System

Abstract

An object of a system according to an embodiment is to enable a user to intuitively grasp the reliability of information of a website.SOLUTION: A system according to an embodiment includes a confirmation unit, an evaluation unit, and a provision unit. The confirmation unit acquires information of the website browsed by the user. The evaluation unit evaluates the reliability based on the reliability of the information source, the past record, the number of citation sources, and the update frequency of the information, based on the information acquired by the confirmation unit. The providing unit provides the reliability obtained by the evaluating unit to the user in a percentage display.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 technologies make it difficult for users to intuitively grasp the reliability of information on websites, and there is a risk of misinformation.

[0005] The system according to the embodiment aims to enable a user to intuitively grasp the reliability of information on a website. [Means for solving the problem]

[0006] The system according to the embodiment includes a verification unit, an evaluation unit, and a provision unit. The verification unit acquires information about the website being viewed by the user. The evaluation unit evaluates the reliability of the information source based on the information acquired by the verification unit, including its reliability, past performance, the number of citations, and the frequency of updates to the information. The provision unit provides the user with the reliability obtained by the evaluation unit as a percentage. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to intuitively grasp the reliability of information on a website. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention, aimed at people who do not know how to verify the reliability of information, displays the information accuracy percentage using a browser plug-in when displaying a website in a browser, making it easier for users to recognize the reliability of the website and correctly recognize the reliability of the information. The system acquires information about the website the user is viewing, and based on the acquired information, evaluates the reliability of the information source, taking into account the reliability of the information source, past performance, number of citations, frequency of information updates, and other factors, and provides the user with the evaluation result displayed as a percentage. For example, when a user is viewing a news site, the system acquires information about the news article. Next, the system evaluates the reliability based on the acquired information so that a high reliability is assigned to a reliable news site and a high reliability is assigned to a site with many citations. Finally, if the reliability is 80%, the system displays "Reliability: 80%." This allows the system to prevent the harmful effects of social networking sites, such as the "echo chamber phenomenon" and the infodemic caused by false images and false news sites. This allows users to correctly recognize the reliability of information and prevent the harmful effects of social networking sites, such as the "echo chamber phenomenon" and the infodemic caused by false images and false news sites.

[0029] An information reliability evaluation system according to an embodiment includes a verification unit, an evaluation unit, and a providing unit. The verification unit acquires information about a website being viewed by a user. For example, when a user is viewing a news site, the verification unit automatically acquires information about the news article. The verification unit can acquire information without requiring user operation. The evaluation unit evaluates reliability based on the information acquired by the verification unit, taking into account the reliability of the information source, past performance, the number of citation sources, and the frequency of information updates. For example, the evaluation unit assigns a high reliability to a news site that is reliable. The evaluation unit can also evaluate the reliability highly when there are many citation sources. The evaluation unit can also evaluate the reliability highly when the information is updated frequently. The providing unit provides the reliability obtained by the evaluation unit to the user as a percentage. For example, if the reliability is 80%, the providing unit displays "Reliability: 80%." The providing unit can display the reliability in a way that allows the user to intuitively understand. This allows the information reliability evaluation system according to an embodiment to intuitively understand the reliability of the information on the website being viewed by the user. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may evaluate the reliability using an AI model that receives information acquired by the confirmation unit as input and outputs a reliability. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit may display the reliability using an AI model that receives the reliability obtained by the evaluation unit as input and outputs a percentage display. This allows the information reliability evaluation system according to the embodiment to intuitively understand the reliability of the information on the website the user is browsing.

[0030] The evaluation unit can evaluate the trustworthiness based on the trustworthiness of the information source. The evaluation unit evaluates the trustworthiness based on, for example, the authority of the information source. For example, the evaluation unit assigns a high trustworthiness to an information source that is an authoritative institution such as a government agency or a university. The evaluation unit can also evaluate the trustworthiness based on past trustworthiness evaluations. For example, the evaluation unit assigns a high trustworthiness to an information source that has provided highly reliable information in the past. This improves the accuracy of the trustworthiness evaluation by taking the trustworthiness of the information source into consideration. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the trustworthiness using an AI model that inputs the trustworthiness of the information source and outputs the trustworthiness.

[0031] The evaluation unit can evaluate the reliability based on past performance. The evaluation unit evaluates the reliability based on, for example, the past success stories of the information source. For example, the evaluation unit assigns a high reliability to an information source that has had successful projects or research in the past. The evaluation unit can also evaluate the reliability based on the past failure stories of the information source. For example, the evaluation unit assigns a low reliability to an information source that has had unsuccessful projects or research in the past. This improves the accuracy of the reliability evaluation by taking past performance into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs past performance and outputs a reliability.

[0032] The evaluation unit can evaluate the trustworthiness based on the number of citation sources. The evaluation unit evaluates the trustworthiness based on, for example, the number of citation sources. For example, the evaluation unit assigns a high trustworthiness if an information source has many citation sources. The evaluation unit can also evaluate the trustworthiness based on the trustworthiness of the citation sources. For example, the evaluation unit assigns a high trustworthiness if the citation source is a reliable information source. In this way, by taking the number of citation sources into consideration, the accuracy of the trustworthiness evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the trustworthiness using an AI model that inputs the number of citation sources and outputs the trustworthiness.

[0033] The evaluation unit can evaluate the reliability based on the update frequency of the information. The evaluation unit evaluates the reliability based on, for example, the update frequency of the information. For example, the evaluation unit assigns a high reliability if the information source is frequently updated. The evaluation unit can also evaluate the reliability based on the importance of the latest information. For example, the evaluation unit assigns a high reliability if the information source provides the latest information. In this way, by taking the update frequency of the information into consideration, the accuracy of the reliability evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the update frequency of the information and outputs the reliability.

[0034] The providing unit can provide the reliability to the user as a percentage. For example, the providing unit provides the reliability to the user as a percentage. For example, if the reliability is 80%, the providing unit displays "Reliability: 80%". The providing unit can also display the reliability in a way that the user can intuitively understand. By displaying the reliability as a percentage, the user can intuitively understand. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the reliability and outputs a percentage.

