system

Abstract

To provide a system for intuitively evaluating the reliability of a WEB site, and for suppressing infodemic.SOLUTION: A system includes a URL analysis part, a reliability evaluation part, and a display part. The URL analysis unit analyzes a URL of a website. The reliability evaluation unit evaluates the reliability based on the information analyzed by the URL analysis unit. The display unit displays the reliability evaluated by the reliability evaluation unit in percentage.SELECTED DRAWING: Figure 1

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 makes it difficult to intuitively evaluate the reliability of websites, posing challenges in curbing the infodemic.

[0005] The system according to the embodiment aims to intuitively evaluate the reliability of websites and curb the infodemic. [Means for solving the problem]

[0006] A system according to an embodiment includes a URL analysis unit, a reliability evaluation unit, and a display unit. The URL analysis unit analyzes the URL of a website. The reliability evaluation unit evaluates reliability based on the information analyzed by the URL analysis unit. The display unit displays the reliability evaluated by the reliability evaluation unit as a percentage. [Effects of the Invention]

[0007] The system according to the embodiment can intuitively evaluate the reliability of websites and curb the infodemic. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information reliability assessment system according to an embodiment of the present invention is a system that expresses the accuracy of information as a percentage based on the URL of a website. This system uses a generation AI to analyze website information and evaluate its reliability. This allows users to determine the reliability of information at a glance, thereby contributing to the prevention of an infodemic.

[0029] An information reliability evaluation system according to an embodiment includes a URL analysis unit, a reliability evaluation unit, and a display unit. The URL analysis unit analyzes the URL of a website. For example, the URL analysis unit evaluates the reliability of a domain. The URL analysis unit can also analyze the link structure. The URL analysis unit can also check whether an SSL certificate is present. For example, the URL analysis unit refers to past reliability data to evaluate the reliability of a domain. To analyze the link structure, the URL analysis unit evaluates the number of links and the reliability of the linked destinations. To check whether an SSL certificate is present, the URL analysis unit checks the issuer and expiration date of the SSL certificate. The reliability evaluation unit evaluates reliability based on the information analyzed by the URL analysis unit. For example, the reliability evaluation unit evaluates the source of the information. The reliability evaluation unit can also evaluate the reliability of a citation source. The reliability evaluation unit can also evaluate a history of past false reports. For example, the reliability evaluation unit calculates a reliability score of the source to evaluate the source of the information. To evaluate the reliability of a citation source, the reliability evaluation unit refers to past reliability data of the citation source. To evaluate a history of past false reports, the reliability evaluation unit analyzes past false reports. The display unit displays the reliability evaluated by the reliability evaluation unit as a percentage. For example, the display unit displays the reliability score in a range of 0 to 100. The display unit can also display the reliability score in a color-coded manner. The display unit can also display the reliability score in a graph. For example, the display unit calculates the reliability score and displays the result to display the reliability score in a range of 0 to 100. To display the reliability score in a color-coded manner, the display unit changes the color depending on the reliability score. To display the reliability score in a graph, the display unit shows fluctuations in the reliability score in a graph. This allows the information reliability evaluation system according to the embodiment to determine the reliability of information at a glance and contribute to suppressing the infodemic. For example, the user can select highly reliable information by referring to the reliability score. The user can also compare the reliability of information based on the reliability score. The user can also evaluate the reliability of information based on the reliability score.

[0030] The URL analyzer can also evaluate the quality of a site's design or user interface and add it to the trustworthiness index. For example, the generation AI analyzes the design elements of a website to evaluate its visual consistency and usability. For example, it checks the consistency of the site's layout and color scheme and adds it to the trustworthiness index. To evaluate the quality of the user interface, the generation AI analyzes the ease of navigation and the application of responsive design. For example, it checks the placement of menus and the operation of links. The URL analyzer also comprehensively evaluates the quality of the site's design and user interface and reflects this in the trustworthiness score. For example, a visually appealing and easy-to-use site will receive a high trustworthiness score. This allows for a more refined trustworthiness index by evaluating the quality of a site's design and user interface.

[0031] The URL analysis unit can also consider the site operator's past activity history or social credibility to evaluate trustworthiness. For example, the generation AI analyzes the site operator's past activity history and adds it to the trustworthiness index. For example, it checks whether the site operator has provided reliable information in the past. To evaluate the site operator's social credibility, the generation AI analyzes the site operator's publicly available information and third-party evaluations. For example, it checks whether the site operator has received certification from a public or specialized institution. The URL analysis unit also comprehensively evaluates the site operator's past activity history and social credibility and reflects this in the trustworthiness score. For example, a site managed by a highly reliable operator will receive a high trustworthiness score. This allows for more accurate trustworthiness evaluations by considering the site operator's past activity history and social credibility.

[0032] The URL analysis unit can analyze not only URLs but also related social media posts or comments to evaluate trustworthiness. For example, the generation AI analyzes social media posts and comments related to the website URL and adds them to the trustworthiness index. For example, a site with many positive comments is judged to be highly trustworthy. The URL analysis unit also collects social media posts and comments in real time and builds a system for the generation AI to analyze them. For example, it analyzes Twitter and Facebook posts and reflects them in the trustworthiness score. The URL analysis unit also comprehensively evaluates the trustworthiness of a site based on social media data. For example, a site with high user ratings receives a high trustworthiness score. This allows the accuracy of trustworthiness assessment to be improved by analyzing social media posts and comments.

