The collection unit can also collect information from academic papers. Furthermore, the collection unit can also collect information from official government databases. For example, the collection unit collects the latest news articles from news websites. The collection unit collects relevant papers from a database of academic papers. The collection unit collects statistical data from official government databases. The generation unit analyzes the information collected by the collection unit and generates answers from multiple different perspectives. For example, the generation unit can generate answers from the perspective of an economic expert. It can also generate answers from the perspective of the government. Furthermore, the generation unit can generate answers from the perspective of citizens. For example, the generation unit generates answers from the perspective of an economic expert. The generation unit generates answers from the perspective of the government. The generation unit generates answers from the perspective of citizens. The provision unit provides explanations of background information related to the answers generated by the generation unit. For example, the provision unit explains the historical background of the economic situation. It can also explain the events leading up to the current situation. Furthermore, the provision unit can provide explanations of background information and historical context related to current events. For example, the provision unit explains past events related to the current economic situation. The provision unit explains comparisons with the economic situation of other countries.As a result, the AI ​​assistant question-answering system for news and current events according to the embodiment can collect and analyze relevant information in response to a user's question and provide answers from different perspectives.

[0030] The reception desk receives user questions. User questions may include, but are not limited to, text or voice formats. The reception desk can, for example, accept text questions. It can also accept voice questions. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can delay accepting the question. Specifically, the reception desk uses natural language processing technology to analyze the user's text questions and understand their intent and content. For voice questions, speech recognition technology is used to convert the audio to text, which is then analyzed. Furthermore, for emotion estimation, machine learning models are used to estimate the user's emotions from the tone of voice and the expression in the text. For example, if the user's voice tone is high and they are in a hurry, the system will determine that the user is anxious and respond quickly. On the other hand, if the user is speaking in a calm tone, a normal response will be given. This allows the reception desk to respond flexibly according to the user's situation and emotions. Furthermore, the reception desk can refer to a user's past question history and prioritize questions based on the user's interests. For example, a user who has asked many economic-related questions in the past will have economic-related questions processed first. This allows the reception desk to provide personalized responses that meet the user's needs.

[0031] The information collection unit collects relevant information from databases containing current or past news information based on questions received by the reception unit. For example, the collection unit collects information from news websites. It can also collect information from academic papers. Furthermore, it can collect information from official government databases. For instance, the collection unit collects the latest news articles from news websites. It collects relevant papers from academic paper databases. It collects statistical data from official government databases. Specifically, the collection unit uses web scraping techniques to automatically collect the latest news articles from news websites on the internet. This includes techniques for analyzing the HTML structure of news websites and extracting necessary information. For academic paper databases, it uses APIs to search for and download relevant papers. Furthermore, it executes queries to retrieve publicly available statistical data and reports from official government databases. The collection unit centrally manages this information and filters and categorizes it as needed. For example, it categorizes collected news articles to enable quick searching of information related to specific topics. Furthermore, the data collection unit uses algorithms to evaluate the reliability of information and prioritizes collecting data from reliable sources. This allows the data collection unit to provide relevant information quickly and accurately in response to user inquiries.

[0032] The generation unit analyzes the information collected by the collection unit and generates answers from multiple different perspectives. For example, the generation unit can generate answers from the perspective of an economic expert. It can also generate answers from the perspective of the government. Furthermore, it can generate answers from the perspective of citizens. Specifically, the generation unit uses natural language generation technology to generate answers based on the collected information. The generation unit first analyzes the collected information and extracts keywords and topics related to the question. Next, based on the extracted keywords and topics, it selects templates for generating answers from different perspectives. For example, when generating answers from the perspective of an economic expert, it uses an AI model with expertise in economics to generate answers that include economic impacts and predictions. When generating answers from the government's perspective, it generates answers based on official government data and policies. When generating answers from citizens' perspectives, it generates answers that reflect the opinions and feelings of the general public. The generation unit integrates these responses from different perspectives and arranges them into the optimal format for providing to the user. Furthermore, the generation unit evaluates the quality of the generated responses and makes corrections and improvements as needed. This allows the generation unit to provide multifaceted and high-quality answers to user questions.

[0033] The provider unit provides explanations of background information related to the answers generated by the generator unit. For example, the provider unit may explain the historical background of the economic situation. It can also explain the events leading up to the current situation. Furthermore, the provider unit may provide explanations of background information and historical context related to current events. For example, the provider unit may explain past events related to the current economic situation. The provider unit may explain comparisons with the economic situations of other countries. Specifically, when the provider unit provides the user with answers generated by the generator unit, it adds background information related to the answers. For example, for questions about the economic situation, it provides explanations about past economic crises and policy changes. It also explains the events leading up to the current situation in chronological order to make it easy for the user to understand. Furthermore, by providing background information and historical context related to current events, the provider unit enables the user to grasp the overall picture of the issue. For example, for questions about the economic situation of a particular country, it provides explanations about that country's past economic policies and international relations. It also enables the user to understand the issue from different perspectives by making comparisons with other countries. The information provider can offer these explanations not only in text format but also visually using graphs and charts. This allows the provider to offer users comprehensive and easy-to-understand information, deepening their understanding of the questions.

[0034] The information provider can provide background information related to current events or explanations of historical context. For example, the information provider can provide background information related to current events. The information provider can also provide explanations of historical context. For example, the information provider can explain past events related to the current economic situation. The information provider can explain comparisons with the economic situation of other countries. By providing background information related to current events and explanations of historical context, the user's understanding can be deepened. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input an explanation of background information related to current events into a generative AI and have the generative AI generate an explanatory text.

[0035] The generation unit can generate answers from different perspectives, one for each perspective. For example, the generation unit can generate answers from the perspective of an economic expert. It can also generate answers from the perspective of the government. Furthermore, the generation unit can generate answers from the perspective of citizens. For example, the generation unit can generate answers from the perspective of an economic expert. The generation unit can generate answers from the perspective of the government. The generation unit can generate answers from the perspective of citizens. Along with the answers for each perspective, the provider unit can provide explanations that describe what each perspective represents. For example, the provider unit can explain what the perspective of an economic expert represents. It can also explain what the perspective of the government represents. Furthermore, the provider unit can explain what the perspective of citizens represents. For example, the provider unit can explain what the perspective of an economic expert represents. The provider unit can explain what the perspective of the government represents. The provider unit can explain what the perspective of citizens represents. By providing answers from different perspectives and explanations of those perspectives, the system can provide users with multifaceted information. Some or all of the above-described processes in the generation unit and the provision unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input answers from different perspectives into the generation AI and cause the generation AI to generate an answer. The provision unit can input an explanation of the perspective into the generation AI and cause the generation AI to generate an explanatory text.