[0035] The confirmation unit can analyze the user's past browsing history and select the optimal information acquisition method. The confirmation unit, for example, prioritizes acquiring information from sites frequently visited by the user. For example, the confirmation unit analyzes the user's past browsing history and identifies frequently visited sites. The confirmation unit can also prioritize selecting sites from which the user has previously acquired highly reliable information. For example, the confirmation unit identifies highly reliable sites based on the user's past browsing history. The confirmation unit can also prioritize acquiring information of a specific genre from the user's browsing history. For example, the confirmation unit analyzes genres that the user has previously viewed and prioritizes acquiring related information. This makes it possible to provide the user with optimal information by analyzing the past browsing history. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can select the information acquisition method using an AI model that inputs the user's past browsing history and outputs the optimal information acquisition method.

[0036] When acquiring information, the verification unit may filter the information based on the user's current areas of interest. For example, the verification unit may preferentially acquire information related to topics in which the user is currently interested. For example, the verification unit may analyze the user's current search history or browsing history to identify topics of interest. The verification unit may also filter the information based on keywords recently searched by the user. For example, the verification unit may acquire relevant information based on keywords recently searched by the user. The verification unit may also acquire information based on posts from social media accounts followed by the user. For example, the verification unit may analyze posts from accounts followed by the user to acquire relevant information. This allows for filtering information based on the user's areas of interest, thereby providing highly relevant information. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit may filter the information using an AI model that receives the user's current areas of interest as input and outputs filtered information.

[0037] When acquiring information, the confirmation unit can select an appropriate acquisition means depending on the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the confirmation unit acquires information using voice recognition technology. For example, the confirmation unit records the user's voice with a microphone and converts it into text data using voice recognition technology. Also, if the user uses text input, the confirmation unit can acquire information using text analysis technology. For example, the confirmation unit analyzes the text entered by the user and acquires related information. Also, if the user uses image input, the confirmation unit can acquire information using image recognition technology. For example, the confirmation unit analyzes images uploaded by the user and acquires related information. This improves the efficiency of information acquisition by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can acquire information using an AI model that receives the user's input method as input and outputs the optimal acquisition means.

[0038] When acquiring information, the confirmation unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. The confirmation unit, for example, prioritizes acquiring news related to the area where the user is currently located. For example, the confirmation unit identifies news related to the area based on the user's geographical location information. The confirmation unit can also acquire nearby event information based on the user's location information. For example, the confirmation unit acquires nearby event information based on the user's location information. The confirmation unit can also prioritize acquiring area-specific information based on the user's geographical location information. For example, the confirmation unit acquires area-specific news and information based on the user's location information. This makes it possible to provide highly relevant information by taking the user's geographical location information into account. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can acquire information using an AI model that inputs the user's geographical location information and outputs highly relevant information.

[0039] When acquiring information, the confirmation unit can analyze the user's social media activity and acquire related information. The confirmation unit can acquire information based on, for example, the content posted by accounts the user follows on social media. For example, the confirmation unit can analyze the content posted by the accounts the user follows to acquire related information. The confirmation unit can also analyze the user's social media posts to acquire related news. For example, the confirmation unit can identify related news based on the user's posts. The confirmation unit can also acquire related information by referring to the activities of the user's friends on social media. For example, the confirmation unit can acquire related information based on information shared by the user's friends. In this way, highly relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the confirmation unit can be performed using, for example, AI, or can be performed without using AI. For example, the confirmation unit can acquire information using an AI model that inputs the user's social media activity and outputs related information.

[0040] When acquiring information, the confirmation unit can customize the acquisition method by reflecting the user's past feedback. The confirmation unit, for example, preferentially acquires information from information sources that the user has previously rated highly. For example, the confirmation unit identifies information sources that the user has previously rated highly based on the user's past feedback. The confirmation unit can also acquire information by excluding information sources that the user has previously rated poorly. For example, the confirmation unit excludes information sources that the user has previously rated poorly based on the user's past feedback. The confirmation unit can also suggest an optimal information acquisition method based on the user's past feedback. For example, the confirmation unit analyzes the user's past feedback and suggests an optimal information acquisition method. In this way, the optimal information acquisition method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can acquire information using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0041] The evaluation unit can analyze the reliability of an information source in detail during reliability evaluation to improve the accuracy of the evaluation. For example, the evaluation unit evaluates the current reliability based on the information source's past reliability evaluation. For example, if an information source has had a high reliability rating in the past, the evaluation unit can also evaluate the current reliability as high. The evaluation unit can also analyze the reliability of the information source's operator or author and reflect this in the evaluation. For example, if the information source's operator or author is a trustworthy person, the evaluation unit assigns a high reliability rating. The evaluation unit can also evaluate the reliability by taking into account the information source's past false reports and correction history. For example, if the information source has issued false reports in the past, the evaluation unit assigns a low reliability rating. This detailed analysis of the reliability of the information source improves the accuracy of the reliability evaluation. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the reliability of the information source and outputs the reliability rating.

[0042] The evaluation unit can optimize the evaluation algorithm by referring to past performance data when evaluating reliability. The evaluation unit, for example, adjusts the evaluation algorithm based on past reliability evaluation data. For example, the evaluation unit analyzes past reliability evaluation data and optimizes the evaluation algorithm. The evaluation unit can also extract specific patterns from past performance data and reflect them in the evaluation. For example, the evaluation unit analyzes past success cases and failure cases and reflects them in the evaluation algorithm. The evaluation unit can also compare past evaluation results with current evaluation results to improve the accuracy of the algorithm. For example, the evaluation unit adjusts current evaluation results based on past evaluation results. In this way, the accuracy of the evaluation algorithm is improved by referring to past performance data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate reliability using an AI model that inputs past performance data and optimizes the evaluation algorithm.

