system

Abstract

The system according to this embodiment aims to automatically search, evaluate, and verify reliable information and provide it to the user. [Solution] The system according to the embodiment comprises a reception unit, a search unit, an evaluation unit, an extraction unit, a verification unit, and a provision unit. The reception unit receives questions from the user. The search unit automatically searches for various sources based on the questions received by the reception unit. The evaluation unit evaluates the reliability of the sources found by the search unit. The extraction unit extracts information from the reliable data sources evaluated by the evaluation unit. The verification unit verifies the accuracy of the information extracted by the extraction unit. The provision unit provides the information verified by the verification unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, due to the spread of automatically generated information by generative AI, there is a risk that the truth will be distorted, and there is a problem that it is difficult to provide reliable information.

[0005] The system according to the embodiment aims to automatically search, evaluate, and confirm reliable information and provide it to the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, an evaluation unit, an extraction unit, a verification unit, and a provision unit. The reception unit receives questions from the user. The search unit automatically searches for various sources based on the questions received by the reception unit. The evaluation unit evaluates the reliability of the sources found by the search unit. The extraction unit extracts information from the reliable data sources evaluated by the evaluation unit. The verification unit verifies the accuracy of the information extracted by the extraction unit. The provision unit provides the information verified by the verification unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can automatically search, evaluate, and verify reliable information and provide it to the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 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 Truthxity AI system according to an embodiment of the present invention is a system designed to prevent the danger of truth being distorted in today's world where automatically generated information by AI is widespread. When a user inputs a question, the Truthxity AI system automatically searches various sources and verifies the accuracy of the information. It outputs and provides to the user only the truth that has been verified and confirmed from reliable data sources. This increases the transparency of information and prevents the spread of misinformation. In terms of service form, the Truthxity AI system is similar to Perplexity AI, generating answers to questions and providing links to the sources. However, what distinguishes the Truthxity AI system from Perplexity AI is that it also scrutinizes the content of the referenced articles. Web articles are already overflowing with information mass-produced by AI generation, and the Truthxity AI system is characterized by its ability to eliminate such articles and deliver only correct information to the user. For example, when a user inputs a question, the Truthxity AI system automatically searches various sources. Next, it evaluates the reliability of the searched sources and extracts only information from reliable data sources. Furthermore, it verifies the accuracy of the extracted information and finally provides it to the user. This process increases information transparency and prevents the spread of misinformation. For example, if a user asks, "Please tell me about the effectiveness of the latest COVID-19 vaccine," the Truthxity AI system automatically searches various sources and extracts data from reliable medical and research institutions. Next, it verifies the accuracy of the extracted data and finally provides it to the user. In this way, users can obtain accurate and reliable information. Furthermore, the Truthxity AI system assigns a truth-of-truth parameter to each site and performs a recursive evaluation, increasing the truth-of-truth parameter for other sites whose information matches that of sites with a high truth-of-truth parameter. Only sites with high evaluation parameters are used as information sources to generate search results. This further increases information transparency and prevents the spread of misinformation even more effectively. In this way, the Truthxity AI system will become a new standard in the information society where reliability is essential, realizing a world where everyone can confidently obtain correct information.This allows the Truthxity AI system to provide users with highly reliable information.

[0029] The Truthxity AI system according to this embodiment comprises a reception unit, a search unit, an evaluation unit, an extraction unit, a confirmation unit, and a provision unit. The reception unit receives questions from the user. For example, the reception unit receives questions entered by the user in text format. The reception unit can also accept voice input. For example, the user enters the question by voice using a microphone. The search unit automatically searches various sources based on the questions received by the reception unit. For example, the search unit searches for academic papers, news articles, websites, etc. on the internet. The search unit can also prioritize searching for data from reliable medical institutions and research institutions. For example, the search unit searches for data from government agencies, universities, and accredited research institutions. The evaluation unit evaluates the reliability of the sources found by the search unit. For example, the evaluation unit evaluates reliability based on the author, publisher, number of citations, etc. of the source. The evaluation unit assigns a truth-degree parameter to each site and performs a recursive evaluation to increase the truth-degree parameter for other sites whose information matches that of sites with a high truth-degree. The extraction unit extracts information from reliable data sources evaluated by the evaluation unit. The extraction unit extracts only information from reliable data sources, for example. The extraction unit can also prioritize the extraction of data from reliable medical and research institutions. The verification unit verifies the accuracy of the information extracted by the extraction unit. The verification unit verifies accuracy based on, for example, the degree of information consistency and the reliability of the sources. The verification unit can also improve the accuracy of verification by comparing it with past verification data. The provision unit provides the information verified by the verification unit to the user. The provision unit displays the verified information in text format, for example. The provision unit can also provide information in audio format. For example, the provision unit provides information in audio format using speech synthesis technology. As a result, the Truthxity AI system according to the embodiment can provide highly reliable information in response to user questions.

[0030] The reception desk receives questions from users. For example, the reception desk accepts questions entered by users in text format. Users can easily submit questions by typing them into the system interface. The reception desk can also accept voice input. For example, users can input questions by voice using a microphone. In the case of voice input, the system uses speech recognition technology to convert the speech into text and process it as a question. Speech recognition technology can be improved in accuracy using machine learning algorithms to accommodate the user's pronunciation and accent. Furthermore, the reception desk uses natural language processing (NLP) technology to analyze the user's input and understand the intent of the question. NLP technology is used to analyze the context and keywords of the question and generate appropriate search queries. This allows the reception desk to accurately understand the user's question and generate appropriate search queries.

[0031] The search unit automatically searches a variety of sources based on the questions received by the reception unit. For example, the search unit searches academic papers, news articles, and websites on the internet. The search unit can also prioritize searching data from reliable medical and research institutions. For example, it searches data from government agencies, universities, and accredited research institutions. The search unit uses a search engine to search extensive databases and collect relevant information. The search engine uses crawling technology to index web pages, allowing it to quickly find pages that match the search query. Furthermore, the search unit can use machine learning algorithms to evaluate the relevance of search results. This allows the search unit to provide the most relevant information to the user's question. The search unit can also filter search results and exclude unreliable information. This allows the search unit to provide reliable information to the user.

[0032] The evaluation unit assesses the reliability of sources retrieved by the search unit. The evaluation unit evaluates reliability based on factors such as the author, publisher, and number of citations. The evaluation unit assigns a truth-of-fact parameter to each site and performs a recursive evaluation, increasing the truth-of-fact parameter for other sites whose information matches that of sites with high truth-of-fact values. The evaluation unit can also automatically evaluate source reliability using machine learning algorithms. For example, the evaluation unit evaluates the expertise and past achievements of the source's author and prioritizes information from highly reliable authors. Furthermore, the evaluation unit evaluates the reliability of the source's publisher and prioritizes information from highly reliable publishers. In addition, the evaluation unit evaluates the number of citations of a source and considers information with many citations to be highly reliable. The evaluation unit combines these evaluation criteria to comprehensively assess the reliability of sources. This allows the evaluation unit to provide users with highly reliable information.