[0036] The data collection unit can collect information from news websites, academic papers, and official government databases. For example, the data collection unit can collect information from news websites. It can also collect information from academic papers. Furthermore, it can collect information from official government databases. For example, the data collection unit can collect the latest news articles from news websites. The data collection unit can collect relevant papers from academic paper databases. The data collection unit can collect statistical data from official government databases. This allows for the provision of reliable information by collecting information from diverse sources. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input information collected from news websites into a generative AI and have the generative AI perform the analysis of the information.

[0037] The generation unit can evaluate and compare collected information based on factors such as reliability, timeliness, and source. For example, the generation unit can evaluate the reliability of the collected information. It can also evaluate the timeliness of the collected information. Furthermore, it can evaluate the source of the collected information. By evaluating and comparing the reliability and timeliness of the information, the reliability of the information provided to the user is improved. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected information into a generation AI and have the generation AI perform the evaluation of the information.

[0038] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can prioritize receiving questions on topics the user has frequently asked about in the past. It can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception desk can analyze the user's past question history to determine when they tend to ask questions and provide the most suitable reception method for that time of day. For example, the reception desk can prioritize receiving questions on topics the user has frequently asked about in the past. The reception desk can prioritize suggesting question formats that the user has used in the past. The reception desk can analyze the user's past question history to determine when they tend to ask questions and provide the most suitable reception method for that time of day. In this way, by analyzing the user's past question history, the reception desk can provide the most suitable reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past question history into a generating AI and have the generating AI select the most suitable reception method.

[0039] The reception unit can filter questions based on the user's current areas of interest when receiving them. For example, the reception unit can prioritize questions related to topics the user has recently been interested in. The reception unit can also analyze the user's social media activity and filter questions based on their areas of interest. Furthermore, the reception unit can filter relevant questions based on keywords the user has previously searched for. For example, the reception unit prioritizes questions related to topics the user has recently been interested in. The reception unit analyzes the user's social media activity and filters questions based on their areas of interest. The reception unit filters relevant questions based on keywords the user has previously searched for. This allows the reception unit to prioritize receiving highly relevant questions by filtering questions based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's areas of interest into a generating AI and have the generating AI perform the question filtering.

[0040] The reception desk can prioritize receiving questions based on the user's geographical location, thereby providing information relevant to the user's location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to news and current events in that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving information related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving questions related to local news and events. This allows the reception desk to provide information relevant to the user's location by prioritizing the receipt of questions based on their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI filter the questions.

[0041] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can prioritize questions related to topics the user has recently shown interest in on social media. The reception desk can also filter questions based on influencers and news sources the user follows. Furthermore, the reception desk can prioritize questions related to articles and posts the user has shared on social media. For example, the reception desk can prioritize questions related to topics the user has recently shown interest in on social media. The reception desk can filter questions based on influencers and news sources the user follows. The reception desk can prioritize questions related to articles and posts the user has shared on social media. This allows the reception desk to prioritize receiving relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI perform question filtering.

[0042] The data collection unit can evaluate the reliability of information during collection and prioritize the collection of highly reliable information. For example, the data collection unit can evaluate the reliability of news sites and prioritize the collection of information from reliable sites. The data collection unit can also evaluate the citation count and author reliability of academic papers and prioritize the collection of highly reliable papers. Furthermore, the data collection unit can prioritize the collection of information from official government databases and exclude unreliable sources. For example, the data collection unit can evaluate the reliability of news sites and prioritize the collection of information from reliable sites. The data collection unit can evaluate the citation count and author reliability of academic papers and prioritize the collection of highly reliable papers. The data collection unit can prioritize the collection of information from official government databases and exclude unreliable sources. This improves the reliability of the information provided to users by evaluating the reliability of information and prioritizing the collection of highly reliable information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the reliability of the collected information into a generating AI and have the generating AI perform a reliability evaluation.

[0043] The data collection unit can apply different collection algorithms depending on the category of information during collection. For example, in the case of economic news, the collection unit can apply an algorithm that prioritizes the collection of economic news sites and academic papers. Similarly, in the case of political news, the collection unit can apply an algorithm that prioritizes the collection of official government databases and political news sites. Furthermore, in the case of science news, the collection unit can apply an algorithm that prioritizes the collection of science news sites and academic papers. For example, in the case of economic news, the collection unit can apply an algorithm that prioritizes the collection of economic news sites and academic papers. In the case of political news, the collection unit can apply an algorithm that prioritizes the collection of official government databases and political news sites. In the case of science news, the collection unit can apply an algorithm that prioritizes the collection of science news sites and academic papers. By applying different collection algorithms depending on the category of information, more appropriate information can be collected. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the category of information into a generating AI and have the generating AI apply the collection algorithm.

[0044] The data collection unit can determine the priority of information collection based on when the information was submitted. For example, the data collection unit can prioritize collecting the latest news and postpone older information. It can also prioritize collecting information of high urgency and postpone less urgency information. Furthermore, the data collection unit can prioritize collecting information that users are interested in at a specific time. For example, the data collection unit can prioritize collecting the latest news and postpone older information. The data collection unit can prioritize collecting information of high urgency and postpone less urgency information. The data collection unit can prioritize collecting information that users are interested in at a specific time. This allows the system to provide up-to-date information by determining the priority of collection based on when the information was submitted. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the submission dates of the information into a generating AI and have the generating AI determine the priority of collection.

[0045] The data collection unit can adjust the order of collection based on the relevance of the information. For example, the data collection unit can prioritize collecting the information most relevant to the user's question. It can also prioritize collecting information related to the user's areas of interest. Furthermore, the data collection unit can prioritize collecting information based on the user's past question history. For example, the data collection unit can prioritize collecting the information most relevant to the user's question. The data collection unit can prioritize collecting information related to the user's areas of interest. The data collection unit can prioritize collecting information based on the user's past question history. By adjusting the order of collection based on the relevance of the information, it is possible to provide the user with information that is highly relevant to them. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the relevance of the information into a generating AI and have the generating AI adjust the order of collection.