[0043] The evaluation unit can evaluate the reliability by taking into account not only the number of sources but also the quality of the sources. For example, the evaluation unit analyzes the reliability of the sources and reflects this in the reliability evaluation. For example, if a source is a reliable information source, the evaluation unit assigns a high reliability. The evaluation unit can also add not only the number of sources but also the quality of the sources to the evaluation criteria. For example, if the quality of a source is high, the evaluation unit assigns a high reliability. The evaluation unit can also evaluate the current reliability based on the source's past reliability evaluation. For example, if a source has had a high reliability in the past, the evaluation unit also assigns a high reliability to the current source. This improves the accuracy of the reliability evaluation by taking the quality of the source into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the quality of the source and outputs a reliability.

[0044] The evaluation unit can take the geographical distribution of information into consideration when evaluating the reliability. The evaluation unit, for example, reflects the geographical reliability of the information source in the evaluation. For example, if the information source is geographically close, the evaluation unit assigns a high reliability. The evaluation unit can also prioritize the evaluation of the reliability of geographically close information sources. For example, if the information source is related to the user's region, the evaluation unit assigns a high reliability. The evaluation unit can also evaluate the reliability of information based on geographical distribution. For example, if information sources are concentrated in a specific region, the evaluation unit reflects the reliability of that region in the evaluation. This improves the accuracy of the reliability evaluation by taking the geographical distribution of information into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the geographical distribution of information and outputs reliability.

[0045] The evaluation unit can improve the accuracy of the evaluation by referring to related literature of the information when evaluating its reliability. The evaluation unit, for example, reflects the reliability of the related literature in the evaluation. For example, if the related literature is a reliable information source, the evaluation unit assigns a high reliability. The evaluation unit can also add the number and quality of related literature to the evaluation criteria. For example, if there are a large number of related literature and the quality is high, the evaluation unit assigns a high reliability. The evaluation unit can also evaluate the current reliability based on past reliability evaluations of the related literature. For example, if the related literature has had a high reliability in the past, the evaluation unit also assigns a high reliability to the current reliability. In this way, referring to the related literature improves the accuracy of the reliability evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs related literature and outputs reliability.

[0046] The evaluation unit can take into account the market value of the information when evaluating the reliability. For example, the evaluation unit adds the market value of the information to the evaluation criteria. For example, if the information has a high market value, the evaluation unit assigns a high reliability. The evaluation unit can also prioritize evaluation of information with a high market value. For example, if the information is in high demand, the evaluation unit assigns a high reliability. The evaluation unit can also evaluate the reliability by taking fluctuations in market value into account. For example, if the market value of the information is increasing, the evaluation unit assigns a high reliability. In this way, by taking the market value of the information into consideration, the accuracy of the reliability evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the market value of the information and outputs the reliability.

[0047] When displaying the reliability, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit preferentially adopts a display method that the user has used preferably in the past. For example, the providing unit identifies the display method that the user has used preferably based on the user's past operation history. The providing unit can also suggest the optimal display method based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and suggests the optimal display method. The providing unit can also customize the display method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and customizes the display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's past operation history and outputs the optimal display method.

[0048] The providing unit can customize the display content according to the user's current task when displaying the reliability. For example, if the user is gathering information, the providing unit displays detailed reliability information. For example, the providing unit analyzes the user's current task and determines that the user is gathering information. The providing unit can also display concise reliability information if the user is in a hurry. For example, the providing unit determines that the user is in a hurry based on the user's current task. The providing unit can also display visually appealing reliability information if the user is relaxed. For example, the providing unit determines that the user is relaxed based on the user's current task. This enables more appropriate information to be provided by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's current task and outputs optimal display content.

[0049] The providing unit can improve the display method by reflecting user feedback when displaying the reliability. The providing unit, for example, adjusts the display method based on feedback provided by the user in the past. For example, the providing unit analyzes the user's past feedback and improves the display method. The providing unit can also improve the display method by reflecting user feedback in real time. For example, the providing unit collects user feedback in real time and adjusts the display method. The providing unit can also analyze user feedback and propose an optimal display method. For example, the providing unit proposes an optimal display method based on user feedback. This improves the display method by reflecting user feedback, making it possible to provide more appropriate information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that uses user feedback as input and improves the display method.

[0050] When displaying the reliability, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit selects a display method optimized for the smartphone based on the user's device information. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit selects a display method optimized for the tablet based on the user's device information. Furthermore, if the user is using a desktop, the providing unit can also display detailed information. For example, the providing unit selects a display method optimized for the desktop based on the user's device information. In this way, the optimal display method can be provided by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's device information and outputs the optimal display method.

[0051] When displaying the reliability, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically translates the reliability information based on the language setting of the user's device. For example, the providing unit translates the reliability information based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit allows the user to select the language they use. Furthermore, if the user selects a specific language, the providing unit can display the reliability information in that language. For example, the providing unit displays the reliability information in the language selected by the user. This makes it possible to accommodate a larger number of users by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's language setting and outputs translated reliability information.

[0052] The providing unit can customize the display design according to the user's visual preferences when displaying the reliability. The providing unit displays the reliability information using, for example, a color or font preferred by the user. For example, the providing unit selects a color or font based on the user's visual preferences. The providing unit can also display the reliability information using an icon or graph based on the user's visual preferences. For example, the providing unit selects an icon or graph based on the user's visual preferences. The providing unit can also suggest an optimal display design based on the user's past selection history. For example, the providing unit analyzes the user's past selection history and suggests an optimal display design. This enables more attractive information to be provided by customizing the display design according to the user's visual preferences. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's visual preferences and outputs an optimal display design.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The confirmation unit can monitor the remaining battery level of the user's device and reduce the frequency of information acquisition when the battery is low. For example, the confirmation unit can limit information acquisition to once per hour when the remaining battery level of the user's device is 20% or less. The confirmation unit can also return to the normal acquisition frequency when the battery is 50% or more. Furthermore, the confirmation unit can suspend information acquisition when the battery is 10% or less. This makes it possible to provide necessary information while reducing battery consumption of the user's device.