[0033] The URL analysis unit can also evaluate the frequency of a site's updates or the availability of the latest information, and add this to the reliability index. For example, the generation AI in the URL analysis unit analyzes the frequency of a website's updates and adds this to the reliability index. For example, a site that is updated regularly may be deemed highly reliable. In addition, the generation AI in the URL analysis unit analyzes the freshness of the information and update history to evaluate the availability of the latest information on the site. For example, a site that provides the latest news or research results will receive a high reliability score. The URL analysis unit also comprehensively evaluates the frequency of a site's updates and the availability of the latest information, and reflects this in the reliability score. For example, a site that is updated frequently will receive a high reliability score. This allows the reliability index to be more refined by evaluating the frequency of a site's updates and the availability of the latest information.

[0034] The reliability evaluation unit can also consider the update history or change history of the information to evaluate its reliability. For example, the generation AI analyzes the information update history of a website and adds it to the reliability index. For example, information that is frequently updated is determined to be highly reliable. In addition, to evaluate the change history of the information, the generation AI compares past versions with the current version and reflects this in the reliability score. For example, information that has changed little is determined to be highly reliable. In addition, the reliability evaluation unit comprehensively evaluates the update history and change history of the information and reflects this in the reliability score. For example, information that is updated frequently and has few changes will receive a high reliability score. In this way, by considering the update history and change history of the information, reliability can be evaluated more accurately.

[0035] The reliability evaluation unit can also add the expertise or qualifications of the information provider to the evaluation. In this case, for example, the generation AI analyzes the expertise and qualifications of the information provider and adds them to the reliability index. For example, it determines that information provided by an expert is highly reliable. In addition, to evaluate the qualifications of the information provider, the generation AI analyzes publicly available qualification information and reflects this in the reliability score. For example, information about a provider with certified qualifications will receive a high reliability score. In addition, the reliability evaluation unit comprehensively evaluates the expertise and qualifications of the information provider and reflects this in the reliability score. For example, information about a provider with specialized knowledge and qualifications will receive a high reliability score. In this way, by adding the expertise and qualifications of the information provider to the evaluation, reliability can be evaluated more accurately.

[0036] The reliability evaluation unit can improve the accuracy of the evaluation by cross-referencing with other reliability evaluation systems. For example, the generation AI cross-references with other reliability evaluation systems to evaluate the reliability of information. For example, the results of multiple evaluation systems are integrated to calculate a reliability score. The reliability evaluation unit also collects data from other reliability evaluation systems in real time and builds a system that the generation AI analyzes. For example, the reliability evaluation unit adjusts the reliability score based on third-party evaluation data. The reliability evaluation unit also uses cross-referencing to comprehensively evaluate the reliability of information. For example, the results of multiple evaluation systems are compared and the most reliable evaluation is adopted. In this way, cross-referencing with other reliability evaluation systems can improve the accuracy of the evaluation.

[0037] The reliability evaluation unit can also include the visual presentation of information in the evaluation. In this case, for example, the generation AI analyzes the visual presentation of information and adds it to the reliability index. For example, it checks whether graphs and charts are accurate. In addition, to evaluate the visual presentation of information, the generation AI analyzes the data source of the graph or chart and reflects this in the reliability score. For example, if a highly reliable data source is used, a high reliability score is obtained. In addition, the reliability evaluation unit comprehensively evaluates the visual presentation of information and its data source and reflects this in the reliability score. For example, information that provides an accurate and reliable visual presentation receives a high reliability score. In this way, by including the visual presentation of information in the evaluation, the reliability evaluation can be made more accurate.

[0038] The display unit can display detailed analysis results that serve as the basis for the reliability in addition to the reliability percentage. For example, the display unit displays detailed analysis results that serve as the basis for the reliability in addition to the reliability percentage assessed by the generating AI. For example, the display unit may provide detailed explanations of the data sources and analysis methods used in the assessment. In addition to the reliability percentage, the display unit may also display specific data points and statistical information that serve as the basis for the assessment. For example, the display unit may show the reliability score of the source and past false alarm history. In addition, the display unit may display an overview of the algorithm and analysis model used in the assessment along with the reliability percentage. For example, the display unit may explain the training data and evaluation criteria for the machine learning model. In this way, by displaying detailed analysis results in addition to the reliability percentage, users can more easily understand the basis for the reliability.

[0039] The display unit can display past evaluation history or trends in addition to the reliability percentage display, allowing the user to understand fluctuations in reliability. For example, the display unit displays past evaluation history and trends in addition to the reliability percentage display evaluated by the generation AI. For example, it displays a graph showing fluctuations in reliability scores over the past year. The display unit also visually displays the evaluation history and trends in addition to the reliability percentage display. For example, it provides a timeline or heat map showing fluctuations in reliability scores. The display unit also analyzes the past evaluation history and trends along with the reliability percentage display, allowing the user to understand fluctuations in reliability. For example, it explains the causes of increases or decreases in reliability scores. In this way, by displaying past evaluation history and trends in addition to the reliability percentage display, the user can more easily understand fluctuations in reliability.