[0033] The extraction unit extracts information from reliable data sources evaluated by the evaluation unit. For example, the extraction unit extracts only information from reliable data sources. The extraction unit can also prioritize the extraction of data from reliable healthcare and research institutions. The extraction unit uses natural language processing (NLP) techniques to extract relevant information from the data sources. NLP techniques are used to analyze the context and keywords of the text to identify relevant information. The extraction unit organizes the extracted information and generates answers to the user's questions. The extraction unit can also compare information from multiple data sources to verify the consistency and accuracy of the information. This allows the extraction unit to provide accurate and reliable information to the user.

[0034] The verification unit verifies the accuracy of the information extracted by the extraction unit. The verification unit verifies accuracy based, for example, on the degree of information consistency and the reliability of the sources. The verification unit can also improve the accuracy of verification by comparing it with past verification data. The verification unit can automatically verify the accuracy of information using machine learning algorithms. For example, the verification unit checks whether the extracted information matches past data. It also verifies the consistency of the information and checks for inconsistencies. Furthermore, the verification unit re-evaluates the reliability of the information sources and prioritizes information from reliable sources. This allows the verification unit to provide users with accurate and reliable information.

[0035] The information provider provides users with information verified by the verification unit. For example, the information provider displays the verified information in text format. Users can easily view the verified information through the system interface. The information provider can also provide information in audio format. For example, the information provider can provide information in audio format using speech synthesis technology. Speech synthesis technology converts text into natural-sounding speech and provides information to users in audio format. This allows the information provider to provide information to users who have difficulty reading text, such as the visually impaired and the elderly. Furthermore, the information provider can collect user feedback and continuously improve the quality of the information it provides. For example, it can allow users to leave ratings and comments on the information provided. This allows the information provider to provide information that meets user needs and improve the overall reliability of the system and user satisfaction.

[0036] The evaluation unit assigns a truth-of-fact parameter to each site and can perform a recursive evaluation that increases the truth-of-fact parameter even for other sites whose information matches that of sites with a high truth-of-fact value. The evaluation unit sets the truth-of-fact parameter based, for example, on the degree of information matching or the reliability of the source. The evaluation unit performs a recursive evaluation that increases the truth-of-fact parameter even for other sites whose information matches that of sites with a high truth-of-fact value. Through this recursive evaluation, the evaluation unit can provide highly reliable information. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can perform a recursive evaluation using an AI model that takes the degree of information matching or the reliability of the source as input and outputs a truth-of-fact parameter. This allows the evaluation unit to provide highly reliable information through recursive evaluation.

[0037] The search unit can search for data from reliable medical and research institutions. For example, the search unit prioritizes searching for data from government agencies, universities, and accredited research institutions. By searching for data from reliable medical and research institutions, the search unit can provide accurate information. Some or all of the above-described processes in the search unit may be performed using AI, or not. For example, the search unit can search for data using an AI model that takes data from reliable medical and research institutions as input and outputs search results. This allows for the provision of accurate information by prioritizing the search for data from reliable medical and research institutions.

[0038] The extraction unit can extract only information from reliable data sources. For example, the extraction unit extracts only information from reliable data sources. The extraction unit can also prioritize the extraction of data from reliable medical and research institutions. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can extract information using an AI model that takes information from reliable data sources as input and outputs extraction results. This makes it possible to provide accurate information by extracting only information from reliable data sources.

[0039] The verification unit can verify the accuracy of the extracted information. The verification unit verifies accuracy based, for example, on the degree of information consistency or the reliability of the sources. The verification unit can also improve the accuracy of the verification by comparing it with past verification data. Some or all of the above-described processes in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can verify the accuracy of the information using an AI model that takes the extracted information as input and outputs accuracy. By doing so, it is possible to provide highly reliable information by verifying the accuracy of the extracted information.

[0040] The information provider can provide the user with the verified information. For example, the information provider can display the verified information in text format. The information provider can also provide the information in audio format. For example, the information provider can provide the information in audio format using speech synthesis technology. 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 provide the information using an AI model that takes the verified information as input and outputs it in a format to be provided to the user. This allows for the provision of accurate information by providing the user with verified information.

[0041] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically suggest relevant questions based on the content of questions the user has frequently asked in the past. The reception desk can also prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest question methods to be used during specific time periods based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. 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 past question history data into a generating AI and have the generating AI select the optimal reception method.

[0042] 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 is currently interested in. The reception unit can also automatically suggest relevant questions based on the user's areas of interest. Furthermore, the reception unit can determine the priority of questions based on the user's areas of interest. This allows the reception unit to receive highly relevant questions by filtering 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 user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0043] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location when receiving inquiries. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. The reception desk can also automatically suggest relevant questions based on the user's current location. Furthermore, the reception desk can determine the priority of questions based on the user's geographical location. This allows for the priority of receiving highly relevant questions by considering the user's geographical location. 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 the user's geographical location data into a generating AI and have the generating AI determine the priority of questions.

[0044] The reception unit can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception unit can prioritize accepting relevant questions based on the user's social media activity. The reception unit can also automatically suggest relevant questions based on the user's social media posts. Furthermore, the reception unit can accept relevant questions based on the activity of the user's social media followers and friends. This allows the reception unit to prioritize accepting relevant questions by analyzing the user's social media activity. 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 social media activity data into a generating AI and have the generating AI perform the task of accepting relevant questions.

[0045] The search unit can adjust the level of detail in search results based on the importance of the source during a search. For example, the search unit can prioritize displaying information from sources with high importance. It can also display information from sources with low importance in a concise manner. Furthermore, the search unit can adjust the level of detail in search results based on the importance of the source. This allows for the priority display of important information by adjusting the level of detail in search results based on the importance of the source. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input source importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in search results.

[0046] The search unit can apply different search algorithms depending on the source category during a search. For example, for sources in the medical category, the search unit can prioritize displaying data from highly reliable medical institutions. Similarly, for sources in the science category, it can prioritize displaying data from highly reliable research institutions. Furthermore, for sources in the news category, it can prioritize displaying data from highly reliable news sources. This allows for the provision of highly reliable information by applying different search algorithms depending on the source category. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input source category data into a generating AI and have the generating AI execute the application of the search algorithm.

[0047] The search unit can determine the priority of search results based on the update frequency of the sources during a search. For example, the search unit may prioritize displaying information from sources with a high update frequency. It can also display information from sources with a low update frequency concisely. Furthermore, the search unit can determine the priority of search results based on the update frequency of the sources. This allows for the display of the latest information preferentially by prioritizing search results based on the update frequency of the sources. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input source update frequency data into a generating AI and have the generating AI perform the determination of the search result priority.