[0055] The evaluation unit can take into account the language of the information source when evaluating the reliability of the information. For example, if the information source is in the user's native language, the evaluation unit can assign a high reliability to the information source. The evaluation unit can also assign an even higher reliability to the information source if it is provided in multiple languages. Furthermore, if the information source uses a lot of technical terminology, the evaluation unit can reflect that technical terminology in the evaluation. In this way, by taking into account the linguistic characteristics of the information source, the accuracy of the reliability evaluation can be improved.

[0056] The checking unit may monitor the user's Internet connection status and suspend information acquisition if the connection is unstable. For example, the checking unit may suspend information acquisition if the user's Internet connection is intermittent. The checking unit may also resume information acquisition if the connection is stable. Furthermore, the checking unit may notify the user if the connection is very unstable and provide an option to manually resume information acquisition. This allows information to be acquired efficiently according to the Internet connection status.

[0057] The providing unit can provide the reliability information by voice, taking into account the user's visual impairment. For example, if the user has a visual impairment, the providing unit can read the reliability information by voice. In addition, if the user is using a voice assistant, the providing unit can also transmit the reliability information to the voice assistant. Furthermore, if the user has a hearing impairment, the providing unit can also display the reliability information in text. This makes it possible to provide the reliability information in an appropriate manner depending on the user's impairment.

[0058] The evaluation unit can take into account the social influence of the information source when evaluating the reliability of information. For example, if the information source has many followers, the evaluation unit can assign a high reliability. The evaluation unit can also assign an even higher reliability if the information source is a well-known expert. Furthermore, if the information source has provided socially important information in the past, the evaluation unit can also reflect that track record in the evaluation. In this way, by taking into account the social influence of the information source, the accuracy of the reliability evaluation can be improved.

[0059] The confirmation unit can monitor the usage status of the user's device and suspend the acquisition of information if the device is under heavy load. For example, the confirmation unit can suspend the acquisition of information if the user's device is simultaneously running many applications. The confirmation unit can also resume the acquisition of information if the load on the device is reduced. Furthermore, the confirmation unit can notify the user if the device load is very high and provide an option to manually resume the acquisition of information. This allows information to be acquired efficiently according to the usage status of the device.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The verification unit acquires information about the website the user is browsing. For example, if the user is browsing a news site, the verification unit automatically acquires information about the news article. The verification unit can also acquire information without requiring user operation. Step 2: The evaluation unit evaluates the reliability of the information source based on the information obtained by the verification unit, taking into account the reliability of the information source, past performance, number of citation sources, frequency of information updates, etc. For example, if the information source is a reliable news site, it can be given a high reliability rating, and if there are many citation sources or the information is updated frequently, the reliability can also be evaluated as high. The processing in the evaluation unit may be performed using AI or without AI. Step 3: The providing unit provides the reliability obtained by the evaluation unit to the user as a percentage. For example, if the reliability is 80%, it displays "Reliability: 80%." The providing unit can also display the reliability in a way that is intuitively understandable to the user. The processing in the providing unit may be performed using AI or without using AI.

[0062] (Example 2) A system according to an embodiment of the present invention, aimed at people who do not know how to verify the reliability of information, displays the information accuracy percentage using a browser plug-in when displaying a website in a browser, making it easier for users to recognize the reliability of the website and correctly recognize the reliability of the information. The system acquires information about the website the user is viewing, and based on the acquired information, evaluates the reliability of the information source, taking into account the reliability of the information source, past performance, number of citations, frequency of information updates, and other factors, and provides the user with the evaluation result displayed as a percentage. For example, when a user is viewing a news site, the system acquires information about the news article. Next, the system evaluates the reliability based on the acquired information so that a high reliability is assigned to a reliable news site and a high reliability is assigned to a site with many citations. Finally, if the reliability is 80%, the system displays "Reliability: 80%." This allows the system to prevent the harmful effects of social networking sites, such as the "echo chamber phenomenon" and the infodemic caused by false images and false news sites. This allows users to correctly recognize the reliability of information and prevent the harmful effects of social networking sites, such as the "echo chamber phenomenon" and the infodemic caused by false images and false news sites.

[0063] An information reliability evaluation system according to an embodiment includes a verification unit, an evaluation unit, and a providing unit. The verification unit acquires information about a website being viewed by a user. For example, when a user is viewing a news site, the verification unit automatically acquires information about the news article. The verification unit can acquire information without requiring user operation. The evaluation unit evaluates reliability based on the information acquired by the verification unit, taking into account the reliability of the information source, past performance, the number of citation sources, and the frequency of information updates. For example, the evaluation unit assigns a high reliability to a news site that is reliable. The evaluation unit can also evaluate the reliability highly when there are many citation sources. The evaluation unit can also evaluate the reliability highly when the information is updated frequently. The providing unit provides the reliability obtained by the evaluation unit to the user as a percentage. For example, if the reliability is 80%, the providing unit displays "Reliability: 80%." The providing unit can display the reliability in a way that allows the user to intuitively understand. This allows the information reliability evaluation system according to an embodiment to intuitively understand the reliability of the information on the website being viewed by the user. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may evaluate the reliability using an AI model that receives information acquired by the confirmation unit as input and outputs a reliability. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit may display the reliability using an AI model that receives the reliability obtained by the evaluation unit as input and outputs a percentage display. This allows the information reliability evaluation system according to the embodiment to intuitively understand the reliability of the information on the website the user is browsing.

[0064] The evaluation unit can evaluate the trustworthiness based on the trustworthiness of the information source. The evaluation unit evaluates the trustworthiness based on, for example, the authority of the information source. For example, the evaluation unit assigns a high trustworthiness to an information source that is an authoritative institution such as a government agency or a university. The evaluation unit can also evaluate the trustworthiness based on past trustworthiness evaluations. For example, the evaluation unit assigns a high trustworthiness to an information source that has provided highly reliable information in the past. This improves the accuracy of the trustworthiness evaluation by taking the trustworthiness of the information source into consideration. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the trustworthiness using an AI model that inputs the trustworthiness of the information source and outputs the trustworthiness.

