system

Abstract

The system according to the embodiment aims to automatically determine whether a website is a fake site and display a warning to the user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects website information. The analysis unit analyzes the information collected by the collection unit and determines whether the website is a fake site. The warning unit displays a warning to the user based on the determination result obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for users to determine on their own whether a website is a fake site.

[0005] The system according to the embodiment aims to automatically determine whether a website is a fake site and display a warning to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects website information. The analysis unit analyzes the information collected by the collection unit and determines whether the website is a fake site. The warning unit displays a warning to the user based on the determination result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically determine whether a website is a fake site and display a warning to the user. [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 fake site detection system according to an embodiment of the present invention incorporates AI into a device or browser to detect fake sites. This fake site detection system collects information about websites accessed by users, analyzes the collected information, and determines whether the website is fake. Based on the determination, a warning is displayed to the user. This mechanism reduces the risk of users accessing fake sites. For example, information about websites accessed by users is collected. This includes detailed information such as the website's URL, content, and metadata. For example, the system checks whether the website's URL is similar to that of the official website and whether the content matches the official website. The collected information is then analyzed by AI. Based on the collected information, the AI ​​determines whether the website is fake. For example, the AI ​​analyzes URL patterns, content similarity, and metadata reliability to determine whether the website is likely to be fake. Based on the determination, a warning is displayed to the user. For example, if the website is likely to be fake, a warning message is displayed to the user, urging them to discontinue access. Even if the website is unlikely to be fake, a warning message can be displayed. This mechanism reduces the risk of users accessing fake sites. For example, the system can avoid the risk of accessing phishing sites or sites containing malware. Furthermore, even when it is difficult to distinguish between official and fake sites, the AI ​​will automatically make the determination, allowing users to use the internet with peace of mind. This means that the fake site detection system can reduce the risk of users accessing fake sites.

[0029] A fake website detection system according to an embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects information about websites accessed by users. The collected information includes, but is not limited to, the website's URL, content, and metadata. For example, the collection unit acquires the website's URL and checks whether the URL is similar to that of the official website. The collection unit can also acquire the website's content and check whether the content matches that of the official website. The collection unit can also acquire the website's metadata and evaluate the reliability of the metadata. For example, the collection unit analyzes the website's HTML source code to extract the URL, content, and metadata. The analysis unit analyzes the information collected by the collection unit to determine whether the website is a fake website. For example, the analysis unit performs URL pattern matching to determine whether the URL is similar to that of the official website. The analysis unit can also perform content similarity analysis to determine whether the content matches that of the official website. The analysis unit can also evaluate the reliability of the metadata to determine whether the metadata is trustworthy. For example, the analysis unit performs URL pattern matching using regular expressions to determine whether the URL matches that of the official website. The analysis unit calculates the similarity of the text and determines whether the content matches the official website. The analysis unit evaluates the origin and update frequency of the metadata and determines whether the metadata is trustworthy. The warning unit displays a warning to the user based on the determination result obtained by the analysis unit. For example, if there is a high possibility that the site is fake, the warning unit displays a warning message to the user and urges them to stop accessing the site. The warning unit can also display a message urging caution even if there is a low possibility that the site is fake. For example, the warning unit displays a pop-up message to warn the user. The warning unit can also warn the user using an audio alert. The warning unit can adjust the content and display method of the warning message to provide a warning that is easy for the user to understand.As a result, the fake site detection system according to the embodiment can reduce the risk of users accessing fake sites.

[0030] The collection unit can collect information including website URLs, content, and metadata. For example, the collection unit can acquire website URLs and check whether the URLs are similar to those of official websites. The collection unit can also acquire website content and check whether the content matches those of official websites. The collection unit can also acquire website metadata and evaluate the reliability of the metadata. For example, the collection unit can analyze the website's HTML source code and extract the URL, content, and metadata. This allows for more detailed website information to be collected, improving the accuracy of identifying fake websites. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the website URL, content, and metadata into a generation AI, causing the generation AI to collect information.

[0031] The analysis unit can perform URL pattern matching based on the collected information. The analysis unit can perform URL pattern matching using, for example, regular expressions to determine whether the URL matches the official website. The analysis unit can also check whether the domain names match and determine whether the URL is similar to the official website. By performing URL pattern matching, the analysis unit can improve the accuracy of detecting fake sites. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected URLs into a generation AI and have the generation AI perform URL pattern matching.