[0048] The search unit can adjust the order of search results based on the relevance of the sources during a search. For example, the search unit may prioritize displaying information from highly relevant sources. It can also display information from less relevant sources concisely. Furthermore, the search unit can adjust the order of search results based on the relevance of the sources. This allows for the priority display of highly relevant information by adjusting the order of search results based on the relevance of the sources. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input source relevance data into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0049] The evaluation unit can improve the accuracy of its evaluations by comparing the reliability of sources with past evaluation data during the evaluation process. For example, the evaluation unit can prioritize evaluating sources that have received high ratings in the past. It can also briefly evaluate sources that have received low ratings in the past. Furthermore, the evaluation unit can evaluate the reliability of sources based on past evaluation data. This improves the accuracy of the evaluations by evaluating the reliability of sources based on past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluations.

[0050] The evaluation unit can perform evaluations while considering the attribute information of the source provider. For example, the evaluation unit may give a high rating if the provider is a highly reliable organization. Conversely, the evaluation unit may also give a low rating if the provider is an unreliable organization. Furthermore, the evaluation unit can evaluate sources based on the provider's attribute information. This allows for highly reliable evaluations by considering the attribute information of the source provider. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the provider's attribute information into a generating AI and have the generating AI perform the evaluation.

[0051] The evaluation unit can perform evaluations while considering the geographical distribution of sources. For example, the evaluation unit can prioritize evaluating sources that are geographically close. It can also perform evaluations of sources that are geographically distant in a concise manner. Furthermore, the evaluation unit can perform evaluations based on the geographical distribution of sources. This allows for more reliable evaluations by considering the geographical distribution of sources. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of sources into a generating AI and have the generating AI perform the evaluation.

[0052] The evaluation unit can improve the accuracy of its evaluation by referring to related literature of the source during the evaluation process. For example, the evaluation unit can evaluate the reliability of the source by referring to related literature. The evaluation unit can also evaluate the source based on evaluation data of related literature. Furthermore, the evaluation unit can improve the accuracy of its evaluation by integrating information from related literature. In this way, the accuracy of the evaluation can be improved by referring to related literature of the source. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input related literature data into a generating AI and have the generating AI perform the improvement of the evaluation accuracy.

[0053] The extraction unit can improve the accuracy of its extraction by considering the interrelationships of sources during the extraction process. For example, the extraction unit analyzes the interrelationships of sources and extracts highly relevant information. The extraction unit can also evaluate the reliability of the information based on the interrelationships of sources. Furthermore, the extraction unit can improve the accuracy of its extraction by considering the interrelationships of sources. This allows for improved extraction accuracy by considering the interrelationships of sources. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input source interrelationship data into a generating AI and have the generating AI perform the task of improving the extraction accuracy.

[0054] The extraction unit can perform extraction while considering the attribute information of the source provider. For example, if the provider is a highly reliable organization, the extraction unit will prioritize extracting that information. The extraction unit can also perform extraction concisely if the provider is a less reliable organization. Furthermore, the extraction unit can perform information extraction based on the provider's attribute information. This allows for the extraction of highly reliable information by considering the attribute information of the source provider. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the provider's attribute information into a generating AI and have the generating AI perform the extraction.

[0055] The extraction unit can perform extraction while considering the geographical distribution of sources. For example, the extraction unit can prioritize extracting information from geographically close sources. It can also concisely extract information from geographically distant sources. Furthermore, the extraction unit can extract information based on the geographical distribution of sources. This allows for the extraction of highly reliable information by considering the geographical distribution of sources. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data of sources into a generating AI and have the generating AI perform the extraction.

[0056] The extraction unit can improve the accuracy of its extraction by referring to related literature of the source during the extraction process. For example, the extraction unit can evaluate the reliability of the source by referring to related literature. The extraction unit can also extract source information based on evaluation data of related literature. Furthermore, the extraction unit can improve the accuracy of its extraction by integrating the information from related literature. In this way, the accuracy of the extraction can be improved by referring to related literature of the source. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input related literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the extraction.

[0057] The verification unit can improve the accuracy of verification by comparing the reliability of the source with past verification data during the verification process. For example, the verification unit can prioritize verification of sources that have received high ratings in the past. It can also briefly verify sources that have received low ratings in the past. Furthermore, the verification unit can verify the reliability of the source based on past verification data. This improves the accuracy of verification by verifying the reliability of the source based on past verification data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past verification data into a generating AI and have the generating AI perform the task of improving the accuracy of verification.

[0058] The verification unit can perform verification while considering the attribute information of the source provider. For example, if the provider is a highly reliable organization, the verification unit will prioritize verifying that information. The verification unit can also perform a brief verification if the provider is a less reliable organization. Furthermore, the verification unit can perform verification based on the provider's attribute information. This allows for highly reliable verification by considering the attribute information of the source provider. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the provider's attribute information into a generating AI and have the generating AI perform the verification.

[0059] The verification unit can perform verification while considering the geographical distribution of sources. For example, the verification unit can prioritize verification of geographically close sources. It can also briefly verify geographically distant sources. Furthermore, the verification unit can perform verification based on the geographical distribution of sources. This allows for more reliable verification by considering the geographical distribution of sources. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input geographical distribution data of sources into a generating AI and have the generating AI perform the verification.

[0060] The verification unit can improve the accuracy of its verification by referring to related literature of the source during the verification process. For example, the verification unit can verify the reliability of the source by referring to related literature. The verification unit can also verify the source information based on evaluation data of related literature. Furthermore, the verification unit can improve the accuracy of its verification by integrating information from related literature. In this way, the accuracy of verification can be improved by referring to related literature of the source. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input related literature data into a generating AI and have the generating AI perform the improvement of verification accuracy.

[0061] The information delivery unit can select the optimal delivery method by referring to the user's past information usage history at the time of delivery. For example, the information delivery unit may prioritize information delivery methods that the user has frequently used in the past. The information delivery unit can also predict the optimal delivery method from the user's past information usage history. Furthermore, the information delivery unit can analyze the user's past information usage history and select the most efficient delivery method. In this way, the optimal delivery method can be selected by referring to the user's past information usage history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past information usage history data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0062] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the delivery unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible display method. This allows the delivery unit to select the optimal delivery method by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

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

[0064] The reception desk can be equipped with natural language processing capabilities to analyze user input and understand the intent of the questions. For example, even if a user enters an ambiguous question, the reception desk can infer its intent and convert it into an appropriate question. Furthermore, the reception desk can refer to the user's past question history and automatically suggest similar questions. In addition, the reception desk can provide relevant additional information based on the user's input. This allows users to ask more specific questions, improving the efficiency of system usage.

[0065] The search engine can analyze a user's search history and customize search results based on their interests. For example, if a user has previously shown interest in a particular topic, the search engine can prioritize displaying the latest information related to that topic. It can also consider the user's geographical location and prioritize displaying information relevant to that region. Furthermore, the search engine can automatically suggest relevant keywords based on the user's search history. This allows users to search for information more efficiently.