[0046] The generation unit can adjust the level of detail in the response based on the importance of the information when generating the response. For example, the generation unit can generate a response that includes a detailed explanation for important information. The generation unit can also generate a concise response for less important information. Furthermore, the generation unit can generate a response with adjusted level of detail according to the user's level of interest. For example, the generation unit can generate a response that includes a detailed explanation for important information. The generation unit can generate a concise response for less important information. The generation unit can generate a response with adjusted level of detail according to the user's level of interest. In this way, by adjusting the level of detail in the response based on the importance of the information, information that is important to the user can be provided in detail. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the information into the generation AI and have the generation AI adjust the level of detail in the response.

[0047] The generation unit can apply different generation algorithms depending on the category of information when generating answers. For example, the generation unit can apply an economics-specific generation algorithm to economic questions. It can also apply a political-specific generation algorithm to political questions. Furthermore, it can apply a scientific-specific generation algorithm to scientific questions. For example, the generation unit can apply an economics-specific generation algorithm to economic questions. It can apply a political-specific generation algorithm to political questions. It can apply a scientific-specific generation algorithm to scientific questions. By applying different generation algorithms depending on the category of information, it is possible to provide more appropriate answers. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of information into a generation AI and cause the generation AI to apply a generation algorithm.

[0048] The generation unit can determine the priority of responses based on the timing of information submission when generating responses. For example, the generation unit can prioritize generating responses based on the latest information. The generation unit can also prioritize generating responses based on information of high urgency. Furthermore, the generation unit can prioritize generating responses based on information that the user is interested in at a specific time. For example, the generation unit can prioritize generating responses based on the latest information. The generation unit can prioritize generating responses based on information of high urgency. The generation unit can prioritize generating responses based on information that the user is interested in at a specific time. This allows the generation unit to provide the latest information by determining the priority of responses based on the timing of information submission. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of information submission into a generation AI and have the generation AI determine the priority of responses.

[0049] The generation unit can adjust the order of answers based on the relevance of the information when generating answers. For example, the generation unit can prioritize generating answers based on the information most relevant to the user's question. The generation unit can also prioritize generating answers based on information related to the user's areas of interest. Furthermore, the generation unit can prioritize generating answers based on information highly relevant based on the user's past question history. For example, the generation unit can prioritize generating answers based on the information most relevant to the user's question. The generation unit can prioritize generating answers based on information related to the user's areas of interest. The generation unit can prioritize generating answers based on information highly relevant based on the user's past question history. By adjusting the order of answers based on the relevance of the information, the generation unit can provide the user with information that is highly relevant to them. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the information into a generation AI and have the generation AI adjust the order of the answers.

[0050] The service provider can select the optimal display method by referring to the user's past question history when providing an answer. For example, the service provider may prioritize display methods that the user has preferred in the past. The service provider may also suggest a specific display method based on the user's past question history. Furthermore, the service provider may select the optimal display method based on the device the user has used in the past. For example, the service provider may prioritize display methods that the user has preferred in the past. The service provider may suggest a specific display method based on the user's past question history. The service provider may select the optimal display method based on the device the user has used in the past. This allows the service provider to provide the optimal display method by referring to the user's past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's past question history into a generating AI and have the generating AI select the optimal display method.

[0051] The information provider can customize the displayed content based on the user's current areas of interest when providing responses. For example, the information provider can prioritize displaying information related to topics the user has recently been interested in. The information provider can also analyze the user's social media activity and customize the displayed content based on their areas of interest. Furthermore, the information provider can display relevant information based on keywords the user has previously searched for. For example, the information provider can prioritize displaying information related to topics the user has recently been interested in. The information provider can analyze the user's social media activity and customize the displayed content based on their areas of interest. The information provider can display relevant information based on keywords the user has previously searched for. This allows the information provider to provide highly relevant information by customizing the displayed content based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's areas of interest into a generating AI and have the generating AI perform the customization of the displayed content.

[0052] The service provider can select the optimal display method when providing responses, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0053] The information provider can analyze the user's social media activity and display relevant information when providing responses. For example, the information provider can display information related to topics the user has recently shown interest in on social media. The information provider can also display information based on influencers and news sources the user follows. Furthermore, the information provider can display information related to articles and posts the user has shared on social media. For example, the information provider can display information related to topics the user has recently shown interest in on social media. The information provider can display information based on influencers and news sources the user follows. The information provider can display information related to articles and posts the user has shared on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media activity into a generating AI and display information related to the generating AI.

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

[0055] An AI assistant question-answering system for news and current events can further analyze a user's past question history and customize answers based on the user's interests. For example, if a user has asked many questions about the economy in the past, it will prioritize providing information related to the economy. It can also prioritize gathering and providing information from specific news sources if the user prefers them. Furthermore, if a user has previously preferred answers in a particular format (text, audio, etc.), it can prioritize providing answers in that format. This allows for more personalized answers by leveraging the user's past question history.

[0056] An AI assistant system that answers questions about news and current events can further leverage the user's geographical location to prioritize providing information relevant to their region. For example, if the user is in a specific area, it can prioritize collecting and providing information about news and current events related to that area. If the user is traveling, it can also prioritize providing information related to their travel destination. Furthermore, if the user is at home, it can prioritize providing information about local news and events. This allows the system to provide more relevant information by utilizing the user's geographical location.

[0057] An AI assistant system that answers questions about news and current events can further analyze a user's social media activity and provide information based on their interests. For example, it can prioritize information related to topics the user has recently shown interest in on social media. It can also provide information based on the influencers and news sources the user follows. Furthermore, it can provide information related to articles and posts the user has shared on social media. This allows the system to leverage the user's social media activity to provide more relevant information.

[0058] An AI assistant question-answering system for news and current events can further consider the user's device information to provide the optimal display method. For example, if the user is using a smartphone, it can provide a display method adapted to the screen size. If the user is using a tablet, it can provide a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, by considering the user's device information, it can provide the optimal display method.