[0032] The analysis unit can perform a content similarity analysis based on the collected information. The analysis unit, for example, calculates text similarity to determine whether the content matches the official website. The analysis unit can also analyze image features to determine whether the image matches the official website. By performing a content similarity analysis, the analysis unit improves the accuracy of detecting fake sites. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected content into a generation AI and have the generation AI perform a content similarity analysis.

[0033] The analysis unit can evaluate the reliability of the metadata based on the collected information. For example, the analysis unit evaluates the origin and update frequency of the metadata to determine whether the metadata is reliable. By evaluating the reliability of the metadata, the analysis unit improves the accuracy of detecting fake sites. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected metadata into a generation AI and have the generation AI perform a reliability evaluation of the metadata.

[0034] The warning unit can display a warning message to the user if there is a possibility that the site is fake. For example, if there is a high possibility that the site is fake, the warning unit displays a warning message to the user and urges the user to stop accessing the site. The warning unit can display a pop-up message to warn the user. The warning unit can also warn the user using an audio alert. The warning unit can adjust the content and display method of the warning message to provide a warning that is easy for the user to understand. In this way, by displaying a warning message when there is a high possibility that the site is fake, the risk of the user accessing the fake site is reduced. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the determination result obtained by the analysis unit into a generation AI and cause the generation AI to display a warning message.

[0035] The warning unit can display a message urging caution even when the possibility of the site being fake is low. The warning unit can, for example, display a message urging caution to the user even when the possibility of the site being fake is low. The warning unit can display a pop-up message to warn the user. The warning unit can also warn the user using an audio alert. The warning unit can adjust the content and display method of the warning message to provide a warning that is easy for the user to understand. This improves user safety by warning the user even when the possibility of the site being fake is low. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input the determination result obtained by the analysis unit into a generation AI and cause the generation AI to display a message urging caution.

[0036] The collection unit can analyze the user's past access history and select the optimal collection method. For example, the collection unit prioritizes collecting information on sites that the user frequently accesses. The collection unit can collect detailed information on sites that the user has not accessed in the past. The collection unit can prioritize collecting information on sites accessed during specific time periods from the user's access history. This makes it possible to select the optimal collection method by analyzing the past access history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past access history into a generation AI, which can then select the optimal collection method.

[0037] When collecting website information, the collection unit can filter the information based on the user's current browsing status and areas of interest. For example, the collection unit collects related information based on the category of the site currently being viewed by the user. The collection unit can preferentially collect information about sites related to the user's areas of interest. The collection unit can filter and collect related information based on the content of the page currently being viewed by the user. This makes it possible to collect highly relevant information by filtering information based on the user's current browsing status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current browsing status and areas of interest into a generation AI, causing the generation AI to filter the information.

[0038] When collecting website information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information related to the area where the user is currently located. The collection unit can collect relevant information based on the user's past location information. If the user is traveling, the collection unit can prioritize collecting information related to the travel destination. This allows highly relevant information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect information.

[0039] When collecting website information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information related to accounts the user follows on social media. The collection unit can collect information related to content the user shared on social media. The collection unit can collect related information based on the user's social media activity history. In this way, the related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into a generation AI and cause the generation AI to collect information.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the website. For example, if the analysis unit determines that the website is an official website, the analysis unit performs a detailed analysis. For an unknown site, the analysis unit can perform a simplified analysis. If the site is determined to be high risk, the analysis unit can perform a detailed analysis. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the website. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the website to the generation AI, causing the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the website. For example, in the case of a shopping site, the analysis unit can apply an algorithm that analyzes the reliability of products. In the case of a news site, the analysis unit can apply an algorithm that analyzes the reliability of information. In the case of a social media site, the analysis unit can apply an algorithm that analyzes the reliability of accounts. By applying different analysis algorithms depending on the category of the website, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the website into the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can determine the analysis priority based on the time of website access. The analysis unit, for example, performs the analysis immediately after a user accesses the website. The analysis unit can perform the analysis a certain amount of time after the user accesses the website. The analysis unit can perform the analysis in advance before the user accesses the website. This enables efficient analysis by determining the analysis priority based on the time of website access. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of website access to the generation AI, causing the generation AI to determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the website. For example, if the analysis unit determines that a website is an official website, the analysis unit performs the analysis first. If the website is unknown, the analysis unit can postpone the analysis. If the website is determined to be high risk, the analysis unit can prioritize the analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the website. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the relevance of the website into the generation AI, causing the generation AI to adjust the order of analysis.