[0066] The evaluation team can consider the frequency of updates and the timeliness of information when assessing the reliability of a source. For example, the evaluation team can rate sources that are regularly updated higher and sources that provide outdated information lower. The evaluation team can also check whether the content of a source is consistent with other reliable sources. Furthermore, the evaluation team can consider the expertise and past performance of the source's author when making its evaluation. This improves the reliability of the information provided to users.

[0067] The extraction unit can analyze the content of the extracted information and extract relevant keywords and phrases in order to evaluate the relevance of the extracted information. For example, the extraction unit can analyze the content of the information using natural language processing techniques and extract relevant keywords. The extraction unit can also verify whether the content of the information is consistent with other reliable information. Furthermore, the extraction unit can evaluate how relevant the content of the information is to the user's question. This improves the relevance of the information provided to the user.

[0068] The verification unit can cross-reference the source of extracted information with multiple reliable sources to confirm its accuracy. For example, the verification unit can check whether the extracted information matches other reliable sources. It can also verify whether the information is based on the latest research and data. Furthermore, it can check whether the information matches past verification data. This improves the accuracy of the information provided to users.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk receives questions from users. The reception desk can accept questions entered by users in text format, or it can accept voice input. For example, the user can use a microphone to input their question by voice. Step 2: The search unit automatically searches a variety of sources based on the questions received by the reception unit. The search unit can search academic papers, news articles, websites, etc. on the internet, and can also prioritize data from reliable medical and research institutions. Step 3: The evaluation unit assesses the reliability of the sources retrieved by the search unit. The evaluation unit evaluates reliability based on the author, publisher, number of citations, etc. of the sources, assigns a truth-of-fact parameter to each site, and performs a recursive evaluation to increase the truth-of-fact parameter for other sites whose information matches that of sites with a high truth-of-fact value. Step 4: The extraction unit extracts information from reliable data sources evaluated by the evaluation unit. The extraction unit can extract only information from reliable data sources and may also prioritize the extraction of data from reliable healthcare and research institutions. Step 5: The verification unit verifies the accuracy of the information extracted by the extraction unit. The verification unit verifies accuracy based on the degree of information consistency and the reliability of the sources, and can also improve the accuracy of the verification by comparing it with past verification data. Step 6: The providing unit provides the user with the information verified by the verification unit. The providing unit can display the verified information in text format, or it can provide the information in audio format. For example, the providing unit can provide the information in audio format using speech synthesis technology.

[0071] (Example of form 2) The Truthxity AI system according to an embodiment of the present invention is a system designed to prevent the danger of truth being distorted in today's world where automatically generated information by AI is widespread. When a user inputs a question, the Truthxity AI system automatically searches various sources and verifies the accuracy of the information. It outputs and provides to the user only the truth that has been verified and confirmed from reliable data sources. This increases the transparency of information and prevents the spread of misinformation. In terms of service form, the Truthxity AI system is similar to Perplexity AI, generating answers to questions and providing links to the sources. However, what distinguishes the Truthxity AI system from Perplexity AI is that it also scrutinizes the content of the referenced articles. Web articles are already overflowing with information mass-produced by AI generation, and the Truthxity AI system is characterized by its ability to eliminate such articles and deliver only correct information to the user. For example, when a user inputs a question, the Truthxity AI system automatically searches various sources. Next, it evaluates the reliability of the searched sources and extracts only information from reliable data sources. Furthermore, it verifies the accuracy of the extracted information and finally provides it to the user. This process increases information transparency and prevents the spread of misinformation. For example, if a user asks, "Please tell me about the effectiveness of the latest COVID-19 vaccine," the Truthxity AI system automatically searches various sources and extracts data from reliable medical and research institutions. Next, it verifies the accuracy of the extracted data and finally provides it to the user. In this way, users can obtain accurate and reliable information. Furthermore, the Truthxity AI system assigns a truth-of-truth parameter to each site and performs a recursive evaluation, increasing the truth-of-truth parameter for other sites whose information matches that of sites with a high truth-of-truth parameter. Only sites with high evaluation parameters are used as information sources to generate search results. This further increases information transparency and prevents the spread of misinformation even more effectively. In this way, the Truthxity AI system will become a new standard in the information society where reliability is essential, realizing a world where everyone can confidently obtain correct information.This allows the Truthxity AI system to provide users with highly reliable information.

[0072] The Truthxity AI system according to this embodiment comprises a reception unit, a search unit, an evaluation unit, an extraction unit, a confirmation unit, and a provision unit. The reception unit receives questions from the user. For example, the reception unit receives questions entered by the user in text format. The reception unit can also accept voice input. For example, the user enters the question by voice using a microphone. The search unit automatically searches various sources based on the questions received by the reception unit. For example, the search unit searches for academic papers, news articles, websites, etc. on the internet. The search unit can also prioritize searching for data from reliable medical institutions and research institutions. For example, the search unit searches for data from government agencies, universities, and accredited research institutions. The evaluation unit evaluates the reliability of the sources found by the search unit. For example, the evaluation unit evaluates reliability based on the author, publisher, number of citations, etc. of the source. The evaluation unit assigns a truth-degree parameter to each site and performs a recursive evaluation to increase the truth-degree parameter for other sites whose information matches that of sites with a high truth-degree. The extraction unit extracts information from reliable data sources evaluated by the evaluation unit. The extraction unit extracts only information from reliable data sources, for example. The extraction unit can also prioritize the extraction of data from reliable medical and research institutions. The verification unit verifies the accuracy of the information extracted by the extraction unit. The verification unit verifies accuracy based on, for example, the degree of information consistency and the reliability of the sources. The verification unit can also improve the accuracy of verification by comparing it with past verification data. The provision unit provides the information verified by the verification unit to the user. The provision unit displays the verified information in text format, for example. The provision unit can also provide information in audio format. For example, the provision unit provides information in audio format using speech synthesis technology. As a result, the Truthxity AI system according to the embodiment can provide highly reliable information in response to user questions.

[0073] The reception desk receives questions from users. For example, the reception desk accepts questions entered by users in text format. Users can easily submit questions by typing them into the system interface. The reception desk can also accept voice input. For example, users can input questions by voice using a microphone. In the case of voice input, the system uses speech recognition technology to convert the speech into text and process it as a question. Speech recognition technology can be improved in accuracy using machine learning algorithms to accommodate the user's pronunciation and accent. Furthermore, the reception desk uses natural language processing (NLP) technology to analyze the user's input and understand the intent of the question. NLP technology is used to analyze the context and keywords of the question and generate appropriate search queries. This allows the reception desk to accurately understand the user's question and generate appropriate search queries.

