system

Abstract

The system according to this embodiment aims to enable users to obtain accurate information without being misled by false information. [Solution] The system according to the embodiment comprises a collection unit, an evaluation unit, a supplement unit, and a provision unit. The collection unit collects information. The evaluation unit evaluates the credibility of the information collected by the collection unit. The supplement unit provides warnings and supplements information based on the information evaluated by the evaluation unit. The provision unit provides the information supplemented by the supplement unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that there is a lot of misinformation and low-reliability information on the Internet, making it difficult for users to identify accurate information.

[0005] The system according to the embodiment aims to enable users to obtain accurate information without being misled by misinformation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an evaluation unit, a supplementation unit, and a provision unit. The collection unit collects information. The evaluation unit evaluates the credibility of the information collected by the collection unit. The supplementation unit provides warnings and supplements based on the information evaluated by the evaluation unit. The provision unit provides the information supplemented by the supplementation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows users to obtain accurate information without being misled by false information. [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 manages communication between multiple computers. Examples of communication standards applicable 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 navigator service "AI Notes" according to an embodiment of the present invention is a system that utilizes generative AI to evaluate information encountered by users in real time, preventing them from being misled by misinformation. In this system, the generative AI constantly analyzes information received by the user from various information devices, evaluates the credibility of the information, and provides warnings or supplementary information regarding misinformation or information with low credibility. Furthermore, the generative AI complements and strengthens the user's information literacy, improving their ability to judge information. Through this mechanism, users can enjoy a reliable information environment and prevent confusion in society as a whole. For example, when a user is viewing a news article, the generative AI analyzes the content of the article in real time and evaluates its credibility. Based on the collected data, the generative AI determines the source of the information and the accuracy of its content. For example, it checks whether the source of the news article is a reliable media outlet and whether the content is consistent with other reliable sources. As a result, the generative AI evaluates the credibility of the information and identifies misinformation or information with low credibility. Furthermore, the generative AI provides warnings or supplementary information regarding misinformation or information with low credibility. For example, if a user is viewing misinformation, the generating AI will notify them that the information is incorrect and provide accurate information. Furthermore, for information of low credibility, it will provide additional information to help users make informed decisions. Through this mechanism, the generating AI complements and enhances users' information literacy, improving their individual information judgment skills. With the support of the generating AI, users can develop the ability to discern accurate information and make appropriate decisions without being misled by misinformation. For instance, if a user encounters misinformation spreading on social media, the generating AI can point out the errors and provide accurate information, allowing the user to make the right decision without being misled. In this way, the AI-powered navigator service "AI Notes" allows users to enjoy a reliable information environment and prevent confusion throughout society. Additionally, revenue can be secured through advertising and API provision, enhancing the sustainability of the service. For example, the service can be provided free of charge to users through browser extensions or apps, while revenue can be generated through advertising and API usage fees.This allows the "AI Notes" navigation service to evaluate the information users encounter in real time, preventing them from being misled by misinformation.

[0029] The navigator service "AI Notes" according to this embodiment comprises a collection unit, an evaluation unit, a supplementary unit, and a provision unit. The collection unit collects information. The collection unit collects data such as news articles, SNS posts, and website content. For example, the collection unit collects news articles from online news sites. The collection unit can also collect SNS posts via APIs. The collection unit can also collect website content using web scraping technology. The evaluation unit evaluates the credibility of the information collected by the collection unit. For example, the evaluation unit determines the source and accuracy of the information. For example, the evaluation unit verifies whether the source of a news article is a reliable media outlet. The evaluation unit can also verify whether the content is consistent with other reliable sources. The evaluation unit evaluates the credibility of the information and identifies misinformation and unreliable information. The supplementary unit provides warnings and supplementary information based on the information evaluated by the evaluation unit. For example, the supplementary unit provides warnings about misinformation and provides accurate information. The supplementary section can also provide additional information for information with low credibility. The supplementary section helps users make accurate judgments. The providing section provides the user with the information supplemented by the supplementary section. The providing section, for example, notifies the user of the supplemented information. The providing section can also display the supplemented information on the dashboard. The providing section can also send the supplemented information via email. As a result, the navigator service "AI Notes" according to this embodiment can efficiently collect information, assess its credibility, issue warnings and supplements, and provide information.

