system

Abstract

The system of the embodiment aims to visualize the reliability of answers provided by the generation AI so that users can obtain appropriate information. [Solution] A system according to an embodiment includes a generation unit, a reliability evaluation unit, a visualization unit, an analysis unit, and a provision unit. The generation unit uses a generation AI to generate answers to questions from users. The reliability evaluation unit evaluates the reliability of the answers generated by the generation unit. The visualization unit visualizes the results evaluated by the reliability evaluation unit. The analysis unit analyzes the user's interests and concerns. The provision unit provides information selected based on specific criteria using the results analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem that the reliability of the answers provided by the generation AI was unclear, making it difficult for users to obtain appropriate information.

[0005] The system of the embodiment aims to visualize the reliability of answers provided by the generation AI so that users can obtain appropriate information. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a reliability evaluation unit, a visualization unit, an analysis unit, and a provision unit. The generation unit uses a generation AI to generate answers to questions from users. The reliability evaluation unit evaluates the reliability of the answers generated by the generation unit. The visualization unit visualizes the results evaluated by the reliability evaluation unit. The analysis unit analyzes the user's interests and concerns. The provision unit provides information selected based on specific criteria, based on the results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment visualizes the reliability of the answers provided by the generation AI, allowing users to obtain appropriate information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides a mechanism for visualizing the reliability of answers generated using a generation AI, improving the usefulness of data, and enabling users to understand the popularity of the answer even when they do not know the search keywords. In this system, the generation AI first generates an answer to a user's question. At the same time, the generation AI simultaneously generates an index for evaluating the reliability of the answer. For example, the reliability of the data underlying the answer and the accuracy of answers to similar questions in the past are evaluated. By visualizing these evaluation results, users can confirm the reliability of the answer. Next, a mechanism for providing highly popular information without the user having to enter a search keyword is provided. The generation AI analyzes the user's interests and presents relevant, highly popular information. For example, highly popular information is automatically displayed based on information the user has previously viewed and current trend information. This mechanism visualizes the reliability of answers provided by the generation AI and improves the usefulness of data. Furthermore, useful information can be provided to users even when the search keywords are unknown. For example, when the generation AI generates an answer to a user's question, the reliability of the data underlying the answer and the accuracy of answers to similar questions in the past are evaluated as indexes for evaluating the reliability of the answer. By visualizing this evaluation result, users can confirm the reliability of the answer. Furthermore, a mechanism is provided to provide highly popular information without the user having to enter search keywords. The generation AI analyzes the user's interests and presents related, highly popular information. For example, it automatically displays highly popular information based on information the user has viewed in the past and current trend information. This mechanism makes it possible to visualize the reliability of the answers provided by the generation AI and improve the usefulness of the data. It is also possible to provide useful information to users even if they do not know the search keywords. This allows the system to visualize the reliability of the answers provided by the generation AI and improve the usefulness of the data. It is also possible to provide useful information to users even if they do not know the search keywords.

[0029] The system according to the embodiment includes a generation unit, a reliability evaluation unit, a visualization unit, an analysis unit, and a provision unit. The generation unit generates answers to questions from users using a generation AI. The generation unit, for example, analyzes questions entered by users and generates appropriate answers. The generation unit can understand the intent of questions and generate appropriate answers using natural language processing technology, for example. The generation unit can also use the generation AI to refer to a database of past questions and answers to generate answers to similar questions. The reliability evaluation unit evaluates the reliability of the answers generated by the generation unit. The reliability evaluation unit, for example, evaluates the reliability of data on which the answers are based. The reliability evaluation unit can evaluate reliability based on, for example, the source of the data and past performance. The reliability evaluation unit can also evaluate the accuracy of answers to similar questions in the past. The reliability evaluation unit can evaluate accuracy based on, for example, the degree of agreement and error rate with past answers. The visualization unit visualizes the results evaluated by the reliability evaluation unit. The visualization unit, for example, displays the results of the reliability evaluation in the form of a graph or chart. The visualization unit can visually display the results of the trustworthiness evaluation using, for example, a bar graph or a pie chart. The visualization unit can also update the results of the trustworthiness evaluation in real time. The visualization unit can update the results of the trustworthiness evaluation, for example, each time new data is added. The analysis unit analyzes the user's interests and concerns. For example, the analysis unit analyzes the user's past browsing history, clicked links, and stay time. The analysis unit can identify the user's interests and concerns using, for example, data mining technology. The analysis unit can also collect trend information on social media and news sites. The analysis unit can collect trend information based on, for example, the number of posts on social media and the number of views on news sites. The provision unit provides highly popular information based on the results of the analysis by the analysis unit. The provision unit provides related information based on, for example, the user's interests and concerns. The provision unit can automatically display highly popular information based on, for example, information the user has viewed in the past or current trend information. The provision unit can also estimate the user's emotions and adjust the method of providing information based on the estimated emotions.For example, the providing unit can provide simple, highly visible information when the user is feeling anxious, and provide detailed information when the user is relaxed. This allows the system according to the embodiment to visualize the reliability of the answers provided by the generation AI and improve the usefulness of the data. Furthermore, it is possible to provide useful information to the user even if the user does not know the search keywords.

[0030] The reliability evaluation unit can evaluate the reliability of data that serves as the basis for an answer. The reliability evaluation unit, for example, analyzes the source of the data and evaluates the reliability. For example, the reliability evaluation unit can evaluate high reliability if the source of the data is reliable, and low reliability if the source is unclear. The reliability evaluation unit can also evaluate the reliability taking into account the update frequency of the data. For example, the reliability evaluation unit can evaluate high reliability if the data is frequently updated, and low reliability if the data is old. Furthermore, the reliability evaluation unit can evaluate the reliability by referring to related literature of the data. For example, the reliability evaluation unit can evaluate high reliability if there is a large number of related literature, and low reliability if there is a small number of related literature. In this way, the reliability evaluation unit can improve the reliability of the answer by evaluating the reliability of the data that serves as the basis for the answer.

[0031] The reliability evaluation unit can evaluate the accuracy of answers to past similar questions. The reliability evaluation unit, for example, analyzes the degree of agreement of past answers and evaluates the accuracy. For example, the reliability evaluation unit can evaluate a past answer as high accuracy if the degree of agreement is high, and as low accuracy if the degree of agreement is low. The reliability evaluation unit can also analyze the error rate of past answers to evaluate the accuracy. For example, the reliability evaluation unit can evaluate a past answer as high accuracy if the error rate is low, and as low accuracy if the error rate is high. Furthermore, the reliability evaluation unit can analyze the evaluation algorithm of past answers to evaluate the accuracy. For example, the reliability evaluation unit can evaluate a past answer as high accuracy if the evaluation algorithm is reliable, and as low accuracy if the evaluation algorithm is unreliable. In this way, the reliability evaluation unit can improve the reliability of the answers by evaluating the accuracy of answers to past similar questions.

[0032] The reliability evaluation unit may include a feedback collection unit that collects user feedback and reflects the collected feedback in the reliability evaluation. The reliability evaluation unit may collect user feedback using, for example, a questionnaire. The reliability evaluation unit may collect user comments and evaluation scores and reflect the collected feedback in the reliability evaluation. The reliability evaluation unit may also analyze the frequency of user feedback to evaluate reliability. For example, the reliability evaluation unit may evaluate high reliability when the frequency of feedback is high, and low reliability when the frequency is low. Furthermore, the reliability evaluation unit may analyze the quality of feedback to evaluate reliability. For example, the reliability evaluation unit may evaluate high reliability when the content of the feedback is specific and detailed, and low reliability when the content is vague and unclear. In this way, the reliability evaluation unit can improve the reliability of answers by collecting user feedback and reflecting the feedback in the reliability evaluation.

[0033] The analysis unit can analyze a user's past browsing history, clicked links, and duration of stay to identify the user's interests and concerns. For example, the analysis unit can analyze a user's past browsing history to identify topics of interest. For example, the analysis unit can analyze the content of pages the user has viewed in the past to identify topics of interest. The analysis unit can also analyze links the user has clicked to identify fields of interest. For example, the analysis unit can analyze the content of links the user has clicked in the past to identify fields of interest. Furthermore, the analysis unit can analyze the duration of stay of the user to identify information in which the user is particularly interested. For example, the analysis unit can analyze the content of pages the user has stayed on for a long time to identify information in which the user is particularly interested. In this way, the analysis unit can identify the user's interests and concerns by analyzing the user's past browsing history, clicked links, and duration of stay to provide related information.

[0034] The analysis unit may include a trend collection unit that collects specific trend information from SNS or news sites. The analysis unit, for example, analyzes the number of posts on SNS to collect trend information. The analysis unit can, for example, analyze information that is frequently shared on SNS to collect trend information. The analysis unit can also analyze the number of views on a news site to collect trend information. The analysis unit can, for example, analyze articles that are frequently viewed on a news site to collect trend information. The analysis unit can also analyze the frequency of use of hashtags to collect trend information. For example, the analysis unit can analyze hashtags that are frequently used on SNS to collect trend information. In this way, the analysis unit can provide the latest, highly popular information by collecting trend information from SNS and news sites.

[0035] The generation unit can analyze the context of the question and apply an algorithm for generating an optimal answer. The generation unit can, for example, analyze background information of the question and generate an answer including related information. The generation unit can, for example, analyze background information of the question entered by the user and generate an answer including related information. The generation unit can also analyze the intent of the question and provide specific information desired by the user. The generation unit can, for example, analyze the intent of the question and provide specific information desired by the user. The generation unit can also analyze the tone of the question and generate an answer with appropriate expression. The generation unit can, for example, analyze the tone of the question and generate an answer with appropriate expression. As a result, the generation unit can provide a more appropriate answer by analyzing the context of the question and applying an algorithm for generating an optimal answer. For example, the generation unit can analyze the background information of the question and generate an answer including related information. The generation unit can also analyze the intent of the question and provide specific information desired by the user. The generation unit can also analyze the tone of the question and generate an answer with appropriate expression. This allows the generator to provide a more appropriate answer by analyzing the context of the question and applying an algorithm to generate the optimal answer.

[0036] The generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit can generate an answer based on expert knowledge for a technical question. For example, the generation unit can generate an answer based on expert knowledge for a technical question. Furthermore, the generation unit can generate an answer that provides an explanation in easy-to-understand language for a general question. For example, the generation unit can generate an answer that provides an explanation in easy-to-understand language for a general question. Furthermore, the generation unit can generate an answer that includes an expression showing empathy for an emotional question. For example, the generation unit can generate an answer that includes an expression showing empathy for an emotional question. In this way, the generation unit can provide a more appropriate answer by applying different generation algorithms depending on the category of the question. For example, the generation unit can generate an answer based on expert knowledge for a technical question. Furthermore, the generation unit can generate an answer that provides an explanation in easy-to-understand language for a general question. Furthermore, the generation unit can generate an answer that includes an expression showing empathy for an emotional question. In this way, the generation unit can provide a more appropriate answer by applying different generation algorithms depending on the category of the question.

[0037] The generation unit can determine the priority of answers based on the time when the question was submitted. The generation unit can, for example, analyze the submission date and time of the question and determine the priority of answers according to the urgency. The generation unit can, for example, analyze the submission date and time of the question and determine the priority of answers according to the urgency. The generation unit can also determine the priority of answers by analyzing the urgency of the question. The generation unit can, for example, analyze the urgency of the question and quickly generate answers for questions with high urgency. Furthermore, the generation unit can also determine the priority of answers by analyzing the deadline of the question. The generation unit can, for example, analyze the deadline of the question and preferentially generate answers for questions whose deadline is approaching. In this way, the generation unit can respond quickly to urgent questions by determining the priority of answers based on the time when the question was submitted. For example, the generation unit can quickly generate answers for urgent questions. Furthermore, the generation unit can generate answers for regular questions with normal priority. Furthermore, the generation unit can postpone generating answers for past questions. This allows the generation unit to prioritize answers based on the time when the question was submitted, thereby enabling a prompt response to urgent questions.

[0038] The generation unit can adjust the order of answers based on the relevance of the question. The generation unit can, for example, analyze the degree of similarity of keywords in the question and generate answers preferentially for highly relevant questions. The generation unit can, for example, analyze the degree of similarity of keywords in the question and generate answers preferentially for highly relevant questions. The generation unit can also adjust the order of answers by analyzing similarity of topics in the questions. The generation unit can, for example, analyze the similarity of topics in the questions and generate answers preferentially for highly relevant questions. Furthermore, the generation unit can also adjust the order of answers by analyzing a user's area of ​​interest. The generation unit can, for example, analyze a user's area of ​​interest and generate answers preferentially for highly relevant questions. In this way, the generation unit can adjust the order of answers based on the relevance of the question, thereby preferentially providing more relevant answers. For example, the generation unit can generate answers preferentially for highly relevant questions. Furthermore, the generation unit can generate answers later for less relevant questions. Furthermore, the generation unit can adjust the order of answers based on the importance of the question. This allows the generation unit to adjust the order of answers based on the relevance of the question, thereby allowing more relevant answers to be provided preferentially.

