system

Abstract

The system according to this embodiment aims to evaluate the reliability of information obtained from the Internet and provide it to users. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a display unit. The reception unit receives keyword input from the user. The analysis unit analyzes the search result web pages based on the keywords received by the reception unit and evaluates the reliability of their content. The display unit displays the reliability evaluated by the analysis unit in the search results.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a general user to determine the reliability of information obtained on the Internet.

[0005] The system according to the embodiment aims to evaluate the reliability of information obtained on the Internet and provide it to the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and a display unit. The reception unit receives keyword input from the user. The analysis unit analyzes the search result web pages based on the keywords received by the reception unit and evaluates the reliability of their content. The display unit displays the reliability evaluated by the analysis unit in the search results. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the reliability of information obtained from the internet and provide it to the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The reliability evaluation system according to an embodiment of the present invention is a system that uses generative AI to evaluate the credibility of information obtained by searching the internet. This reliability evaluation system operates when a user enters keywords into a search engine. The generative AI analyzes the web pages of the search results and evaluates the reliability of their content. The evaluation criteria include whether technical terms are used correctly and whether the information is based on reliable sources. The evaluation results are displayed in the search results, allowing the user to check the reliability of the information. For example, a user enters keywords such as "health information" or "financial information." This information is input into the generative AI. Next, the generative AI analyzes the web pages of the search results based on the entered keywords. The generative AI analyzes the content of the web pages and evaluates the usage of technical terms and the reliability of the information sources. For example, in the case of medical information, it evaluates whether technical terms are used correctly and whether the information is based on reliable medical institutions. The evaluation results are displayed in the search results. For example, a highly reliable web page will display "Reliability: High," and a low-reliability web page will display "Reliability: Low." This allows the user to check the reliability of the information by looking at the search results. This system eliminates the need for users to verify the reliability of information, protecting them from unreliable sources. Furthermore, implementing this system in search engines differentiates them from competitors, enabling them to provide users with more trustworthy information. For example, when a user searches for "health information," the generating AI analyzes the search results web pages and evaluates their reliability. Highly reliable web pages display "Reliability: High," allowing users to use the information with confidence. Conversely, unreliable web pages display "Reliability: Low," allowing users to avoid that information. In this way, a web page reliability evaluation system utilizing generating AI allows users to easily verify the reliability of information obtained on the internet, reducing the risk of misinformation. Thus, the reliability evaluation system enables users to easily verify the reliability of information obtained on the internet, reducing the risk of misinformation.

[0029] The reliability evaluation system according to the embodiment comprises a reception unit, an analysis unit, and a display unit. The reception unit receives keyword input from the user. The reception unit operates, for example, when the user enters keywords into a search engine. The reception unit inputs the keywords into a generation AI. The analysis unit analyzes the search result web pages based on the keywords received by the reception unit and evaluates the reliability of their content. The analysis unit analyzes the search result web pages, for example, using a generation AI. The analysis unit evaluates whether technical terms are used correctly, whether the information is based on reliable sources, etc. For example, in the case of medical information, it evaluates whether technical terms are used correctly and whether the information is based on reliable medical institutions. The analysis unit analyzes the content of the web pages using a generation AI and evaluates the usage of technical terms and the reliability of the information sources. The display unit displays the reliability evaluated by the analysis unit in the search results. The display unit displays, for example, "Reliability: High" for highly reliable web pages and "Reliability: Low" for unreliable web pages. The display unit allows the user to view the search results and confirm the reliability of the information. This allows the reliability evaluation system according to the embodiment to easily verify the reliability of search results. Some or all of the above-described processes in the analysis unit are performed using a generating AI. For example, the analysis unit inputs the search result web page into the generating AI, which analyzes the content of the web page and evaluates the usage of technical terms and the reliability of information sources. Some or all of the above-described processes in the display unit may be performed using AI or not. For example, the display unit inputs the reliability evaluated by the analysis unit into an AI model, which then displays the reliability.

[0030] The reception unit receives keyword input from users. Specifically, it operates when a user enters keywords into a search engine. For example, if a user enters keywords such as "latest medical technology" or "reliable news sites," the reception unit receives the keywords and proceeds to the next step. The reception unit inputs the keywords into a generating AI. This generating AI uses natural language processing technology to analyze the meaning of the keywords and generates prompts to obtain relevant search results. For example, the generating AI understands the context of the keywords and constructs appropriate search queries. This allows the reception unit to efficiently process the keywords entered by the user and prepare them for passing on to the next analysis unit. Furthermore, the reception unit can learn from the user's input history and past search patterns to perform more accurate keyword analysis. For example, if a user has frequently searched for "medical" related keywords in the past, the reception unit takes that information into consideration and inputs prompts into the generating AI to provide more relevant search results. This allows the reception unit to respond flexibly to user needs and improve the overall efficiency and accuracy of the system.