[0044] When displaying a warning message, the warning unit can select the optimal display method by referring to the user's past warning history. For example, the warning unit can re-display warning messages that the user has previously ignored. The warning unit can preferentially display warning messages that the user has previously complied with. The warning unit can select the optimal display method from the user's past warning history. This makes it possible to display the optimal warning message by referring to the user's past warning history. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, AI, for example. For example, the warning unit can input the user's past warning history into a generation AI, and have the generation AI select the optimal display method.

[0045] When displaying a warning message, the warning unit can customize the warning method based on the user's current browsing status. For example, if the user has multiple tabs open, the warning unit can display the warning message as a pop-up. If the user is browsing in full-screen mode, the warning unit can display the warning message at the top of the screen. If the user is watching a video, the warning unit can pause video playback and display the warning message. This allows for appropriate warnings to be provided by customizing the warning method based on the user's current browsing status. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's current browsing status to a generation AI and have the generation AI customize the warning method.

[0046] When displaying a warning message, the warning unit can select the optimal warning method by taking into account the user's geographical location information. For example, when the user is at home, the warning unit can display a normal warning message. When the user is in a public place, the warning unit can display a warning message that takes privacy into consideration. When the user is traveling, the warning unit can display the warning message in the language of the destination. This makes it possible to display an appropriate warning message by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input the user's geographical location information to a generation AI and have the generation AI select a warning method.

[0047] When displaying a warning message, the warning unit can analyze the user's social media activity and suggest warning measures. For example, the warning unit can display a warning message related to a link the user shared on social media. The warning unit can display a warning message related to an account the user follows on social media. The warning unit can display an optimal warning message based on the user's social media activity history. This makes it possible to suggest appropriate warning measures by analyzing the user's social media activity. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's social media activity into a generation AI and have the generation AI suggest warning measures.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The collection unit can analyze the user's past access history and select the optimal collection method. For example, it can prioritize collecting information about sites that the user frequently accesses. The collection unit can collect detailed information about sites that the user has not accessed in the past. The collection unit can prioritize collecting information about sites accessed during specific time periods from the user's access history. This allows the optimal collection method to be selected by analyzing the past access history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past access history into a generation AI, which can then select the optimal collection method.

[0050] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the website. For example, if a website is determined to be an official website, a detailed analysis is performed. If the website is unknown, the analysis unit can perform a simplified analysis. If the website is determined to be high risk, the analysis unit can perform a detailed analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the website. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the website to the generation AI, causing the generation AI to adjust the level of detail of the analysis.

[0051] When collecting website information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, it can prioritize collecting information related to the area where the user is currently located. The collection unit can collect relevant information based on the user's past location information. If the user is traveling, the collection unit can prioritize collecting information related to the travel destination. This allows highly relevant information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect information.

[0052] When displaying a warning message, the warning unit can select the optimal display method by referring to the user's past warning history. For example, the warning unit can re-display warning messages that the user previously ignored. The warning unit can preferentially display warning messages that the user previously followed. The warning unit can select the optimal display method from the user's past warning history. This allows the optimal warning message to be displayed by referring to the user's past warning history. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's past warning history into a generation AI, and have the generation AI select the optimal display method.

[0053] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the website. For example, in the case of a shopping site, the analysis unit can apply an algorithm that analyzes the reliability of products. In the case of a news site, the analysis unit can apply an algorithm that analyzes the reliability of information. In the case of a social media site, the analysis unit can apply an algorithm that analyzes the reliability of accounts. By applying different analysis algorithms depending on the category of the website, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the website into the generation AI and cause the generation AI to apply the analysis algorithm.