[0030] The data collection unit collects information. For example, it collects data such as news articles, social media posts, and website content. Specifically, the unit uses RSS feeds and APIs to collect news articles from online news sites, allowing it to obtain the latest news articles in real time. For social media posts, it collects posts related to specific keywords and hashtags through APIs provided by each social media platform. For example, it can use social media APIs to collect posts on specific topics. For website content, it extracts necessary information from specific web pages using web scraping techniques. Web scraping can utilize libraries such as Python's BeautifulSoup and Scrapy. This allows the data collection unit to efficiently collect a wide range of data from diverse sources and build an information infrastructure for the entire system. Furthermore, the data collection unit centrally manages the collected data and stores it in a database. The database also includes metadata such as collection date and time, source, and content, enabling efficient use in subsequent processing. The data collection unit regularly updates the data to always maintain the latest information. Furthermore, the data collection unit can dynamically change the information sources it collects, allowing it to respond flexibly to specific events and topics. This enables the data collection unit to establish a foundation for consistently providing up-to-date and diverse information.

[0031] The evaluation unit assesses the credibility of the information collected by the collection unit. For example, the evaluation unit determines the source and accuracy of the information. Specifically, the evaluation unit compares collected news articles against a predefined list of reliable sources to confirm that the source is a trustworthy media outlet. The list of reliable sources includes major news media and public institution websites. The evaluation unit prioritizes evaluating data from these sources and handles data from less reliable sources with caution. The evaluation unit also performs cross-referencing to confirm that the content of the collected information is consistent with other reliable sources. For example, it compares data from multiple sources on the same news topic and determines that the information is highly credible if there is a lot of agreement. Furthermore, the evaluation unit uses natural language processing techniques to analyze the content of the information and identify misinformation and biased information. For example, it performs sentiment analysis of the text and determines that the information is less credible if it contains many extreme expressions or emotional words. Based on these evaluation results, the evaluation unit assigns a credibility score to each piece of information, distinguishing between highly reliable and less reliable information. This allows the evaluation unit to assess the credibility of the collected information with high accuracy and improve the overall quality of information within the system.

[0032] The supplementary section provides warnings and supplementary information based on the information evaluated by the evaluation section. Specifically, the supplementary section warns against misinformation and provides accurate information. For example, if the evaluation section identifies information with low credibility, the supplementary section will issue a warning about that information and alert the user. The supplementary section can also provide additional information regarding information with low credibility. For example, if a particular news article is determined to be misinformation, the supplementary section will provide data from reliable sources related to that article to help the user make an accurate judgment. The supplementary section uses natural language generation technology to provide information in a way that is easy for users to understand. For example, it uses generation AI to generate and present to the user a concise summary of accurate information regarding misinformation. Furthermore, the supplementary section can customize how information is provided according to the user's interests and needs. For example, users interested in a particular topic will be given priority in receiving reliable information related to that topic. In this way, the supplementary section can support users in obtaining accurate and reliable information and improve the quality of information.

[0033] The information provider provides users with information supplemented by the supplementary information provider. Specifically, the information provider notifies users of the supplementary information. For example, it provides real-time notifications when important information is collected based on notification conditions set by the user. Notifications can be sent via smartphone push notifications, email, SMS, etc. The information provider can also display the supplementary information on a dashboard. The dashboard is an interface that allows users to centrally view collected information, evaluation results, and supplementary information. The dashboard visually displays the credibility score of the information and the content of warnings, allowing users to intuitively grasp the quality of the information. Furthermore, the information provider can also send the supplementary information via email. It periodically sends summaries of information and important notifications to the email address registered by the user, ensuring that the user always has access to the latest information. The information provider can also collect user feedback and use it to improve the delivery method. For example, when users provide evaluations and comments on the information provided, the information provider can use that feedback to improve the way and content of the information provided. This allows the information provider to deliver information to users efficiently and effectively, improving user satisfaction.

[0034] The data collection unit can collect data such as news articles, social media posts, and website content. For example, the data collection unit can collect news articles from online news sites. The data collection unit can also collect social media posts via APIs. The data collection unit can also collect website content using web scraping techniques. This improves the comprehensiveness of information by collecting data from diverse sources. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, when collecting news articles, the data collection unit can use generative AI to analyze the content of the news articles and extract important information.