[0059] The AI ​​assistant question-answering system for news and current events can further analyze the user's past question history and select the optimal response method. For example, it can prioritize topics the user has frequently asked about in the past. It can also prioritize suggesting question formats the user has used in the past (text, voice, etc.). Furthermore, it can analyze the user's past question history to determine when they tend to ask questions and provide the most suitable response method for that time of day. In this way, by analyzing the user's past question history, it can provide the most optimal response method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives user questions. These questions can be in text or audio format. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if the user is feeling anxious, the acceptance of questions can be delayed. Step 2: Based on the questions received by the reception department, the collection department gathers relevant information from databases containing current or past news information. The collection department can gather information from news websites, academic papers, official government databases, etc. Step 3: The generation unit analyzes the information collected by the collection unit and generates responses from multiple different perspectives. For example, it can generate responses from the perspective of an economic expert, a government, and a citizen. Step 4: The providing unit provides explanations of background information related to the answer, along with the answer generated by the generating unit. For example, it can provide explanations of the historical background of the economic situation, the process leading up to the current situation, and background information and historical context related to current events.

[0062] (Example of form 2) The AI ​​assistant question-answering system for news and current events according to an embodiment of the present invention is a system that collects and analyzes relevant information in response to a user's question and provides answers from different perspectives. The AI ​​assistant question-answering system for news and current events starts when the user inputs a question. For example, the user might input a question such as, "Please tell me about the current economic situation." This question is received by the reception unit. Next, based on the question received by the reception unit, the collection unit collects relevant information from a database where current or past news information is stored. For example, it collects information from news sites, academic papers, and official government databases. The collected information is analyzed by the generation unit, and answers from multiple different perspectives are generated. For example, answers from different perspectives such as the perspective of an economic expert, the government, and the citizens are generated. The generated answers are provided to the user by the provision unit. Along with the generated answers, the provision unit provides explanations of background information related to the answers. For example, it explains the historical background of the economic situation and the process leading up to the current situation. Furthermore, the provision unit can provide explanations of background information and historical context related to current events. For example, it explains past events related to the current economic situation and comparisons with the economic situation of other countries. Furthermore, the generation unit can evaluate and compare the collected information based on factors such as reliability, timeliness, and source. This improves the reliability of the information provided to the user. In addition, the reception unit can estimate the user's emotions and adjust the timing of question submission based on the estimated emotions. For example, if the user is feeling anxious, the timing of question submission may be delayed. The generation unit can also determine the perspective based on the user's areas of interest at the time of question submission. For example, if the user is interested in economics, an answer from an economic perspective will be generated. Furthermore, the generation unit can estimate the user's emotions and generate an answer from a perspective related to the estimated emotions of the user. For example, if the user is feeling anxious, an answer from a perspective that alleviates anxiety will be generated.This allows the AI ​​assistant question-answering system for news and current events to collect and analyze relevant information and provide answers from different perspectives in response to user questions.

[0063] The AI ​​assistant question-answering system for news and current events according to this embodiment comprises a reception unit, a collection unit, a generation unit, and a provision unit. The reception unit receives questions from the user. User questions include, but are not limited to, text format, voice format, etc. The reception unit can, for example, receive questions in text format. The reception unit can also receive questions in voice format. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of question acceptance based on the estimated user emotions. For example, if the user is feeling anxious, the acceptance of the question can be delayed. The collection unit collects relevant information from a database where current or past news information is stored, based on the questions received by the reception unit. The collection unit can, for example, collect information from news websites. The collection unit can also collect information from academic papers. Furthermore, the collection unit can also collect information from official government databases. For example, the collection unit collects the latest news articles from news websites. The collection unit collects relevant papers from a database of academic papers. The collection unit collects statistical data from official government databases. The generation unit analyzes the information collected by the collection unit and generates answers from multiple different perspectives. For example, the generation unit can generate answers from the perspective of an economic expert. It can also generate answers from the perspective of the government. Furthermore, the generation unit can generate answers from the perspective of citizens. For example, the generation unit generates answers from the perspective of an economic expert. The generation unit generates answers from the perspective of the government. The generation unit generates answers from the perspective of citizens. The provision unit provides explanations of background information related to the answers generated by the generation unit. For example, the provision unit explains the historical background of the economic situation. It can also explain the events leading up to the current situation. Furthermore, the provision unit can provide explanations of background information and historical context related to current events. For example, the provision unit explains past events related to the current economic situation. The provision unit explains comparisons with the economic situation of other countries.As a result, the AI ​​assistant question-answering system for news and current events according to the embodiment can collect and analyze relevant information in response to a user's question and provide answers from different perspectives.

[0064] The reception desk receives user questions. User questions may include, but are not limited to, text or voice formats. The reception desk can, for example, accept text questions. It can also accept voice questions. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can delay accepting the question. Specifically, the reception desk uses natural language processing technology to analyze the user's text questions and understand their intent and content. For voice questions, speech recognition technology is used to convert the audio to text, which is then analyzed. Furthermore, for emotion estimation, machine learning models are used to estimate the user's emotions from the tone of voice and the expression in the text. For example, if the user's voice tone is high and they are in a hurry, the system will determine that the user is anxious and respond quickly. On the other hand, if the user is speaking in a calm tone, a normal response will be given. This allows the reception desk to respond flexibly according to the user's situation and emotions. Furthermore, the reception desk can refer to a user's past question history and prioritize questions based on the user's interests. For example, a user who has asked many economic-related questions in the past will have economic-related questions processed first. This allows the reception desk to provide personalized responses that meet the user's needs.

[0065] The information collection unit collects relevant information from databases containing current or past news information based on questions received by the reception unit. For example, the collection unit collects information from news websites. It can also collect information from academic papers. Furthermore, it can collect information from official government databases. For instance, the collection unit collects the latest news articles from news websites. It collects relevant papers from academic paper databases. It collects statistical data from official government databases. Specifically, the collection unit uses web scraping techniques to automatically collect the latest news articles from news websites on the internet. This includes techniques for analyzing the HTML structure of news websites and extracting necessary information. For academic paper databases, it uses APIs to search for and download relevant papers. Furthermore, it executes queries to retrieve publicly available statistical data and reports from official government databases. The collection unit centrally manages this information and filters and categorizes it as needed. For example, it categorizes collected news articles to enable quick searching of information related to specific topics. Furthermore, the data collection unit uses algorithms to evaluate the reliability of information and prioritizes collecting data from reliable sources. This allows the data collection unit to provide relevant information quickly and accurately in response to user inquiries.