[0040] The display unit can integrate the reliability percentage display with other reliability evaluation systems to provide an overall reliability score. For example, the display unit can integrate the reliability percentage display evaluated by the generation AI with other reliability evaluation systems to provide an overall reliability score. For example, the display unit can integrate the results of multiple evaluation systems to calculate a reliability score. The display unit can also collect data from other reliability evaluation systems in real time and build a system for the generation AI to analyze. For example, the display unit can adjust the reliability score based on third-party evaluation data. The display unit can also use cross-referencing to comprehensively evaluate the reliability of information. For example, the display unit can compare the results of multiple evaluation systems and adopt the most reliable evaluation. This makes it possible to provide an overall reliability score by integrating with other reliability evaluation systems.

[0041] The display unit can customize the reliability percentage display based on the user's areas of interest or past browsing history. For example, the display unit customizes the reliability percentage display evaluated by the generation AI based on the user's areas of interest and past browsing history. For example, the display unit highlights the reliability score of information related to topics that interest the user. The display unit also analyzes the user's past browsing history and builds a system that customizes the reliability percentage display. For example, the display unit prioritizes displaying the reliability scores of sites that the user frequently visits. The display unit also comprehensively evaluates the user's areas of interest and past browsing history and customizes the reliability percentage display. For example, the display unit adjusts the reliability score according to the user's level of interest. This allows the display unit to provide more useful information to the user by customizing the reliability percentage display based on the user's areas of interest and past browsing history.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The URL analyzer can also evaluate the quality of a site's design or user interface and add it to the trustworthiness index. For example, the generation AI analyzes the design elements of a website to evaluate its visual consistency and usability. For example, it checks the consistency of the site's layout and color scheme and adds it to the trustworthiness index. To evaluate the quality of the user interface, the generation AI analyzes the ease of navigation and the application of responsive design. For example, it checks the placement of menus and the operation of links. The URL analyzer also comprehensively evaluates the quality of the site's design and user interface and reflects this in the trustworthiness score. For example, a visually appealing and easy-to-use site will receive a high trustworthiness score. This allows for a more refined trustworthiness index by evaluating the quality of a site's design and user interface.

[0044] The URL analysis unit can also consider the site operator's past activity history or social credibility to evaluate trustworthiness. For example, the generation AI can analyze the site operator's past activity history and add it to the trustworthiness index. For example, it can check whether the site operator has provided reliable information in the past. To evaluate the site operator's social credibility, the generation AI can analyze the site operator's publicly available information and third-party evaluations. For example, it can check whether the site operator has been certified by a public or specialized institution. The URL analysis unit also comprehensively evaluates the site operator's past activity history and social credibility and reflects this in the trustworthiness score. For example, a site managed by a highly reliable operator will receive a high trustworthiness score. This allows for more accurate trustworthiness evaluations by taking into account the site operator's past activity history and social credibility.

[0045] The URL analysis unit can analyze not only URLs but also related social media posts or comments to evaluate trustworthiness. For example, the generation AI can analyze social media posts and comments related to a website's URL and add them to its trustworthiness index. For example, a site with many positive comments can be judged to be highly trustworthy. The URL analysis unit also builds a system that collects social media posts and comments in real time and allows the generation AI to analyze them. For example, it can analyze Twitter and Facebook posts and reflect them in the trustworthiness score. The URL analysis unit also comprehensively evaluates the trustworthiness of a site based on social media data. For example, a site with high user ratings will receive a high trustworthiness score. This allows the accuracy of trustworthiness assessment to be improved by analyzing social media posts and comments.

[0046] The URL analysis unit can also evaluate the frequency of a site's updates or the availability of the latest information, and add this to the reliability index. For example, the generation AI can analyze the frequency of a website's updates and add this to the reliability index. For example, a site that is updated regularly can be judged to be highly reliable. The URL analysis unit also analyzes the freshness of the information and update history to evaluate the site's availability of the latest information. For example, a site that provides the latest news or research results will receive a high reliability score. The URL analysis unit also comprehensively evaluates the frequency of a site's updates and the availability of the latest information, and reflects this in the reliability score. For example, a site that is updated frequently will receive a high reliability score. This allows the reliability index to be more refined by evaluating the frequency of a site's updates and the availability of the latest information.

[0047] The reliability evaluation unit can also consider the update history or change history of the information to evaluate its reliability. For example, the generation AI analyzes the information update history of a website and adds it to the reliability index. For example, information that is frequently updated is determined to be highly reliable. In addition, to evaluate the change history of the information, the generation AI compares past versions with the current version and reflects this in the reliability score. For example, information with few changes is determined to be highly reliable. The reliability evaluation unit also comprehensively evaluates the update history and change history of the information and reflects this in the reliability score. For example, information that is frequently updated and has few changes will receive a high reliability score. This allows for more accurate reliability evaluation by considering the update history and change history of the information.

[0048] The reliability evaluation unit can also include the expertise or qualifications of the information provider in the evaluation. For example, the generation AI analyzes the expertise and qualifications of the information provider and adds them to the reliability index. For example, information provided by an expert is judged to be highly reliable. In addition, to evaluate the qualifications of the information provider, the generation AI analyzes publicly available qualification information and reflects this in the reliability score. For example, information from a provider with certified qualifications will receive a high reliability score. The reliability evaluation unit also comprehensively evaluates the expertise and qualifications of the information provider and reflects this in the reliability score. For example, information from a provider with specialized knowledge and qualifications will receive a high reliability score. In this way, by adding the expertise and qualifications of the information provider to the evaluation, reliability can be evaluated more accurately.