[0054] When displaying a warning message, the warning unit can analyze the user's social media activity and suggest warning measures. For example, the warning unit can display a warning message related to a link the user shared on social media. The warning unit can display a warning message related to an account the user follows on social media. The warning unit can display an optimal warning message based on the user's social media activity history. In this way, appropriate warning measures can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's social media activity into a generation AI and have the generation AI suggest warning measures.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The collection unit collects information about websites accessed by users. The collected information includes the website's URL, content, metadata, etc. For example, the collection unit obtains the website's URL and checks whether the URL is similar to that of the official website. The collection unit can also obtain the website's content and check whether the content matches that of the official website. Furthermore, the collection unit can obtain the website's metadata and evaluate the reliability of the metadata. For example, the collection unit analyzes the website's HTML source code and extracts the URL, content, and metadata. Step 2: The analysis unit analyzes the information collected by the collection unit and determines whether the website is a fake site. The analysis unit performs URL pattern matching to determine whether the URL is similar to the official website. The analysis unit can also perform content similarity analysis to determine whether the content matches the official website. The analysis unit can also evaluate the reliability of the metadata to determine whether the metadata is trustworthy. For example, the analysis unit uses regular expressions to perform URL pattern matching to determine whether the URL matches the official website. The analysis unit calculates text similarity to determine whether the content matches the official website. The analysis unit evaluates the origin and update frequency of the metadata to determine whether the metadata is trustworthy. Step 3: The warning unit displays a warning to the user based on the judgment result obtained by the analysis unit. If the warning unit is highly likely to be a fake site, it displays a warning message to the user and urges them to stop accessing the site. The warning unit can also display a message urging caution even if the site is unlikely to be a fake site. For example, the warning unit displays a pop-up message to warn the user. The warning unit can also warn the user using an audio alert. The warning unit can adjust the content and display method of the warning message to provide a warning that is easy for the user to understand.

[0057] (Example 2) A fake site detection system according to an embodiment of the present invention incorporates AI into a device or browser to detect fake sites. This fake site detection system collects information about websites accessed by users, analyzes the collected information, and determines whether the website is fake. Based on the determination, a warning is displayed to the user. This mechanism reduces the risk of users accessing fake sites. For example, information about websites accessed by users is collected. This includes detailed information such as the website's URL, content, and metadata. For example, the system checks whether the website's URL is similar to that of the official website and whether the content matches the official website. The collected information is then analyzed by AI. Based on the collected information, the AI ​​determines whether the website is fake. For example, the AI ​​analyzes URL patterns, content similarity, and metadata reliability to determine whether the website is likely to be fake. Based on the determination, a warning is displayed to the user. For example, if the website is likely to be fake, a warning message is displayed to the user, urging them to discontinue access. Even if the website is unlikely to be fake, a warning message can be displayed. This mechanism reduces the risk of users accessing fake sites. For example, the system can avoid the risk of accessing phishing sites or sites containing malware. Furthermore, even when it is difficult to distinguish between official and fake sites, the AI ​​will automatically make the determination, allowing users to use the internet with peace of mind. This means that the fake site detection system can reduce the risk of users accessing fake sites.

[0058] A fake website detection system according to an embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects information about websites accessed by users. The collected information includes, but is not limited to, the website's URL, content, and metadata. For example, the collection unit acquires the website's URL and checks whether the URL is similar to that of the official website. The collection unit can also acquire the website's content and check whether the content matches that of the official website. The collection unit can also acquire the website's metadata and evaluate the reliability of the metadata. For example, the collection unit analyzes the website's HTML source code to extract the URL, content, and metadata. The analysis unit analyzes the information collected by the collection unit to determine whether the website is a fake website. For example, the analysis unit performs URL pattern matching to determine whether the URL is similar to that of the official website. The analysis unit can also perform content similarity analysis to determine whether the content matches that of the official website. The analysis unit can also evaluate the reliability of the metadata to determine whether the metadata is trustworthy. For example, the analysis unit performs URL pattern matching using regular expressions to determine whether the URL matches that of the official website. The analysis unit calculates the similarity of the text and determines whether the content matches the official website. The analysis unit evaluates the origin and update frequency of the metadata and determines whether the metadata is trustworthy. The warning unit displays a warning to the user based on the determination result obtained by the analysis unit. For example, if there is a high possibility that the site is fake, the warning unit displays a warning message to the user and urges them to stop accessing the site. The warning unit can also display a message urging caution even if there is a low possibility that the site is fake. For example, the warning unit displays a pop-up message to warn the user. The warning unit can also warn the user using an audio alert. The warning unit can adjust the content and display method of the warning message to provide a warning that is easy for the user to understand.As a result, the fake site detection system according to the embodiment can reduce the risk of users accessing fake sites.