[0066] The generation unit analyzes the information collected by the collection unit and generates answers from multiple different perspectives. For example, the generation unit can generate answers from the perspective of an economic expert. It can also generate answers from the perspective of the government. Furthermore, it can generate answers from the perspective of citizens. Specifically, the generation unit uses natural language generation technology to generate answers based on the collected information. The generation unit first analyzes the collected information and extracts keywords and topics related to the question. Next, based on the extracted keywords and topics, it selects templates for generating answers from different perspectives. For example, when generating answers from the perspective of an economic expert, it uses an AI model with expertise in economics to generate answers that include economic impacts and predictions. When generating answers from the government's perspective, it generates answers based on official government data and policies. When generating answers from citizens' perspectives, it generates answers that reflect the opinions and feelings of the general public. The generation unit integrates these responses from different perspectives and arranges them into the optimal format for providing to the user. Furthermore, the generation unit evaluates the quality of the generated responses and makes corrections and improvements as needed. This allows the generation unit to provide multifaceted and high-quality answers to user questions.

[0067] The provider unit provides explanations of background information related to the answers generated by the generator unit. For example, the provider unit may explain the historical background of the economic situation. It can also explain the events leading up to the current situation. Furthermore, the provider unit may provide explanations of background information and historical context related to current events. For example, the provider unit may explain past events related to the current economic situation. The provider unit may explain comparisons with the economic situations of other countries. Specifically, when the provider unit provides the user with answers generated by the generator unit, it adds background information related to the answers. For example, for questions about the economic situation, it provides explanations about past economic crises and policy changes. It also explains the events leading up to the current situation in chronological order to make it easy for the user to understand. Furthermore, by providing background information and historical context related to current events, the provider unit enables the user to grasp the overall picture of the issue. For example, for questions about the economic situation of a particular country, it provides explanations about that country's past economic policies and international relations. It also enables the user to understand the issue from different perspectives by making comparisons with other countries. The information provider can offer these explanations not only in text format but also visually using graphs and charts. This allows the provider to offer users comprehensive and easy-to-understand information, deepening their understanding of the questions.

[0068] The information provider can provide background information related to current events or explanations of historical context. For example, the information provider can provide background information related to current events. The information provider can also provide explanations of historical context. For example, the information provider can explain past events related to the current economic situation. The information provider can explain comparisons with the economic situation of other countries. By providing background information related to current events and explanations of historical context, the user's understanding can be deepened. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input an explanation of background information related to current events into a generative AI and have the generative AI generate an explanatory text.

[0069] The generation unit can generate answers from different perspectives, one for each perspective. For example, the generation unit can generate answers from the perspective of an economic expert. It can also generate answers from the perspective of the government. Furthermore, the generation unit can generate answers from the perspective of citizens. For example, the generation unit can generate answers from the perspective of an economic expert. The generation unit can generate answers from the perspective of the government. The generation unit can generate answers from the perspective of citizens. Along with the answers for each perspective, the provider unit can provide explanations that describe what each perspective represents. For example, the provider unit can explain what the perspective of an economic expert represents. It can also explain what the perspective of the government represents. Furthermore, the provider unit can explain what the perspective of citizens represents. For example, the provider unit can explain what the perspective of an economic expert represents. The provider unit can explain what the perspective of the government represents. The provider unit can explain what the perspective of citizens represents. By providing answers from different perspectives and explanations of those perspectives, the system can provide users with multifaceted information. Some or all of the above-described processes in the generation unit and the provision unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input answers from different perspectives into the generation AI and cause the generation AI to generate an answer. The provision unit can input an explanation of the perspective into the generation AI and cause the generation AI to generate an explanatory text.

[0070] The data collection unit can collect information from news websites, academic papers, and official government databases. For example, the data collection unit can collect information from news websites. It can also collect information from academic papers. Furthermore, it can collect information from official government databases. For example, the data collection unit can collect the latest news articles from news websites. The data collection unit can collect relevant papers from academic paper databases. The data collection unit can collect statistical data from official government databases. This allows for the provision of reliable information by collecting information from diverse sources. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input information collected from news websites into a generative AI and have the generative AI perform the analysis of the information.

[0071] The generation unit can evaluate and compare collected information based on factors such as reliability, timeliness, and source. For example, the generation unit can evaluate the reliability of the collected information. It can also evaluate the timeliness of the collected information. Furthermore, it can evaluate the source of the collected information. By evaluating and comparing the reliability and timeliness of the information, the reliability of the information provided to the user is improved. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected information into a generation AI and have the generation AI perform the evaluation of the information.

[0072] The reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can delay accepting questions. Conversely, if the user is excited, the reception desk can accept questions immediately. Furthermore, if the user is tired, the reception desk can temporarily suspend accepting questions. For example, if the user is feeling anxious, the reception desk can delay accepting questions to give the user time to relax. If the user is excited, the reception desk can accept questions immediately and provide answers quickly. If the user is tired, the reception desk can temporarily suspend accepting questions and display a message encouraging rest. This reduces user stress by adjusting the timing of question acceptance 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0073] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can prioritize receiving questions on topics the user has frequently asked about in the past. It can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception desk can analyze the user's past question history to determine when they tend to ask questions and provide the most suitable reception method for that time of day. For example, the reception desk can prioritize receiving questions on topics the user has frequently asked about in the past. The reception desk can prioritize suggesting question formats that the user has used in the past. The reception desk can analyze the user's past question history to determine when they tend to ask questions and provide the most suitable reception method for that time of day. In this way, by analyzing the user's past question history, the reception desk can provide the most suitable reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past question history into a generating AI and have the generating AI select the most suitable reception method.

[0074] The reception unit can filter questions based on the user's current areas of interest when receiving them. For example, the reception unit can prioritize questions related to topics the user has recently been interested in. The reception unit can also analyze the user's social media activity and filter questions based on their areas of interest. Furthermore, the reception unit can filter relevant questions based on keywords the user has previously searched for. For example, the reception unit prioritizes questions related to topics the user has recently been interested in. The reception unit analyzes the user's social media activity and filters questions based on their areas of interest. The reception unit filters relevant questions based on keywords the user has previously searched for. This allows the reception unit to prioritize receiving highly relevant questions by filtering questions based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's areas of interest into a generating AI and have the generating AI perform the question filtering.