[0065] The evaluation unit can evaluate the reliability based on past performance. The evaluation unit evaluates the reliability based on, for example, the past success stories of the information source. For example, the evaluation unit assigns a high reliability to an information source that has had successful projects or research in the past. The evaluation unit can also evaluate the reliability based on the past failure stories of the information source. For example, the evaluation unit assigns a low reliability to an information source that has had unsuccessful projects or research in the past. This improves the accuracy of the reliability evaluation by taking past performance into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs past performance and outputs a reliability.

[0066] The evaluation unit can evaluate the trustworthiness based on the number of citation sources. The evaluation unit evaluates the trustworthiness based on, for example, the number of citation sources. For example, the evaluation unit assigns a high trustworthiness if an information source has many citation sources. The evaluation unit can also evaluate the trustworthiness based on the trustworthiness of the citation sources. For example, the evaluation unit assigns a high trustworthiness if the citation source is a reliable information source. In this way, by taking the number of citation sources into consideration, the accuracy of the trustworthiness evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the trustworthiness using an AI model that inputs the number of citation sources and outputs the trustworthiness.

[0067] The evaluation unit can evaluate the reliability based on the update frequency of the information. The evaluation unit evaluates the reliability based on, for example, the update frequency of the information. For example, the evaluation unit assigns a high reliability if the information source is frequently updated. The evaluation unit can also evaluate the reliability based on the importance of the latest information. For example, the evaluation unit assigns a high reliability if the information source provides the latest information. In this way, by taking the update frequency of the information into consideration, the accuracy of the reliability evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the update frequency of the information and outputs the reliability.

[0068] The providing unit can provide the reliability to the user as a percentage. For example, the providing unit provides the reliability to the user as a percentage. For example, if the reliability is 80%, the providing unit displays "Reliability: 80%". The providing unit can also display the reliability in a way that the user can intuitively understand. By displaying the reliability as a percentage, the user can intuitively understand. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the reliability and outputs a percentage.

[0069] The confirmation unit can estimate the user's emotions and adjust the timing of information acquisition based on the emotions. For example, if the user is feeling stressed, the confirmation unit delays information acquisition until the user relaxes. For example, the confirmation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the confirmation unit can immediately acquire information and quickly evaluate reliability. For example, the confirmation unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is concentrating, the confirmation unit can periodically acquire information so as not to disturb the user's work. For example, the confirmation unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. This allows the timing of information acquisition to be adjusted according to the user's emotions, thereby reducing the user's stress. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input image data of the user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0070] The confirmation unit can analyze the user's past browsing history and select the optimal information acquisition method. The confirmation unit, for example, prioritizes acquiring information from sites frequently visited by the user. For example, the confirmation unit analyzes the user's past browsing history and identifies frequently visited sites. The confirmation unit can also prioritize selecting sites from which the user has previously acquired highly reliable information. For example, the confirmation unit identifies highly reliable sites based on the user's past browsing history. The confirmation unit can also prioritize acquiring information of a specific genre from the user's browsing history. For example, the confirmation unit analyzes genres that the user has previously viewed and prioritizes acquiring related information. This makes it possible to provide the user with optimal information by analyzing the past browsing history. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can select the information acquisition method using an AI model that inputs the user's past browsing history and outputs the optimal information acquisition method.

[0071] When acquiring information, the verification unit may filter the information based on the user's current areas of interest. For example, the verification unit may preferentially acquire information related to topics in which the user is currently interested. For example, the verification unit may analyze the user's current search history or browsing history to identify topics of interest. The verification unit may also filter the information based on keywords recently searched by the user. For example, the verification unit may acquire relevant information based on keywords recently searched by the user. The verification unit may also acquire information based on posts from social media accounts followed by the user. For example, the verification unit may analyze posts from accounts followed by the user to acquire relevant information. This allows for filtering information based on the user's areas of interest, thereby providing highly relevant information. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit may filter the information using an AI model that receives the user's current areas of interest as input and outputs filtered information.

[0072] When acquiring information, the confirmation unit can select an appropriate acquisition means depending on the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the confirmation unit acquires information using voice recognition technology. For example, the confirmation unit records the user's voice with a microphone and converts it into text data using voice recognition technology. Also, if the user uses text input, the confirmation unit can acquire information using text analysis technology. For example, the confirmation unit analyzes the text entered by the user and acquires related information. Also, if the user uses image input, the confirmation unit can acquire information using image recognition technology. For example, the confirmation unit analyzes images uploaded by the user and acquires related information. This improves the efficiency of information acquisition by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can acquire information using an AI model that receives the user's input method as input and outputs the optimal acquisition means.

[0073] The confirmation unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. For example, if the user is feeling anxious, the confirmation unit prioritizes acquiring information with high reliability. For example, the confirmation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the confirmation unit can prioritize acquiring the latest information. For example, the confirmation unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is relaxed, the confirmation unit can prioritize acquiring detailed information. For example, the confirmation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for prioritizing information according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may input image data of the user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0074] When acquiring information, the confirmation unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. The confirmation unit, for example, prioritizes acquiring news related to the area where the user is currently located. For example, the confirmation unit identifies news related to the area based on the user's geographical location information. The confirmation unit can also acquire nearby event information based on the user's location information. For example, the confirmation unit acquires nearby event information based on the user's location information. The confirmation unit can also prioritize acquiring area-specific information based on the user's geographical location information. For example, the confirmation unit acquires area-specific news and information based on the user's location information. This makes it possible to provide highly relevant information by taking the user's geographical location information into account. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can acquire information using an AI model that inputs the user's geographical location information and outputs highly relevant information.