[0039] The reliability evaluation unit can analyze the source of the data that forms the basis of the answer and evaluate its reliability. The reliability evaluation unit, for example, analyzes the source of the data and evaluates its reliability. For example, the reliability evaluation unit can evaluate high reliability if the source of the data is reliable, and low reliability if the source is unclear. The reliability evaluation unit can also evaluate reliability comprehensively when there are multiple sources of data. For example, when there are multiple sources of data, the reliability evaluation unit can evaluate the reliability of each source and evaluate the reliability comprehensively. Furthermore, the reliability evaluation unit can evaluate high reliability if the source of the data is official data or an academic paper. For example, the reliability evaluation unit can evaluate high reliability if the source of the data is official data or an academic paper. In this way, the reliability evaluation unit can provide a highly reliable answer by analyzing the source of the data that forms the basis of the answer and evaluating its reliability. For example, the reliability evaluation unit can evaluate high reliability if the source of the data is reliable. Furthermore, the reliability evaluation unit can evaluate low reliability if the source of the data is unclear. Furthermore, when there are multiple data sources, the reliability evaluation unit can comprehensively evaluate reliability, thereby analyzing the source of the data that forms the basis of the answer and evaluating its reliability, thereby providing a highly reliable answer.

[0040] The reliability evaluation unit can analyze the process of generating an answer and evaluate its reliability. For example, if the answer is generated based on multiple data sources, the reliability evaluation unit can evaluate the reliability as high. For example, if the answer is generated based on multiple data sources, the reliability evaluation unit can evaluate the reliability as high. Furthermore, if the answer is generated based on a single data source, the reliability evaluation unit can evaluate the reliability as low. For example, if the answer is generated based on a single data source, the reliability evaluation unit can evaluate the reliability as low. Furthermore, if there are unclear points in the process of generating an answer, the reliability evaluation unit can evaluate the reliability as low. For example, if there are unclear points in the process of generating an answer, the reliability evaluation unit can evaluate the reliability as low. In this way, the reliability evaluation unit can provide a highly reliable answer by analyzing the process of generating an answer and evaluating its reliability. For example, if the answer is generated based on multiple data sources, the reliability evaluation unit can evaluate the reliability as high. Furthermore, if the answer is generated based on a single data source, the reliability evaluation unit can evaluate the reliability as low. Furthermore, if there are unclear points in the process of generating an answer, the reliability evaluation unit can evaluate the reliability as low. This allows the reliability evaluation unit to analyze the process of generating an answer and evaluate the reliability, thereby providing a highly reliable answer.

[0041] The reliability evaluation unit can evaluate the reliability of an answer by taking into account the update frequency of the data. For example, the reliability evaluation unit analyzes the update frequency of the data and evaluates the reliability. For example, the reliability evaluation unit can evaluate high reliability when the update frequency of the data is high, and low reliability when the update frequency is low. The reliability evaluation unit can also evaluate the reliability by analyzing the update date and time of the data. For example, the reliability evaluation unit can evaluate high reliability when the update date and time of the data is recent, and low reliability when the update date and time is old. Furthermore, the reliability evaluation unit can evaluate the reliability by analyzing the number of updates of the data. For example, the reliability evaluation unit can evaluate high reliability when the update frequency of the data is high, and low reliability when the update frequency is low. As a result, the reliability evaluation unit can provide a more reliable answer by taking the update frequency of the data into account when evaluating the reliability of an answer. For example, the reliability evaluation unit can evaluate high reliability when the update frequency of the data is high. Furthermore, the reliability evaluation unit can evaluate low reliability when the update frequency of the data is low. Furthermore, the reliability evaluation unit can evaluate the reliability by analyzing the update date and time of the data. This allows the reliability evaluation unit to take into consideration the update frequency of data when evaluating the reliability of an answer, thereby providing a more reliable answer.

[0042] The reliability evaluation unit can improve the accuracy of the evaluation by referring to related literature of the data when evaluating the reliability of an answer. The reliability evaluation unit, for example, analyzes related literature of the data and evaluates reliability. For example, the reliability evaluation unit can evaluate high reliability when there are many related literatures, and low reliability when there are few related literatures. The reliability evaluation unit can also evaluate reliability by analyzing the quality of the related literature. For example, the reliability evaluation unit can evaluate high reliability when the quality of the related literature is high, and low reliability when the quality is low. Furthermore, the reliability evaluation unit can evaluate reliability by analyzing the number of citations of the related literature. For example, the reliability evaluation unit can evaluate high reliability when the number of citations of the related literature is high, and low reliability when the number of citations is low. As a result, the reliability evaluation unit can improve the accuracy of the evaluation by referring to related literature of the data when evaluating the reliability of an answer, thereby providing a more reliable answer. For example, the reliability evaluation unit can evaluate high reliability when there are many related literatures. Furthermore, the reliability evaluation unit can evaluate low reliability when there are few related literatures. Furthermore, the reliability evaluation unit can evaluate reliability by analyzing the quality of the related literature. This allows the reliability evaluation unit to improve the accuracy of the evaluation by referring to literature related to the data when evaluating the reliability of the answer, thereby providing a more reliable answer.

[0043] The visualization unit can display the results of the reliability evaluation in the form of a graph or a chart. The visualization unit, for example, displays the results of the reliability evaluation in the form of a bar graph. The visualization unit, for example, can display the results of the reliability evaluation in the form of a bar graph. Furthermore, the visualization unit can display the results of the reliability evaluation in the form of a pie chart. For example, the visualization unit can display the results of the reliability evaluation in the form of a pie chart. Furthermore, the visualization unit can display the results of the reliability evaluation in the form of a line graph. For example, the visualization unit can display the results of the reliability evaluation in the form of a line graph. By displaying the results of the reliability evaluation in the form of a graph or a chart, the visualization unit can make it easier for the user to understand. For example, the visualization unit can display the results of the reliability evaluation in the form of a bar graph. Furthermore, the visualization unit can display the results of the reliability evaluation in the form of a pie chart. Furthermore, the visualization unit can display the results of the reliability evaluation in the form of a line graph. By displaying the results of the reliability evaluation in the form of a graph or a chart, the visualization unit can make it easier for the user to understand.

[0044] The visualization unit can update the result of the trustworthiness evaluation in real time. For example, the visualization unit updates the result of the trustworthiness evaluation in real time every time new data is added. For example, the visualization unit can update the result of the trustworthiness evaluation in real time every time new data is added. Furthermore, the visualization unit can update the result of the trustworthiness evaluation in real time every time a user provides feedback. For example, the visualization unit can update the result of the trustworthiness evaluation in real time every time a user provides feedback. Furthermore, the visualization unit can update the result of the trustworthiness evaluation in real time in accordance with the update frequency of the data. For example, the visualization unit can update the result of the trustworthiness evaluation in real time in accordance with the update frequency of the data. In this way, the visualization unit can provide the latest information by updating the result of the trustworthiness evaluation in real time. For example, the visualization unit can update the result of the trustworthiness evaluation in real time every time new data is added. Furthermore, the visualization unit can update the result of the trustworthiness evaluation in real time every time a user provides feedback. Furthermore, the visualization unit can update the result of the trustworthiness evaluation in real time in accordance with the update frequency of the data. In this way, the visualization unit can provide the latest information by updating the result of the trustworthiness evaluation in real time.

[0045] The visualization unit can select an optimal display method in consideration of the user's device information when visualizing. For example, when the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. For example, when the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. Furthermore, when the user is using a tablet, the visualization unit can provide a display method optimized for a large screen. For example, when the user is using a tablet, the visualization unit can provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the visualization unit can provide a display method that includes detailed information. For example, when the user is using a desktop, the visualization unit can provide a display method that includes detailed information. As a result, the visualization unit selects the optimal display method in accordance with the user's device information, thereby improving visibility. For example, when the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. Furthermore, when the user is using a tablet, the visualization unit can provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the visualization unit can provide a display method that includes detailed information. As a result, the visualization unit selects the optimal display method in accordance with the user's device information, thereby improving visibility.

[0046] The visualization unit can select an optimal display method by referring to the user's past browsing history when visualizing the content. For example, the visualization unit can preferentially provide a display method that the user has used preferentially in the past. For example, the visualization unit can preferentially provide a display method that the user has used preferentially in the past. The visualization unit can also suggest an optimal display method based on the user's past browsing history. For example, the visualization unit can suggest an optimal display method based on the user's past browsing history. Furthermore, the visualization unit can analyze the user's past browsing history and provide a display method with high visibility. For example, the visualization unit can analyze the user's past browsing history and provide a display method with high visibility. As a result, the visualization unit selects an optimal display method based on the user's past browsing history, thereby improving user convenience. For example, the visualization unit can preferentially provide a display method that the user has used preferentially in the past. Furthermore, the visualization unit can suggest an optimal display method based on the user's past browsing history. Furthermore, the visualization unit can analyze the user's past browsing history and provide a display method with high visibility. This allows the visualization unit to select the optimal display method based on the user's past browsing history, thereby improving user convenience.

[0047] The analysis unit can analyze the user's past browsing history to identify the user's interests and concerns. For example, the analysis unit can analyze the content of pages the user has viewed in the past to identify topics of interest. For example, the analysis unit can analyze the content of pages the user has viewed in the past to identify topics of interest. The analysis unit can also analyze links the user has clicked in the past to identify areas of interest. For example, the analysis unit can analyze the content of links the user has clicked in the past to identify areas of interest. Furthermore, the analysis unit can analyze the user's stay time to identify information in which the user is particularly interested. For example, the analysis unit can analyze the content of pages the user has stayed on for a long time to identify information in which the user is particularly interested. In this way, the analysis unit can identify the user's interests and concerns by analyzing the user's past browsing history and provide related information. For example, the analysis unit can analyze the content of pages the user has viewed in the past to identify topics of interest. For example, the analysis unit can analyze links the user has clicked in the past to identify areas of interest. Furthermore, the analysis unit can analyze the user's stay time to identify information in which the user is particularly interested. This allows the analysis unit to analyze the user's past browsing history to identify the user's interests and concerns and provide related information.

[0048] The analysis unit can identify the user's interests and concerns by analyzing the links clicked by the user and the time spent on the site. For example, the analysis unit can analyze the content of the links clicked by the user to identify topics of interest. For example, the analysis unit can analyze the content of the links clicked by the user to identify topics of interest. The analysis unit can also analyze the user's time spent on the site to identify information in which the user is particularly interested. For example, the analysis unit can analyze the user's time spent on the site to identify information in which the user is particularly interested. Furthermore, the analysis unit can analyze the user's click patterns to identify areas of interest. For example, the analysis unit can analyze the user's click patterns to identify areas of interest. As a result, the analysis unit can identify the user's interests and concerns by analyzing the links clicked by the user and the time spent on the site, and provide related information. For example, the analysis unit can analyze the content of the links clicked by the user to identify topics of interest. For example, the analysis unit can analyze the user's time spent on the site to identify information in which the user is particularly interested. Furthermore, the analysis unit can analyze the user's click patterns to identify areas of interest. This allows the analysis unit to identify the user's interests and concerns by analyzing the links the user clicks and the length of time the user stays on the site, and to provide related information.

[0049] The analysis unit can perform the analysis taking into account the user's geographical location information. For example, the analysis unit can prioritize analyzing related information based on the user's current location. For example, the analysis unit can prioritize analyzing related information based on the user's current location. The analysis unit can also analyze information about areas of interest based on the user's past location information. For example, the analysis unit can analyze information about areas of interest based on the user's past location information. Furthermore, the analysis unit can update the user's location information in real time and analyze optimal information. For example, the analysis unit can update the user's location information in real time and analyze optimal information. In this way, the analysis unit can provide more relevant information by performing the analysis taking into account the user's geographical location information. For example, the analysis unit can prioritize analyzing related information based on the user's current location. The analysis unit can also analyze information about areas of interest based on the user's past location information. Furthermore, the analysis unit can update the user's location information in real time and analyze optimal information. In this way, the analysis unit can provide more relevant information by performing the analysis taking into account the user's geographical location information.

[0050] During the analysis, the analysis unit can analyze the user's social media activities and identify their interests. For example, the analysis unit can analyze the content shared by the user on social media to identify topics of interest. For example, the analysis unit can analyze the content shared by the user on social media to identify topics of interest. The analysis unit can also analyze the accounts the user follows on social media to identify areas of interest. For example, the analysis unit can analyze the accounts the user follows on social media to identify areas of interest. Furthermore, the analysis unit can analyze the frequency of the user's social media activities to identify information in which the user is particularly interested. For example, the analysis unit can analyze the frequency of the user's social media activities to identify information in which the user is particularly interested. Thus, by analyzing the user's social media activities, the analysis unit can identify the user's interests and provide related information. For example, the analysis unit can analyze the content shared by the user on social media to identify topics of interest. For example, the analysis unit can analyze the accounts the user follows on social media to identify areas of interest. Furthermore, the analysis unit can analyze the frequency of the user's social media activities to identify information in which the user is particularly interested. This allows the analysis unit to analyze the user's social media activity to identify the user's interests and provide related information.