[0031] The analysis department analyzes web pages in search results based on keywords received by the reception department and evaluates the reliability of their content. Specifically, it uses generative AI to analyze web pages in search results. The generative AI utilizes natural language processing technology to analyze the content of web pages in detail. For example, the generative AI analyzes the text on the web page and evaluates whether technical terms are used correctly. In the case of medical information, the generative AI checks whether medical terminology is used accurately and whether it is based on information from a reliable medical institution. Furthermore, the generative AI evaluates the information sources of the web page and determines whether they are based on reliable sources. For example, information from academic papers or public institution websites is evaluated as highly reliable. On the other hand, information from personal blogs or unreliable sources is evaluated as unreliable. Based on these evaluation criteria, the analysis department makes a comprehensive judgment on the reliability of the web page. Furthermore, the analysis department can also utilize historical data and statistical information to conduct long-term reliability evaluations. For example, by analyzing the reliability of information that a particular website has provided in the past and understanding the trends, it can predict future reliability. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only perform real-time reliability assessments but also long-term risk management and anomaly detection, thereby improving the overall reliability and safety of the system.

[0032] The display unit shows the reliability evaluated by the analysis unit in the search results. Specifically, it displays "Reliability: High" for highly reliable web pages and "Reliability: Low" for less reliable web pages. The display unit allows users to check the reliability of information when viewing search results. For example, it displays the reliability rating next to each link in the search results, allowing users to judge reliability at a glance. Furthermore, the display unit can also provide detailed information about the reliability rating. For example, clicking on the details of a web page displayed as "Reliability: High" displays the evaluation criteria and detailed analysis results. This allows users to understand why that web page was rated as highly reliable. The display unit can collect user feedback and continuously improve the accuracy and effectiveness of the reliability rating. For example, by having users actually view web pages rated as "Reliability: High" and provide feedback on whether they were satisfied with the content, the evaluation criteria of the analysis unit can be reviewed. In addition, the display unit supports multiple devices and platforms, allowing users to check the reliability rating from any device. For example, it optimizes the display format for different devices such as smartphones, tablets, and desktop computers to improve the user experience. This allows the display unit to provide users with a quick and accurate reliability assessment, making it easy to verify the reliability of the information.

[0033] The analysis unit has a function to refer to a specialized terminology dictionary in order to evaluate whether the usage of specialized terminology is correct. For example, the analysis unit uses a generating AI to refer to the specialized terminology dictionary and evaluate whether the usage of specialized terminology is correct. The analysis unit can refer to specialized terminology dictionaries such as medical terminology dictionaries and technical terminology dictionaries. For example, the analysis unit refers to a medical terminology dictionary and evaluates whether the usage of specialized terminology in medical information is correct. The analysis unit also refers to a technical terminology dictionary and evaluates whether the usage of specialized terminology in technical information is correct. In this way, the analysis unit can evaluate the accurate use of specialized terminology. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs a specialized terminology dictionary into the generating AI, and the generating AI evaluates the usage of the specialized terminology.

[0034] The analysis unit has a function to refer to a list of reliable sources in order to evaluate the reliability of the information source. The analysis unit, for example, uses a generative AI to refer to the list of reliable sources and evaluate the reliability of the information source. The analysis unit can refer to lists of reliable sources such as government agency websites and academic papers. For example, the analysis unit can refer to government agency websites and evaluate the reliability of the information source. The analysis unit can also refer to academic papers and evaluate the reliability of the information source. This allows the analysis unit to make evaluations based on reliable sources. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs a list of reliable sources into the generative AI, and the generative AI evaluates the reliability of the information source.

[0035] The display unit has the function of displaying "Reliability: High" for highly reliable web pages and "Reliability: Low" for unreliable web pages. For example, the display unit displays "Reliability: High" for highly reliable web pages, allowing users to use the information with confidence. Also, the display unit displays "Reliability: Low" for unreliable web pages, allowing users to avoid that information. The display unit needs to clearly define the reliability evaluation criteria and specific display methods. For example, the display unit displays reliability using evaluation scores or reliability indicators. This allows the display unit to easily identify highly reliable information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit inputs the reliability evaluation results into an AI model, and the AI ​​model displays the reliability.

[0036] The reception desk can analyze the user's past search history and suggest the most suitable keywords. For example, the reception desk can automatically display keywords that the user has frequently searched for in the past as suggestions. The reception desk can also prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest keywords to be used at specific times based on the user's past search history. This allows the reception desk to suggest the most suitable keywords based on the user's past search history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's search history data into an AI model, and the AI ​​model can suggest the most suitable keywords.

[0037] The input system can automatically complete relevant keywords based on the user's current areas of interest when keywords are entered. For example, the input system can suggest relevant keywords based on topics the user has recently searched for. It can also automatically complete relevant keywords based on the content of the web page the user is currently viewing. Furthermore, the input system can analyze the user's social media activity and suggest relevant keywords. This allows the input system to automatically complete relevant keywords based on the user's areas of interest. Some or all of the above processing in the input system may be performed using AI or not. For example, the input system can input the user's areas of interest data into an AI model, and the AI ​​model can automatically complete relevant keywords.