[0059] The collection unit can collect information including website URLs, content, and metadata. For example, the collection unit can acquire website URLs and check whether the URLs are similar to those of official websites. The collection unit can also acquire website content and check whether the content matches those of official websites. The collection unit can also acquire website metadata and evaluate the reliability of the metadata. For example, the collection unit can analyze the website's HTML source code and extract the URL, content, and metadata. This allows for more detailed website information to be collected, improving the accuracy of identifying fake websites. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the website URL, content, and metadata into a generation AI, causing the generation AI to collect information.

[0060] The analysis unit can perform URL pattern matching based on the collected information. The analysis unit can perform URL pattern matching using, for example, regular expressions to determine whether the URL matches the official website. The analysis unit can also check whether the domain names match and determine whether the URL is similar to the official website. By performing URL pattern matching, the analysis unit can improve the accuracy of detecting fake sites. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected URLs into a generation AI and have the generation AI perform URL pattern matching.

[0061] The analysis unit can perform a content similarity analysis based on the collected information. The analysis unit, for example, calculates text similarity to determine whether the content matches the official website. The analysis unit can also analyze image features to determine whether the image matches the official website. By performing a content similarity analysis, the analysis unit improves the accuracy of detecting fake sites. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected content into a generation AI and have the generation AI perform a content similarity analysis.

[0062] The analysis unit can evaluate the reliability of the metadata based on the collected information. For example, the analysis unit evaluates the origin and update frequency of the metadata to determine whether the metadata is reliable. By evaluating the reliability of the metadata, the analysis unit improves the accuracy of detecting fake sites. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected metadata into a generation AI and have the generation AI perform a reliability evaluation of the metadata.

[0063] The warning unit can display a warning message to the user if there is a possibility that the site is fake. For example, if there is a high possibility that the site is fake, the warning unit displays a warning message to the user and urges the user to stop accessing the site. The warning unit can display a pop-up message to warn the user. The warning unit can also warn the user using an audio alert. The warning unit can adjust the content and display method of the warning message to provide a warning that is easy for the user to understand. In this way, by displaying a warning message when there is a high possibility that the site is fake, the risk of the user accessing the fake site is reduced. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the determination result obtained by the analysis unit into a generation AI and cause the generation AI to display a warning message.

[0064] The warning unit can display a message urging caution even when the possibility of the site being fake is low. The warning unit can, for example, display a message urging caution to the user even when the possibility of the site being fake is low. The warning unit can display a pop-up message to warn the user. The warning unit can also warn the user using an audio alert. The warning unit can adjust the content and display method of the warning message to provide a warning that is easy for the user to understand. This improves user safety by warning the user even when the possibility of the site being fake is low. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input the determination result obtained by the analysis unit into a generation AI and cause the generation AI to display a message urging caution.

[0065] The collection unit can estimate the user's emotions and adjust the timing of collecting website information based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects website information at a normal timing. When the user is stressed, the collection unit can delay the collection timing to reduce the user's burden. When the user is in a hurry, the collection unit can advance the collection timing to quickly acquire information. This reduces the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing.

[0066] The collection unit can analyze the user's past access history and select the optimal collection method. For example, the collection unit prioritizes collecting information on sites that the user frequently accesses. The collection unit can collect detailed information on sites that the user has not accessed in the past. The collection unit can prioritize collecting information on sites accessed during specific time periods from the user's access history. This makes it possible to select the optimal collection method by analyzing the past access history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past access history into a generation AI, which can then select the optimal collection method.

[0067] When collecting website information, the collection unit can filter the information based on the user's current browsing status and areas of interest. For example, the collection unit collects related information based on the category of the site currently being viewed by the user. The collection unit can preferentially collect information about sites related to the user's areas of interest. The collection unit can filter and collect related information based on the content of the page currently being viewed by the user. This makes it possible to collect highly relevant information by filtering information based on the user's current browsing status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current browsing status and areas of interest into a generation AI, causing the generation AI to filter the information.