[0051] The providing unit can adjust the level of detail of the information to be provided based on the importance of the information to be provided. For example, the providing unit selects a providing method including a detailed description for information with a high level of importance. For example, the providing unit can select a providing method including a detailed description for information with a high level of importance. The providing unit can also select a providing method including a brief description for information with a low level of importance. For example, the providing unit can select a providing method including a brief description for information with a low level of importance. Furthermore, the providing unit can gradually adjust the level of detail of the information according to the importance. For example, the providing unit can gradually adjust the level of detail of the information according to the importance. As a result, the providing unit can provide more appropriate information by adjusting the level of detail of the information based on the importance of the information. For example, the providing unit can select a providing method including a detailed description for information with a high level of importance. Also, the providing unit can select a providing method including a brief description for information with a low level of importance. Furthermore, the providing unit can gradually adjust the level of detail of the information according to the importance. As a result, the providing unit can provide more appropriate information by adjusting the level of detail of the information based on the importance of the information.

[0052] The providing unit can apply different providing algorithms depending on the category of information to be provided. For example, the providing unit can select a providing method based on specialized knowledge for technical information. For example, the providing unit can select a providing method based on specialized knowledge for technical information. Furthermore, the providing unit can select a providing method that explains general information in easy-to-understand terms. For example, the providing unit can select a providing method that explains general information in easy-to-understand terms. Furthermore, the providing unit can select a providing method that includes expressions showing empathy for emotional information. For example, the providing unit can select a providing method that includes expressions showing empathy for emotional information. In this way, the providing unit can provide more appropriate information by applying an appropriate providing algorithm depending on the category of information. For example, the providing unit can select a providing method based on specialized knowledge for technical information. Furthermore, the providing unit can select a providing method that explains general information in easy-to-understand terms. Furthermore, the providing unit can select a providing method that includes expressions showing empathy for emotional information. In this way, the providing unit can provide more appropriate information by applying an appropriate providing algorithm depending on the category of information.

[0053] The providing unit can determine the priority of provision based on the time of submission of the information when the information is provided. The providing unit, for example, analyzes the submission date and time of the information and determines the priority of provision according to the urgency. The providing unit, for example, analyzes the submission date and time of the information and determines the priority of provision according to the urgency. The providing unit can also analyze the urgency of the information and determine the priority of provision. For example, the providing unit can analyze the urgency of the information and quickly provide information with a high level of urgency. Furthermore, the providing unit can analyze the deadline of the information and determine the priority of provision. For example, the providing unit can analyze the deadline of the information and provide information with priority when the deadline is approaching. In this way, the providing unit can respond quickly to urgent information by determining the priority of provision based on the time of submission of the information. For example, the providing unit can provide urgent information quickly. In addition, the providing unit can provide periodic information with normal priority. Furthermore, the providing unit can provide past information at a later date. In this way, the providing unit can respond quickly to urgent information by determining the priority of provision based on the time of submission of the information.

[0054] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. The providing unit can, for example, analyze the degree of coincidence of keywords of the information and provide highly relevant information preferentially. The providing unit can, for example, analyze the degree of coincidence of keywords of the information and provide highly relevant information preferentially. The providing unit can also adjust the order of provision by analyzing similarity of topics of the information. The providing unit can, for example, analyze similarity of topics of the information and provide highly relevant information preferentially. Furthermore, the providing unit can also adjust the order of provision by analyzing a user's area of ​​interest. The providing unit can, for example, analyze a user's area of ​​interest and provide highly relevant information preferentially. In this way, the providing unit can adjust the order of provision based on the relevance of the information, thereby providing more relevant information preferentially. For example, the providing unit can provide highly relevant information preferentially. Furthermore, the providing unit can provide less relevant information later. Furthermore, the providing unit can adjust the order of provision based on the importance of the information. This allows the providing unit to adjust the order of providing information based on the relevance of the information, thereby enabling the providing unit to provide more relevant information with priority.

[0055] The feedback collecting unit can analyze the content of the feedback and reflect it in the reliability evaluation. The feedback collecting unit, for example, analyzes the content of the user's feedback and reflects it in the reliability evaluation. The feedback collecting unit, for example, can analyze the content of the user's feedback and reflect it in the reliability evaluation. The feedback collecting unit can also analyze the frequency of feedback and reflect it in the reliability evaluation. The feedback collecting unit can, for example, analyze the frequency of feedback and reflect it in the reliability evaluation. Furthermore, the feedback collecting unit can also analyze the quality of feedback and reflect it in the reliability evaluation. The feedback collecting unit can, for example, analyze the quality of feedback and reflect it in the reliability evaluation. Thereby, the feedback collecting unit can analyze the content of the feedback and reflect it in the reliability evaluation, thereby improving the accuracy of the reliability evaluation. For example, the feedback collecting unit can analyze the content of the user's feedback and reflect it in the reliability evaluation. Furthermore, the feedback collecting unit can analyze the frequency of feedback and reflect it in the reliability evaluation. Furthermore, the feedback collecting unit can analyze the quality of feedback and reflect it in the reliability evaluation. This allows the feedback collection unit to analyze the content of the feedback and reflect it in the reliability evaluation, thereby improving the accuracy of the reliability evaluation.

[0056] When collecting feedback, the feedback collection unit can select an optimal collection method by referring to the user's past feedback history. The feedback collection unit, for example, can select the optimal collection method by referring to the user's past feedback history. The feedback collection unit can select the optimal collection method by referring to the user's past feedback history. The feedback collection unit can also suggest appropriate questions from the user's feedback history. The feedback collection unit can also suggest appropriate questions from the user's feedback history. The feedback collection unit can also adjust the feedback collection method based on the user's feedback history. The feedback collection unit can adjust the feedback collection method based on the user's feedback history. Thereby, the feedback collection unit can select a more appropriate feedback collection method by referring to the user's past feedback history. For example, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history. The feedback collection unit can also suggest appropriate questions from the user's feedback history. The feedback collection unit can also adjust the feedback collection method based on the user's feedback history. This allows the feedback collection unit to select a more appropriate feedback collection method by referring to the user's past feedback history.

[0057] The feedback collection unit can collect feedback taking into consideration geographical location information of the user when collecting feedback. The feedback collection unit, for example, can prioritize collecting relevant feedback based on the user's current location. The feedback collection unit can prioritize collecting relevant feedback based on the user's current location. The feedback collection unit can also collect feedback on areas of interest based on the user's past location information. The feedback collection unit can collect feedback on areas of interest based on the user's past location information. The feedback collection unit can also update the user's location information in real time to collect optimal feedback. The feedback collection unit can, for example, update the user's location information in real time to collect optimal feedback. In this way, the feedback collection unit can collect more relevant feedback by collecting feedback taking into consideration the user's geographical location information. For example, the feedback collection unit can prioritize collecting relevant feedback based on the user's current location. The feedback collection unit can also collect feedback on areas of interest based on the user's past location information. The feedback collection unit can also update the user's location information in real time to collect optimal feedback. This allows the feedback collection unit to collect feedback taking into account the geographical location information of the user, thereby allowing the collection of more relevant feedback.

[0058] The feedback collection unit can analyze the user's social media activities when collecting feedback and collect relevant feedback. For example, the feedback collection unit can analyze content shared by the user on social media and collect relevant feedback. For example, the feedback collection unit can analyze content shared by the user on social media and collect relevant feedback. The feedback collection unit can also analyze accounts the user follows on social media to collect feedback on areas of interest. For example, the feedback collection unit can analyze accounts the user follows on social media and collect feedback on areas of interest. Furthermore, the feedback collection unit can analyze the frequency of the user's social media activities to collect feedback on information in which the user is particularly interested. For example, the feedback collection unit can analyze the frequency of the user's social media activities to collect feedback on information in which the user is particularly interested. In this way, the feedback collection unit can collect more relevant feedback by analyzing the user's social media activities. For example, the feedback collection unit can analyze content shared by the user on social media and collect relevant feedback. For example, the feedback collection unit can analyze accounts the user follows on social media to collect feedback on areas of interest. Furthermore, the feedback collection unit can analyze the frequency of the user's social media activities to collect feedback on information that the user is particularly interested in. This allows the feedback collection unit to collect more relevant feedback by analyzing the user's social media activities.

[0059] The trend collection unit can analyze trend information on SNS and news sites to identify information that is attracting a lot of attention. The trend collection unit can, for example, analyze information that is shared frequently on SNS to identify information that is attracting a lot of attention. The trend collection unit can also analyze articles that are viewed frequently on news sites to identify information that is attracting a lot of attention. The trend collection unit can, for example, analyze articles that are viewed frequently on news sites to identify information that is attracting a lot of attention. The trend collection unit can also analyze the number of comments on SNS and news sites to identify information that is attracting a lot of attention. The trend collection unit can, for example, analyze the number of comments on SNS and news sites to identify information that is attracting a lot of attention. In this way, the trend collection unit can identify information that is attracting a lot of attention by analyzing trend information on SNS and news sites. For example, the trend collection unit can analyze information that is shared frequently on SNS to identify information that is attracting a lot of attention. The trend collection unit can also analyze articles that are viewed frequently on news sites to identify information that is attracting a lot of attention. Furthermore, the trend collection unit can identify information that is attracting a lot of attention by analyzing the number of comments on social media and news sites. This allows the trend collection unit to identify information that is attracting a lot of attention by analyzing trend information on social media and news sites.

[0060] The trend collection unit can predict a current trend by referring to past trend data when collecting trend information. The trend collection unit can predict a current trend based on, for example, past trend data. The trend collection unit can predict a current trend based on, for example, past trend data. The trend collection unit can also predict a trend change by comparing past trend data with current data. The trend collection unit can predict a trend change by comparing, for example, past trend data with current data. Furthermore, the trend collection unit can predict a future trend by analyzing past trend data. The trend collection unit can predict a future trend by analyzing, for example, past trend data. As a result, the trend collection unit can predict a current trend by referring to past trend data when collecting trend information, thereby providing more appropriate information. For example, the trend collection unit can predict a current trend based on past trend data. The trend collection unit can predict a trend change by comparing past trend data with current data. Furthermore, the trend collection unit can predict a future trend by analyzing past trend data. As a result, the trend collection unit can predict a current trend by referring to past trend data when collecting trend information, thereby providing more appropriate information.

[0061] The trend collection unit can collect trend information taking into consideration the user's geographical location information. For example, the trend collection unit can collect relevant trend information preferentially based on the user's current location. For example, the trend collection unit can collect relevant trend information preferentially based on the user's current location. The trend collection unit can also collect trend information for areas of interest based on the user's past location information. For example, the trend collection unit can collect trend information for areas of interest based on the user's past location information. Furthermore, the trend collection unit can update the user's location information in real time and collect optimal trend information. For example, the trend collection unit can update the user's location information in real time and collect optimal trend information. In this way, the trend collection unit can provide more relevant information by collecting trend information taking into consideration the user's geographical location information. For example, the trend collection unit can collect relevant trend information preferentially based on the user's current location. Furthermore, the trend collection unit can collect trend information for areas of interest based on the user's past location information. Furthermore, the trend collection unit can update the user's location information in real time and collect optimal trend information. This allows the trend collection unit to collect trend information taking into account the user's geographical location information, thereby enabling the provision of more relevant information.

[0062] When collecting trend information, the trend collection unit can analyze the user's social media activities and collect related trend information. For example, the trend collection unit can analyze content shared by the user on social media to collect related trend information. For example, the trend collection unit can analyze content shared by the user on social media to collect related trend information. The trend collection unit can also analyze accounts the user follows on social media to collect trend information on areas of interest. For example, the trend collection unit can analyze accounts the user follows on social media to collect trend information on areas of interest. Furthermore, the trend collection unit can analyze the frequency of the user's social media activities to collect trend information on information in which the user is particularly interested. For example, the trend collection unit can analyze the frequency of the user's social media activities to collect trend information on information in which the user is particularly interested. In this way, the trend collection unit can analyze the user's social media activities to collect more relevant trend information. For example, the trend collection unit can analyze content shared by the user on social media to collect related trend information. For example, the trend collection unit can analyze accounts the user follows on social media to collect trend information on areas of interest. Furthermore, the trend collection unit can analyze the frequency of a user's social media activity to collect trend information about information that the user is particularly interested in. This allows the trend collection unit to collect more relevant trend information by analyzing the user's social media activity.

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

[0064] When evaluating the reliability of the data that forms the basis of an answer, the reliability evaluation unit can evaluate the reliability as high if the data source is official data or an academic paper. The reliability evaluation unit can also evaluate the reliability comprehensively if there are multiple data sources. Furthermore, the reliability evaluation unit can evaluate the reliability as low if the source of the data is unclear. This allows the reliability evaluation unit to analyze the source of the data that forms the basis of an answer and evaluate its reliability, thereby providing a highly reliable answer.