[0074] The search unit automatically searches a variety of sources based on the questions received by the reception unit. For example, the search unit searches academic papers, news articles, and websites on the internet. The search unit can also prioritize searching data from reliable medical and research institutions. For example, it searches data from government agencies, universities, and accredited research institutions. The search unit uses a search engine to search extensive databases and collect relevant information. The search engine uses crawling technology to index web pages, allowing it to quickly find pages that match the search query. Furthermore, the search unit can use machine learning algorithms to evaluate the relevance of search results. This allows the search unit to provide the most relevant information to the user's question. The search unit can also filter search results and exclude unreliable information. This allows the search unit to provide reliable information to the user.

[0075] The evaluation unit assesses the reliability of sources retrieved by the search unit. The evaluation unit evaluates reliability based on factors such as the author, publisher, and number of citations. The evaluation unit assigns a truth-of-fact parameter to each site and performs a recursive evaluation, increasing the truth-of-fact parameter for other sites whose information matches that of sites with high truth-of-fact values. The evaluation unit can also automatically evaluate source reliability using machine learning algorithms. For example, the evaluation unit evaluates the expertise and past achievements of the source's author and prioritizes information from highly reliable authors. Furthermore, the evaluation unit evaluates the reliability of the source's publisher and prioritizes information from highly reliable publishers. In addition, the evaluation unit evaluates the number of citations of a source and considers information with many citations to be highly reliable. The evaluation unit combines these evaluation criteria to comprehensively assess the reliability of sources. This allows the evaluation unit to provide users with highly reliable information.

[0076] The extraction unit extracts information from reliable data sources evaluated by the evaluation unit. For example, the extraction unit extracts only information from reliable data sources. The extraction unit can also prioritize the extraction of data from reliable healthcare and research institutions. The extraction unit uses natural language processing (NLP) techniques to extract relevant information from the data sources. NLP techniques are used to analyze the context and keywords of the text to identify relevant information. The extraction unit organizes the extracted information and generates answers to the user's questions. The extraction unit can also compare information from multiple data sources to verify the consistency and accuracy of the information. This allows the extraction unit to provide accurate and reliable information to the user.

[0077] The verification unit verifies the accuracy of the information extracted by the extraction unit. The verification unit verifies accuracy based, for example, on the degree of information consistency and the reliability of the sources. The verification unit can also improve the accuracy of verification by comparing it with past verification data. The verification unit can automatically verify the accuracy of information using machine learning algorithms. For example, the verification unit checks whether the extracted information matches past data. It also verifies the consistency of the information and checks for inconsistencies. Furthermore, the verification unit re-evaluates the reliability of the information sources and prioritizes information from reliable sources. This allows the verification unit to provide users with accurate and reliable information.

[0078] The information provider provides users with information verified by the verification unit. For example, the information provider displays the verified information in text format. Users can easily view the verified information through the system interface. The information provider can also provide information in audio format. For example, the information provider can provide information in audio format using speech synthesis technology. Speech synthesis technology converts text into natural-sounding speech and provides information to users in audio format. This allows the information provider to provide information to users who have difficulty reading text, such as the visually impaired and the elderly. Furthermore, the information provider can collect user feedback and continuously improve the quality of the information it provides. For example, it can allow users to leave ratings and comments on the information provided. This allows the information provider to provide information that meets user needs and improve the overall reliability of the system and user satisfaction.

[0079] The evaluation unit assigns a truth-of-fact parameter to each site and can perform a recursive evaluation that increases the truth-of-fact parameter even for other sites whose information matches that of sites with a high truth-of-fact value. The evaluation unit sets the truth-of-fact parameter based, for example, on the degree of information matching or the reliability of the source. The evaluation unit performs a recursive evaluation that increases the truth-of-fact parameter even for other sites whose information matches that of sites with a high truth-of-fact value. Through this recursive evaluation, the evaluation unit can provide highly reliable information. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can perform a recursive evaluation using an AI model that takes the degree of information matching or the reliability of the source as input and outputs a truth-of-fact parameter. This allows the evaluation unit to provide highly reliable information through recursive evaluation.

[0080] The search unit can search for data from reliable medical and research institutions. For example, the search unit prioritizes searching for data from government agencies, universities, and accredited research institutions. By searching for data from reliable medical and research institutions, the search unit can provide accurate information. Some or all of the above-described processes in the search unit may be performed using AI, or not. For example, the search unit can search for data using an AI model that takes data from reliable medical and research institutions as input and outputs search results. This allows for the provision of accurate information by prioritizing the search for data from reliable medical and research institutions.

[0081] The extraction unit can extract only information from reliable data sources. For example, the extraction unit extracts only information from reliable data sources. The extraction unit can also prioritize the extraction of data from reliable medical and research institutions. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can extract information using an AI model that takes information from reliable data sources as input and outputs extraction results. This makes it possible to provide accurate information by extracting only information from reliable data sources.

[0082] The verification unit can verify the accuracy of the extracted information. The verification unit verifies accuracy based, for example, on the degree of information consistency or the reliability of the sources. The verification unit can also improve the accuracy of the verification by comparing it with past verification data. Some or all of the above-described processes in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit can verify the accuracy of the information using an AI model that takes the extracted information as input and outputs accuracy. By doing so, it is possible to provide highly reliable information by verifying the accuracy of the extracted information.

[0083] The information provider can provide the user with the verified information. For example, the information provider can display the verified information in text format. The information provider can also provide the information in audio format. For example, the information provider can provide the information in audio format using speech synthesis technology. 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 provide the information using an AI model that takes the verified information as input and outputs it in a format to be provided to the user. This allows for the provision of accurate information by providing the user with verified information.

[0084] The reception desk can estimate the user's emotions and adjust the timing of question submission based on the estimated emotions. For example, if the user is stressed, the reception desk can quickly process the questions to reduce the user's burden. If the user is relaxed, the reception desk can process the questions slowly and collect detailed information. Furthermore, if the user is in a hurry, the reception desk can quickly process the questions and collect only the minimum necessary information. In this way, the user's burden can be reduced by adjusting the timing of question submission according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically suggest relevant questions based on the content of questions the user has frequently asked in the past. The reception desk can also prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest question methods to be used during specific time periods based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. 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 past question history data into a generating AI and have the generating AI select the optimal reception method.

[0086] 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 is currently interested in. The reception unit can also automatically suggest relevant questions based on the user's areas of interest. Furthermore, the reception unit can determine the priority of questions based on the user's areas of interest. This allows the reception unit to receive highly relevant questions by filtering 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 user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0087] The reception desk can estimate the user's emotions and determine the priority of questions to be received based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize important questions. If the user is relaxed, the reception desk may also prioritize detailed questions. Furthermore, if the user is in a hurry, the reception desk may prioritize concise questions. This allows for prioritizing important questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the 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 generative AI and have the generative AI determine the priority of questions.

[0088] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location when receiving inquiries. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. The reception desk can also automatically suggest relevant questions based on the user's current location. Furthermore, the reception desk can determine the priority of questions based on the user's geographical location. This allows for the priority of receiving highly relevant questions by considering the user's geographical location. 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 the user's geographical location data into a generating AI and have the generating AI determine the priority of questions.