[0075] When acquiring information, the confirmation unit can analyze the user's social media activity and acquire related information. The confirmation unit can acquire information based on, for example, the content posted by accounts the user follows on social media. For example, the confirmation unit can analyze the content posted by the accounts the user follows to acquire related information. The confirmation unit can also analyze the user's social media posts to acquire related news. For example, the confirmation unit can identify related news based on the user's posts. The confirmation unit can also acquire related information by referring to the activities of the user's friends on social media. For example, the confirmation unit can acquire related information based on information shared by the user's friends. In this way, highly relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the confirmation unit can be performed using, for example, AI, or can be performed without using AI. For example, the confirmation unit can acquire information using an AI model that inputs the user's social media activity and outputs related information.

[0076] When acquiring information, the confirmation unit can customize the acquisition method by reflecting the user's past feedback. The confirmation unit, for example, preferentially acquires information from information sources that the user has previously rated highly. For example, the confirmation unit identifies information sources that the user has previously rated highly based on the user's past feedback. The confirmation unit can also acquire information by excluding information sources that the user has previously rated poorly. For example, the confirmation unit excludes information sources that the user has previously rated poorly based on the user's past feedback. The confirmation unit can also suggest an optimal information acquisition method based on the user's past feedback. For example, the confirmation unit analyzes the user's past feedback and suggests an optimal information acquisition method. In this way, the optimal information acquisition method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can acquire information using an AI model that uses the user's past feedback as input and customizes the acquisition method.

[0077] The evaluation unit can estimate the user's emotions and adjust the reliability evaluation criteria based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit tightens the reliability evaluation criteria. For example, the evaluation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The evaluation unit can also loosen the reliability evaluation criteria if the user is relaxed. For example, the evaluation unit records the user's voice and estimates the emotion using voice analysis technology. The evaluation unit can also maintain the reliability evaluation criteria neutral if the user is excited. For example, the evaluation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for more appropriate evaluation by adjusting the reliability evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0078] The evaluation unit can analyze the reliability of an information source in detail during reliability evaluation to improve the accuracy of the evaluation. For example, the evaluation unit evaluates the current reliability based on the information source's past reliability evaluation. For example, if an information source has had a high reliability rating in the past, the evaluation unit can also evaluate the current reliability as high. The evaluation unit can also analyze the reliability of the information source's operator or author and reflect this in the evaluation. For example, if the information source's operator or author is a trustworthy person, the evaluation unit assigns a high reliability rating. The evaluation unit can also evaluate the reliability by taking into account the information source's past false reports and correction history. For example, if the information source has issued false reports in the past, the evaluation unit assigns a low reliability rating. This detailed analysis of the reliability of the information source improves the accuracy of the reliability evaluation. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the reliability of the information source and outputs the reliability rating.

[0079] The evaluation unit can optimize the evaluation algorithm by referring to past performance data when evaluating reliability. The evaluation unit, for example, adjusts the evaluation algorithm based on past reliability evaluation data. For example, the evaluation unit analyzes past reliability evaluation data and optimizes the evaluation algorithm. The evaluation unit can also extract specific patterns from past performance data and reflect them in the evaluation. For example, the evaluation unit analyzes past success cases and failure cases and reflects them in the evaluation algorithm. The evaluation unit can also compare past evaluation results with current evaluation results to improve the accuracy of the algorithm. For example, the evaluation unit adjusts current evaluation results based on past evaluation results. In this way, the accuracy of the evaluation algorithm is improved by referring to past performance data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate reliability using an AI model that inputs past performance data and optimizes the evaluation algorithm.

[0080] The evaluation unit can evaluate the reliability by taking into account not only the number of sources but also the quality of the sources. For example, the evaluation unit analyzes the reliability of the sources and reflects this in the reliability evaluation. For example, if a source is a reliable information source, the evaluation unit assigns a high reliability. The evaluation unit can also add not only the number of sources but also the quality of the sources to the evaluation criteria. For example, if the quality of a source is high, the evaluation unit assigns a high reliability. The evaluation unit can also evaluate the current reliability based on the source's past reliability evaluation. For example, if a source has had a high reliability in the past, the evaluation unit also assigns a high reliability to the current source. This improves the accuracy of the reliability evaluation by taking the quality of the source into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the quality of the source and outputs a reliability.

[0081] The evaluation unit can estimate the user's emotions and adjust the display order of the reliability evaluation results based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can prioritize displaying information with high reliability. For example, the evaluation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the evaluation unit can prioritize displaying detailed information. For example, the evaluation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is excited, the evaluation unit can prioritize displaying the latest information. For example, the evaluation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for adjusting the display order according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0082] The evaluation unit can take the geographical distribution of information into consideration when evaluating the reliability. The evaluation unit, for example, reflects the geographical reliability of the information source in the evaluation. For example, if the information source is geographically close, the evaluation unit assigns a high reliability. The evaluation unit can also prioritize the evaluation of the reliability of geographically close information sources. For example, if the information source is related to the user's region, the evaluation unit assigns a high reliability. The evaluation unit can also evaluate the reliability of information based on geographical distribution. For example, if information sources are concentrated in a specific region, the evaluation unit reflects the reliability of that region in the evaluation. This improves the accuracy of the reliability evaluation by taking the geographical distribution of information into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the geographical distribution of information and outputs reliability.

[0083] The evaluation unit can improve the accuracy of the evaluation by referring to related literature of the information when evaluating its reliability. The evaluation unit, for example, reflects the reliability of the related literature in the evaluation. For example, if the related literature is a reliable information source, the evaluation unit assigns a high reliability. The evaluation unit can also add the number and quality of related literature to the evaluation criteria. For example, if there are a large number of related literature and the quality is high, the evaluation unit assigns a high reliability. The evaluation unit can also evaluate the current reliability based on past reliability evaluations of the related literature. For example, if the related literature has had a high reliability in the past, the evaluation unit also assigns a high reliability to the current reliability. In this way, referring to the related literature improves the accuracy of the reliability evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs related literature and outputs reliability.