[0035] The evaluation unit can determine the source and accuracy of the collected data. For example, the evaluation unit can verify that the source of the information is a reliable media. The evaluation unit can also verify that the content is consistent with other reliable sources. The evaluation unit assesses the credibility of the information and identifies misinformation and unreliable information. This allows for the provision of reliable information by determining the source and accuracy of the data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the collected data into a generating AI, which can then evaluate the credibility of the data.

[0036] The supplementary section can alert users to misinformation and provide accurate information. For example, the supplementary section alerts users to misinformation and provides accurate information. If a user is viewing misinformation, the supplementary section notifies them that the information is incorrect. The supplementary section provides accurate information and helps users make accurate judgments. In this way, by alerting users to misinformation, users can obtain accurate information. Some or all of the above processing in the supplementary section may be performed using AI, for example, or without AI. For example, the supplementary section can input misinformation into a generating AI, which can identify the misinformation and provide accurate information.

[0037] The supplementary section can provide additional information for information of low credibility. For example, the supplementary section provides additional information for information of low credibility. If the user is viewing information of low credibility, the supplementary section provides additional information to help the user make an accurate judgment. This enables the user to make an accurate judgment by providing additional information for information of low credibility. Some or all of the processing described above in the supplementary section may be performed using AI, for example, or not using AI. For example, the supplementary section can input information of low credibility into a generating AI, and the generating AI can provide additional information.

[0038] The information provider can provide supplementary information to users, thereby complementing and strengthening their information literacy. For example, the information provider can notify users of the supplementary information. The information provider can also display the supplementary information on a dashboard. The information provider can also send the supplementary information via email. This improves the user's information literacy by providing supplementary information. Some or all of the above-described processes in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the supplementary information into a generating AI, and the generating AI can generate information to provide to the user.

[0039] The service provider can monetize by displaying advertisements or providing APIs. For example, the service provider can display advertisements. The service provider can also provide APIs and earn usage fees. This improves the sustainability of the service by monetizing through advertising and API provision. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the content of advertisements into a generation AI, and the generation AI can generate advertisements.

[0040] The data collection unit can analyze the user's past information browsing history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information sources that the user has frequently viewed in the past. The data collection unit can also prioritize collecting information of a specific genre from the user's past browsing history. The data collection unit can also analyze the user's past browsing history and select and collect highly reliable information sources. In this way, the optimal information collection method can be selected by analyzing the user's past information browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information browsing history into a generating AI, which can then select the optimal information collection method.

[0041] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to topics the user is currently interested in. The data collection unit can also filter information containing relevant keywords based on the user's current areas of interest. The data collection unit can also prioritize collecting information from specific categories based on the user's current areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into a generating AI, which can then filter the information.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during information gathering. For example, the data collection unit can prioritize the collection of news and event information related to the user's current location. The data collection unit can also collect region-specific information based on the user's geographical location. The data collection unit can also prioritize the collection of urgent information by considering the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also prioritize collecting information shared by the user's followers and friends on social media. The data collection unit can also analyze the user's social media activity and collect information related to trends. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into a generating AI, and the generating AI can collect relevant information.

[0044] The evaluation unit can determine the reliability of the information source by comparing it with a past database during the evaluation process. For example, the evaluation unit can check whether the information source is a media outlet that has been evaluated as highly reliable in the past. The evaluation unit can also check with the database whether the information source has ever provided misinformation in the past. The evaluation unit can also check whether the information source has ever provided unreliable information in the past. By doing so, reliable information can be provided by comparing the reliability of the information source with a past database. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the information source into a generating AI, and the generating AI can determine the reliability by comparing it with a past database.

[0045] The evaluation unit can verify during the evaluation whether the content of the information is consistent with other reliable sources. For example, the evaluation unit can verify whether the content of the information is consistent with other reliable news media. The evaluation unit can also verify whether the content of the information is consistent with expert opinions. The evaluation unit can also verify whether the content of the information is consistent with announcements from public institutions. This ensures that reliable information is provided by verifying that the content of the information is consistent with other reliable sources. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the content of the information into a generating AI and verify that the generating AI is consistent with other reliable sources.