[0049] The reliability evaluation unit can improve the accuracy of the evaluation by cross-referencing with other reliability evaluation systems. For example, the generation AI cross-references with other reliability evaluation systems to evaluate the reliability of information. For example, it integrates the results of multiple evaluation systems and calculates a reliability score. The reliability evaluation unit also collects data from other reliability evaluation systems in real time, and builds a system that the generation AI analyzes. For example, it adjusts the reliability score based on third-party evaluation data. The reliability evaluation unit also uses cross-referencing to comprehensively evaluate the reliability of information. For example, it compares the results of multiple evaluation systems and adopts the most reliable evaluation. In this way, cross-referencing with other reliability evaluation systems can improve the accuracy of the evaluation.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The URL analysis unit analyzes the website URL. For example, the URL analysis unit evaluates the reliability of the domain, analyzes the link structure, and checks whether or not it has an SSL certificate. To evaluate the reliability of the domain, it refers to past reliability data, to analyze the link structure it evaluates the number of links and the reliability of the linked destinations, and to check whether or not it has an SSL certificate, it checks the issuer and expiration date of the SSL certificate. Step 2: The credibility evaluation unit evaluates the credibility based on the information analyzed by the URL analysis unit. For example, the credibility evaluation unit evaluates the source of the information, evaluates the credibility of the source, and evaluates the history of past misinformation. To evaluate the source of the information, the credibility evaluation unit calculates the credibility score of the source, refers to the past credibility data of the source to evaluate the credibility of the source, and analyzes the past misinformation data to evaluate the history of past misinformation. Step 3: The display unit displays the reliability evaluated by the reliability evaluation unit as a percentage. For example, the display unit displays the reliability score in a range of 0 to 100, displays the reliability score in a color-coded manner, and displays the reliability score in a graph. To display the reliability score in a range of 0 to 100, the display unit calculates the reliability score and displays the result; to display the reliability score in a color-coded manner, the display unit changes the color according to the reliability score; and to display the reliability score in a graph, the display unit shows the fluctuation of the reliability score in a graph.

[0052] (Example 2) The information reliability assessment system according to an embodiment of the present invention is a system that expresses the accuracy of information as a percentage based on the URL of a website. This system uses a generation AI to analyze website information and evaluate its reliability. This allows users to determine the reliability of information at a glance, thereby contributing to the prevention of an infodemic.

[0053] An information reliability evaluation system according to an embodiment includes a URL analysis unit, a reliability evaluation unit, and a display unit. The URL analysis unit analyzes the URL of a website. For example, the URL analysis unit evaluates the reliability of a domain. The URL analysis unit can also analyze the link structure. The URL analysis unit can also check whether an SSL certificate is present. For example, the URL analysis unit refers to past reliability data to evaluate the reliability of a domain. To analyze the link structure, the URL analysis unit evaluates the number of links and the reliability of the linked destinations. To check whether an SSL certificate is present, the URL analysis unit checks the issuer and expiration date of the SSL certificate. The reliability evaluation unit evaluates reliability based on the information analyzed by the URL analysis unit. For example, the reliability evaluation unit evaluates the source of the information. The reliability evaluation unit can also evaluate the reliability of a citation source. The reliability evaluation unit can also evaluate a history of past false reports. For example, the reliability evaluation unit calculates a reliability score of the source to evaluate the source of the information. To evaluate the reliability of a citation source, the reliability evaluation unit refers to past reliability data of the citation source. To evaluate a history of past false reports, the reliability evaluation unit analyzes past false reports. The display unit displays the reliability evaluated by the reliability evaluation unit as a percentage. For example, the display unit displays the reliability score in a range of 0 to 100. The display unit can also display the reliability score in a color-coded manner. The display unit can also display the reliability score in a graph. For example, the display unit calculates the reliability score and displays the result to display the reliability score in a range of 0 to 100. To display the reliability score in a color-coded manner, the display unit changes the color depending on the reliability score. To display the reliability score in a graph, the display unit shows fluctuations in the reliability score in a graph. This allows the information reliability evaluation system according to the embodiment to determine the reliability of information at a glance and contribute to suppressing the infodemic. For example, the user can select highly reliable information by referring to the reliability score. The user can also compare the reliability of information based on the reliability score. The user can also evaluate the reliability of information based on the reliability score.

[0054] The URL analyzer can also evaluate the quality of a site's design or user interface and add it to the trustworthiness index. For example, the generation AI analyzes the design elements of a website to evaluate its visual consistency and usability. For example, it checks the consistency of the site's layout and color scheme and adds it to the trustworthiness index. To evaluate the quality of the user interface, the generation AI analyzes the ease of navigation and the application of responsive design. For example, it checks the placement of menus and the operation of links. The URL analyzer also comprehensively evaluates the quality of the site's design and user interface and reflects this in the trustworthiness score. For example, a visually appealing and easy-to-use site will receive a high trustworthiness score. This allows for a more refined trustworthiness index by evaluating the quality of a site's design and user interface.