[0065] When evaluating the accuracy of answers to past similar questions, the reliability evaluation unit can analyze the degree of agreement and error rate of past answers. For example, if past answers show a high degree of agreement, the reliability evaluation unit can evaluate the accuracy as high, and if the degree of agreement is low, the reliability evaluation unit can evaluate the accuracy as low. Furthermore, if the error rate is low, the reliability evaluation unit can evaluate the accuracy as high, and if the error rate is high, the reliability evaluation unit can evaluate the accuracy as low. Furthermore, if the evaluation algorithm is reliable, the reliability evaluation unit can evaluate the accuracy as high, and if it is unreliable, the reliability evaluation unit can evaluate the accuracy of answers to past similar questions, thereby improving the reliability of the answers.

[0066] The reliability evaluation unit may include a feedback collection unit that collects user feedback and reflects it in the reliability evaluation. For example, user feedback can be collected using a questionnaire, and comments and evaluation scores can be reflected in the reliability evaluation. Furthermore, if the frequency of feedback is high, high reliability can be evaluated, and if the frequency is low, low reliability can be evaluated. Furthermore, if the content of the feedback is specific and detailed, high reliability can be evaluated, and if the content is vague and unclear, low reliability can be evaluated. In this way, the reliability evaluation unit can improve the reliability of answers by collecting user feedback and reflecting it in the reliability evaluation.

[0067] The analysis unit can identify the user's interests and concerns by analyzing the user's past browsing history, clicked links, and time spent on the site. For example, it can analyze the content of pages the user has previously viewed to identify topics of interest. It can also analyze the content of links the user has clicked to identify areas of interest. It can also analyze the content of pages the user has spent a long time on to identify information in which the user is particularly interested. In this way, the analysis unit can identify the user's interests and concerns and provide related information by analyzing the user's past browsing history, clicked links, and time spent on the site.

[0068] The analysis unit may include a trend collection unit that collects specific trend information from social media or news sites. For example, the number of posts on social media may be analyzed to collect trend information. Trend information may also be collected by analyzing the number of views on news sites. Trend information may also be collected by analyzing the frequency of hashtag usage. In this way, the analysis unit can provide the latest, most popular information by collecting trend information from social media and news sites.

[0069] The generation unit can analyze the context of the question and apply an algorithm for generating an optimal answer. For example, it can analyze background information of the question and generate an answer that includes relevant information. It can also analyze the intent of the question and provide specific information that the user is looking for. It can also analyze the tone of the question and generate an answer with appropriate expression. As a result, the generation unit can provide a more appropriate answer by analyzing the context of the question and applying an algorithm for generating an optimal answer.

[0070] The generator can apply different generation algorithms depending on the category of the question. For example, it can generate an answer based on specialized knowledge for a technical question. It can also generate an answer that explains things in easy-to-understand language for a general question. It can also generate an answer that includes expressions showing empathy for an emotional question. This allows the generator to provide more appropriate answers by applying different generation algorithms depending on the category of the question.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The generation unit uses a generation AI to generate an answer to a question from a user. For example, the generation unit analyzes the question entered by the user and generates an appropriate answer. For example, the generation unit can use natural language processing technology to understand the intent of the question and generate an appropriate answer. The generation unit can also use the generation AI to refer to a database of past questions and answers to generate answers to similar questions. Step 2: The reliability evaluation unit evaluates the reliability of the answer generated by the generation unit. The reliability evaluation unit evaluates, for example, the reliability of the data on which the answer is based. The reliability evaluation unit can evaluate reliability based on, for example, the source of the data or past performance. The reliability evaluation unit can also evaluate the accuracy of answers to similar questions in the past. The reliability evaluation unit can evaluate accuracy based on, for example, the degree of agreement or error rate with past answers. Step 3: The visualization unit visualizes the results of the evaluation by the reliability evaluation unit. The visualization unit, for example, displays the results of the reliability evaluation in the form of a graph or chart. The visualization unit can visually display the results of the reliability evaluation using, for example, a bar graph or a pie chart. The visualization unit can also update the results of the reliability evaluation in real time. For example, the visualization unit can update the results of the reliability evaluation every time new data is added. Step 4: The analysis unit analyzes the user's interests and concerns. For example, the analysis unit analyzes the user's past browsing history, clicked links, and time spent on the site. The analysis unit can identify the user's interests and concerns using, for example, data mining technology. The analysis unit can also collect trend information from social networking sites and news sites. For example, the analysis unit can collect trend information based on the number of posts on social networking sites and the number of views on news sites. Step 5: The providing unit provides information that is of high interest based on the results of the analysis by the analyzing unit. The providing unit provides relevant information based on, for example, the user's interests and concerns. The providing unit can automatically display information that is of high interest based on, for example, information that the user has viewed in the past or current trend information. The providing unit can also estimate the user's emotions and adjust the method of providing information based on the estimated emotions. For example, the providing unit can provide simple, highly visible information when the user is feeling anxious, and provide detailed information when the user is relaxed.

[0073] (Example 2) A system according to an embodiment of the present invention provides a mechanism for visualizing the reliability of answers generated using a generation AI, improving the usefulness of data, and enabling users to understand the popularity of the answer even when they do not know the search keywords. In this system, the generation AI first generates an answer to a user's question. At the same time, the generation AI simultaneously generates an index for evaluating the reliability of the answer. For example, the reliability of the data underlying the answer and the accuracy of answers to similar questions in the past are evaluated. By visualizing these evaluation results, users can confirm the reliability of the answer. Next, a mechanism for providing highly popular information without the user having to enter a search keyword is provided. The generation AI analyzes the user's interests and presents relevant, highly popular information. For example, highly popular information is automatically displayed based on information the user has previously viewed and current trend information. This mechanism visualizes the reliability of answers provided by the generation AI and improves the usefulness of data. Furthermore, useful information can be provided to users even when the search keywords are unknown. For example, when the generation AI generates an answer to a user's question, the reliability of the data underlying the answer and the accuracy of answers to similar questions in the past are evaluated as indexes for evaluating the reliability of the answer. By visualizing this evaluation result, users can confirm the reliability of the answer. Furthermore, a mechanism is provided to provide highly popular information without the user having to enter search keywords. The generation AI analyzes the user's interests and presents related, highly popular information. For example, it automatically displays highly popular information based on information the user has viewed in the past and current trend information. This mechanism makes it possible to visualize the reliability of the answers provided by the generation AI and improve the usefulness of the data. It is also possible to provide useful information to users even if they do not know the search keywords. This allows the system to visualize the reliability of the answers provided by the generation AI and improve the usefulness of the data. It is also possible to provide useful information to users even if they do not know the search keywords.

[0074] The system according to the embodiment includes a generation unit, a reliability evaluation unit, a visualization unit, an analysis unit, and a provision unit. The generation unit generates answers to questions from users using a generation AI. The generation unit, for example, analyzes questions entered by users and generates appropriate answers. The generation unit can understand the intent of questions and generate appropriate answers using natural language processing technology, for example. The generation unit can also use the generation AI to refer to a database of past questions and answers to generate answers to similar questions. The reliability evaluation unit evaluates the reliability of the answers generated by the generation unit. The reliability evaluation unit, for example, evaluates the reliability of data on which the answers are based. The reliability evaluation unit can evaluate reliability based on, for example, the source of the data and past performance. The reliability evaluation unit can also evaluate the accuracy of answers to similar questions in the past. The reliability evaluation unit can evaluate accuracy based on, for example, the degree of agreement and error rate with past answers. The visualization unit visualizes the results evaluated by the reliability evaluation unit. The visualization unit, for example, displays the results of the reliability evaluation in the form of a graph or chart. The visualization unit can visually display the results of the trustworthiness evaluation using, for example, a bar graph or a pie chart. The visualization unit can also update the results of the trustworthiness evaluation in real time. The visualization unit can update the results of the trustworthiness evaluation, for example, each time new data is added. The analysis unit analyzes the user's interests and concerns. For example, the analysis unit analyzes the user's past browsing history, clicked links, and stay time. The analysis unit can identify the user's interests and concerns using, for example, data mining technology. The analysis unit can also collect trend information on social media and news sites. The analysis unit can collect trend information based on, for example, the number of posts on social media and the number of views on news sites. The provision unit provides highly popular information based on the results of the analysis by the analysis unit. The provision unit provides related information based on, for example, the user's interests and concerns. The provision unit can automatically display highly popular information based on, for example, information the user has viewed in the past or current trend information. The provision unit can also estimate the user's emotions and adjust the method of providing information based on the estimated emotions.For example, the providing unit can provide simple, highly visible information when the user is feeling anxious, and provide detailed information when the user is relaxed. This allows the system according to the embodiment to visualize the reliability of the answers provided by the generation AI and improve the usefulness of the data. Furthermore, it is possible to provide useful information to the user even if the user does not know the search keywords.

[0075] The reliability evaluation unit can evaluate the reliability of data that serves as the basis for an answer. The reliability evaluation unit, for example, analyzes the source of the data and evaluates the reliability. For example, the reliability evaluation unit can evaluate high reliability if the source of the data is reliable, and low reliability if the source is unclear. The reliability evaluation unit can also evaluate the reliability taking into account the update frequency of the data. For example, the reliability evaluation unit can evaluate high reliability if the data is frequently updated, and low reliability if the data is old. Furthermore, the reliability evaluation unit can evaluate the reliability by referring to related literature of the data. For example, the reliability evaluation unit can evaluate high reliability if there is a large number of related literature, and low reliability if there is a small number of related literature. In this way, the reliability evaluation unit can improve the reliability of the answer by evaluating the reliability of the data that serves as the basis for the answer.

[0076] The reliability evaluation unit can evaluate the accuracy of answers to past similar questions. The reliability evaluation unit, for example, analyzes the degree of agreement of past answers and evaluates the accuracy. For example, the reliability evaluation unit can evaluate a past answer as high accuracy if the degree of agreement is high, and as low accuracy if the degree of agreement is low. The reliability evaluation unit can also analyze the error rate of past answers to evaluate the accuracy. For example, the reliability evaluation unit can evaluate a past answer as high accuracy if the error rate is low, and as low accuracy if the error rate is high. Furthermore, the reliability evaluation unit can analyze the evaluation algorithm of past answers to evaluate the accuracy. For example, the reliability evaluation unit can evaluate a past answer as high accuracy if the evaluation algorithm is reliable, and as low accuracy if the evaluation algorithm is unreliable. In this way, the reliability evaluation unit can improve the reliability of the answers by evaluating the accuracy of answers to past similar questions.

[0077] The reliability evaluation unit may include a feedback collection unit that collects user feedback and reflects the collected feedback in the reliability evaluation. The reliability evaluation unit may collect user feedback using, for example, a questionnaire. The reliability evaluation unit may collect user comments and evaluation scores and reflect the collected feedback in the reliability evaluation. The reliability evaluation unit may also analyze the frequency of user feedback to evaluate reliability. For example, the reliability evaluation unit may evaluate high reliability when the frequency of feedback is high, and low reliability when the frequency is low. Furthermore, the reliability evaluation unit may analyze the quality of feedback to evaluate reliability. For example, the reliability evaluation unit may evaluate high reliability when the content of the feedback is specific and detailed, and low reliability when the content is vague and unclear. In this way, the reliability evaluation unit can improve the reliability of answers by collecting user feedback and reflecting the feedback in the reliability evaluation.

[0078] The analysis unit can analyze a user's past browsing history, clicked links, and duration of stay to identify the user's interests and concerns. For example, the analysis unit can analyze a user's past browsing history to identify topics of interest. For example, the analysis unit can analyze the content of pages the user has viewed in the past to identify topics of interest. The analysis unit can also analyze links the user has clicked to identify fields of interest. For example, the analysis unit can analyze the content of links the user has clicked in the past to identify fields of interest. Furthermore, the analysis unit can analyze the duration of stay of the user to identify information in which the user is particularly interested. For example, the analysis unit can analyze the content of pages the user has stayed on for a long time to identify information in which the user is particularly interested. In this way, the analysis unit can identify the user's interests and concerns by analyzing the user's past browsing history, clicked links, and duration of stay to provide related information.

[0079] The analysis unit may include a trend collection unit that collects specific trend information from SNS or news sites. The analysis unit, for example, analyzes the number of posts on SNS to collect trend information. The analysis unit can, for example, analyze information that is frequently shared on SNS to collect trend information. The analysis unit can also analyze the number of views on a news site to collect trend information. The analysis unit can, for example, analyze articles that are frequently viewed on a news site to collect trend information. The analysis unit can also analyze the frequency of use of hashtags to collect trend information. For example, the analysis unit can analyze hashtags that are frequently used on SNS to collect trend information. In this way, the analysis unit can provide the latest, highly popular information by collecting trend information from SNS and news sites.