[0046] The evaluation unit can assess the credibility of information by considering its geographical distribution during the evaluation process. For example, the evaluation unit can verify whether the source of the information is a highly reliable region. The evaluation unit can also verify whether the source of the information is a region that has previously provided misinformation. The evaluation unit can also verify whether the source of the information is a less reliable region. By considering the geographical distribution of the information, it is possible to provide highly credible information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical distribution of the information into a generating AI, which can then evaluate its credibility.

[0047] The evaluation unit can improve the accuracy of its credibility assessment by referring to relevant literature during the evaluation process. For example, the evaluation unit can verify whether the content of the information is consistent with relevant academic papers. The evaluation unit can also verify whether the content of the information is consistent with relevant books. The evaluation unit can also verify whether the content of the information is consistent with relevant reports. This improves the accuracy of credibility assessment by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the content of the information into a generating AI, which can then evaluate its credibility by referring to relevant literature.

[0048] The supplementary section can adjust the level of detail of the warning based on the impact of the misinformation when supplementing it. For example, if the impact of the misinformation is high, the supplementary section will provide a detailed warning. If the impact of the misinformation is moderate, the supplementary section may provide a concise warning. If the impact of the misinformation is low, the supplementary section may provide a simple warning. This allows for appropriate warnings by adjusting the level of detail of the warning according to the impact of the misinformation. Some or all of the above processing in the supplementary section may be performed using AI, for example, or without AI. For example, the supplementary section can input the impact of the misinformation into a generating AI, which can then adjust the level of detail of the warning.

[0049] The supplementary component can apply different supplementary algorithms depending on the category of information during the supplementation process. For example, for news articles, the supplementary component supplements information by referring to reliable sources. For social media posts, the supplementary component can also supplement information by referring to the opinions of relevant experts. For website content, the supplementary component can also supplement information by referring to announcements from relevant public institutions. This enables appropriate supplementation according to the category of information. Some or all of the above processing in the supplementary component may be performed using AI, for example, or without AI. For example, the supplementary component can input the category of information into a generating AI, and the generating AI can apply different supplementary algorithms.

[0050] The supplementary unit can determine the priority of supplementation based on the information submission date. For example, the supplementary unit can prioritize supplementing the most recent information. It can also lower the priority of supplementing older information. For information whose submission date is unknown, the supplementary unit can set the priority to a medium level. This allows for the provision of appropriate information by determining the priority of supplementation based on the information submission date. Some or all of the above processing in the supplementary unit may be performed using AI, for example, or not using AI. For example, the supplementary unit can input the information submission date into a generating AI, which can then determine the priority of supplementation.

[0051] The supplementary unit can adjust the order of supplementation based on the relevance of the information. For example, the supplementary unit can prioritize supplementing information with high relevance. For information with moderate relevance, the supplementary unit can set the supplementation order to a moderate level. For information with low relevance, the supplementary unit can postpone the supplementation order. This allows for the provision of appropriate information by adjusting the order of supplementation based on the relevance of the information. Some or all of the above processing in the supplementary unit may be performed using AI, for example, or without AI. For example, the supplementary unit can input the relevance of the information into a generating AI, which can then adjust the order of supplementation.

[0052] The information provider can select the optimal method of information delivery by referring to the user's past information browsing history at the time of delivery. For example, the provider may prioritize providing information sources that the user has frequently viewed in the past. The provider may also prioritize providing information of a specific genre based on the user's past browsing history. The provider may also analyze the user's past browsing history and select and provide highly reliable information sources. This allows the provider to select the optimal method of information delivery by referring to the user's past information browsing history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider may input the user's past information browsing history into a generating AI, which can then select the optimal method of information delivery.

[0053] The information provider can customize the means of providing information based on the user's current areas of interest at the time of delivery. For example, the provider may prioritize providing information related to topics the user is currently interested in. The provider may also provide information that includes relevant keywords based on the user's current areas of interest. The provider may also prioritize providing information from specific categories based on the user's current areas of interest. This makes it possible to provide appropriate information by customizing the means of providing information based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider may input the user's current areas of interest into a generating AI, which can then customize the means of providing information.

[0054] The information delivery unit can select the optimal information delivery method by considering the user's geographical location information at the time of delivery. For example, the information delivery unit may prioritize providing news and event information related to the user's current location. The information delivery unit may also provide region-specific information based on the user's geographical location information. The information delivery unit may also prioritize providing information of high urgency by considering the user's geographical location information. In this way, the optimal information delivery method can be selected by considering the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit may input the user's geographical location information into a generating AI, and the generating AI may select the optimal information delivery method.