[0038] The reception desk can prioritize and present highly relevant keywords when the user enters keywords, taking into account the user's geographical location. For example, the reception desk can suggest keywords related to the user's region based on the user's current location. Furthermore, if the user is traveling, the reception desk can prioritize displaying keywords related to their destination. In addition, the reception desk can analyze the user's past location data and suggest relevant keywords. This allows the reception desk to present highly relevant keywords based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's location data into an AI model, and the AI ​​model presents highly relevant keywords.

[0039] The reception desk can analyze the user's social media activity when keywords are entered and suggest relevant keywords. For example, the reception desk can suggest relevant keywords based on the user's recent posts. It can also display relevant keywords based on the topics of accounts the user follows. Furthermore, the reception desk can analyze the user's social media activity history and suggest relevant keywords. This allows the reception desk to suggest relevant keywords based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI model, and the AI ​​model can suggest relevant keywords.

[0040] The analysis unit can evaluate reliability by considering the update frequency of web pages during analysis. For example, the analysis unit can evaluate web pages that are frequently updated as highly reliable. Conversely, the analysis unit can evaluate web pages that are not frequently updated as less reliable. Furthermore, the analysis unit can also verify the consistency between update frequency and content and evaluate reliability. This allows the analysis unit to evaluate reliability based on the update frequency of web pages. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs web page update history data into the generation AI, which evaluates the update frequency and then evaluates reliability.

[0041] The analysis unit can refer to an author's past writing history to assess the author's expertise during analysis. For example, the analysis unit can evaluate the reliability of articles the author has written in the past. The analysis unit can also evaluate reliability based on the author's field of expertise. Furthermore, the analysis unit can analyze the author's past writing history and evaluate reliability. This allows the analysis unit to evaluate reliability based on the author's expertise. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the author's writing history data into the generative AI, which then evaluates the author's expertise and reliability.

[0042] The analysis unit can evaluate reliability by considering the geographical information of a web page during analysis. For example, the analysis unit may evaluate geographically close information sources as highly reliable. Conversely, it may evaluate geographically distant information sources as less reliable. Furthermore, the analysis unit can also verify the consistency between geographical information and content and evaluate reliability. This allows the analysis unit to evaluate reliability based on geographical information. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs geographical information data of a web page into the generative AI, which evaluates the geographical information and assesses reliability.

[0043] The analysis unit can evaluate the reliability of a webpage by referring to related literature during the analysis. For example, the analysis unit can evaluate a webpage with many related documents as highly reliable. Conversely, the analysis unit can evaluate a webpage with few related documents as less reliable. Furthermore, the analysis unit can also evaluate the quality of the related documents and assess their reliability. This allows the analysis unit to evaluate reliability based on related literature. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs related literature data into the generation AI, which evaluates the related documents and assesses their reliability.

[0044] The display unit can select the optimal display method by referring to the user's past browsing history when displaying information. For example, the display unit may prioritize displaying web pages that the user has frequently viewed in the past. The display unit can also suggest the optimal display method based on the user's past browsing history. Furthermore, the display unit can analyze the user's past browsing history and display relevant information. This allows the display unit to provide the optimal display method based on the user's past browsing history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit may input the user's browsing history data into an AI model, and the AI ​​model may select the optimal display method.

[0045] The display unit can apply different display formats depending on the category of the web page when displaying it. For example, in the case of medical information, the display unit can provide a display format that includes explanations of technical terms. In the case of financial information, the display unit can also provide a display format that includes graphs and charts. Furthermore, in the case of entertainment information, the display unit can provide a visually appealing display format. In this way, the display unit can provide a display format that is appropriate for the category of the web page. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit inputs web page category data into an AI model, and the AI ​​model applies different display formats.

[0046] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit inputs user device information data into an AI model, and the AI ​​model selects the optimal display method.

[0047] The display unit can analyze the user's social media activity and display relevant information when displaying information. For example, the display unit can display relevant information based on the user's recent posts. It can also display relevant information based on the topics of accounts the user follows. Furthermore, the display unit can analyze the user's social media activity history and display relevant information. This allows the display unit to provide relevant information based on the user's social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit inputs the user's social media data into an AI model, and the AI ​​model displays relevant information.

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

[0049] The analysis unit can evaluate the readability of a web page when analyzing its content. For example, it can evaluate the length and complexity of the text, the frequency of technical terms, and calculate a readability score. It can also consider the page layout and design elements to evaluate visual readability. Furthermore, the analysis unit can set appropriate readability standards based on the user's age and education level. This allows the analysis unit to conduct reliability assessments that take readability into account, ensuring users can easily understand the information.

[0050] The analysis unit can consider the page's update history when analyzing the content of a web page. For example, the analysis unit can evaluate whether a page is frequently updated and rate pages with high update frequency as more reliable. The analysis unit can also analyze the content of page updates and evaluate the freshness and accuracy of the information. Furthermore, the analysis unit can verify the consistency between the page's update history and its content and evaluate its reliability. This allows the analysis unit to assess reliability based on the web page's update history.

[0051] The reception desk can analyze the user's past search history and suggest the most suitable keywords. For example, it can automatically display keywords that the user has frequently searched in the past as suggestions. The reception desk can also prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest keywords that the user will use at specific times of day based on their past search history. This allows the reception desk to suggest the most suitable keywords based on the user's past search history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's search history data into an AI model, and the AI ​​model can suggest the most suitable keywords.