[0068] The collection unit can estimate the user's emotions and determine the priority of website information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects information with normal priority. When the user is stressed, the collection unit can prioritize collecting highly important information. When the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. This allows important information to be collected preferentially by determining the priority of information according to 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0069] When collecting website information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information related to the area where the user is currently located. The collection unit can collect relevant information based on the user's past location information. If the user is traveling, the collection unit can prioritize collecting information related to the travel destination. This allows highly relevant information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect information.

[0070] When collecting website information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information related to accounts the user follows on social media. The collection unit can collect information related to content the user shared on social media. The collection unit can collect related information based on the user's social media activity history. In this way, the related information can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activities into a generation AI and cause the generation AI to collect information.

[0071] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is stressed, the analysis unit can display concise analysis results. If the user is in a hurry, the analysis unit can display analysis results that are concise. This allows the analysis results to be easily understood by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the website. For example, if the analysis unit determines that the website is an official website, the analysis unit performs a detailed analysis. For an unknown site, the analysis unit can perform a simplified analysis. If the site is determined to be high risk, the analysis unit can perform a detailed analysis. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the website. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the website to the generation AI, causing the generation AI to adjust the level of detail of the analysis.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the website. For example, in the case of a shopping site, the analysis unit can apply an algorithm that analyzes the reliability of products. In the case of a news site, the analysis unit can apply an algorithm that analyzes the reliability of information. In the case of a social media site, the analysis unit can apply an algorithm that analyzes the reliability of accounts. By applying different analysis algorithms depending on the category of the website, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the website into the generation AI and cause the generation AI to apply the analysis algorithm.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise analysis results. If the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing an appropriate analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0075] During analysis, the analysis unit can determine the analysis priority based on the time of website access. The analysis unit, for example, performs the analysis immediately after a user accesses the website. The analysis unit can perform the analysis a certain amount of time after the user accesses the website. The analysis unit can perform the analysis in advance before the user accesses the website. This enables efficient analysis by determining the analysis priority based on the time of website access. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of website access to the generation AI, causing the generation AI to determine the analysis priority.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the website. For example, if the analysis unit determines that a website is an official website, the analysis unit performs the analysis first. If the website is unknown, the analysis unit can postpone the analysis. If the website is determined to be high risk, the analysis unit can prioritize the analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the website. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the relevance of the website into the generation AI, causing the generation AI to adjust the order of analysis.

[0077] The warning unit can estimate the user's emotions and adjust the display method of the warning message based on the estimated user emotions. For example, when the user is relaxed, the warning unit can display a normal warning message. When the user is stressed, the warning unit can display a concise and easy-to-understand warning message. When the user is in a hurry, the warning unit can display a warning message that can be quickly understood. This allows the user to be provided with a warning that is easy to understand by adjusting the display method of the warning message according to 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method of the warning message.

[0078] When displaying a warning message, the warning unit can select the optimal display method by referring to the user's past warning history. For example, the warning unit can re-display warning messages that the user has previously ignored. The warning unit can preferentially display warning messages that the user has previously complied with. The warning unit can select the optimal display method from the user's past warning history. This makes it possible to display the optimal warning message by referring to the user's past warning history. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, AI, for example. For example, the warning unit can input the user's past warning history into a generation AI, and have the generation AI select the optimal display method.

[0079] When displaying a warning message, the warning unit can customize the warning method based on the user's current browsing status. For example, if the user has multiple tabs open, the warning unit can display the warning message as a pop-up. If the user is browsing in full-screen mode, the warning unit can display the warning message at the top of the screen. If the user is watching a video, the warning unit can pause video playback and display the warning message. This allows for appropriate warnings to be provided by customizing the warning method based on the user's current browsing status. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's current browsing status to a generation AI and have the generation AI customize the warning method.