[0084] The evaluation unit can take into account the market value of the information when evaluating the reliability. For example, the evaluation unit adds the market value of the information to the evaluation criteria. For example, if the information has a high market value, the evaluation unit assigns a high reliability. The evaluation unit can also prioritize evaluation of information with a high market value. For example, if the information is in high demand, the evaluation unit assigns a high reliability. The evaluation unit can also evaluate the reliability by taking fluctuations in market value into account. For example, if the market value of the information is increasing, the evaluation unit assigns a high reliability. In this way, by taking the market value of the information into consideration, the accuracy of the reliability evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the reliability using an AI model that inputs the market value of the information and outputs the reliability.

[0085] The providing unit can estimate the user's emotion and adjust the reliability display method based on the estimated user emotion. For example, if the user is feeling anxious, the providing unit can emphasize the reliability display. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can also display detailed reliability information. For example, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is excited, the providing unit can adopt a visually stimulating display method. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate information provision by adjusting the reliability display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken with a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0086] When displaying the reliability, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit preferentially adopts a display method that the user has used preferably in the past. For example, the providing unit identifies the display method that the user has used preferably based on the user's past operation history. The providing unit can also suggest the optimal display method based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and suggests the optimal display method. The providing unit can also customize the display method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and customizes the display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's past operation history and outputs the optimal display method.

[0087] The providing unit can customize the display content according to the user's current task when displaying the reliability. For example, if the user is gathering information, the providing unit displays detailed reliability information. For example, the providing unit analyzes the user's current task and determines that the user is gathering information. The providing unit can also display concise reliability information if the user is in a hurry. For example, the providing unit determines that the user is in a hurry based on the user's current task. The providing unit can also display visually appealing reliability information if the user is relaxed. For example, the providing unit determines that the user is relaxed based on the user's current task. This enables more appropriate information to be provided by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's current task and outputs optimal display content.

[0088] The providing unit can improve the display method by reflecting user feedback when displaying the reliability. The providing unit, for example, adjusts the display method based on feedback provided by the user in the past. For example, the providing unit analyzes the user's past feedback and improves the display method. The providing unit can also improve the display method by reflecting user feedback in real time. For example, the providing unit collects user feedback in real time and adjusts the display method. The providing unit can also analyze user feedback and propose an optimal display method. For example, the providing unit proposes an optimal display method based on user feedback. This improves the display method by reflecting user feedback, making it possible to provide more appropriate information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that uses user feedback as input and improves the display method.

[0089] The providing unit can estimate the user's emotions and determine the priority of reliability display based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can prioritize displaying information with high reliability. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can prioritize displaying detailed information. For example, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is excited, the providing unit can prioritize displaying the latest information. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables more appropriate information to be provided by determining the priority of reliability display based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken with a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0090] When displaying the reliability, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit selects a display method optimized for the smartphone based on the user's device information. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit selects a display method optimized for the tablet based on the user's device information. Furthermore, if the user is using a desktop, the providing unit can also display detailed information. For example, the providing unit selects a display method optimized for the desktop based on the user's device information. In this way, the optimal display method can be provided by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's device information and outputs the optimal display method.

[0091] When displaying the reliability, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically translates the reliability information based on the language setting of the user's device. For example, the providing unit translates the reliability information based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit allows the user to select the language they use. Furthermore, if the user selects a specific language, the providing unit can display the reliability information in that language. For example, the providing unit displays the reliability information in the language selected by the user. This makes it possible to accommodate a larger number of users by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's language setting and outputs translated reliability information.

[0092] The providing unit can customize the display design according to the user's visual preferences when displaying the reliability. The providing unit displays the reliability information using, for example, a color or font preferred by the user. For example, the providing unit selects a color or font based on the user's visual preferences. The providing unit can also display the reliability information using an icon or graph based on the user's visual preferences. For example, the providing unit selects an icon or graph based on the user's visual preferences. The providing unit can also suggest an optimal display design based on the user's past selection history. For example, the providing unit analyzes the user's past selection history and suggests an optimal display design. This enables more attractive information to be provided by customizing the display design according to the user's visual preferences. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can display the reliability using an AI model that inputs the user's visual preferences and outputs an optimal display design. === Hard Collateral 1-1 === Each of the multiple elements including the above-described confirmation unit, evaluation unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the confirmation unit can acquire information about the website being viewed by the user via the control unit 46A of the smart device 14. The evaluation unit can evaluate the reliability based on the acquired information via the specific processing unit 290 of the data processing device 12. The providing unit can provide the user with the evaluated reliability as a percentage via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described confirmation unit, evaluation unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the confirmation unit can acquire information about a website being viewed by a user via the control unit 46A of the smart glasses 214. The evaluation unit can evaluate the reliability based on the acquired information via the specific processing unit 290 of the data processing device 12. The providing unit can provide the user with the evaluated reliability as a percentage via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned confirmation unit, evaluation unit, and providing unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the confirmation unit can acquire information about the website being viewed by the user via the control unit 46A of the headset type terminal 314. The evaluation unit can evaluate the reliability based on the acquired information via the specific processing unit 290 of the data processing device 12. The providing unit can provide the user with the evaluated reliability displayed as a percentage via the control unit 46A of the headset type terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned confirmation unit, evaluation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the confirmation unit can acquire information about the website being viewed by the user via the control unit 46A of the robot 414. The evaluation unit can evaluate the trustworthiness based on the acquired information via the specific processing unit 290 of the data processing device 12. The provision unit can provide the user with the assessed trustworthiness displayed as a percentage via the control unit 46A of the robot 414, for example.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The confirmation unit can monitor the remaining battery level of the user's device and reduce the frequency of information acquisition when the battery is low. For example, the confirmation unit can limit information acquisition to once per hour when the remaining battery level of the user's device is 20% or less. The confirmation unit can also return to the normal acquisition frequency when the battery is 50% or more. Furthermore, the confirmation unit can suspend information acquisition when the battery is 10% or less. This makes it possible to provide necessary information while reducing battery consumption of the user's device.