[0089] The reception unit can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception unit can prioritize accepting relevant questions based on the user's social media activity. The reception unit can also automatically suggest relevant questions based on the user's social media posts. Furthermore, the reception unit can accept relevant questions based on the activity of the user's social media followers and friends. This allows the reception unit to prioritize accepting relevant questions by analyzing the user's social media activity. 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 social media activity data into a generating AI and have the generating AI perform the task of accepting relevant questions.

[0090] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit can display simple and highly visible search results. If the user is relaxed, the search unit can also display search results containing detailed information. Furthermore, if the user is in a hurry, the search unit can display concise search results. By adjusting how search results are displayed according to the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into the generative AI and have the generative AI adjust how search results are displayed.

[0091] The search unit can adjust the level of detail in search results based on the importance of the source during a search. For example, the search unit can prioritize displaying information from sources with high importance. It can also display information from sources with low importance in a concise manner. Furthermore, the search unit can adjust the level of detail in search results based on the importance of the source. This allows for the priority display of important information by adjusting the level of detail in search results based on the importance of the source. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input source importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in search results.

[0092] The search unit can apply different search algorithms depending on the source category during a search. For example, for sources in the medical category, the search unit can prioritize displaying data from highly reliable medical institutions. Similarly, for sources in the science category, it can prioritize displaying data from highly reliable research institutions. Furthermore, for sources in the news category, it can prioritize displaying data from highly reliable news sources. This allows for the provision of highly reliable information by applying different search algorithms depending on the source category. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input source category data into a generating AI and have the generating AI execute the application of the search algorithm.

[0093] The search unit can estimate the user's emotions and adjust the length of search results based on the estimated emotions. For example, if the user is stressed, the search unit can display short, concise search results. If the user is relaxed, the search unit can display longer search results with more detailed explanations. Furthermore, if the user is in a hurry, the search unit can display concise and quick search results. By adjusting the length of search results according to the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust the length of the search results.

[0094] The search unit can determine the priority of search results based on the update frequency of the sources during a search. For example, the search unit may prioritize displaying information from sources with a high update frequency. It can also display information from sources with a low update frequency concisely. Furthermore, the search unit can determine the priority of search results based on the update frequency of the sources. This allows for the display of the latest information preferentially by prioritizing search results based on the update frequency of the sources. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input source update frequency data into a generating AI and have the generating AI perform the determination of the search result priority.

[0095] The search unit can adjust the order of search results based on the relevance of the sources during a search. For example, the search unit may prioritize displaying information from highly relevant sources. It can also display information from less relevant sources concisely. Furthermore, the search unit can adjust the order of search results based on the relevance of the sources. This allows for the priority display of highly relevant information by adjusting the order of search results based on the relevance of the sources. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input source relevance data into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0096] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit may prioritize evaluating reliable sources. If the user is relaxed, the evaluation unit may also prioritize evaluating sources containing detailed information. Furthermore, if the user is in a hurry, the evaluation unit may prioritize evaluating concise and to-the-point sources. This reduces the user's burden by adjusting the evaluation criteria according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0097] The evaluation unit can improve the accuracy of its evaluations by comparing the reliability of sources with past evaluation data during the evaluation process. For example, the evaluation unit can prioritize evaluating sources that have received high ratings in the past. It can also briefly evaluate sources that have received low ratings in the past. Furthermore, the evaluation unit can evaluate the reliability of sources based on past evaluation data. This improves the accuracy of the evaluations by evaluating the reliability of sources based on past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluations.

[0098] The evaluation unit can perform evaluations while considering the attribute information of the source provider. For example, the evaluation unit may give a high rating if the provider is a highly reliable organization. Conversely, the evaluation unit may also give a low rating if the provider is an unreliable organization. Furthermore, the evaluation unit can evaluate sources based on the provider's attribute information. This allows for highly reliable evaluations by considering the attribute information of the source provider. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the provider's attribute information into a generating AI and have the generating AI perform the evaluation.

[0099] The evaluation unit can estimate the user's emotions and adjust the display order of evaluation results based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can prioritize displaying important evaluation results. It can also display detailed evaluation results if the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can display concise evaluation results. This reduces the user's burden by adjusting the display order of evaluation results according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the display order of evaluation results.

[0100] The evaluation unit can perform evaluations while considering the geographical distribution of sources. For example, the evaluation unit can prioritize evaluating sources that are geographically close. It can also perform evaluations of sources that are geographically distant in a concise manner. Furthermore, the evaluation unit can perform evaluations based on the geographical distribution of sources. This allows for more reliable evaluations by considering the geographical distribution of sources. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of sources into a generating AI and have the generating AI perform the evaluation.

[0101] The evaluation unit can improve the accuracy of its evaluation by referring to related literature of the source during the evaluation process. For example, the evaluation unit can evaluate the reliability of the source by referring to related literature. The evaluation unit can also evaluate the source based on evaluation data of related literature. Furthermore, the evaluation unit can improve the accuracy of its evaluation by integrating information from related literature. In this way, the accuracy of the evaluation can be improved by referring to related literature of the source. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input related literature data into a generating AI and have the generating AI perform the improvement of the evaluation accuracy.

[0102] The extraction unit can estimate the user's emotions and determine the priority of information to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit will prioritize extracting important information. It can also extract detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the extraction unit can extract concise information. This allows for the priority of information to be extracted according to the user's emotions, thereby prioritizing the extraction of important information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI, or not. For example, the extraction unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0103] The extraction unit can improve the accuracy of its extraction by considering the interrelationships of sources during the extraction process. For example, the extraction unit analyzes the interrelationships of sources and extracts highly relevant information. The extraction unit can also evaluate the reliability of the information based on the interrelationships of sources. Furthermore, the extraction unit can improve the accuracy of its extraction by considering the interrelationships of sources. This allows for improved extraction accuracy by considering the interrelationships of sources. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input source interrelationship data into a generating AI and have the generating AI perform the task of improving the extraction accuracy.

[0104] The extraction unit can perform extraction while considering the attribute information of the source provider. For example, if the provider is a highly reliable organization, the extraction unit will prioritize extracting that information. The extraction unit can also perform extraction concisely if the provider is a less reliable organization. Furthermore, the extraction unit can perform information extraction based on the provider's attribute information. This allows for the extraction of highly reliable information by considering the attribute information of the source provider. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the provider's attribute information into a generating AI and have the generating AI perform the extraction.

[0105] The extraction unit can estimate the user's emotions and adjust how the extracted information is displayed based on the estimated emotions. For example, if the user is stressed, the extraction unit can provide a simple and highly visible display method. If the user is relaxed, the extraction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can provide a concise and to-the-point display method. This reduces the user's burden by adjusting how information is displayed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can input user emotion data into the generative AI and have the generative AI adjust how the information is displayed.