[0052] The analysis unit can evaluate the expertise of the page's author when analyzing the content of a webpage. For example, the analysis unit can evaluate the reliability of articles previously written by the author. Furthermore, the analysis unit can evaluate reliability based on the author's area of ​​expertise. In addition, the analysis unit can analyze the author's past writing history and evaluate its reliability. This allows the analysis unit to evaluate reliability based on the author's expertise. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the author's writing history data into the generative AI, which then evaluates the author's expertise and reliability.

[0053] The analysis unit can evaluate the reliability of a webpage by referring to related literature when analyzing the content of the webpage. For example, it can evaluate a webpage with many related documents as highly reliable. Conversely, the analysis unit can evaluate a webpage with few related documents as less reliable. Furthermore, the analysis unit can evaluate the quality of the related documents and assess their reliability. This allows the analysis unit to evaluate reliability based on related literature. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs related literature data into the generation AI, which evaluates the related documents and assesses their reliability.

[0054] The reception desk can prioritize and present highly relevant keywords when the user enters keywords, taking into account the user's geographical location. For example, it can suggest keywords related to the user's region based on the user's current location. Furthermore, if the user is traveling, the reception desk can prioritize displaying keywords related to their destination. In addition, the reception desk can analyze the user's past location data and suggest relevant keywords. This allows the reception desk to present highly relevant keywords based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's location data into an AI model, and the AI ​​model presents highly relevant keywords.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk receives keyword input from the user. For example, it operates when the user enters keywords into a search engine, and then inputs them into the keyword generation AI. Step 2: The analysis unit analyzes the search result web pages based on the keywords received by the reception unit and evaluates the reliability of their content. For example, it uses generative AI to evaluate whether technical terms are used correctly and whether they are based on reliable sources. Step 3: The display unit displays the reliability evaluated by the analysis unit in the search results. For example, it displays "Reliability: High" for highly reliable web pages and "Reliability: Low" for less reliable web pages.

[0057] (Example of form 2) The reliability evaluation system according to an embodiment of the present invention is a system that uses generative AI to evaluate the credibility of information obtained by searching the internet. This reliability evaluation system operates when a user enters keywords into a search engine. The generative AI analyzes the web pages of the search results and evaluates the reliability of their content. The evaluation criteria include whether technical terms are used correctly and whether the information is based on reliable sources. The evaluation results are displayed in the search results, allowing the user to check the reliability of the information. For example, a user enters keywords such as "health information" or "financial information." This information is input into the generative AI. Next, the generative AI analyzes the web pages of the search results based on the entered keywords. The generative AI analyzes the content of the web pages and evaluates the usage of technical terms and the reliability of the information sources. For example, in the case of medical information, it evaluates whether technical terms are used correctly and whether the information is based on reliable medical institutions. The evaluation results are displayed in the search results. For example, a highly reliable web page will display "Reliability: High," and a low-reliability web page will display "Reliability: Low." This allows the user to check the reliability of the information by looking at the search results. This system eliminates the need for users to verify the reliability of information, protecting them from unreliable sources. Furthermore, implementing this system in search engines differentiates them from competitors, enabling them to provide users with more trustworthy information. For example, when a user searches for "health information," the generating AI analyzes the search results web pages and evaluates their reliability. Highly reliable web pages display "Reliability: High," allowing users to use the information with confidence. Conversely, unreliable web pages display "Reliability: Low," allowing users to avoid that information. In this way, a web page reliability evaluation system utilizing generating AI allows users to easily verify the reliability of information obtained on the internet, reducing the risk of misinformation. Thus, the reliability evaluation system enables users to easily verify the reliability of information obtained on the internet, reducing the risk of misinformation.

[0058] The reliability evaluation system according to the embodiment comprises a reception unit, an analysis unit, and a display unit. The reception unit receives keyword input from the user. The reception unit operates, for example, when the user enters keywords into a search engine. The reception unit inputs the keywords into a generation AI. The analysis unit analyzes the search result web pages based on the keywords received by the reception unit and evaluates the reliability of their content. The analysis unit analyzes the search result web pages, for example, using a generation AI. The analysis unit evaluates whether technical terms are used correctly, whether the information is based on reliable sources, etc. For example, in the case of medical information, it evaluates whether technical terms are used correctly and whether the information is based on reliable medical institutions. The analysis unit analyzes the content of the web pages using a generation AI and evaluates the usage of technical terms and the reliability of the information sources. The display unit displays the reliability evaluated by the analysis unit in the search results. The display unit displays, for example, "Reliability: High" for highly reliable web pages and "Reliability: Low" for unreliable web pages. The display unit allows the user to view the search results and confirm the reliability of the information. This allows the reliability evaluation system according to the embodiment to easily verify the reliability of search results. Some or all of the above-described processes in the analysis unit are performed using a generating AI. For example, the analysis unit inputs the search result web page into the generating AI, which analyzes the content of the web page and evaluates the usage of technical terms and the reliability of information sources. Some or all of the above-described processes in the display unit may be performed using AI or not. For example, the display unit inputs the reliability evaluated by the analysis unit into an AI model, which then displays the reliability.