[0075] 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 is feeling anxious, the reception desk will prioritize questions that provide reassurance. Similarly, if the user is excited, the reception desk may prioritize questions that pique their interest. Furthermore, if the user is tired, the reception desk may prioritize simple and short questions. This allows for prioritizing questions according to the user's emotions, thereby ensuring that more appropriate questions are accepted. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0076] The reception desk can prioritize receiving questions based on the user's geographical location, thereby providing information relevant to the user's location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions related to news and current events in that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving information related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving questions related to local news and events. This allows the reception desk to provide information relevant to the user's location by prioritizing the receipt of questions based on their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI filter the questions.

[0077] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can prioritize questions related to topics the user has recently shown interest in on social media. The reception desk can also filter questions based on influencers and news sources the user follows. Furthermore, the reception desk can prioritize questions related to articles and posts the user has shared on social media. For example, the reception desk can prioritize questions related to topics the user has recently shown interest in on social media. The reception desk can filter questions based on influencers and news sources the user follows. The reception desk can prioritize questions related to articles and posts the user has shared on social media. This allows the reception desk to prioritize receiving relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI perform question filtering.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can delay information collection to give the user time to relax. Alternatively, if the user is excited, the data collection unit can collect information immediately and provide it quickly. Furthermore, if the user is tired, the data collection unit can temporarily stop information collection and display a message encouraging rest. For example, if the user is feeling anxious, the data collection unit can delay information collection to give the user time to relax. If the user is excited, the data collection unit can collect information immediately and provide it quickly. If the user is tired, the data collection unit can temporarily stop information collection and display a message encouraging rest. This allows for the reduction of user stress by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then perform emotion estimation.

[0079] The data collection unit can evaluate the reliability of information during collection and prioritize the collection of highly reliable information. For example, the data collection unit can evaluate the reliability of news sites and prioritize the collection of information from reliable sites. The data collection unit can also evaluate the citation count and author reliability of academic papers and prioritize the collection of highly reliable papers. Furthermore, the data collection unit can prioritize the collection of information from official government databases and exclude unreliable sources. For example, the data collection unit can evaluate the reliability of news sites and prioritize the collection of information from reliable sites. The data collection unit can evaluate the citation count and author reliability of academic papers and prioritize the collection of highly reliable papers. The data collection unit can prioritize the collection of information from official government databases and exclude unreliable sources. This improves the reliability of the information provided to users by evaluating the reliability of information and prioritizing the collection of highly reliable information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the reliability of the collected information into a generating AI and have the generating AI perform a reliability evaluation.

[0080] The data collection unit can apply different collection algorithms depending on the category of information during collection. For example, in the case of economic news, the collection unit can apply an algorithm that prioritizes the collection of economic news sites and academic papers. Similarly, in the case of political news, the collection unit can apply an algorithm that prioritizes the collection of official government databases and political news sites. Furthermore, in the case of science news, the collection unit can apply an algorithm that prioritizes the collection of science news sites and academic papers. For example, in the case of economic news, the collection unit can apply an algorithm that prioritizes the collection of economic news sites and academic papers. In the case of political news, the collection unit can apply an algorithm that prioritizes the collection of official government databases and political news sites. In the case of science news, the collection unit can apply an algorithm that prioritizes the collection of science news sites and academic papers. By applying different collection algorithms depending on the category of information, more appropriate information can be collected. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the category of information into a generating AI and have the generating AI apply the collection algorithm.

[0081] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information that provides a sense of security. Similarly, if the user is excited, the data collection unit can prioritize collecting information that is interesting. Furthermore, if the user is tired, the data collection unit can prioritize collecting information that is simple and easy to understand. This allows for the provision of more appropriate information by prioritizing the information collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generating AI, which can then perform emotion estimation.

[0082] The data collection unit can determine the priority of information collection based on when the information was submitted. For example, the data collection unit can prioritize collecting the latest news and postpone older information. It can also prioritize collecting information of high urgency and postpone less urgency information. Furthermore, the data collection unit can prioritize collecting information that users are interested in at a specific time. For example, the data collection unit can prioritize collecting the latest news and postpone older information. The data collection unit can prioritize collecting information of high urgency and postpone less urgency information. The data collection unit can prioritize collecting information that users are interested in at a specific time. This allows the system to provide up-to-date information by determining the priority of collection based on when the information was submitted. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the submission dates of the information into a generating AI and have the generating AI determine the priority of collection.

[0083] The data collection unit can adjust the order of collection based on the relevance of the information. For example, the data collection unit can prioritize collecting the information most relevant to the user's question. It can also prioritize collecting information related to the user's areas of interest. Furthermore, the data collection unit can prioritize collecting information based on the user's past question history. For example, the data collection unit can prioritize collecting the information most relevant to the user's question. The data collection unit can prioritize collecting information related to the user's areas of interest. The data collection unit can prioritize collecting information based on the user's past question history. By adjusting the order of collection based on the relevance of the information, it is possible to provide the user with information that is highly relevant to them. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the relevance of the information into a generating AI and have the generating AI adjust the order of collection.

[0084] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will generate a response expressed in a way that provides reassurance. The generation unit can also generate a response expressed in an engaging way if the user is excited. Furthermore, if the user is tired, the generation unit can generate a response expressed in a simple and easy-to-understand way. For example, if the user is feeling anxious, the generation unit will generate a response expressed in a way that provides reassurance. If the user is excited, the generation unit will generate a response expressed in an engaging way. If the user is tired, the generation unit will generate a response expressed in a simple and easy-to-understand way. By adjusting the way the response is expressed according to the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, which can then perform emotion estimation.

[0085] The generation unit can adjust the level of detail in the response based on the importance of the information when generating the response. For example, the generation unit can generate a response that includes a detailed explanation for important information. The generation unit can also generate a concise response for less important information. Furthermore, the generation unit can generate a response with adjusted level of detail according to the user's level of interest. For example, the generation unit can generate a response that includes a detailed explanation for important information. The generation unit can generate a concise response for less important information. The generation unit can generate a response with adjusted level of detail according to the user's level of interest. In this way, by adjusting the level of detail in the response based on the importance of the information, information that is important to the user can be provided in detail. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the information into the generation AI and have the generation AI adjust the level of detail in the response.