[0095] The evaluation unit can take into account the language of the information source when evaluating the reliability of the information. For example, if the information source is in the user's native language, the evaluation unit can assign a high reliability to the information source. The evaluation unit can also assign an even higher reliability to the information source if it is provided in multiple languages. Furthermore, if the information source uses a lot of technical terminology, the evaluation unit can reflect that technical terminology in the evaluation. In this way, by taking into account the linguistic characteristics of the information source, the accuracy of the reliability evaluation can be improved.

[0096] The providing unit can estimate the user's emotion and change the color of the reliability display based on the estimated user's emotion. For example, the providing unit can change the reliability display to blue when the user is feeling anxious. The providing unit can also change the reliability display to green when the user is relaxed. Furthermore, the providing unit can change the reliability display to red when the user is excited. This makes it possible to provide a visually easy-to-understand reliability display according to the user's emotion.

[0097] The checking unit may monitor the user's Internet connection status and suspend information acquisition if the connection is unstable. For example, the checking unit may suspend information acquisition if the user's Internet connection is intermittent. The checking unit may also resume information acquisition if the connection is stable. Furthermore, the checking unit may notify the user if the connection is very unstable and provide an option to manually resume information acquisition. This allows information to be acquired efficiently according to the Internet connection status.

[0098] The evaluation unit can estimate the user's emotions and dynamically adjust the reliability evaluation algorithm based on the estimated user's emotions. For example, the evaluation unit can tighten the reliability evaluation criteria when the user is feeling anxious. The evaluation unit can also loosen the reliability evaluation criteria when the user is relaxed. Furthermore, the evaluation unit can keep the reliability evaluation criteria neutral when the user is excited. This makes it possible to improve the accuracy of the reliability evaluation according to the user's emotions.

[0099] The providing unit can provide the reliability information by voice, taking into account the user's visual impairment. For example, if the user has a visual impairment, the providing unit can read the reliability information by voice. In addition, if the user is using a voice assistant, the providing unit can also transmit the reliability information to the voice assistant. Furthermore, if the user has a hearing impairment, the providing unit can also display the reliability information in text. This makes it possible to provide the reliability information in an appropriate manner depending on the user's impairment.

[0100] The confirmation unit can estimate the user's emotions and adjust the frequency of information acquisition based on the estimated user's emotions. For example, the confirmation unit can reduce the frequency of information acquisition when the user is feeling stressed. The confirmation unit can also increase the frequency of information acquisition when the user is relaxed. Furthermore, the confirmation unit can maintain the normal frequency of information acquisition when the user is excited. In this way, by adjusting the frequency of information acquisition according to the user's emotions, it is possible to reduce the user's stress.

[0101] The evaluation unit can take into account the social influence of the information source when evaluating the reliability of information. For example, if the information source has many followers, the evaluation unit can assign a high reliability. The evaluation unit can also assign an even higher reliability if the information source is a well-known expert. Furthermore, if the information source has provided socially important information in the past, the evaluation unit can also reflect that track record in the evaluation. In this way, by taking into account the social influence of the information source, the accuracy of the reliability evaluation can be improved.

[0102] The providing unit can estimate the user's emotion and change the display format of the reliability information based on the estimated user's emotion. For example, if the user is feeling anxious, the providing unit can display the reliability information in a concise manner. If the user is feeling relaxed, the providing unit can also display detailed reliability information. Furthermore, if the user is excited, the providing unit can also adopt a visually attractive display format. This makes it possible to provide an optimal display format according to the user's emotion.

[0103] The confirmation unit can monitor the usage status of the user's device and suspend the acquisition of information if the device is under heavy load. For example, the confirmation unit can suspend the acquisition of information if the user's device is simultaneously running many applications. The confirmation unit can also resume the acquisition of information if the load on the device is reduced. Furthermore, the confirmation unit can notify the user if the device load is very high and provide an option to manually resume the acquisition of information. This allows information to be acquired efficiently according to the usage status of the device.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The verification unit acquires information about the website the user is browsing. For example, if the user is browsing a news site, the verification unit automatically acquires information about the news article. The verification unit can also acquire information without requiring user operation. Step 2: The evaluation unit evaluates the reliability of the information source based on the information obtained by the verification unit, taking into account the reliability of the information source, past performance, number of citation sources, frequency of information updates, etc. For example, if the information source is a reliable news site, it can be given a high reliability rating, and if there are many citation sources or the information is updated frequently, the reliability can also be evaluated as high. The processing in the evaluation unit may be performed using AI or without AI. Step 3: The providing unit provides the reliability obtained by the evaluation unit to the user as a percentage. For example, if the reliability is 80%, it displays "Reliability: 80%." The providing unit can also display the reliability in a way that is intuitively understandable to the user. The processing in the providing unit may be performed using AI or without using AI.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 verification unit that acquires information about the website that the user is browsing; an evaluation unit that evaluates the reliability of the information source based on the information acquired by the confirmation unit, such as the reliability of the information source, its past performance, the number of citation sources, and the frequency of information updates; a providing unit that provides the reliability obtained by the evaluation unit to a user in percentage format. A system characterized by:

2. The evaluation unit Evaluate trustworthiness based on the reliability of the source 2. The system of claim 1.

3. The evaluation unit Evaluate reliability based on past performance 2. The system of claim 1.

4. The evaluation unit Evaluate trustworthiness based on the number of sources cited 2. The system of claim 1.

5. The evaluation unit Evaluate reliability based on how frequently information is updated 2. The system of claim 1.

6. The providing unit Provide users with a confidence percentage 2. The system of claim 1.

7. The confirmation unit Estimates user emotions and adjusts information acquisition timing based on the estimated user emotions 2. The system of claim 1.

8. The confirmation unit Analyze the user's browsing history and select the appropriate method of obtaining information 2. The system of claim 1.

Images