[0055] The URL analysis unit can also consider the site operator's past activity history or social credibility to evaluate trustworthiness. For example, the generation AI analyzes the site operator's past activity history and adds it to the trustworthiness index. For example, it checks whether the site operator has provided reliable information in the past. To evaluate the site operator's social credibility, the generation AI analyzes the site operator's publicly available information and third-party evaluations. For example, it checks whether the site operator has received certification from a public or specialized institution. The URL analysis unit also comprehensively evaluates the site operator's past activity history and social credibility and reflects this in the trustworthiness score. For example, a site managed by a highly reliable operator will receive a high trustworthiness score. This allows for more accurate trustworthiness evaluations by considering the site operator's past activity history and social credibility.

[0056] The URL analysis unit uses the emotion estimation function to analyze the emotional impact that a site's content has on users and reflect this in its trustworthiness evaluation. For example, the URL analysis unit uses a generation AI to analyze the content of a site and evaluate its emotional impact on users. For example, it determines that content that evokes positive emotions is highly trustworthy. The URL analysis unit also uses the emotion estimation function to analyze the emotional impact that a site's content has on users in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The URL analysis unit also comprehensively evaluates the site's content and the user's emotional response and reflects this in the trustworthiness score. For example, a site that evokes positive emotions receives a high trustworthiness score. As a result, the emotion estimation function makes it possible to evaluate trustworthiness by taking into account the emotional impact that a site's content has on users.

[0057] The URL analysis unit can analyze not only URLs but also related social media posts or comments to evaluate trustworthiness. For example, the generation AI analyzes social media posts and comments related to the website URL and adds them to the trustworthiness index. For example, a site with many positive comments is judged to be highly trustworthy. The URL analysis unit also collects social media posts and comments in real time and builds a system for the generation AI to analyze them. For example, it analyzes Twitter and Facebook posts and reflects them in the trustworthiness score. The URL analysis unit also comprehensively evaluates the trustworthiness of a site based on social media data. For example, a site with high user ratings receives a high trustworthiness score. This allows the accuracy of trustworthiness assessment to be improved by analyzing social media posts and comments.

[0058] The URL analysis unit can also evaluate the frequency of a site's updates or the availability of the latest information, and add this to the reliability index. For example, the generation AI in the URL analysis unit analyzes the frequency of a website's updates and adds this to the reliability index. For example, a site that is updated regularly may be deemed highly reliable. In addition, the generation AI in the URL analysis unit analyzes the freshness of the information and update history to evaluate the availability of the latest information on the site. For example, a site that provides the latest news or research results will receive a high reliability score. The URL analysis unit also comprehensively evaluates the frequency of a site's updates and the availability of the latest information, and reflects this in the reliability score. For example, a site that is updated frequently will receive a high reliability score. This allows the reliability index to be more refined by evaluating the frequency of a site's updates and the availability of the latest information.

[0059] The URL analysis unit uses the emotion estimation function to analyze the emotion a user feels when entering a URL in real time, and can use this emotion as a reference for evaluating trustworthiness. For example, the URL analysis unit uses a generation AI to analyze the user's emotion in real time and use the emotion at the time of URL entry as a reference for evaluating trustworthiness. For example, a URL entered with positive emotion may be determined to be highly trustworthy. The URL analysis unit also uses the emotion estimation function to analyze the user's facial expressions and voice and calculate an emotion score at the time of URL entry. For example, a URL entered with a smile may be determined to be highly trustworthy. The URL analysis unit also comprehensively evaluates the trustworthiness of the URL based on the user's emotion data. For example, a URL entered with positive emotion may receive a high trust score. This allows analyzing the emotion a user feels when entering a URL to be used as a reference for evaluating trustworthiness.

[0060] The reliability evaluation unit can also consider the update history or change history of the information to evaluate its reliability. For example, the generation AI analyzes the information update history of a website and adds it to the reliability index. For example, information that is frequently updated is determined to be highly reliable. In addition, to evaluate the change history of the information, the generation AI compares past versions with the current version and reflects this in the reliability score. For example, information that has changed little is determined to be highly reliable. In addition, the reliability evaluation unit comprehensively evaluates the update history and change history of the information and reflects this in the reliability score. For example, information that is updated frequently and has few changes will receive a high reliability score. In this way, by considering the update history and change history of the information, reliability can be evaluated more accurately.

[0061] The reliability evaluation unit can also add the expertise or qualifications of the information provider to the evaluation. In this case, for example, the generation AI analyzes the expertise and qualifications of the information provider and adds them to the reliability index. For example, it determines that information provided by an expert is highly reliable. In addition, to evaluate the qualifications of the information provider, the generation AI analyzes publicly available qualification information and reflects this in the reliability score. For example, information about a provider with certified qualifications will receive a high reliability score. In addition, the reliability evaluation unit comprehensively evaluates the expertise and qualifications of the information provider and reflects this in the reliability score. For example, information about a provider with specialized knowledge and qualifications will receive a high reliability score. In this way, by adding the expertise and qualifications of the information provider to the evaluation, reliability can be evaluated more accurately.

[0062] The credibility evaluation unit can use the emotion estimation function to analyze the emotional impact that the reliability of information has on the user and reflect this in the credibility evaluation. For example, when the generation AI evaluates the reliability of information, the credibility evaluation unit uses the emotion estimation function to analyze the emotional impact on the user. For example, it determines that information that evokes positive emotions is highly reliable. The credibility evaluation unit also uses the emotion estimation function to analyze the emotional impact that the reliability of information has on the user in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The credibility evaluation unit also comprehensively evaluates the reliability of the information and the user's emotional response and reflects this in the credibility score. For example, information that evokes positive emotions will receive a high credibility score. As a result, using the emotion estimation function makes it possible to evaluate credibility taking into account the emotional impact that the reliability of information has on the user.