[0059] The reception unit receives keyword input from users. Specifically, it operates when a user enters keywords into a search engine. For example, if a user enters keywords such as "latest medical technology" or "reliable news sites," the reception unit receives the keywords and proceeds to the next step. The reception unit inputs the keywords into a generating AI. This generating AI uses natural language processing technology to analyze the meaning of the keywords and generates prompts to obtain relevant search results. For example, the generating AI understands the context of the keywords and constructs appropriate search queries. This allows the reception unit to efficiently process the keywords entered by the user and prepare them for passing on to the next analysis unit. Furthermore, the reception unit can learn from the user's input history and past search patterns to perform more accurate keyword analysis. For example, if a user has frequently searched for "medical" related keywords in the past, the reception unit takes that information into consideration and inputs prompts into the generating AI to provide more relevant search results. This allows the reception unit to respond flexibly to user needs and improve the overall efficiency and accuracy of the system.

[0060] The analysis department analyzes web pages in search results based on keywords received by the reception department and evaluates the reliability of their content. Specifically, it uses generative AI to analyze web pages in search results. The generative AI utilizes natural language processing technology to analyze the content of web pages in detail. For example, the generative AI analyzes the text on the web page and evaluates whether technical terms are used correctly. In the case of medical information, the generative AI checks whether medical terminology is used accurately and whether it is based on information from a reliable medical institution. Furthermore, the generative AI evaluates the information sources of the web page and determines whether they are based on reliable sources. For example, information from academic papers or public institution websites is evaluated as highly reliable. On the other hand, information from personal blogs or unreliable sources is evaluated as unreliable. Based on these evaluation criteria, the analysis department makes a comprehensive judgment on the reliability of the web page. Furthermore, the analysis department can also utilize historical data and statistical information to conduct long-term reliability evaluations. For example, by analyzing the reliability of information that a particular website has provided in the past and understanding the trends, it can predict future reliability. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only perform real-time reliability assessments but also long-term risk management and anomaly detection, thereby improving the overall reliability and safety of the system.

[0061] The display unit shows the reliability evaluated by the analysis unit in the search results. Specifically, it displays "Reliability: High" for highly reliable web pages and "Reliability: Low" for less reliable web pages. The display unit allows users to check the reliability of information when viewing search results. For example, it displays the reliability rating next to each link in the search results, allowing users to judge reliability at a glance. Furthermore, the display unit can also provide detailed information about the reliability rating. For example, clicking on the details of a web page displayed as "Reliability: High" displays the evaluation criteria and detailed analysis results. This allows users to understand why that web page was rated as highly reliable. The display unit can collect user feedback and continuously improve the accuracy and effectiveness of the reliability rating. For example, by having users actually view web pages rated as "Reliability: High" and provide feedback on whether they were satisfied with the content, the evaluation criteria of the analysis unit can be reviewed. In addition, the display unit supports multiple devices and platforms, allowing users to check the reliability rating from any device. For example, it optimizes the display format for different devices such as smartphones, tablets, and desktop computers to improve the user experience. This allows the display unit to provide users with a quick and accurate reliability assessment, making it easy to verify the reliability of the information.

[0062] The analysis unit has a function to refer to a specialized terminology dictionary in order to evaluate whether the usage of specialized terminology is correct. For example, the analysis unit uses a generating AI to refer to the specialized terminology dictionary and evaluate whether the usage of specialized terminology is correct. The analysis unit can refer to specialized terminology dictionaries such as medical terminology dictionaries and technical terminology dictionaries. For example, the analysis unit refers to a medical terminology dictionary and evaluates whether the usage of specialized terminology in medical information is correct. The analysis unit also refers to a technical terminology dictionary and evaluates whether the usage of specialized terminology in technical information is correct. In this way, the analysis unit can evaluate the accurate use of specialized terminology. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs a specialized terminology dictionary into the generating AI, and the generating AI evaluates the usage of the specialized terminology.

[0063] The analysis unit has a function to refer to a list of reliable sources in order to evaluate the reliability of the information source. The analysis unit, for example, uses a generative AI to refer to the list of reliable sources and evaluate the reliability of the information source. The analysis unit can refer to lists of reliable sources such as government agency websites and academic papers. For example, the analysis unit can refer to government agency websites and evaluate the reliability of the information source. The analysis unit can also refer to academic papers and evaluate the reliability of the information source. This allows the analysis unit to make evaluations based on reliable sources. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs a list of reliable sources into the generative AI, and the generative AI evaluates the reliability of the information source.

[0064] The display unit has the function of displaying "Reliability: High" for highly reliable web pages and "Reliability: Low" for unreliable web pages. For example, the display unit displays "Reliability: High" for highly reliable web pages, allowing users to use the information with confidence. Also, the display unit displays "Reliability: Low" for unreliable web pages, allowing users to avoid that information. The display unit needs to clearly define the reliability evaluation criteria and specific display methods. For example, the display unit displays reliability using evaluation scores or reliability indicators. This allows the display unit to easily identify highly reliable information. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit inputs the reliability evaluation results into an AI model, and the AI ​​model displays the reliability.