[0086] The generation unit can apply different generation algorithms depending on the category of information when generating answers. For example, the generation unit can apply an economics-specific generation algorithm to economic questions. It can also apply a political-specific generation algorithm to political questions. Furthermore, it can apply a scientific-specific generation algorithm to scientific questions. For example, the generation unit can apply an economics-specific generation algorithm to economic questions. It can apply a political-specific generation algorithm to political questions. It can apply a scientific-specific generation algorithm to scientific questions. By applying different generation algorithms depending on the category of information, it is possible to provide more appropriate answers. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of information into a generation AI and cause the generation AI to apply a generation algorithm.

[0087] 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 feeling anxious, the generation unit will generate a short, to-the-point response. If the user is relaxed, the generation unit can also generate a longer response with more detailed explanations. Furthermore, if the user is excited, the generation unit can generate a response with visually stimulating effects. For example, if the user is feeling anxious, the generation unit will generate a short, to-the-point response. If the user is relaxed, the generation unit will generate a longer response with more detailed explanations. If the user is excited, the generation unit will generate a response with visually stimulating effects. This allows for the provision of more appropriate responses by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, which can then perform emotion estimation.

[0088] The generation unit can determine the priority of responses based on the timing of information submission when generating responses. For example, the generation unit can prioritize generating responses based on the latest information. The generation unit can also prioritize generating responses based on information of high urgency. Furthermore, the generation unit can prioritize generating responses based on information that the user is interested in at a specific time. For example, the generation unit can prioritize generating responses based on the latest information. The generation unit can prioritize generating responses based on information of high urgency. The generation unit can prioritize generating responses based on information that the user is interested in at a specific time. This allows the generation unit to provide the latest information by determining the priority of responses based on the timing of information submission. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of information submission into a generation AI and have the generation AI determine the priority of responses.

[0089] The generation unit can adjust the order of answers based on the relevance of the information when generating answers. For example, the generation unit can prioritize generating answers based on the information most relevant to the user's question. The generation unit can also prioritize generating answers based on information related to the user's areas of interest. Furthermore, the generation unit can prioritize generating answers based on information highly relevant based on the user's past question history. For example, the generation unit can prioritize generating answers based on the information most relevant to the user's question. The generation unit can prioritize generating answers based on information related to the user's areas of interest. The generation unit can prioritize generating answers based on information highly relevant based on the user's past question history. By adjusting the order of answers based on the relevance of the information, the generation unit can provide the user with information that is highly relevant to them. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the information into a generation AI and have the generation AI adjust the order of the answers.

[0090] The service provider can estimate the user's emotions and adjust how the response is displayed based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide a response in a reassuring way. Similarly, if the user is excited, the service provider can provide a response in an engaging way. Furthermore, if the user is tired, the service provider can provide a response in a simple and easy-to-understand way. This allows for more appropriate responses by adjusting the response display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0091] The service provider can select the optimal display method by referring to the user's past question history when providing an answer. For example, the service provider may prioritize display methods that the user has preferred in the past. The service provider may also suggest a specific display method based on the user's past question history. Furthermore, the service provider may select the optimal display method based on the device the user has used in the past. For example, the service provider may prioritize display methods that the user has preferred in the past. The service provider may suggest a specific display method based on the user's past question history. The service provider may select the optimal display method based on the device the user has used in the past. This allows the service provider to provide the optimal display method by referring to the user's past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's past question history into a generating AI and have the generating AI select the optimal display method.

[0092] The information provider can customize the displayed content based on the user's current areas of interest when providing responses. For example, the information provider can prioritize displaying information related to topics the user has recently been interested in. The information provider can also analyze the user's social media activity and customize the displayed content based on their areas of interest. Furthermore, the information provider can display relevant information based on keywords the user has previously searched for. For example, the information provider can prioritize displaying information related to topics the user has recently been interested in. The information provider can analyze the user's social media activity and customize the displayed content based on their areas of interest. The information provider can display relevant information based on keywords the user has previously searched for. This allows the information provider to provide highly relevant information by customizing the displayed content based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's areas of interest into a generating AI and have the generating AI perform the customization of the displayed content.

[0093] The service provider can estimate the user's emotions and adjust the display order of responses based on the estimated emotions. For example, if the user is feeling anxious, the service provider can display reassuring information first. Similarly, if the user is excited, the service provider can display interesting information first. Furthermore, if the user is tired, the service provider can display simple and easy-to-understand information first. This allows the service provider to provide more appropriate information by adjusting the display order of responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The service provider can select the optimal display method when providing responses, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0095] The information provider can analyze the user's social media activity and display relevant information when providing responses. For example, the information provider can display information related to topics the user has recently shown interest in on social media. The information provider can also display information based on influencers and news sources the user follows. Furthermore, the information provider can display information related to articles and posts the user has shared on social media. For example, the information provider can display information related to topics the user has recently shown interest in on social media. The information provider can display information based on influencers and news sources the user follows. The information provider can display information related to articles and posts the user has shared on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's social media activity into a generating AI and display information related to the generating AI.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] An AI assistant question-answering system for news and current events can further analyze a user's past question history and customize answers based on the user's interests. For example, if a user has asked many questions about the economy in the past, it will prioritize providing information related to the economy. It can also prioritize gathering and providing information from specific news sources if the user prefers them. Furthermore, if a user has previously preferred answers in a particular format (text, audio, etc.), it can prioritize providing answers in that format. This allows for more personalized answers by leveraging the user's past question history.

[0098] An AI assistant system that answers questions about news and current events can further leverage the user's geographical location to prioritize providing information relevant to their region. For example, if the user is in a specific area, it can prioritize collecting and providing information about news and current events related to that area. If the user is traveling, it can also prioritize providing information related to their travel destination. Furthermore, if the user is at home, it can prioritize providing information about local news and events. This allows the system to provide more relevant information by utilizing the user's geographical location.