[0106] The extraction unit can perform extraction while considering the geographical distribution of sources. For example, the extraction unit can prioritize extracting information from geographically close sources. It can also concisely extract information from geographically distant sources. Furthermore, the extraction unit can extract information based on the geographical distribution of sources. This allows for the extraction of highly reliable information by considering the geographical distribution of sources. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data of sources into a generating AI and have the generating AI perform the extraction.

[0107] The extraction unit can improve the accuracy of its extraction by referring to related literature of the source during the extraction process. For example, the extraction unit can evaluate the reliability of the source by referring to related literature. The extraction unit can also extract source information based on evaluation data of related literature. Furthermore, the extraction unit can improve the accuracy of its extraction by integrating the information from related literature. In this way, the accuracy of the extraction can be improved by referring to related literature of the source. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input related literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the extraction.

[0108] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated emotions. For example, if the user is stressed, the verification unit can perform a quick verification to reduce the user's burden. If the user is relaxed, the verification unit can also perform a detailed verification to improve the accuracy of the information. Furthermore, if the user is in a hurry, the verification unit can perform a concise verification to provide information quickly. In this way, the user's burden can be reduced by adjusting the verification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the verification method.

[0109] The verification unit can improve the accuracy of verification by comparing the reliability of the source with past verification data during the verification process. For example, the verification unit can prioritize verification of sources that have received high ratings in the past. It can also briefly verify sources that have received low ratings in the past. Furthermore, the verification unit can verify the reliability of the source based on past verification data. This improves the accuracy of verification by verifying the reliability of the source based on past verification data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past verification data into a generating AI and have the generating AI perform the task of improving the accuracy of verification.

[0110] The verification unit can perform verification while considering the attribute information of the source provider. For example, if the provider is a highly reliable organization, the verification unit will prioritize verifying that information. The verification unit can also perform a brief verification if the provider is a less reliable organization. Furthermore, the verification unit can perform verification based on the provider's attribute information. This allows for highly reliable verification by considering the attribute information of the source provider. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the provider's attribute information into a generating AI and have the generating AI perform the verification.

[0111] The verification unit can estimate the user's emotions and adjust the display order of the verification results based on the estimated emotions. For example, if the user is feeling stressed, the verification unit will prioritize displaying important verification results. It can also display detailed verification results if the user is relaxed. Furthermore, if the user is in a hurry, the verification unit can display concise verification results. This reduces the user's burden by adjusting the display order of verification results according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the verification unit may be performed using AI, or not. For example, the verification unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the verification results.

[0112] The verification unit can perform verification while considering the geographical distribution of sources. For example, the verification unit can prioritize verification of geographically close sources. It can also briefly verify geographically distant sources. Furthermore, the verification unit can perform verification based on the geographical distribution of sources. This allows for more reliable verification by considering the geographical distribution of sources. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input geographical distribution data of sources into a generating AI and have the generating AI perform the verification.

[0113] The verification unit can improve the accuracy of its verification by referring to related literature of the source during the verification process. For example, the verification unit can verify the reliability of the source by referring to related literature. The verification unit can also verify the source information based on evaluation data of related literature. Furthermore, the verification unit can improve the accuracy of its verification by integrating information from related literature. In this way, the accuracy of verification can be improved by referring to related literature of the source. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input related literature data into a generating AI and have the generating AI perform the improvement of verification accuracy.

[0114] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise and to-the-point display method. This reduces the user's burden by adjusting how information is displayed according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the information is displayed.

[0115] The information delivery unit can select the optimal delivery method by referring to the user's past information usage history at the time of delivery. For example, the information delivery unit may prioritize information delivery methods that the user has frequently used in the past. The information delivery unit can also predict the optimal delivery method from the user's past information usage history. Furthermore, the information delivery unit can analyze the user's past information usage history and select the most efficient delivery method. In this way, the optimal delivery method can be selected by referring to the user's past information usage history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past information usage history data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0116] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the information provider will prioritize providing important information. It can also provide detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can provide concise information. This allows for the prioritization of important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0117] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the delivery unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible display method. This allows the delivery unit to select the optimal delivery method by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The reception desk can be equipped with natural language processing capabilities to analyze user input and understand the intent of the questions. For example, even if a user enters an ambiguous question, the reception desk can infer its intent and convert it into an appropriate question. Furthermore, the reception desk can refer to the user's past question history and automatically suggest similar questions. In addition, the reception desk can provide relevant additional information based on the user's input. This allows users to ask more specific questions, improving the efficiency of system usage.

[0120] The search engine can analyze a user's search history and customize search results based on their interests. For example, if a user has previously shown interest in a particular topic, the search engine can prioritize displaying the latest information related to that topic. It can also consider the user's geographical location and prioritize displaying information relevant to that region. Furthermore, the search engine can automatically suggest relevant keywords based on the user's search history. This allows users to search for information more efficiently.

[0121] The evaluation team can consider the frequency of updates and the timeliness of information when assessing the reliability of a source. For example, the evaluation team can rate sources that are regularly updated higher and sources that provide outdated information lower. The evaluation team can also check whether the content of a source is consistent with other reliable sources. Furthermore, the evaluation team can consider the expertise and past performance of the source's author when making its evaluation. This improves the reliability of the information provided to users.

[0122] The extraction unit can analyze the content of the extracted information and extract relevant keywords and phrases in order to evaluate the relevance of the extracted information. For example, the extraction unit can analyze the content of the information using natural language processing techniques and extract relevant keywords. The extraction unit can also verify whether the content of the information is consistent with other reliable information. Furthermore, the extraction unit can evaluate how relevant the content of the information is to the user's question. This improves the relevance of the information provided to the user.

[0123] The verification unit can cross-reference the source of extracted information with multiple reliable sources to confirm its accuracy. For example, the verification unit can check whether the extracted information matches other reliable sources. It can also verify whether the information is based on the latest research and data. Furthermore, it can check whether the information matches past verification data. This improves the accuracy of the information provided to users.

[0124] The information delivery system can estimate the user's emotions and adjust the way information is delivered based on those estimates. For example, if the user is stressed, the system can provide simple, easy-to-understand information. If the user is relaxed, the system can provide detailed information. Furthermore, if the user is in a hurry, the system can provide concise, to-the-point information. By adjusting the way information is delivered according to the user's emotions, the system can reduce the user's burden.

[0125] The reception desk can estimate the user's emotions and adjust the timing of question submission based on those emotions. For example, if the user is stressed, questions will be submitted quickly to reduce the user's burden. If the user is relaxed, questions will be submitted slowly to gather detailed information. Furthermore, if the user is in a hurry, questions will be submitted quickly to gather only the minimum necessary information. In this way, the user's burden can be reduced by adjusting the timing of question submission according to the user's emotions.

[0126] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is stressed, it can display simple, easy-to-read search results. If the user is relaxed, it can display search results with more detailed information. Furthermore, if the user is in a hurry, it can display concise search results. By adjusting how search results are displayed according to the user's emotions, the system can reduce the user's burden.