[0065] The reception desk can estimate the user's emotions and adjust the keyword input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick keyword entry. In this way, the reception desk can provide an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's facial expression data into the generative AI, which estimates the emotions and adjusts the interface.

[0066] The reception desk can analyze the user's past search history and suggest the most suitable keywords. For example, the reception desk can automatically display keywords that the user has frequently searched for in the past as suggestions. The reception desk can also prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest keywords to be used at specific times based on the user's past search history. This allows the reception desk to suggest the most suitable keywords based on the user's past search history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's search history data into an AI model, and the AI ​​model can suggest the most suitable keywords.

[0067] The input system can automatically complete relevant keywords based on the user's current areas of interest when keywords are entered. For example, the input system can suggest relevant keywords based on topics the user has recently searched for. It can also automatically complete relevant keywords based on the content of the web page the user is currently viewing. Furthermore, the input system can analyze the user's social media activity and suggest relevant keywords. This allows the input system to automatically complete relevant keywords based on the user's areas of interest. Some or all of the above processing in the input system may be performed using AI or not. For example, the input system can input the user's areas of interest data into an AI model, and the AI ​​model can automatically complete relevant keywords.

[0068] The reception desk can estimate the user's emotions and determine the priority of keyword input based on the estimated emotions. For example, if the user is nervous, the reception desk can prioritize displaying simple and highly visible keywords. If the user is relaxed, the reception desk can provide more detailed keyword options. Furthermore, if the user is in a hurry, the reception desk can quickly display the most relevant keywords. This allows the reception desk to determine the priority of keyword input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's facial expression data into a generative AI, which estimates the emotions and determines the priority of keyword input.

[0069] The reception desk can prioritize and present highly relevant keywords when the user enters keywords, taking into account the user's geographical location. For example, the reception desk can suggest keywords related to the user's region based on the user's current location. Furthermore, if the user is traveling, the reception desk can prioritize displaying keywords related to their destination. In addition, the reception desk can analyze the user's past location data and suggest relevant keywords. This allows the reception desk to present highly relevant keywords based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's location data into an AI model, and the AI ​​model presents highly relevant keywords.

[0070] The reception desk can analyze the user's social media activity when keywords are entered and suggest relevant keywords. For example, the reception desk can suggest relevant keywords based on the user's recent posts. It can also display relevant keywords based on the topics of accounts the user follows. Furthermore, the reception desk can analyze the user's social media activity history and suggest relevant keywords. This allows the reception desk to suggest relevant keywords based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI model, and the AI ​​model can suggest relevant keywords.

[0071] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide reliable information. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise information. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. In this way, the analysis unit can adjust the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, the generative AI estimates the emotions, and adjusts the accuracy of the analysis.

[0072] The analysis unit can evaluate reliability by considering the update frequency of web pages during analysis. For example, the analysis unit can evaluate web pages that are frequently updated as highly reliable. Conversely, the analysis unit can evaluate web pages that are not frequently updated as less reliable. Furthermore, the analysis unit can also verify the consistency between update frequency and content and evaluate reliability. This allows the analysis unit to evaluate reliability based on the update frequency of web pages. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs web page update history data into the generation AI, which evaluates the update frequency and then evaluates reliability.

[0073] The analysis unit can refer to an author's past writing history to assess the author's expertise during analysis. For example, the analysis unit can evaluate the reliability of articles the author has written in the past. The analysis unit can also evaluate reliability based on the author's field of expertise. Furthermore, the analysis unit can analyze the author's past writing history and evaluate reliability. This allows the analysis unit to evaluate reliability based on the author's expertise. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the author's writing history data into the generative AI, which then evaluates the author's expertise and reliability.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the analysis unit can provide a display method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the display method.

[0075] The analysis unit can evaluate reliability by considering the geographical information of a web page during analysis. For example, the analysis unit may evaluate geographically close information sources as highly reliable. Conversely, it may evaluate geographically distant information sources as less reliable. Furthermore, the analysis unit can also verify the consistency between geographical information and content and evaluate reliability. This allows the analysis unit to evaluate reliability based on geographical information. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs geographical information data of a web page into the generative AI, which evaluates the geographical information and assesses reliability.

[0076] The analysis unit can evaluate the reliability of a webpage by referring to related literature during the analysis. For example, the analysis unit can evaluate a webpage with many related documents as highly reliable. Conversely, the analysis unit can evaluate a webpage with few related documents as less reliable. Furthermore, the analysis unit can also evaluate the quality of the related documents and assess their reliability. This allows the analysis unit to evaluate reliability based on related literature. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs related literature data into the generation AI, which evaluates the related documents and assesses their reliability.