[0055] The information provider can analyze the user's social media activity and propose means of information provision at the time of provision. For example, the information provider can provide information related to topics the user has shown interest in on social media. The information provider can also prioritize providing information shared by the user's followers and friends on social media. The information provider can also analyze the user's social media activity and provide information related to trends. In this way, by analyzing the user's social media activity, it is possible to propose appropriate means of information provision. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's social media activity into a generating AI, and the generating AI can propose means of information provision.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze a user's past information browsing history and select the most suitable information collection method. For example, it can prioritize collecting information from sources that the user has frequently viewed in the past. The data collection unit can also prioritize collecting information of a specific genre based on the user's past browsing history. The data collection unit can also analyze a user's past browsing history and select and collect highly reliable information sources. In this way, by analyzing a user's past information browsing history, the optimal information collection method can be selected.

[0058] The evaluation unit can determine the reliability of the information source by comparing it with past databases during the evaluation process. For example, it can check whether the information source is a media outlet that has been previously evaluated as highly reliable. The evaluation unit can also check whether the information source has previously provided misinformation with the database. The evaluation unit can also check whether the information source has previously provided unreliable information. By comparing the reliability of the information source with past databases, the evaluation unit can provide highly reliable information.

[0059] The supplementary section can adjust the level of detail in its warnings based on the impact of the misinformation. For example, if the impact of the misinformation is high, the supplementary section will provide a detailed warning. If the impact is moderate, the supplementary section can provide a concise warning. If the impact is low, the supplementary section can provide a simple warning. This allows for appropriate warnings by adjusting the level of detail in the warnings according to the impact of the misinformation.

[0060] The information provider can select the most appropriate method of information delivery by considering the user's geographical location. For example, it can prioritize providing news and event information relevant to the user's current location. The information provider can also provide region-specific information based on the user's geographical location. Furthermore, it can prioritize providing information of high urgency by considering the user's geographical location. This allows for the selection of the most appropriate method of information delivery by considering the user's geographical location.

[0061] The information provider can analyze the user's social media activity at the time of delivery and propose methods for providing information. For example, it can provide information related to topics the user has shown interest in on social media. The information provider can also prioritize providing information shared by the user's followers and friends on social media. The information provider can also analyze the user's social media activity and provide information related to trends. In this way, by analyzing the user's social media activity, it can propose appropriate methods for providing information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects information. The collection unit collects data such as news articles, social media posts, and website content. The collection unit can collect news articles from online news sites, social media posts via APIs, and website content using web scraping technology. Step 2: The evaluation unit assesses the credibility of the information collected by the collection unit. The evaluation unit determines the source and accuracy of the information, for example, verifying that news articles originate from reliable media outlets and that their content is consistent with other reliable sources. The evaluation unit assesses the credibility of the information and identifies misinformation and unreliable information. Step 3: The supplementary section provides warnings and supplementary information based on the information evaluated by the evaluation section. The supplementary section warns against misinformation, provides accurate information, and provides additional information for information with low credibility. The supplementary section helps users make accurate judgments. Step 4: The provider unit provides the user with the information supplemented by the supplementary unit. The provider unit can notify the user of the supplementary information, display it on the dashboard, or send it via email.

[0064] (Example of form 2) The navigator service "AI Notes" according to an embodiment of the present invention is a system that utilizes generative AI to evaluate information encountered by users in real time, preventing them from being misled by misinformation. In this system, the generative AI constantly analyzes information received by the user from various information devices, evaluates the credibility of the information, and provides warnings or supplementary information regarding misinformation or information with low credibility. Furthermore, the generative AI complements and strengthens the user's information literacy, improving their ability to judge information. Through this mechanism, users can enjoy a reliable information environment and prevent confusion in society as a whole. For example, when a user is viewing a news article, the generative AI analyzes the content of the article in real time and evaluates its credibility. Based on the collected data, the generative AI determines the source of the information and the accuracy of its content. For example, it checks whether the source of the news article is a reliable media outlet and whether the content is consistent with other reliable sources. As a result, the generative AI evaluates the credibility of the information and identifies misinformation or information with low credibility. Furthermore, the generative AI provides warnings or supplementary information regarding misinformation or information with low credibility. For example, if a user is viewing misinformation, the generating AI will notify them that the information is incorrect and provide accurate information. Furthermore, for information of low credibility, it will provide additional information to help users make informed decisions. Through this mechanism, the generating AI complements and enhances users' information literacy, improving their individual information judgment skills. With the support of the generating AI, users can develop the ability to discern accurate information and make appropriate decisions without being misled by misinformation. For instance, if a user encounters misinformation spreading on social media, the generating AI can point out the errors and provide accurate information, allowing the user to make the right decision without being misled. In this way, the AI-powered navigator service "AI Notes" allows users to enjoy a reliable information environment and prevent confusion throughout society. Additionally, revenue can be secured through advertising and API provision, enhancing the sustainability of the service. For example, the service can be provided free of charge to users through browser extensions or apps, while revenue can be generated through advertising and API usage fees.This allows the "AI Notes" navigation service to evaluate the information users encounter in real time, preventing them from being misled by misinformation.