[0063] The reliability evaluation unit can improve the accuracy of the evaluation by cross-referencing with other reliability evaluation systems. For example, the generation AI cross-references with other reliability evaluation systems to evaluate the reliability of information. For example, the results of multiple evaluation systems are integrated to calculate a reliability score. The reliability evaluation unit also collects data from other reliability evaluation systems in real time and builds a system that the generation AI analyzes. For example, the reliability evaluation unit adjusts the reliability score based on third-party evaluation data. The reliability evaluation unit also uses cross-referencing to comprehensively evaluate the reliability of information. For example, the results of multiple evaluation systems are compared and the most reliable evaluation is adopted. In this way, cross-referencing with other reliability evaluation systems can improve the accuracy of the evaluation.

[0064] The reliability evaluation unit can also include the visual presentation of information in the evaluation. In this case, for example, the generation AI analyzes the visual presentation of information and adds it to the reliability index. For example, it checks whether graphs and charts are accurate. In addition, to evaluate the visual presentation of information, the generation AI analyzes the data source of the graph or chart and reflects this in the reliability score. For example, if a highly reliable data source is used, a high reliability score is obtained. In addition, the reliability evaluation unit comprehensively evaluates the visual presentation of information and its data source and reflects this in the reliability score. For example, information that provides an accurate and reliable visual presentation receives a high reliability score. In this way, by including the visual presentation of information in the evaluation, the reliability evaluation can be made more accurate.

[0065] The credibility evaluation unit uses the emotion estimation function to analyze the emotions of the user when viewing information in real time and use the results as a reference for credibility evaluation. For example, the credibility evaluation unit uses a generation AI to analyze the user's emotions in real time and use the emotions when viewing information as a reference for credibility evaluation. For example, information viewed with positive emotions is determined to be highly reliable. The credibility evaluation unit also uses the emotion estimation function to analyze the user's facial expressions and voice and calculate an emotion score when viewing information. For example, information viewed with a smile is determined to be highly reliable. The credibility evaluation unit also comprehensively evaluates the credibility of information based on the user's emotion data. For example, information viewed with positive emotions receives a high credibility score. In this way, analyzing the emotions of the user when viewing information can be used as a reference for credibility evaluation.

[0066] The display unit can display detailed analysis results that serve as the basis for the reliability in addition to the reliability percentage. For example, the display unit displays detailed analysis results that serve as the basis for the reliability in addition to the reliability percentage assessed by the generating AI. For example, the display unit may provide detailed explanations of the data sources and analysis methods used in the assessment. In addition to the reliability percentage, the display unit may also display specific data points and statistical information that serve as the basis for the assessment. For example, the display unit may show the reliability score of the source and past false alarm history. In addition, the display unit may display an overview of the algorithm and analysis model used in the assessment along with the reliability percentage. For example, the display unit may explain the training data and evaluation criteria for the machine learning model. In this way, by displaying detailed analysis results in addition to the reliability percentage, users can more easily understand the basis for the reliability.

[0067] The display unit can display past evaluation history or trends in addition to the reliability percentage display, allowing the user to understand fluctuations in reliability. For example, the display unit displays past evaluation history and trends in addition to the reliability percentage display evaluated by the generation AI. For example, it displays a graph showing fluctuations in reliability scores over the past year. The display unit also visually displays the evaluation history and trends in addition to the reliability percentage display. For example, it provides a timeline or heat map showing fluctuations in reliability scores. The display unit also analyzes the past evaluation history and trends along with the reliability percentage display, allowing the user to understand fluctuations in reliability. For example, it explains the causes of increases or decreases in reliability scores. In this way, by displaying past evaluation history and trends in addition to the reliability percentage display, the user can more easily understand fluctuations in reliability.

[0068] The display unit can use the emotion estimation function to analyze the emotional impact that the reliability percentage display has on the user and optimize the display method. For example, the display unit uses a generation AI to analyze the emotional impact that the reliability percentage display has on the user and optimize the display method. For example, a display method that evokes positive emotions is adopted. The display unit also uses the emotion estimation function to analyze the emotional impact that the reliability percentage display has on the user in real time. For example, the display unit analyzes the user's facial expressions and voice and calculates an emotion score. The display unit also comprehensively evaluates the reliability percentage display and the user's emotional response and optimizes the display method. For example, a display method that evokes positive emotions is adopted. In this way, by using the emotion estimation function, it is possible to optimize the display method taking into account the emotional impact that the reliability percentage display has on the user.

[0069] The display unit can integrate the reliability percentage display with other reliability evaluation systems to provide an overall reliability score. For example, the display unit can integrate the reliability percentage display evaluated by the generation AI with other reliability evaluation systems to provide an overall reliability score. For example, the display unit can integrate the results of multiple evaluation systems to calculate a reliability score. The display unit can also collect data from other reliability evaluation systems in real time and build a system for the generation AI to analyze. For example, the display unit can adjust the reliability score based on third-party evaluation data. The display unit can also use cross-referencing to comprehensively evaluate the reliability of information. For example, the display unit can compare the results of multiple evaluation systems and adopt the most reliable evaluation. This makes it possible to provide an overall reliability score by integrating with other reliability evaluation systems.