[0080] The warning unit can estimate the user's emotions and determine the priority of warning messages based on the estimated user emotions. For example, when the user is relaxed, the warning unit can display warning messages with normal priority. When the user is stressed, the warning unit can prioritize displaying warning messages with high importance. When the user is in a hurry, the warning unit can prioritize displaying warning messages that can be displayed quickly. This allows important warnings to be displayed preferentially by determining the priority of warning messages according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the warning unit can input user emotion data into the generation AI and have the generation AI determine the priority of warning messages.

[0081] When displaying a warning message, the warning unit can select the optimal warning method by taking into account the user's geographical location information. For example, when the user is at home, the warning unit can display a normal warning message. When the user is in a public place, the warning unit can display a warning message that takes privacy into consideration. When the user is traveling, the warning unit can display the warning message in the language of the destination. This makes it possible to display an appropriate warning message by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input the user's geographical location information to a generation AI and have the generation AI select a warning method.

[0082] When displaying a warning message, the warning unit can analyze the user's social media activity and suggest warning measures. For example, the warning unit can display a warning message related to a link the user shared on social media. The warning unit can display a warning message related to an account the user follows on social media. The warning unit can display an optimal warning message based on the user's social media activity history. This makes it possible to suggest appropriate warning measures by analyzing the user's social media activity. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's social media activity into a generation AI and have the generation AI suggest warning measures. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and warning unit described above may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit may collect website URLs, content, and metadata using the control unit 46A of the smart device 14. The analysis unit may analyze the information collected by the identification processing unit 290 of the data processing device 12 and determine whether the site is fake. The warning unit may display a warning message to the user using the output device 40 of the smart device 14. Some or all of the collection unit, analysis unit, and warning unit may be realized, for example, by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and warning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect website URLs, content, and metadata by the control unit 46A of the smart glasses 214. The analysis unit can analyze the information collected by, for example, the identification processing unit 290 of the data processing device 12 and determine whether the site is fake. The warning unit can provide an audio warning message to the user by, for example, the speaker 240 of the smart glasses 214. Some or all of the collection unit, analysis unit, and warning unit may be realized, for example, by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and warning unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect website URLs, content, and metadata by the control unit 46A of the headset type terminal 314. The analysis unit can analyze the information collected by, for example, the identification processing unit 290 of the data processing device 12 and determine whether the site is fake. The warning unit can display a warning message to the user on, for example, the display 343 of the headset type terminal 314. Some or all of the collection unit, analysis unit, and warning unit may be realized, for example, by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and warning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect website URLs, content, and metadata by the control unit 46A of the robot 414. The analysis unit can analyze the information collected by, for example, the identification processing unit 290 of the data processing device 12 and determine whether the site is fake. The warning unit can provide a warning message to the user by voice by, for example, the speaker 240 of the robot 414. Some or all of the collection unit, analysis unit, and warning unit may be realized, for example, by the identification processing unit 290 of the data processing device 12.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is relaxed, analysis can be performed with normal priority. If the user is stressed, analysis of high importance can be prioritized. If the user is in a hurry, information that can be analyzed quickly can be prioritized. This allows important information to be analyzed preferentially by determining the analysis priority according to 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0085] The collection unit can analyze the user's past access history and select the optimal collection method. For example, it can prioritize collecting information about sites that the user frequently accesses. The collection unit can collect detailed information about sites that the user has not accessed in the past. The collection unit can prioritize collecting information about sites accessed during specific time periods from the user's access history. This allows the optimal collection method to be selected by analyzing the past access history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past access history into a generation AI, which can then select the optimal collection method.

[0086] The warning unit can estimate the user's emotions and adjust the display method of the warning message based on the estimated user emotions. For example, if the user is relaxed, a normal warning message can be displayed. If the user is stressed, a concise and easy-to-understand warning message can be displayed. If the user is in a hurry, a warning message that can be quickly understood can be displayed. This allows the user to easily understand the warning by adjusting the display method of the warning message according to 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method of the warning message.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the website. For example, if a website is determined to be an official website, a detailed analysis is performed. If the website is unknown, the analysis unit can perform a simplified analysis. If the website is determined to be high risk, the analysis unit can perform a detailed analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the website. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the website to the generation AI, causing the generation AI to adjust the level of detail of the analysis.