[0077] The display unit can estimate the user's emotions and adjust the display method of the reliability evaluation based on the estimated user emotions. For example, if the user is nervous, the display unit can provide a simple and highly visible display method. If the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a concise display method. In this way, the display unit can provide a display method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the display unit may be performed using AI or not using AI. For example, the display unit inputs user emotion data into the generative AI, the generative AI estimates the emotions, and adjusts the display method.

[0078] The display unit can select the optimal display method by referring to the user's past browsing history when displaying information. For example, the display unit may prioritize displaying web pages that the user has frequently viewed in the past. The display unit can also suggest the optimal display method based on the user's past browsing history. Furthermore, the display unit can analyze the user's past browsing history and display relevant information. This allows the display unit to provide the optimal display method based on the user's past browsing history. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit may input the user's browsing history data into an AI model, and the AI ​​model may select the optimal display method.

[0079] The display unit can apply different display formats depending on the category of the web page when displaying it. For example, in the case of medical information, the display unit can provide a display format that includes explanations of technical terms. In the case of financial information, the display unit can also provide a display format that includes graphs and charts. Furthermore, in the case of entertainment information, the display unit can provide a visually appealing display format. In this way, the display unit can provide a display format that is appropriate for the category of the web page. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit inputs web page category data into an AI model, and the AI ​​model applies different display formats.

[0080] The display unit can estimate the user's emotions and adjust the display order of reliability evaluations based on the estimated user emotions. For example, if the user is nervous, the display unit will prioritize displaying reliable information. If the user is relaxed, the display unit can provide a display order that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a display order that gets straight to the point. In this way, the display unit can provide a display order that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the display order.

[0081] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit inputs user device information data into an AI model, and the AI ​​model selects the optimal display method.

[0082] The display unit can analyze the user's social media activity and display relevant information when displaying information. For example, the display unit can display relevant information based on the user's recent posts. It can also display relevant information based on the topics of accounts the user follows. Furthermore, the display unit can analyze the user's social media activity history and display relevant information. This allows the display unit to provide relevant information based on the user's social media activity. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit inputs the user's social media data into an AI model, and the AI ​​model displays relevant information.

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

[0084] The reception system can analyze the user's voice input and extract keywords from the audio. For example, if a user says "Tell me the latest health information" by voice, the reception system converts the audio to text and extracts the keyword "latest health information." The reception system can also analyze the user's accent and intonation during voice input and estimate the user's emotions. For example, if a user is speaking in an excited voice, the reception system can adjust the interface to take that emotion into account. Furthermore, the reception system can remove background noise during voice input and generate clear audio data. This allows the reception system to accurately grasp the user's intent through voice input and extract appropriate keywords.

[0085] The analysis unit can evaluate the readability of a web page when analyzing its content. For example, it can evaluate the length and complexity of the text, the frequency of technical terms, and calculate a readability score. It can also consider the page layout and design elements to evaluate visual readability. Furthermore, the analysis unit can set appropriate readability standards based on the user's age and education level. This allows the analysis unit to conduct reliability assessments that take readability into account, ensuring users can easily understand the information.

[0086] The analysis unit can consider the page's update history when analyzing the content of a web page. For example, the analysis unit can evaluate whether a page is frequently updated and rate pages with high update frequency as more reliable. The analysis unit can also analyze the content of page updates and evaluate the freshness and accuracy of the information. Furthermore, the analysis unit can verify the consistency between the page's update history and its content and evaluate its reliability. This allows the analysis unit to assess reliability based on the web page's update history.

[0087] The display unit can estimate the user's emotions and adjust the display method of search results based on the estimated emotions. For example, if the user is stressed, the display unit can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, the display unit can provide a display method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not using AI. For example, the display unit inputs the user's facial expression data into the generative AI, the generative AI estimates the emotions, and adjusts the display method.

[0088] The reception desk can analyze the user's past search history and suggest the most suitable keywords. For example, it can automatically display keywords that the user has frequently searched in the past as suggestions. The reception desk can also prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest keywords that the user will use at specific times of day based on their past search history. This allows the reception desk to suggest the most suitable keywords based on the user's past search history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's search history data into an AI model, and the AI ​​model can suggest the most suitable keywords.

[0089] The analysis unit can evaluate the expertise of the page's author when analyzing the content of a webpage. For example, the analysis unit can evaluate the reliability of articles previously written by the author. Furthermore, the analysis unit can evaluate reliability based on the author's area of ​​expertise. In addition, the analysis unit can analyze the author's past writing history and evaluate its reliability. This allows the analysis unit to evaluate reliability based on the author's expertise. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the author's writing history data into the generative AI, which then evaluates the author's expertise and reliability.

[0090] The reception desk can estimate the user's emotions and prioritize keyword input based on the estimated emotions. For example, if the user is nervous, simple and highly visible keywords will be displayed preferentially. If the user is relaxed, more detailed keyword options can be provided. Furthermore, if the user is in a hurry, the most relevant keywords can be displayed quickly. This allows the reception desk to prioritize keyword input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's facial expression data into a generative AI, which estimates the emotions and determines the priority of keyword input.