[0070] The display unit can customize the reliability percentage display based on the user's areas of interest or past browsing history. For example, the display unit customizes the reliability percentage display evaluated by the generation AI based on the user's areas of interest and past browsing history. For example, the display unit highlights the reliability score of information related to topics that interest the user. The display unit also analyzes the user's past browsing history and builds a system that customizes the reliability percentage display. For example, the display unit prioritizes displaying the reliability scores of sites that the user frequently visits. The display unit also comprehensively evaluates the user's areas of interest and past browsing history and customizes the reliability percentage display. For example, the display unit adjusts the reliability score according to the user's level of interest. This allows the display unit to provide more useful information to the user by customizing the reliability percentage display based on the user's areas of interest and past browsing history.

[0071] The display unit can use the emotion estimation function to analyze the emotions of the user when viewing the reliability percentage display in real time and optimize the display method. For example, the display unit uses a generation AI to analyze the emotional impact of the reliability percentage display on the user and optimize the display method. For example, a display method that evokes positive emotions is adopted. The display unit also uses the emotion estimation function to analyze the emotional impact of the reliability percentage display on the user in real time. For example, the display unit analyzes the user's facial expressions and voice and calculates an emotion score. The display unit also comprehensively evaluates the reliability percentage display and the user's emotional response and optimizes the display method. For example, a display method that evokes positive emotions is adopted. In this way, by using the emotion estimation function, it is possible to optimize the display method taking into account the emotions of the user when viewing the reliability percentage display.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The URL analyzer can also evaluate the quality of a site's design or user interface and add it to the trustworthiness index. For example, the generation AI analyzes the design elements of a website to evaluate its visual consistency and usability. For example, it checks the consistency of the site's layout and color scheme and adds it to the trustworthiness index. To evaluate the quality of the user interface, the generation AI analyzes the ease of navigation and the application of responsive design. For example, it checks the placement of menus and the operation of links. The URL analyzer also comprehensively evaluates the quality of the site's design and user interface and reflects this in the trustworthiness score. For example, a visually appealing and easy-to-use site will receive a high trustworthiness score. This allows for a more refined trustworthiness index by evaluating the quality of a site's design and user interface.

[0074] The URL analysis unit can also consider the site operator's past activity history or social credibility to evaluate trustworthiness. For example, the generation AI can analyze the site operator's past activity history and add it to the trustworthiness index. For example, it can check whether the site operator has provided reliable information in the past. To evaluate the site operator's social credibility, the generation AI can analyze the site operator's publicly available information and third-party evaluations. For example, it can check whether the site operator has been certified by a public or specialized institution. The URL analysis unit also comprehensively evaluates the site operator's past activity history and social credibility and reflects this in the trustworthiness score. For example, a site managed by a highly reliable operator will receive a high trustworthiness score. This allows for more accurate trustworthiness evaluations by taking into account the site operator's past activity history and social credibility.

[0075] The URL analysis unit uses the emotion estimation function to analyze the emotional impact that a site's content has on users and reflect this in its trustworthiness evaluation. For example, a generation AI analyzes the content of a site and evaluates its emotional impact on users. For example, content that evokes positive emotions is determined to be highly trustworthy. The URL analysis unit also uses the emotion estimation function to analyze the emotional impact that a site's content has on users in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The URL analysis unit also comprehensively evaluates the site's content and the user's emotional response and reflects this in the trustworthiness score. For example, a site that evokes positive emotions receives a high trustworthiness score. As a result, the emotion estimation function makes it possible to evaluate trustworthiness by taking into account the emotional impact that a site's content has on users.

[0076] The URL analysis unit can analyze not only URLs but also related social media posts or comments to evaluate trustworthiness. For example, the generation AI can analyze social media posts and comments related to a website's URL and add them to its trustworthiness index. For example, a site with many positive comments can be judged to be highly trustworthy. The URL analysis unit also builds a system that collects social media posts and comments in real time and allows the generation AI to analyze them. For example, it can analyze Twitter and Facebook posts and reflect them in the trustworthiness score. The URL analysis unit also comprehensively evaluates the trustworthiness of a site based on social media data. For example, a site with high user ratings will receive a high trustworthiness score. This allows the accuracy of trustworthiness assessment to be improved by analyzing social media posts and comments.

[0077] The URL analysis unit can also evaluate the frequency of a site's updates or the availability of the latest information, and add this to the reliability index. For example, the generation AI can analyze the frequency of a website's updates and add this to the reliability index. For example, a site that is updated regularly can be judged to be highly reliable. The URL analysis unit also analyzes the freshness of the information and update history to evaluate the site's availability of the latest information. For example, a site that provides the latest news or research results will receive a high reliability score. The URL analysis unit also comprehensively evaluates the frequency of a site's updates and the availability of the latest information, and reflects this in the reliability score. For example, a site that is updated frequently will receive a high reliability score. This allows the reliability index to be more refined by evaluating the frequency of a site's updates and the availability of the latest information.