[0080] The generation unit can estimate the user's emotion and adjust the way the answer is expressed based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. The generation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotion using text analysis technology. The generation unit can, for example, analyze the content of text entered by the user and estimate the emotion. The generation unit can also estimate the user's emotion using voice analysis technology. The generation unit can, for example, record the user's voice and estimate the emotion using a voice analysis algorithm. This allows the generation unit to estimate the user's emotion and adjust the way the answer is expressed based on the estimated emotion, thereby providing a more appropriate answer. For example, if the user is stressed, the generation unit can generate a concise and easy-to-understand answer. Also, if the user is relaxed, the generation unit can generate an answer that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a short answer that gets to the point. This allows the generation unit to provide a more appropriate answer by adjusting the way the answer is expressed depending on the user's feelings.

[0081] The generation unit can analyze the context of the question and apply an algorithm for generating an optimal answer. The generation unit can, for example, analyze background information of the question and generate an answer including related information. The generation unit can, for example, analyze background information of the question entered by the user and generate an answer including related information. The generation unit can also analyze the intent of the question and provide specific information desired by the user. The generation unit can, for example, analyze the intent of the question and provide specific information desired by the user. The generation unit can also analyze the tone of the question and generate an answer with appropriate expression. The generation unit can, for example, analyze the tone of the question and generate an answer with appropriate expression. As a result, the generation unit can provide a more appropriate answer by analyzing the context of the question and applying an algorithm for generating an optimal answer. For example, the generation unit can analyze the background information of the question and generate an answer including related information. The generation unit can also analyze the intent of the question and provide specific information desired by the user. The generation unit can also analyze the tone of the question and generate an answer with appropriate expression. This allows the generator to provide a more appropriate answer by analyzing the context of the question and applying an algorithm to generate the optimal answer.

[0082] The generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit can generate an answer based on expert knowledge for a technical question. For example, the generation unit can generate an answer based on expert knowledge for a technical question. Furthermore, the generation unit can generate an answer that provides an explanation in easy-to-understand language for a general question. For example, the generation unit can generate an answer that provides an explanation in easy-to-understand language for a general question. Furthermore, the generation unit can generate an answer that includes an expression showing empathy for an emotional question. For example, the generation unit can generate an answer that includes an expression showing empathy for an emotional question. In this way, the generation unit can provide a more appropriate answer by applying different generation algorithms depending on the category of the question. For example, the generation unit can generate an answer based on expert knowledge for a technical question. Furthermore, the generation unit can generate an answer that provides an explanation in easy-to-understand language for a general question. Furthermore, the generation unit can generate an answer that includes an expression showing empathy for an emotional question. In this way, the generation unit can provide a more appropriate answer by applying different generation algorithms depending on the category of the question.

[0083] The generation unit can estimate the user's emotion and adjust the level of detail of the answer based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. The generation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotion using text analysis technology. The generation unit can, for example, analyze the content of text entered by the user and estimate the emotion. The generation unit can also estimate the user's emotion using voice analysis technology. The generation unit can, for example, record the user's voice and estimate the emotion using a voice analysis algorithm. In this way, the generation unit can estimate the user's emotion and adjust the level of detail of the answer based on the estimated emotion, thereby providing a more appropriate answer. For example, if the user is stressed, the generation unit can generate a concise and to-the-point answer. Also, if the user is relaxed, the generation unit can generate an answer including detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a short and to-the-point answer. This allows the generation unit to adjust the level of detail of the answer depending on the user's feelings, thereby enabling the generation unit to provide a more appropriate answer.

[0084] The generation unit can determine the priority of answers based on the time when the question was submitted. The generation unit can, for example, analyze the submission date and time of the question and determine the priority of answers according to the urgency. The generation unit can, for example, analyze the submission date and time of the question and determine the priority of answers according to the urgency. The generation unit can also determine the priority of answers by analyzing the urgency of the question. The generation unit can, for example, analyze the urgency of the question and quickly generate answers for questions with high urgency. Furthermore, the generation unit can also determine the priority of answers by analyzing the deadline of the question. The generation unit can, for example, analyze the deadline of the question and preferentially generate answers for questions whose deadline is approaching. In this way, the generation unit can respond quickly to urgent questions by determining the priority of answers based on the time when the question was submitted. For example, the generation unit can quickly generate answers for urgent questions. Furthermore, the generation unit can generate answers for regular questions with normal priority. Furthermore, the generation unit can postpone generating answers for past questions. This allows the generation unit to prioritize answers based on the time when the question was submitted, thereby enabling a prompt response to urgent questions.

[0085] The generation unit can adjust the order of answers based on the relevance of the question. The generation unit can, for example, analyze the degree of similarity of keywords in the question and generate answers preferentially for highly relevant questions. The generation unit can, for example, analyze the degree of similarity of keywords in the question and generate answers preferentially for highly relevant questions. The generation unit can also adjust the order of answers by analyzing similarity of topics in the questions. The generation unit can, for example, analyze the similarity of topics in the questions and generate answers preferentially for highly relevant questions. Furthermore, the generation unit can also adjust the order of answers by analyzing a user's area of ​​interest. The generation unit can, for example, analyze a user's area of ​​interest and generate answers preferentially for highly relevant questions. In this way, the generation unit can adjust the order of answers based on the relevance of the question, thereby preferentially providing more relevant answers. For example, the generation unit can generate answers preferentially for highly relevant questions. Furthermore, the generation unit can generate answers later for less relevant questions. Furthermore, the generation unit can adjust the order of answers based on the importance of the question. This allows the generation unit to adjust the order of answers based on the relevance of the question, thereby allowing more relevant answers to be provided preferentially.

[0086] The trustworthiness evaluation unit can estimate the user's emotion and adjust the trustworthiness evaluation criteria based on the estimated user's emotion. The trustworthiness evaluation unit can estimate the user's emotion using, for example, facial expression recognition technology. The trustworthiness evaluation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The trustworthiness evaluation unit can also estimate the user's emotion using text analysis technology. The trustworthiness evaluation unit can, for example, analyze the content of text entered by the user and estimate the emotion. The trustworthiness evaluation unit can also estimate the user's emotion using voice analysis technology. The trustworthiness evaluation unit can, for example, record the user's voice and estimate the emotion using a voice analysis algorithm. This allows the trustworthiness evaluation unit to estimate the user's emotion and adjust the trustworthiness evaluation criteria based on the estimated emotion, thereby performing a more appropriate trustworthiness evaluation. For example, if the user is feeling anxious, the trustworthiness evaluation unit can evaluate the trustworthiness using strict criteria. On the other hand, if the user is relaxed, the trustworthiness evaluation unit can evaluate the trustworthiness using flexible criteria. Furthermore, if the user is in a hurry, the trustworthiness evaluation unit can quickly evaluate the trustworthiness. This allows the reliability evaluation unit to adjust the reliability evaluation criteria in accordance with the user's feelings, thereby enabling a more appropriate reliability evaluation.

[0087] The reliability evaluation unit can analyze the source of the data that forms the basis of the answer and evaluate its reliability. The reliability evaluation unit, for example, analyzes the source of the data and evaluates its reliability. For example, the reliability evaluation unit can evaluate high reliability if the source of the data is reliable, and low reliability if the source is unclear. The reliability evaluation unit can also evaluate reliability comprehensively when there are multiple sources of data. For example, when there are multiple sources of data, the reliability evaluation unit can evaluate the reliability of each source and evaluate the reliability comprehensively. Furthermore, the reliability evaluation unit can evaluate high reliability if the source of the data is official data or an academic paper. For example, the reliability evaluation unit can evaluate high reliability if the source of the data is official data or an academic paper. In this way, the reliability evaluation unit can provide a highly reliable answer by analyzing the source of the data that forms the basis of the answer and evaluating its reliability. For example, the reliability evaluation unit can evaluate high reliability if the source of the data is reliable. Furthermore, the reliability evaluation unit can evaluate low reliability if the source of the data is unclear. Furthermore, when there are multiple data sources, the reliability evaluation unit can comprehensively evaluate reliability, thereby analyzing the source of the data that forms the basis of the answer and evaluating its reliability, thereby providing a highly reliable answer.

[0088] The reliability evaluation unit can analyze the process of generating an answer and evaluate its reliability. For example, if the answer is generated based on multiple data sources, the reliability evaluation unit can evaluate the reliability as high. For example, if the answer is generated based on multiple data sources, the reliability evaluation unit can evaluate the reliability as high. Furthermore, if the answer is generated based on a single data source, the reliability evaluation unit can evaluate the reliability as low. For example, if the answer is generated based on a single data source, the reliability evaluation unit can evaluate the reliability as low. Furthermore, if there are unclear points in the process of generating an answer, the reliability evaluation unit can evaluate the reliability as low. For example, if there are unclear points in the process of generating an answer, the reliability evaluation unit can evaluate the reliability as low. In this way, the reliability evaluation unit can provide a highly reliable answer by analyzing the process of generating an answer and evaluating its reliability. For example, if the answer is generated based on multiple data sources, the reliability evaluation unit can evaluate the reliability as high. Furthermore, if the answer is generated based on a single data source, the reliability evaluation unit can evaluate the reliability as low. Furthermore, if there are unclear points in the process of generating an answer, the reliability evaluation unit can evaluate the reliability as low. This allows the reliability evaluation unit to analyze the process of generating an answer and evaluate the reliability, thereby providing a highly reliable answer.

[0089] The reliability evaluation unit can estimate the user's emotion and adjust the order in which the reliability evaluation results are displayed based on the estimated user's emotion. The reliability evaluation unit can estimate the user's emotion using, for example, facial expression recognition technology. The reliability evaluation unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reliability evaluation unit can also estimate the user's emotion using text analysis technology. The reliability evaluation unit can, for example, analyze the content of text entered by the user and estimate the emotion. The reliability evaluation unit can also estimate the user's emotion using voice analysis technology. The reliability evaluation unit can, for example, record the user's voice and estimate the emotion using a voice analysis algorithm. This allows the reliability evaluation unit to estimate the user's emotion and adjust the order in which the reliability evaluation results are displayed based on the estimated emotion, thereby providing more appropriate information. For example, if the user is feeling anxious, the reliability evaluation unit can prioritize displaying highly reliable results. Furthermore, if the user is relaxed, the reliability evaluation unit can also display results with low reliability. Furthermore, if the user is in a hurry, the reliability evaluation unit can quickly display highly reliable results. This allows the reliability evaluation unit to adjust the order in which the reliability evaluation results are displayed depending on the user's feelings, thereby providing more appropriate information.

[0090] The reliability evaluation unit can evaluate the reliability of an answer by taking into account the update frequency of the data. For example, the reliability evaluation unit analyzes the update frequency of the data and evaluates the reliability. For example, the reliability evaluation unit can evaluate high reliability when the update frequency of the data is high, and low reliability when the update frequency is low. The reliability evaluation unit can also evaluate the reliability by analyzing the update date and time of the data. For example, the reliability evaluation unit can evaluate high reliability when the update date and time of the data is recent, and low reliability when the update date and time is old. Furthermore, the reliability evaluation unit can evaluate the reliability by analyzing the number of updates of the data. For example, the reliability evaluation unit can evaluate high reliability when the update frequency of the data is high, and low reliability when the update frequency is low. As a result, the reliability evaluation unit can provide a more reliable answer by taking the update frequency of the data into account when evaluating the reliability of an answer. For example, the reliability evaluation unit can evaluate high reliability when the update frequency of the data is high. Furthermore, the reliability evaluation unit can evaluate low reliability when the update frequency of the data is low. Furthermore, the reliability evaluation unit can evaluate the reliability by analyzing the update date and time of the data. This allows the reliability evaluation unit to take into consideration the update frequency of data when evaluating the reliability of an answer, thereby providing a more reliable answer.

[0091] The reliability evaluation unit can improve the accuracy of the evaluation by referring to related literature of the data when evaluating the reliability of an answer. The reliability evaluation unit, for example, analyzes related literature of the data and evaluates reliability. For example, the reliability evaluation unit can evaluate high reliability when there are many related literatures, and low reliability when there are few related literatures. The reliability evaluation unit can also evaluate reliability by analyzing the quality of the related literature. For example, the reliability evaluation unit can evaluate high reliability when the quality of the related literature is high, and low reliability when the quality is low. Furthermore, the reliability evaluation unit can evaluate reliability by analyzing the number of citations of the related literature. For example, the reliability evaluation unit can evaluate high reliability when the number of citations of the related literature is high, and low reliability when the number of citations is low. As a result, the reliability evaluation unit can improve the accuracy of the evaluation by referring to related literature of the data when evaluating the reliability of an answer, thereby providing a more reliable answer. For example, the reliability evaluation unit can evaluate high reliability when there are many related literatures. Furthermore, the reliability evaluation unit can evaluate low reliability when there are few related literatures. Furthermore, the reliability evaluation unit can evaluate reliability by analyzing the quality of the related literature. This allows the reliability evaluation unit to improve the accuracy of the evaluation by referring to literature related to the data when evaluating the reliability of the answer, thereby providing a more reliable answer.