[0088] When collecting website information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, it can prioritize collecting information related to the area where the user is currently located. The collection unit can collect relevant information based on the user's past location information. If the user is traveling, the collection unit can prioritize collecting information related to the travel destination. This allows highly relevant information to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect information.

[0089] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be displayed. If the user is stressed, concise analysis results can be displayed. If the user is in a hurry, analysis results that focus on the main points can be displayed. By adjusting the way the analysis is presented according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0090] When displaying a warning message, the warning unit can select the optimal display method by referring to the user's past warning history. For example, the warning unit can re-display warning messages that the user previously ignored. The warning unit can preferentially display warning messages that the user previously followed. The warning unit can select the optimal display method from the user's past warning history. This allows the optimal warning message to be displayed by referring to the user's past warning history. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's past warning history into a generation AI, and have the generation AI select the optimal display method.

[0091] The collection unit can estimate the user's emotions and prioritize the website information to be collected based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can collect information with normal priority. If the user is stressed, the collection unit can prioritize collecting information of high importance. If the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. This allows important information to be collected preferentially by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0092] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the website. For example, in the case of a shopping site, the analysis unit can apply an algorithm that analyzes the reliability of products. In the case of a news site, the analysis unit can apply an algorithm that analyzes the reliability of information. In the case of a social media site, the analysis unit can apply an algorithm that analyzes the reliability of accounts. By applying different analysis algorithms depending on the category of the website, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the website into the generation AI and cause the generation AI to apply the analysis algorithm.

[0093] When displaying a warning message, the warning unit can analyze the user's social media activity and suggest warning measures. For example, the warning unit can display a warning message related to a link the user shared on social media. The warning unit can display a warning message related to an account the user follows on social media. The warning unit can display an optimal warning message based on the user's social media activity history. In this way, appropriate warning measures can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's social media activity into a generation AI and have the generation AI suggest warning measures.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The collection unit collects information about websites accessed by users. The collected information includes the website's URL, content, metadata, etc. For example, the collection unit obtains the website's URL and checks whether the URL is similar to that of the official website. The collection unit can also obtain the website's content and check whether the content matches that of the official website. Furthermore, the collection unit can obtain the website's metadata and evaluate the reliability of the metadata. For example, the collection unit analyzes the website's HTML source code and extracts the URL, content, and metadata. Step 2: The analysis unit analyzes the information collected by the collection unit and determines whether the website is a fake site. The analysis unit performs URL pattern matching to determine whether the URL is similar to the official website. The analysis unit can also perform content similarity analysis to determine whether the content matches the official website. The analysis unit can also evaluate the reliability of the metadata to determine whether the metadata is trustworthy. For example, the analysis unit uses regular expressions to perform URL pattern matching to determine whether the URL matches the official website. The analysis unit calculates text similarity to determine whether the content matches the official website. The analysis unit evaluates the origin and update frequency of the metadata to determine whether the metadata is trustworthy. Step 3: The warning unit displays a warning to the user based on the judgment result obtained by the analysis unit. If the warning unit is highly likely to be a fake site, it displays a warning message to the user and urges them to stop accessing the site. The warning unit can also display a message urging caution even if the site is unlikely to be a fake site. For example, the warning unit displays a pop-up message to warn the user. The warning unit can also warn the user using an audio alert. The warning unit can adjust the content and display method of the warning message to provide a warning that is easy for the user to understand.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0098] 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.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Explanation of symbols]

[0168] 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 collection unit that collects website information; an analysis unit that analyzes the information collected by the collection unit and determines whether a website is a fake site; a warning unit that displays a warning to a user based on the determination result obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect information including website URLs, content, and metadata The system of claim 1 .

3. The analysis unit Based on the collected information, URL pattern matching is performed. The system of claim 1 .

4. The analysis unit Based on the collected information, content matching analysis is performed. The system of claim 1 .

5. The analysis unit Based on the collected information, the reliability of the metadata is evaluated. The system of claim 1 .

6. The warning unit If the site is suspected to be fake, a warning message will be displayed to the user. The system of claim 1 .

7. The warning unit Even if the site is unlikely to be fake, a warning message will be displayed. The system of claim 1 .

8. The collecting unit To estimate a user's emotions and adjust the timing of collecting website information based on the estimated user's emotions. The system of claim 1 .

Images