[0078] The URL analysis unit uses the emotion estimation function to analyze the user's emotions in real time when entering a URL, and can use this as a reference for evaluating trustworthiness. For example, the generation AI analyzes the user's emotions in real time and uses the emotions at the time of URL entry as a reference for evaluating trustworthiness. For example, a URL entered with positive emotions may be determined to be highly trustworthy. The URL analysis unit also uses the emotion estimation function to analyze the user's facial expressions and voice and calculate an emotion score at the time of URL entry. For example, a URL entered with a smiling face may be determined to be highly trustworthy. The URL analysis unit also comprehensively evaluates the trustworthiness of the URL based on the user's emotion data. For example, a URL entered with positive emotions receives a high trust score. This allows analyzing the user's emotions when entering a URL to be used as a reference for evaluating trustworthiness.

[0079] The reliability evaluation unit can also consider the update history or change history of the information to evaluate its reliability. For example, the generation AI analyzes the information update history of a website and adds it to the reliability index. For example, information that is frequently updated is determined to be highly reliable. In addition, to evaluate the change history of the information, the generation AI compares past versions with the current version and reflects this in the reliability score. For example, information with few changes is determined to be highly reliable. The reliability evaluation unit also comprehensively evaluates the update history and change history of the information and reflects this in the reliability score. For example, information that is frequently updated and has few changes will receive a high reliability score. This allows for more accurate reliability evaluation by considering the update history and change history of the information.

[0080] The reliability evaluation unit can also include the expertise or qualifications of the information provider in the evaluation. For example, the generation AI analyzes the expertise and qualifications of the information provider and adds them to the reliability index. For example, information provided by an expert is judged to be highly reliable. In addition, to evaluate the qualifications of the information provider, the generation AI analyzes publicly available qualification information and reflects this in the reliability score. For example, information from a provider with certified qualifications will receive a high reliability score. The reliability evaluation unit also comprehensively evaluates the expertise and qualifications of the information provider and reflects this in the reliability score. For example, information from a provider with specialized knowledge and qualifications will receive a high reliability score. In this way, by adding the expertise and qualifications of the information provider to the evaluation, reliability can be evaluated more accurately.

[0081] The credibility evaluation unit can use the emotion estimation function to analyze the emotional impact that the credibility of information has on the user and reflect this in the credibility evaluation. For example, when the generation AI evaluates the credibility of information, it uses the emotion estimation function to analyze the emotional impact on the user. For example, it determines that information that evokes positive emotions is highly credible. The credibility evaluation unit also uses the emotion estimation function to analyze the emotional impact that the credibility of information has on the user in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The credibility evaluation unit also comprehensively evaluates the credibility of the information and the user's emotional response and reflects this in the credibility score. For example, information that evokes positive emotions will receive a high credibility score. As a result, using the emotion estimation function makes it possible to evaluate credibility by taking into account the emotional impact that the credibility of information has on the user.

[0082] The reliability evaluation unit can improve the accuracy of the evaluation by cross-referencing with other reliability evaluation systems. For example, the generation AI cross-references with other reliability evaluation systems to evaluate the reliability of information. For example, it integrates the results of multiple evaluation systems and calculates a reliability score. The reliability evaluation unit also collects data from other reliability evaluation systems in real time, and builds a system that the generation AI analyzes. For example, it adjusts the reliability score based on third-party evaluation data. The reliability evaluation unit also uses cross-referencing to comprehensively evaluate the reliability of information. For example, it compares the results of multiple evaluation systems and adopts the most reliable evaluation. In this way, cross-referencing with other reliability evaluation systems can improve the accuracy of the evaluation.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The URL analysis unit analyzes the website URL. For example, the URL analysis unit evaluates the reliability of the domain, analyzes the link structure, and checks whether or not it has an SSL certificate. To evaluate the reliability of the domain, it refers to past reliability data, to analyze the link structure it evaluates the number of links and the reliability of the linked destinations, and to check whether or not it has an SSL certificate, it checks the issuer and expiration date of the SSL certificate. Step 2: The credibility evaluation unit evaluates the credibility based on the information analyzed by the URL analysis unit. For example, the credibility evaluation unit evaluates the source of the information, evaluates the credibility of the source, and evaluates the history of past misinformation. To evaluate the source of the information, the credibility evaluation unit calculates the credibility score of the source, refers to the past credibility data of the source to evaluate the credibility of the source, and analyzes the past misinformation data to evaluate the history of past misinformation. Step 3: The display unit displays the reliability evaluated by the reliability evaluation unit as a percentage. For example, the display unit displays the reliability score in a range of 0 to 100, displays the reliability score in a color-coded manner, and displays the reliability score in a graph. To display the reliability score in a range of 0 to 100, the display unit calculates the reliability score and displays the result; to display the reliability score in a color-coded manner, the display unit changes the color according to the reliability score; and to display the reliability score in a graph, the display unit shows the fluctuation of the reliability score in a graph.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 robot 414 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 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 URL analysis unit that analyzes the URL of a website; a reliability evaluation unit that evaluates reliability based on the information analyzed by the URL analysis unit; a display unit that displays the reliability evaluated by the reliability evaluation unit in percentage. A system characterized by:

2. The URL analysis unit Also evaluate the quality of a site's design or user interface and factor that into your credibility metrics.

2. The system of claim 1.

3. The URL analysis unit We evaluate the reliability of the site operator, taking into account their past activity history and social credibility.

2. The system of claim 1.

4. The URL analysis unit Analyze the emotional impact of the site's content on users and reflect this in your credibility assessment 2. The system of claim 1.

5. The URL analysis unit Analyze not only URLs but also related social media posts or comments to assess their trustworthiness 2. The system of claim 1.

Images