[0092] The visualization unit can estimate the user's emotion and adjust the display method of the visualization based on the estimated user's emotion. The visualization unit can estimate the user's emotion using, for example, facial expression recognition technology. The visualization unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The visualization unit can also estimate the user's emotion using text analysis technology. The visualization unit can, for example, analyze the content of text entered by the user and estimate the emotion. The visualization unit can also estimate the user's emotion using voice analysis technology. The visualization unit can, for example, record the user's voice and estimate the emotion using a voice analysis algorithm. This allows the visualization unit to estimate the user's emotion and adjust the display method of the visualization based on the estimated emotion, thereby providing more appropriate information. For example, if the user is feeling anxious, the visualization unit can provide a simple, highly visible display method. If the user is relaxed, the visualization unit can provide a display method including detailed information. If the user is in a hurry, the visualization unit can provide a display method that focuses on the main points. This allows the visualization unit to provide more appropriate information by adjusting the visualization display method in accordance with the user's emotions.

[0093] The visualization unit can display the results of the reliability evaluation in the form of a graph or a chart. The visualization unit, for example, displays the results of the reliability evaluation in the form of a bar graph. The visualization unit, for example, can display the results of the reliability evaluation in the form of a bar graph. Furthermore, the visualization unit can display the results of the reliability evaluation in the form of a pie chart. For example, the visualization unit can display the results of the reliability evaluation in the form of a pie chart. Furthermore, the visualization unit can display the results of the reliability evaluation in the form of a line graph. For example, the visualization unit can display the results of the reliability evaluation in the form of a line graph. By displaying the results of the reliability evaluation in the form of a graph or a chart, the visualization unit can make it easier for the user to understand. For example, the visualization unit can display the results of the reliability evaluation in the form of a bar graph. Furthermore, the visualization unit can display the results of the reliability evaluation in the form of a pie chart. Furthermore, the visualization unit can display the results of the reliability evaluation in the form of a line graph. By displaying the results of the reliability evaluation in the form of a graph or a chart, the visualization unit can make it easier for the user to understand.

[0094] The visualization unit can update the result of the trustworthiness evaluation in real time. For example, the visualization unit updates the result of the trustworthiness evaluation in real time every time new data is added. For example, the visualization unit can update the result of the trustworthiness evaluation in real time every time new data is added. Furthermore, the visualization unit can update the result of the trustworthiness evaluation in real time every time a user provides feedback. For example, the visualization unit can update the result of the trustworthiness evaluation in real time every time a user provides feedback. Furthermore, the visualization unit can update the result of the trustworthiness evaluation in real time in accordance with the update frequency of the data. For example, the visualization unit can update the result of the trustworthiness evaluation in real time in accordance with the update frequency of the data. In this way, the visualization unit can provide the latest information by updating the result of the trustworthiness evaluation in real time. For example, the visualization unit can update the result of the trustworthiness evaluation in real time every time new data is added. Furthermore, the visualization unit can update the result of the trustworthiness evaluation in real time every time a user provides feedback. Furthermore, the visualization unit can update the result of the trustworthiness evaluation in real time in accordance with the update frequency of the data. In this way, the visualization unit can provide the latest information by updating the result of the trustworthiness evaluation in real time.

[0095] The visualization unit can estimate the user's emotions and determine the priority of visualization based on the estimated user's emotions. The visualization unit can estimate the user's emotions using, for example, facial expression recognition technology. The visualization unit can, for example, capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The visualization unit can also estimate the user's emotions using text analysis technology. The visualization unit can, for example, analyze the content of text entered by the user and estimate the emotions. The visualization unit can also estimate the user's emotions using voice analysis technology. The visualization unit can, for example, record the user's voice and estimate the emotions using a voice analysis algorithm. This allows the visualization unit to estimate the user's emotions and determine the priority of visualization based on the estimated emotions, thereby providing more appropriate information. For example, if the user is feeling anxious, the visualization unit can prioritize displaying highly reliable information. Furthermore, if the user is relaxed, the visualization unit can prioritize displaying detailed information. Furthermore, if the user is in a hurry, the visualization unit can prioritize displaying information that focuses on the main points. This allows the visualization unit to determine the priority of visualization according to the user's emotions, thereby providing more appropriate information.

[0096] The visualization unit can select an optimal display method in consideration of the user's device information when visualizing. For example, when the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. For example, when the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. Furthermore, when the user is using a tablet, the visualization unit can provide a display method optimized for a large screen. For example, when the user is using a tablet, the visualization unit can provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the visualization unit can provide a display method that includes detailed information. For example, when the user is using a desktop, the visualization unit can provide a display method that includes detailed information. As a result, the visualization unit selects the optimal display method in accordance with the user's device information, thereby improving visibility. For example, when the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. Furthermore, when the user is using a tablet, the visualization unit can provide a display method optimized for a large screen. Furthermore, when the user is using a desktop, the visualization unit can provide a display method that includes detailed information. As a result, the visualization unit selects the optimal display method in accordance with the user's device information, thereby improving visibility.

[0097] The visualization unit can select an optimal display method by referring to the user's past browsing history when visualizing the content. For example, the visualization unit can preferentially provide a display method that the user has used preferentially in the past. For example, the visualization unit can preferentially provide a display method that the user has used preferentially in the past. The visualization unit can also suggest an optimal display method based on the user's past browsing history. For example, the visualization unit can suggest an optimal display method based on the user's past browsing history. Furthermore, the visualization unit can analyze the user's past browsing history and provide a display method with high visibility. For example, the visualization unit can analyze the user's past browsing history and provide a display method with high visibility. As a result, the visualization unit selects an optimal display method based on the user's past browsing history, thereby improving user convenience. For example, the visualization unit can preferentially provide a display method that the user has used preferentially in the past. Furthermore, the visualization unit can suggest an optimal display method based on the user's past browsing history. Furthermore, the visualization unit can analyze the user's past browsing history and provide a display method with high visibility. This allows the visualization unit to select the optimal display method based on the user's past browsing history, thereby improving user convenience.

[0098] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, facial expression recognition technology. The analysis unit can estimate the user's emotion using, for example, an emotion estimation algorithm by capturing the user's facial expression with a camera. The analysis unit can also estimate the user's emotion using text analysis technology. The analysis unit can estimate the user's emotion by analyzing the content of text entered by the user. The analysis unit can also estimate the user's emotion using voice analysis technology. The analysis unit can record the user's voice and estimate the emotion using a voice analysis algorithm. This allows the analysis unit to estimate the user's emotion and adjust the analysis method based on the estimated emotion, thereby providing more appropriate information. For example, if the user is feeling anxious, the analysis unit can perform a detailed analysis and provide reliable information. Furthermore, if the user is relaxed, the analysis unit can perform a flexible analysis and provide diverse information. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide information that focuses on the main points. This allows the analysis unit to adjust the analysis method according to the user's emotions, thereby providing more appropriate information.

[0099] The analysis unit can analyze the user's past browsing history to identify the user's interests and concerns. For example, the analysis unit can analyze the content of pages the user has viewed in the past to identify topics of interest. For example, the analysis unit can analyze the content of pages the user has viewed in the past to identify topics of interest. The analysis unit can also analyze links the user has clicked in the past to identify areas of interest. For example, the analysis unit can analyze the content of links the user has clicked in the past to identify areas of interest. Furthermore, the analysis unit can analyze the user's stay time to identify information in which the user is particularly interested. For example, the analysis unit can analyze the content of pages the user has stayed on for a long time to identify information in which the user is particularly interested. In this way, the analysis unit can identify the user's interests and concerns by analyzing the user's past browsing history and provide related information. For example, the analysis unit can analyze the content of pages the user has viewed in the past to identify topics of interest. For example, the analysis unit can analyze links the user has clicked in the past to identify areas of interest. Furthermore, the analysis unit can analyze the user's stay time to identify information in which the user is particularly interested. This allows the analysis unit to analyze the user's past browsing history to identify the user's interests and concerns and provide related information.

[0100] The analysis unit can identify the user's interests and concerns by analyzing the links clicked by the user and the time spent on the site. For example, the analysis unit can analyze the content of the links clicked by the user to identify topics of interest. For example, the analysis unit can analyze the content of the links clicked by the user to identify topics of interest. The analysis unit can also analyze the user's time spent on the site to identify information in which the user is particularly interested. For example, the analysis unit can analyze the user's time spent on the site to identify information in which the user is particularly interested. Furthermore, the analysis unit can analyze the user's click patterns to identify areas of interest. For example, the analysis unit can analyze the user's click patterns to identify areas of interest. As a result, the analysis unit can identify the user's interests and concerns by analyzing the links clicked by the user and the time spent on the site, and provide related information. For example, the analysis unit can analyze the content of the links clicked by the user to identify topics of interest. For example, the analysis unit can analyze the user's time spent on the site to identify information in which the user is particularly interested. Furthermore, the analysis unit can analyze the user's click patterns to identify areas of interest. This allows the analysis unit to identify the user's interests and concerns by analyzing the links the user clicks and the length of time the user stays on the site, and to provide related information.

[0101] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. The analysis unit can, for example, capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using text analysis technology. The analysis unit can, for example, analyze the content of text entered by the user and estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. The analysis unit can, for example, record the user's voice and estimate the emotions using a voice analysis algorithm. This allows the analysis unit to estimate the user's emotions and determine analysis priorities based on the estimated emotions, thereby providing more appropriate information. For example, if the user is feeling anxious, the analysis unit can prioritize analyzing highly reliable information. Also, if the user is relaxed, the analysis unit can prioritize analyzing diverse information. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing information that focuses on the main points. This allows the analysis unit to determine the priority of analysis according to the user's emotions, thereby providing more appropriate information.

[0102] The analysis unit can perform the analysis taking into account the user's geographical location information. For example, the analysis unit can prioritize analyzing related information based on the user's current location. For example, the analysis unit can prioritize analyzing related information based on the user's current location. The analysis unit can also analyze information about areas of interest based on the user's past location information. For example, the analysis unit can analyze information about areas of interest based on the user's past location information. Furthermore, the analysis unit can update the user's location information in real time and analyze optimal information. For example, the analysis unit can update the user's location information in real time and analyze optimal information. In this way, the analysis unit can provide more relevant information by performing the analysis taking into account the user's geographical location information. For example, the analysis unit can prioritize analyzing related information based on the user's current location. The analysis unit can also analyze information about areas of interest based on the user's past location information. Furthermore, the analysis unit can update the user's location information in real time and analyze optimal information. In this way, the analysis unit can provide more relevant information by performing the analysis taking into account the user's geographical location information.

[0103] During the analysis, the analysis unit can analyze the user's social media activities and identify their interests. For example, the analysis unit can analyze the content shared by the user on social media to identify topics of interest. For example, the analysis unit can analyze the content shared by the user on social media to identify topics of interest. The analysis unit can also analyze the accounts the user follows on social media to identify areas of interest. For example, the analysis unit can analyze the accounts the user follows on social media to identify areas of interest. Furthermore, the analysis unit can analyze the frequency of the user's social media activities to identify information in which the user is particularly interested. For example, the analysis unit can analyze the frequency of the user's social media activities to identify information in which the user is particularly interested. Thus, by analyzing the user's social media activities, the analysis unit can identify the user's interests and provide related information. For example, the analysis unit can analyze the content shared by the user on social media to identify topics of interest. For example, the analysis unit can analyze the accounts the user follows on social media to identify areas of interest. Furthermore, the analysis unit can analyze the frequency of the user's social media activities to identify information in which the user is particularly interested. This allows the analysis unit to analyze the user's social media activity to identify the user's interests and provide related information.

[0104] The providing unit can estimate the user's emotion and adjust the information provision method based on the estimated user's emotion. The providing unit can estimate the user's emotion using, for example, facial expression recognition technology. The providing unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotion using text analysis technology. The providing unit can, for example, analyze the content of text entered by the user and estimate the emotion. The providing unit can also estimate the user's emotion using voice analysis technology. The providing unit can, for example, record the user's voice and estimate the emotion using a voice analysis algorithm. In this way, the providing unit can estimate the user's emotion and adjust the information provision method based on the estimated emotion, thereby providing more appropriate information. For example, if the user is feeling anxious, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can select a provision method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can quickly provide information that covers the main points. This allows the providing unit to adjust the method of providing information in accordance with the user's emotions, thereby providing more appropriate information.

[0105] The providing unit can adjust the level of detail of the information to be provided based on the importance of the information to be provided. For example, the providing unit selects a providing method including a detailed description for information with a high level of importance. For example, the providing unit can select a providing method including a detailed description for information with a high level of importance. The providing unit can also select a providing method including a brief description for information with a low level of importance. For example, the providing unit can select a providing method including a brief description for information with a low level of importance. Furthermore, the providing unit can gradually adjust the level of detail of the information according to the importance. For example, the providing unit can gradually adjust the level of detail of the information according to the importance. As a result, the providing unit can provide more appropriate information by adjusting the level of detail of the information based on the importance of the information. For example, the providing unit can select a providing method including a detailed description for information with a high level of importance. Also, the providing unit can select a providing method including a brief description for information with a low level of importance. Furthermore, the providing unit can gradually adjust the level of detail of the information according to the importance. As a result, the providing unit can provide more appropriate information by adjusting the level of detail of the information based on the importance of the information.