[0099] An AI assistant system that answers questions about news and current events can further analyze a user's social media activity and provide information based on their interests. For example, it can prioritize information related to topics the user has recently shown interest in on social media. It can also provide information based on the influencers and news sources the user follows. Furthermore, it can provide information related to articles and posts the user has shared on social media. This allows the system to leverage the user's social media activity to provide more relevant information.

[0100] An AI assistant question-answering system for news and current events can further consider the user's device information to provide the optimal display method. For example, if the user is using a smartphone, it can provide a display method adapted to the screen size. If the user is using a tablet, it can provide a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, by considering the user's device information, it can provide the optimal display method.

[0101] The AI ​​assistant question-answering system for news and current events can further estimate the user's emotions and adjust how answers are displayed based on that estimation. For example, if the user is feeling anxious, the system can provide answers in a reassuring way. If the user is excited, it can provide answers in an engaging way. Furthermore, if the user is tired, it can provide answers in a simple and easy-to-understand way. By adjusting how answers are displayed according to the user's emotions, the system can provide more appropriate information.

[0102] AI assistant question answering systems for news and current events can further estimate the user's emotions and adjust the way they express their answers based on that estimation. For example, if the user is feeling anxious, the system can generate answers using reassuring language. If the user is excited, it can generate answers using engaging language. Furthermore, if the user is tired, it can generate answers using simple and easy-to-understand language. By adjusting the way answers are expressed according to the user's emotions, the system can provide more appropriate answers.

[0103] An AI assistant question-answering system for news and current events can further estimate the user's emotions and prioritize the information it collects based on those emotions. For example, if the user is feeling anxious, it will prioritize information that provides reassurance. If the user is excited, it can prioritize information that is interesting. Furthermore, if the user is tired, it can prioritize information that is simple and easy to understand. By prioritizing the information collected according to the user's emotions, it can provide more relevant information.

[0104] The AI ​​assistant question-answering system for news and current events can also estimate the user's emotions and adjust the timing of question acceptance based on those emotions. For example, if the user is feeling anxious, the system can delay accepting questions. Conversely, if the user is excited, it can accept questions immediately. Furthermore, if the user is tired, it can temporarily stop accepting questions. By adjusting the timing of question acceptance according to the user's emotions, this system can reduce user stress.

[0105] AI assistant question answering systems for news and current events can further estimate the user's emotions and adjust the length of their answers based on that estimation. For example, if the user is feeling anxious, it can generate short, to-the-point answers. If the user is relaxed, it can generate longer answers with more detailed explanations. Furthermore, if the user is excited, it can generate answers with visually stimulating effects. By adjusting the length of answers according to the user's emotions, it can provide more appropriate responses.

[0106] The AI ​​assistant question-answering system for news and current events can further analyze the user's past question history and select the optimal response method. For example, it can prioritize topics the user has frequently asked about in the past. It can also prioritize suggesting question formats the user has used in the past (text, voice, etc.). Furthermore, it can analyze the user's past question history to determine when they tend to ask questions and provide the most suitable response method for that time of day. In this way, by analyzing the user's past question history, it can provide the most optimal response method.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives user questions. These questions can be in text or audio format. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of question acceptance based on the estimated emotions. For example, if the user is feeling anxious, the acceptance of questions can be delayed. Step 2: Based on the questions received by the reception department, the collection department gathers relevant information from databases containing current or past news information. The collection department can gather information from news websites, academic papers, official government databases, etc. Step 3: The generation unit analyzes the information collected by the collection unit and generates responses from multiple different perspectives. For example, it can generate responses from the perspective of an economic expert, a government, and a citizen. Step 4: The providing unit provides explanations of background information related to the answer, along with the answer generated by the generating unit. For example, it can provide explanations of the historical background of the economic situation, the process leading up to the current situation, and background information and historical context related to current events.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the reception unit is implemented by the reception device 38 of the smart device 14. For example, the collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the output device 40 of the smart device 14. For example, some or all of the reception unit, collection unit, generation unit, and provision unit may be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214. For example, the collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the delivery unit is implemented by the speaker 240 of the smart glasses 214. For example, some or all of the reception unit, collection unit, generation unit, and delivery unit may be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314. For example, the collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the speaker 240 of the headset terminal 314. For example, some or all of the reception unit, collection unit, generation unit, and provision unit may be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the reception unit is implemented by the microphone 238 of the robot 414. For example, the collection unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the delivery unit is implemented by the speaker 240 of the robot 414. For example, some or all of the reception unit, collection unit, generation unit, and delivery unit may be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception desk that handles user inquiries, A collection unit collects relevant information from a database containing current or past news information based on the questions received by the reception unit, A generation unit analyzes the information collected by the collection unit and generates answers from multiple different perspectives, The system includes a providing unit that provides an explanation of background information related to the answer generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provides background information related to current events or explanations of historical context. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It generates answers from different perspectives, The aforementioned supply unit is, Along with the answers for each perspective, we provide explanations describing what each perspective entails. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Gather information from news websites, academic papers, and official government databases. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The collected information is evaluated and compared based on factors such as reliability, timeliness, and source. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the reliability of the information is evaluated, and reliable information is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, different collection algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting information, prioritize collection based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, adjust the order of collection based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The generating unit is When generating responses, adjust the level of detail in the responses based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating responses, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The generating unit is When generating responses, the system prioritizes responses based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating responses, adjust the order of responses based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how responses are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing an answer, the system will refer to the user's past question history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing responses, the displayed content is customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the display order of responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing responses, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When users provide responses, their social media activity is analyzed and relevant information is displayed. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 collection unit collects relevant information from a database containing current or past news information based on the questions received by the reception unit, A generation unit analyzes the information collected by the collection unit and generates answers from multiple different perspectives, The system includes a providing unit that provides an explanation of background information related to the answer generated by the generation unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provides background information related to current events or explanations of historical context. The system according to feature 1.

3. The generating unit is It generates answers from different perspectives, The aforementioned supply unit is, Along with the answers for each perspective, we provide explanations describing what each perspective entails. The system according to feature 1.

4. The aforementioned collection unit is Gather information from news websites, academic papers, and official government databases. The system according to feature 1.

5. The generating unit is The collected information is evaluated and compared based on factors such as reliability, timeliness, and source. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system according to feature 1.

7. 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.

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

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant based on the user's geographical location. The system according to feature 1.

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