[0127] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those emotions. For example, if the user is stressed, it can prioritize evaluating reliable sources. If the user is relaxed, it can prioritize evaluating sources that contain detailed information. Furthermore, if the user is in a hurry, it can prioritize evaluating sources that are concise and to the point. By adjusting the evaluation criteria according to the user's emotions, the burden on the user can be reduced.

[0128] The information delivery unit can estimate the user's emotions and determine the priority of the information to be delivered based on those emotions. For example, if the user is stressed, important information will be prioritized. If the user is relaxed, detailed information may be provided. Furthermore, if the user is in a hurry, concise information may be provided. In this way, by prioritizing information according to the user's emotions, important information can be delivered preferentially.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The reception desk receives questions from users. The reception desk can accept questions entered by users in text format, or it can accept voice input. For example, the user can use a microphone to input their question by voice. Step 2: The search unit automatically searches a variety of sources based on the questions received by the reception unit. The search unit can search academic papers, news articles, websites, etc. on the internet, and can also prioritize data from reliable medical and research institutions. Step 3: The evaluation unit assesses the reliability of the sources retrieved by the search unit. The evaluation unit evaluates reliability based on the author, publisher, number of citations, etc. of the sources, assigns a truth-of-fact parameter to each site, and performs a recursive evaluation to increase the truth-of-fact parameter for other sites whose information matches that of sites with a high truth-of-fact value. Step 4: The extraction unit extracts information from reliable data sources evaluated by the evaluation unit. The extraction unit can extract only information from reliable data sources and may also prioritize the extraction of data from reliable healthcare and research institutions. Step 5: The verification unit verifies the accuracy of the information extracted by the extraction unit. The verification unit verifies accuracy based on the degree of information consistency and the reliability of the sources, and can also improve the accuracy of the verification by comparing it with past verification data. Step 6: The providing unit provides the user with the information verified by the verification unit. The providing unit can display the verified information in text format, or it can provide the information in audio format. For example, the providing unit can provide the information in audio format using speech synthesis technology.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, search unit, evaluation unit, extraction unit, verification unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives questions from the user. The search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches for academic papers and news articles on the internet. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the retrieved sources. The extraction unit is implemented by the specific processing unit 290 of the data processing device 12 and extracts information from reliable data sources. The verification unit is implemented by the specific processing unit 290 of the data processing device 12 and confirms the accuracy of the extracted information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the verified information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the reception unit, search unit, evaluation unit, extraction unit, verification unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives questions from the user. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for academic papers and news articles on the internet. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the reliability of the retrieved sources. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts information from reliable data sources. The verification unit is implemented by the identification processing unit 290 of the data processing unit 12 and confirms the accuracy of the extracted information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the verified information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, search unit, evaluation unit, extraction unit, verification unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives questions from the user. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for academic papers and news articles on the internet. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the reliability of the retrieved sources. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts information from reliable data sources. The verification unit is implemented by the specific processing unit 290 of the data processing unit 12 and confirms the accuracy of the extracted information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the verified information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the reception unit, search unit, evaluation unit, extraction unit, verification unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives questions from the user. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for academic papers and news articles on the internet. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the reliability of the retrieved sources. The extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and extracts information from reliable data sources. The verification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and verifies the accuracy of the extracted information. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the verified information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) A reception desk that receives questions from users, A search unit that automatically searches for various sources based on the questions received by the reception unit, An evaluation unit that evaluates the reliability of the sources retrieved by the search unit, An extraction unit that extracts information from reliable data sources evaluated by the evaluation unit, A verification unit that verifies the accuracy of the information extracted by the extraction unit, The system includes a providing unit that provides the user with the information confirmed by the confirmation unit. A system characterized by the following features. (Note 2) The evaluation unit, Each site is assigned a truth-of-fact parameter, and a recursive evaluation is performed to increase the truth-of-fact parameter for other sites whose information matches that of sites with a high truth-of-fact value. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Search for data from reliable medical and research institutions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The extraction unit is Extract only information from reliable data sources. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned verification unit is Verify the accuracy of the extracted information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the verified information to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions, 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 10) 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 11) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving questions, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, adjust the level of detail in search results based on the importance of the source. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, different search algorithms are applied depending on the source category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, It estimates the user's sentiment and adjusts the length of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching, search results are prioritized based on how frequently the source is updated. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, the order of search results is adjusted based on the relevance of the source. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During evaluation, the reliability of the source is compared with past evaluation data to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation process, the attribute information of the source provider will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, The system estimates the user's emotions and adjusts the display order of evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation, the geographical distribution of the sources will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During evaluation, we improve the accuracy of the evaluation by referring to related literature in the source. The system described in Appendix 1, characterized by the features described herein. (Note 25) The extraction unit is It estimates the user's emotions and determines the priority of information to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The extraction unit is During extraction, the interrelationships between sources are taken into consideration to improve the accuracy of the extraction. The system described in Appendix 1, characterized by the features described herein. (Note 27) The extraction unit is During extraction, the attribute information of the source provider is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The extraction unit is It estimates the user's emotions and adjusts how the information extracted based on those estimated emotions is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 29) The extraction unit is During the extraction process, the geographical distribution of the sources will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The extraction unit is During extraction, we improve the accuracy of the extraction by referring to related literature of the source. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned verification unit is During verification, the reliability of the source is compared with past verification data to improve the accuracy of the verification. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned verification unit is During verification, the attribute information of the source provider will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned verification unit is The system estimates the user's emotions and adjusts the display order of the confirmation results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned verification unit is When verifying, the geographical distribution of the sources will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned verification unit is During verification, we improve the accuracy of the verification by referring to related literature of the source. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When providing data, the system will select the most suitable method of delivery by referring to the user's past information usage history. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0203] 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 receives questions from users, A search unit that automatically searches for various sources based on the questions received by the reception unit, An evaluation unit that evaluates the reliability of the sources retrieved by the search unit, An extraction unit that extracts information from reliable data sources evaluated by the evaluation unit, A verification unit that verifies the accuracy of the information extracted by the extraction unit, The system includes a providing unit that provides the user with the information confirmed by the confirmation unit. A system characterized by the following features.

2. The evaluation unit, Each site is assigned a truth-of-fact parameter, and a recursive evaluation is performed to increase the truth-of-fact parameter for other sites whose information matches that of sites with a high truth-of-fact value. The system according to feature 1.

3. The aforementioned search unit, Search for data from reliable medical and research institutions. The system according to feature 1.

4. The extraction unit is Extract only information from reliable data sources. The system according to feature 1.

5. The aforementioned verification unit is Verify the accuracy of the extracted information. The system according to feature 1.

6. The aforementioned supply unit is, Provide the verified information to the user. The system according to feature 1.

7. 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.

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

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

10. 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.

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