[0106] The providing unit can apply different providing algorithms depending on the category of information to be provided. For example, the providing unit can select a providing method based on specialized knowledge for technical information. For example, the providing unit can select a providing method based on specialized knowledge for technical information. Furthermore, the providing unit can select a providing method that explains general information in easy-to-understand terms. For example, the providing unit can select a providing method that explains general information in easy-to-understand terms. Furthermore, the providing unit can select a providing method that includes expressions showing empathy for emotional information. For example, the providing unit can select a providing method that includes expressions showing empathy for emotional information. In this way, the providing unit can provide more appropriate information by applying an appropriate providing algorithm depending on the category of information. For example, the providing unit can select a providing method based on specialized knowledge for technical information. Furthermore, the providing unit can select a providing method that explains general information in easy-to-understand terms. Furthermore, the providing unit can select a providing method that includes expressions showing empathy for emotional information. In this way, the providing unit can provide more appropriate information by applying an appropriate providing algorithm depending on the category of information.

[0107] The providing unit can estimate the user's emotion and determine the priority of information provision based on the estimated user's emotion. The providing unit can estimate the user's emotion using, for example, facial expression recognition technology. The providing unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotion using text analysis technology. The providing unit can, for example, analyze the content of text entered by the user and estimate the emotion. The providing unit can also estimate the user's emotion using voice analysis technology. The providing unit can, for example, record the user's voice and estimate the emotion using a voice analysis algorithm. In this way, the providing unit can estimate the user's emotion and determine the priority of information provision based on the estimated emotion, thereby providing more appropriate information. For example, if the user is feeling anxious, the providing unit can prioritize providing highly reliable information. Furthermore, if the user is relaxed, the providing unit can prioritize providing diverse information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information that focuses on the main points. This allows the providing unit to determine the priority of providing information according to the user's emotions, thereby enabling the providing unit to provide more appropriate information.

[0108] The providing unit can determine the priority of provision based on the time of submission of the information when the information is provided. The providing unit, for example, analyzes the submission date and time of the information and determines the priority of provision according to the urgency. The providing unit, for example, analyzes the submission date and time of the information and determines the priority of provision according to the urgency. The providing unit can also analyze the urgency of the information and determine the priority of provision. For example, the providing unit can analyze the urgency of the information and quickly provide information with a high level of urgency. Furthermore, the providing unit can analyze the deadline of the information and determine the priority of provision. For example, the providing unit can analyze the deadline of the information and provide information with priority when the deadline is approaching. In this way, the providing unit can respond quickly to urgent information by determining the priority of provision based on the time of submission of the information. For example, the providing unit can provide urgent information quickly. In addition, the providing unit can provide periodic information with normal priority. Furthermore, the providing unit can provide past information at a later date. In this way, the providing unit can respond quickly to urgent information by determining the priority of provision based on the time of submission of the information.

[0109] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. The providing unit can, for example, analyze the degree of coincidence of keywords of the information and provide highly relevant information preferentially. The providing unit can, for example, analyze the degree of coincidence of keywords of the information and provide highly relevant information preferentially. The providing unit can also adjust the order of provision by analyzing similarity of topics of the information. The providing unit can, for example, analyze similarity of topics of the information and provide highly relevant information preferentially. Furthermore, the providing unit can also adjust the order of provision by analyzing a user's area of ​​interest. The providing unit can, for example, analyze a user's area of ​​interest and provide highly relevant information preferentially. In this way, the providing unit can adjust the order of provision based on the relevance of the information, thereby providing more relevant information preferentially. For example, the providing unit can provide highly relevant information preferentially. Furthermore, the providing unit can provide less relevant information later. Furthermore, the providing unit can adjust the order of provision based on the importance of the information. This allows the providing unit to adjust the order of providing information based on the relevance of the information, thereby enabling the providing unit to provide more relevant information with priority.

[0110] The feedback collection unit can estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. The feedback collection unit can estimate the user's emotion using, for example, facial expression recognition technology. The feedback collection unit can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The feedback collection unit can also estimate the user's emotion using text analysis technology. The feedback collection unit can, for example, analyze the content of text entered by the user and estimate the emotion. The feedback collection unit can also estimate the user's emotion using voice analysis technology. For example, the feedback collection unit can record the user's voice and estimate the emotion using a voice analysis algorithm. This allows the feedback collection unit to estimate the user's emotion and adjust the feedback collection method based on the estimated emotion, thereby collecting more appropriate feedback. For example, if the user is feeling anxious, the feedback collection unit can provide a simple and easy-to-understand feedback form. On the other hand, if the user is relaxed, the feedback collection unit can provide a form requesting detailed feedback. Furthermore, if the user is in a hurry, the feedback collection unit can provide a method for quickly collecting feedback. This allows the feedback collection unit to adjust the feedback collection method according to the user's emotions, thereby allowing more appropriate feedback to be collected.

[0111] The feedback collecting unit can analyze the content of the feedback and reflect it in the reliability evaluation. The feedback collecting unit, for example, analyzes the content of the user's feedback and reflects it in the reliability evaluation. The feedback collecting unit, for example, can analyze the content of the user's feedback and reflect it in the reliability evaluation. The feedback collecting unit can also analyze the frequency of feedback and reflect it in the reliability evaluation. The feedback collecting unit can, for example, analyze the frequency of feedback and reflect it in the reliability evaluation. Furthermore, the feedback collecting unit can also analyze the quality of feedback and reflect it in the reliability evaluation. The feedback collecting unit can, for example, analyze the quality of feedback and reflect it in the reliability evaluation. Thereby, the feedback collecting unit can analyze the content of the feedback and reflect it in the reliability evaluation, thereby improving the accuracy of the reliability evaluation. For example, the feedback collecting unit can analyze the content of the user's feedback and reflect it in the reliability evaluation. Furthermore, the feedback collecting unit can analyze the frequency of feedback and reflect it in the reliability evaluation. Furthermore, the feedback collecting unit can analyze the quality of feedback and reflect it in the reliability evaluation. This allows the feedback collection unit to analyze the content of the feedback and reflect it in the reliability evaluation, thereby improving the accuracy of the reliability evaluation.

[0112] When collecting feedback, the feedback collection unit can select an optimal collection method by referring to the user's past feedback history. The feedback collection unit, for example, can select the optimal collection method by referring to the user's past feedback history. The feedback collection unit can select the optimal collection method by referring to the user's past feedback history. The feedback collection unit can also suggest appropriate questions from the user's feedback history. The feedback collection unit can also suggest appropriate questions from the user's feedback history. The feedback collection unit can also adjust the feedback collection method based on the user's feedback history. The feedback collection unit can adjust the feedback collection method based on the user's feedback history. Thereby, the feedback collection unit can select a more appropriate feedback collection method by referring to the user's past feedback history. For example, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history. The feedback collection unit can also suggest appropriate questions from the user's feedback history. The feedback collection unit can also adjust the feedback collection method based on the user's feedback history. This allows the feedback collection unit to select a more appropriate feedback collection method by referring to the user's past feedback history.

[0113] The feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback collection unit can estimate the user's emotions using, for example, facial expression recognition technology. The feedback collection unit can, for example, capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The feedback collection unit can also estimate the user's emotions using text analysis technology. The feedback collection unit can, for example, analyze the content of text entered by the user and estimate the emotions. The feedback collection unit can also estimate the user's emotions using voice analysis technology. The feedback collection unit can, for example, record the user's voice and estimate the emotions using a voice analysis algorithm. In this way, the feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions, thereby preferentially collecting more important feedback. For example, if the user is feeling anxious, the feedback collection unit can preferentially collect important feedback. Also, if the user is relaxed, the feedback collection unit can preferentially collect diverse feedback. Furthermore, if the user is in a hurry, the feedback collection unit can preferentially collect feedback that focuses on the main points. This allows the feedback collection unit to prioritize the feedback according to the user's emotions, thereby allowing the more important feedback to be collected preferentially.

[0114] The feedback collection unit can collect feedback taking into consideration geographical location information of the user when collecting feedback. The feedback collection unit, for example, can prioritize collecting relevant feedback based on the user's current location. The feedback collection unit can prioritize collecting relevant feedback based on the user's current location. The feedback collection unit can also collect feedback on areas of interest based on the user's past location information. The feedback collection unit can collect feedback on areas of interest based on the user's past location information. The feedback collection unit can also update the user's location information in real time to collect optimal feedback. The feedback collection unit can, for example, update the user's location information in real time to collect optimal feedback. In this way, the feedback collection unit can collect more relevant feedback by collecting feedback taking into consideration the user's geographical location information. For example, the feedback collection unit can prioritize collecting relevant feedback based on the user's current location. The feedback collection unit can also collect feedback on areas of interest based on the user's past location information. The feedback collection unit can also update the user's location information in real time to collect optimal feedback. This allows the feedback collection unit to collect feedback taking into account the geographical location information of the user, thereby allowing the collection of more relevant feedback.

[0115] The feedback collection unit can analyze the user's social media activities when collecting feedback and collect relevant feedback. For example, the feedback collection unit can analyze content shared by the user on social media and collect relevant feedback. For example, the feedback collection unit can analyze content shared by the user on social media and collect relevant feedback. The feedback collection unit can also analyze accounts the user follows on social media to collect feedback on areas of interest. For example, the feedback collection unit can analyze accounts the user follows on social media and collect feedback on areas of interest. Furthermore, the feedback collection unit can analyze the frequency of the user's social media activities to collect feedback on information in which the user is particularly interested. For example, the feedback collection unit can analyze the frequency of the user's social media activities to collect feedback on information in which the user is particularly interested. In this way, the feedback collection unit can collect more relevant feedback by analyzing the user's social media activities. For example, the feedback collection unit can analyze content shared by the user on social media and collect relevant feedback. For example, the feedback collection unit can analyze accounts the user follows on social media to collect feedback on areas of interest. Furthermore, the feedback collection unit can analyze the frequency of the user's social media activities to collect feedback on information that the user is particularly interested in. This allows the feedback collection unit to collect more relevant feedback by analyzing the user's social media activities.

[0116] The trend collection unit can estimate a user's emotion and adjust a trend information collection method based on the estimated user's emotion. The trend collection unit can estimate a user's emotion using, for example, facial expression recognition technology. The trend collection unit can, for example, capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The trend collection unit can also estimate a user's emotion using text analysis technology. The trend collection unit can, for example, analyze the content of text entered by the user and estimate the emotion. The trend collection unit can also estimate a user's emotion using voice analysis technology. The trend collection unit can, for example, record the user's voice and estimate the emotion using a voice analysis algorithm. This allows the trend collection unit to estimate a user's emotion and adjust a trend information collection method based on the estimated emotion, thereby collecting more appropriate trend information. For example, if the user is feeling anxious, the trend collection unit can prioritize collecting reliable trend information. Also, if the user is relaxed, the trend collection unit can collect a variety of trend information. Furthermore, if the user is in a hurry, the trend collection unit can quickly collect trend information that is concise. This allows the trend collection unit to adjust the trend information collection method in accordance with the user's emotions, thereby enabling more appropriate trend information to be collected.

[0117] The trend collection unit can analyze trend information on SNS and news sites to identify information that is attracting a lot of attention. The trend collection unit can, for example, analyze information that is shared frequently on SNS to identify information that is attracting a lot of attention. The trend collection unit can also analyze articles that are viewed frequently on news sites to identify information that is attracting a lot of attention. The trend collection unit can, for example, analyze articles that are viewed frequently on news sites to identify information that is attracting a lot of attention. The trend collection unit can also analyze the number of comments on SNS and news sites to identify information that is attracting a lot of attention. The trend collection unit can, for example, analyze the number of comments on SNS and news sites to identify information that is attracting a lot of attention. In this way, the trend collection unit can identify information that is attracting a lot of attention by analyzing trend information on SNS and news sites. For example, the trend collection unit can analyze information that is shared frequently on SNS to identify information that is attracting a lot of attention. The trend collection unit can also analyze articles that are viewed frequently on news sites to identify information that is attracting a lot of attention. Furthermore, the trend collection unit can identify information that is attracting a lot of attention by analyzing the number of comments on social media and news sites. This allows the trend collection unit to identify information that is attracting a lot of attention by analyzing trend information on social media and news sites.