[0091] The analysis unit can evaluate the reliability of a webpage by referring to related literature when analyzing the content of the webpage. For example, it can evaluate a webpage with many related documents as highly reliable. Conversely, the analysis unit can evaluate a webpage with few related documents as less reliable. Furthermore, the analysis unit can evaluate the quality of the related documents and assess their reliability. This allows the analysis unit to evaluate reliability based on related literature. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs related literature data into the generation AI, which evaluates the related documents and assesses their reliability.

[0092] The display unit can estimate the user's emotions and adjust the display order of reliability evaluations based on the estimated user emotions. For example, if the user is nervous, it can prioritize displaying reliable information. If the user is relaxed, it can provide a display order that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display order that gets straight to the point. In this way, the display unit can provide a display order that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not using AI. For example, the display unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the display order.

[0093] The reception desk can prioritize and present highly relevant keywords when the user enters keywords, taking into account the user's geographical location. For example, it can suggest keywords related to the user's region based on the user's current location. Furthermore, if the user is traveling, the reception desk can prioritize displaying keywords related to their destination. In addition, the reception desk can analyze the user's past location data and suggest relevant keywords. This allows the reception desk to present highly relevant keywords based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's location data into an AI model, and the AI ​​model presents highly relevant keywords.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The reception desk receives keyword input from the user. For example, it operates when the user enters keywords into a search engine, and then inputs them into the keyword generation AI. Step 2: The analysis unit analyzes the search result web pages based on the keywords received by the reception unit and evaluates the reliability of their content. For example, it uses generative AI to evaluate whether technical terms are used correctly and whether they are based on reliable sources. Step 3: The display unit displays the reliability evaluated by the analysis unit in the search results. For example, it displays "Reliability: High" for highly reliable web pages and "Reliability: Low" for less reliable web pages.

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements described above, including the reception unit, analysis unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and operates when the user enters keywords into the search engine. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the search result web pages using generating AI and evaluates the reliability of their content. The display unit is implemented by the output device 40 of the smart device 14 and displays the reliability evaluated by the analysis unit in the search results. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

[0101] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0107] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and operates when the user enters keywords into the search engine. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the search result web pages using generating AI and evaluates the reliability of their content. The display unit is implemented by the speaker 240 of the smart glasses 214 and displays the reliability evaluated by the analysis unit in the search results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and operates when the user enters keywords into the search engine. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the search result web pages using generating AI and evaluates the reliability of their content. The display unit is implemented by the display 343 of the headset terminal 314 and displays the reliability evaluated by the analysis unit in the search results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0133] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0140] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, and display unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and operates when the user enters keywords into the search engine. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the search result web pages using generating AI and evaluates the reliability of their content. The display unit is implemented by, for example, the speaker 240 of the robot 414 and displays the reliability evaluated by the analysis unit in the search results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0151] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0152] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0153] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0157] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[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] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0160] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0161] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0162] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0164] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0165] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0166] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0167] (Note 1) A reception area that accepts keyword input from users, An analysis unit analyzes the search results web pages based on the keywords received by the reception unit and evaluates the reliability of their content. The system includes a display unit that displays the reliability evaluated by the analysis unit in the search results. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a function to refer to a technical term dictionary to evaluate whether technical terms are being used correctly. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It includes a feature to refer to a list of reliable sources in order to evaluate the reliability of the information source. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is It has a feature that displays "Reliability: High" for highly reliable web pages and "Reliability: Low" for less reliable web pages. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the keyword input interface based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past search history and suggests the most suitable keywords. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering keywords, the system automatically completes related keywords based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of keyword input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter keywords, the system prioritizes and displays highly relevant keywords, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When you enter keywords, the system analyzes your social media activity and suggests relevant keywords. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, reliability is evaluated by considering the frequency of web page updates. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, we refer to the author's past writing history to assess the expertise of the web page's author. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the reliability of a webpage is evaluated by considering its geographical information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, reliability is evaluated by referring to related literature on the web page. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is The system estimates user sentiment and adjusts how reliability ratings are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying content, the system selects the optimal display method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is When displaying a webpage, apply different display formats depending on the webpage's category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is It estimates the user's sentiment and adjusts the display order of reliability ratings based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying information, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts keyword input from users, An analysis unit analyzes the search results web pages based on the keywords received by the reception unit and evaluates the reliability of their content. The system includes a display unit that displays the reliability evaluated by the analysis unit in the search results. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a function to refer to a technical term dictionary to evaluate whether technical terms are being used correctly. The system according to feature 1.

3. The aforementioned analysis unit, It includes a feature to refer to a list of reliable sources in order to evaluate the reliability of the information source. The system according to feature 1.

4. The aforementioned display unit is It has a function that displays information indicating the high reliability of highly reliable web pages and information indicating the low reliability of unreliable web pages. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the keyword input interface based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes the user's past search history and suggests the most suitable keywords. The system according to feature 1.

7. The aforementioned reception unit is When entering keywords, the system automatically completes related keywords based on the user's current areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of keyword input based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When users enter keywords, the system prioritizes and displays highly relevant keywords, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When you enter keywords, the system analyzes your social media activity and suggests relevant keywords. The system according to feature 1.

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