[0118] The trend collection unit can predict a current trend by referring to past trend data when collecting trend information. The trend collection unit can predict a current trend based on, for example, past trend data. The trend collection unit can predict a current trend based on, for example, past trend data. The trend collection unit can also predict a trend change by comparing past trend data with current data. The trend collection unit can predict a trend change by comparing, for example, past trend data with current data. Furthermore, the trend collection unit can predict a future trend by analyzing past trend data. The trend collection unit can predict a future trend by analyzing, for example, past trend data. As a result, the trend collection unit can predict a current trend by referring to past trend data when collecting trend information, thereby providing more appropriate information. For example, the trend collection unit can predict a current trend based on past trend data. The trend collection unit can predict a trend change by comparing past trend data with current data. Furthermore, the trend collection unit can predict a future trend by analyzing past trend data. As a result, the trend collection unit can predict a current trend by referring to past trend data when collecting trend information, thereby providing more appropriate information.

[0119] The trend collection unit can estimate a user's emotions and prioritize trend information based on the estimated user emotions. The trend collection unit can estimate a user's emotions using, for example, facial expression recognition technology. The trend collection unit can, for example, capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The trend collection unit can also estimate a user's emotions using text analysis technology. The trend collection unit can, for example, analyze the content of text entered by a user and estimate the emotion. The trend collection unit can also estimate a user's emotions using voice analysis technology. The trend collection unit can, for example, record a user's voice and estimate the emotion using a voice analysis algorithm. This allows the trend collection unit to estimate a user's emotions and prioritize trend information based on the estimated emotion, thereby providing more appropriate information. For example, if a user is feeling anxious, the trend collection unit can prioritize providing reliable trend information. Also, if a user is relaxed, the trend collection unit can prioritize providing diverse trend information. Furthermore, if a user is in a hurry, the trend collection unit can prioritize providing trend information that is concise. This allows the trend collection unit to determine the priority of trend information according to the user's emotions, thereby enabling more appropriate information to be provided.

[0120] The trend collection unit can collect trend information taking into consideration the user's geographical location information. For example, the trend collection unit can collect relevant trend information preferentially based on the user's current location. For example, the trend collection unit can collect relevant trend information preferentially based on the user's current location. The trend collection unit can also collect trend information for areas of interest based on the user's past location information. For example, the trend collection unit can collect trend information for areas of interest based on the user's past location information. Furthermore, the trend collection unit can update the user's location information in real time and collect optimal trend information. For example, the trend collection unit can update the user's location information in real time and collect optimal trend information. In this way, the trend collection unit can provide more relevant information by collecting trend information taking into consideration the user's geographical location information. For example, the trend collection unit can collect relevant trend information preferentially based on the user's current location. Furthermore, the trend collection unit can collect trend information for areas of interest based on the user's past location information. Furthermore, the trend collection unit can update the user's location information in real time and collect optimal trend information. This allows the trend collection unit to collect trend information taking into account the user's geographical location information, thereby enabling the provision of more relevant information.

[0121] When collecting trend information, the trend collection unit can analyze the user's social media activities and collect related trend information. For example, the trend collection unit can analyze content shared by the user on social media to collect related trend information. For example, the trend collection unit can analyze content shared by the user on social media to collect related trend information. The trend collection unit can also analyze accounts the user follows on social media to collect trend information on areas of interest. For example, the trend collection unit can analyze accounts the user follows on social media to collect trend information on areas of interest. Furthermore, the trend collection unit can analyze the frequency of the user's social media activities to collect trend information on information in which the user is particularly interested. For example, the trend collection unit can analyze the frequency of the user's social media activities to collect trend information on information in which the user is particularly interested. In this way, the trend collection unit can analyze the user's social media activities to collect more relevant trend information. For example, the trend collection unit can analyze content shared by the user on social media to collect related trend information. For example, the trend collection unit can analyze accounts the user follows on social media to collect trend information on areas of interest. Furthermore, the trend collection unit can analyze the frequency of a user's social media activity to collect trend information about information that the user is particularly interested in. This allows the trend collection unit to collect more relevant trend information by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the generation unit, reliability evaluation unit, visualization unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the smart device 14 and generates an answer to a question from a user. The reliability evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the generated answer. The visualization unit is realized, for example, by the display 40A of the smart device 14 and displays the results of the reliability evaluation in the form of a graph or chart. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's interests and concerns. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides highly visible information based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned generation unit, reliability evaluation unit, visualization unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the smart glasses 214 and generates an answer to a question from a user. The reliability evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the generated answer. The visualization unit is realized, for example, by the display of the smart glasses 214 and displays the results of the reliability evaluation in the form of a graph or chart. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's interests and concerns. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides highly visible information based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, reliability evaluation unit, visualization unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the headset type terminal 314 and generates an answer to a question from a user. The reliability evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the generated answer. The visualization unit is realized, for example, by the display 343 of the headset type terminal 314 and displays the results of the reliability evaluation in the form of a graph or chart. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's interests and concerns. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides highly popular information based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, reliability evaluation unit, visualization unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the robot 414 and generates an answer to a question from a user. The reliability evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the generated answer. The visualization unit is realized, for example, by the display of the robot 414 and displays the results of the reliability evaluation in the form of a graph or chart. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's interests and concerns. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides highly visible information based on the analysis results.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can generate a concise and easy-to-understand answer. If the user is relaxed, the generation unit can generate an answer that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a short answer that gets to the point. In this way, the generation unit can provide a more appropriate answer by adjusting the way the answer is expressed based on the user's emotions.

[0124] When evaluating the reliability of the data that forms the basis of an answer, the reliability evaluation unit can evaluate the reliability as high if the data source is official data or an academic paper. The reliability evaluation unit can also evaluate the reliability comprehensively if there are multiple data sources. Furthermore, the reliability evaluation unit can evaluate the reliability as low if the source of the data is unclear. This allows the reliability evaluation unit to analyze the source of the data that forms the basis of an answer and evaluate its reliability, thereby providing a highly reliable answer.

[0125] When evaluating the accuracy of answers to past similar questions, the reliability evaluation unit can analyze the degree of agreement and error rate of past answers. For example, if past answers show a high degree of agreement, the reliability evaluation unit can evaluate the accuracy as high, and if the degree of agreement is low, the reliability evaluation unit can evaluate the accuracy as low. Furthermore, if the error rate is low, the reliability evaluation unit can evaluate the accuracy as high, and if the error rate is high, the reliability evaluation unit can evaluate the accuracy as low. Furthermore, if the evaluation algorithm is reliable, the reliability evaluation unit can evaluate the accuracy as high, and if it is unreliable, the reliability evaluation unit can evaluate the accuracy of answers to past similar questions, thereby improving the reliability of the answers.

[0126] The reliability evaluation unit may include a feedback collection unit that collects user feedback and reflects it in the reliability evaluation. For example, user feedback can be collected using a questionnaire, and comments and evaluation scores can be reflected in the reliability evaluation. Furthermore, if the frequency of feedback is high, high reliability can be evaluated, and if the frequency is low, low reliability can be evaluated. Furthermore, if the content of the feedback is specific and detailed, high reliability can be evaluated, and if the content is vague and unclear, low reliability can be evaluated. In this way, the reliability evaluation unit can improve the reliability of answers by collecting user feedback and reflecting it in the reliability evaluation.

[0127] The analysis unit can identify the user's interests and concerns by analyzing the user's past browsing history, clicked links, and time spent on the site. For example, it can analyze the content of pages the user has previously viewed to identify topics of interest. It can also analyze the content of links the user has clicked to identify areas of interest. It can also analyze the content of pages the user has spent a long time on to identify information in which the user is particularly interested. In this way, the analysis unit can identify the user's interests and concerns and provide related information by analyzing the user's past browsing history, clicked links, and time spent on the site.

[0128] The analysis unit may include a trend collection unit that collects specific trend information from social media or news sites. For example, the number of posts on social media may be analyzed to collect trend information. Trend information may also be collected by analyzing the number of views on news sites. Trend information may also be collected by analyzing the frequency of hashtag usage. In this way, the analysis unit can provide the latest, most popular information by collecting trend information from social media and news sites.

[0129] The generation unit can estimate the user's emotion and adjust the way the answer is expressed based on the estimated user's emotion. For example, the user's emotion can be estimated using facial expression recognition technology, the user's facial expression can be captured with a camera, and the emotion can be estimated using an emotion estimation algorithm. The user's emotion can also be estimated using text analysis technology. Furthermore, the user's emotion can also be estimated using voice analysis technology. In this way, the generation unit can provide a more appropriate answer by estimating the user's emotion and adjusting the way the answer is expressed based on the estimated emotion.

[0130] The generation unit can analyze the context of the question and apply an algorithm for generating an optimal answer. For example, it can analyze background information of the question and generate an answer that includes relevant information. It can also analyze the intent of the question and provide specific information that the user is looking for. It can also analyze the tone of the question and generate an answer with appropriate expression. As a result, the generation unit can provide a more appropriate answer by analyzing the context of the question and applying an algorithm for generating an optimal answer.

[0131] The generator can apply different generation algorithms depending on the category of the question. For example, it can generate an answer based on specialized knowledge for a technical question. It can also generate an answer that explains things in easy-to-understand language for a general question. It can also generate an answer that includes expressions showing empathy for an emotional question. This allows the generator to provide more appropriate answers by applying different generation algorithms depending on the category of the question.

[0132] The generation unit can estimate the user's emotion and adjust the level of detail of the answer based on the estimated user's emotion. For example, the user's emotion can be estimated using facial expression recognition technology, the user's facial expression can be captured with a camera, and the emotion can be estimated using an emotion estimation algorithm. The user's emotion can also be estimated using text analysis technology. Furthermore, the user's emotion can also be estimated using voice analysis technology. In this way, the generation unit can provide a more appropriate answer by estimating the user's emotion and adjusting the level of detail of the answer based on the estimated emotion.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The generation unit uses a generation AI to generate an answer to a question from a user. For example, the generation unit analyzes the question entered by the user and generates an appropriate answer. For example, the generation unit can use natural language processing technology to understand the intent of the question and generate an appropriate answer. The generation unit can also use the generation AI to refer to a database of past questions and answers to generate answers to similar questions. Step 2: The reliability evaluation unit evaluates the reliability of the answer generated by the generation unit. The reliability evaluation unit evaluates, for example, the reliability of the data on which the answer is based. The reliability evaluation unit can evaluate reliability based on, for example, the source of the data or past performance. The reliability evaluation unit can also evaluate the accuracy of answers to similar questions in the past. The reliability evaluation unit can evaluate accuracy based on, for example, the degree of agreement or error rate with past answers. Step 3: The visualization unit visualizes the results of the evaluation by the reliability evaluation unit. The visualization unit, for example, displays the results of the reliability evaluation in the form of a graph or chart. The visualization unit can visually display the results of the reliability evaluation using, for example, a bar graph or a pie chart. The visualization unit can also update the results of the reliability evaluation in real time. For example, the visualization unit can update the results of the reliability evaluation every time new data is added. Step 4: The analysis unit analyzes the user's interests and concerns. For example, the analysis unit analyzes the user's past browsing history, clicked links, and time spent on the site. The analysis unit can identify the user's interests and concerns using, for example, data mining technology. The analysis unit can also collect trend information from social networking sites and news sites. For example, the analysis unit can collect trend information based on the number of posts on social networking sites and the number of views on news sites. Step 5: The providing unit provides information that is of high interest based on the results of the analysis by the analyzing unit. The providing unit provides relevant information based on, for example, the user's interests and concerns. The providing unit can automatically display information that is of high interest based on, for example, information that the user has viewed in the past or current trend information. The providing unit can also estimate the user's emotions and adjust the method of providing information based on the estimated emotions. For example, the providing unit can provide simple, highly visible information when the user is feeling anxious, and provide detailed information when the user is relaxed.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0137] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a 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.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0179] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0190] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0191] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0196] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0197] 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.

[0198] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0199] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0200] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0201] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0203] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0204] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0205] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0206] [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generation unit in which a generation AI generates an answer to a question from a user; a reliability evaluation unit that evaluates the reliability of the answer generated by the generation unit; a visualization unit that visualizes the results of the evaluation by the reliability evaluation unit; an analysis unit that analyzes the user's interests and concerns; a providing unit that provides information selected based on specific criteria based on the results of the analysis by the analyzing unit. A system characterized by:

2. The reliability evaluation unit Evaluate the reliability of the data on which your answers are based 2. The system of claim 1.

3. The reliability evaluation unit Evaluate the accuracy of answers to similar questions in the past 2. The system of claim 1.

4. The reliability evaluation unit A feedback collection unit is provided to collect user feedback and reflect it in the evaluation of reliability.

2. The system of claim 1.

5. The analysis unit Analyzing users' browsing history, clicked links, and time spent on the site to identify their interests 2. The system of claim 1.

6. The analysis unit Equipped with a trend collection section that collects specific trend information from social media or news sites 2. The system of claim 1.

7. The generation unit Inferring user sentiment and adjusting the way responses are phrased based on the inferred sentiment 2. The system of claim 1.

8. The generation unit Analyzes the context of the question and applies algorithms to generate the best answer 2. The system of claim 1.

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