[0065] The navigator service "AI Notes" according to this embodiment comprises a collection unit, an evaluation unit, a supplementary unit, and a provision unit. The collection unit collects information. The collection unit collects data such as news articles, SNS posts, and website content. For example, the collection unit collects news articles from online news sites. The collection unit can also collect SNS posts via APIs. The collection unit can also collect website content using web scraping technology. The evaluation unit evaluates the credibility of the information collected by the collection unit. For example, the evaluation unit determines the source and accuracy of the information. For example, the evaluation unit verifies whether the source of a news article is a reliable media outlet. The evaluation unit can also verify whether the content is consistent with other reliable sources. The evaluation unit evaluates the credibility of the information and identifies misinformation and unreliable information. The supplementary unit provides warnings and supplementary information based on the information evaluated by the evaluation unit. For example, the supplementary unit provides warnings about misinformation and provides accurate information. The supplementary section can also provide additional information for information with low credibility. The supplementary section helps users make accurate judgments. The providing section provides the user with the information supplemented by the supplementary section. The providing section, for example, notifies the user of the supplemented information. The providing section can also display the supplemented information on the dashboard. The providing section can also send the supplemented information via email. As a result, the navigator service "AI Notes" according to this embodiment can efficiently collect information, assess its credibility, issue warnings and supplements, and provide information.

[0066] The data collection unit collects information. For example, it collects data such as news articles, social media posts, and website content. Specifically, the unit uses RSS feeds and APIs to collect news articles from online news sites, allowing it to obtain the latest news articles in real time. For social media posts, it collects posts related to specific keywords and hashtags through APIs provided by each social media platform. For example, it can use social media APIs to collect posts on specific topics. For website content, it extracts necessary information from specific web pages using web scraping techniques. Web scraping can utilize libraries such as Python's BeautifulSoup and Scrapy. This allows the data collection unit to efficiently collect a wide range of data from diverse sources and build an information infrastructure for the entire system. Furthermore, the data collection unit centrally manages the collected data and stores it in a database. The database also includes metadata such as collection date and time, source, and content, enabling efficient use in subsequent processing. The data collection unit regularly updates the data to always maintain the latest information. Furthermore, the data collection unit can dynamically change the information sources it collects, allowing it to respond flexibly to specific events and topics. This enables the data collection unit to establish a foundation for consistently providing up-to-date and diverse information.

[0067] The evaluation unit assesses the credibility of the information collected by the collection unit. For example, the evaluation unit determines the source and accuracy of the information. Specifically, the evaluation unit compares collected news articles against a predefined list of reliable sources to confirm that the source is a trustworthy media outlet. The list of reliable sources includes major news media and public institution websites. The evaluation unit prioritizes evaluating data from these sources and handles data from less reliable sources with caution. The evaluation unit also performs cross-referencing to confirm that the content of the collected information is consistent with other reliable sources. For example, it compares data from multiple sources on the same news topic and determines that the information is highly credible if there is a lot of agreement. Furthermore, the evaluation unit uses natural language processing techniques to analyze the content of the information and identify misinformation and biased information. For example, it performs sentiment analysis of the text and determines that the information is less credible if it contains many extreme expressions or emotional words. Based on these evaluation results, the evaluation unit assigns a credibility score to each piece of information, distinguishing between highly reliable and less reliable information. This allows the evaluation unit to assess the credibility of the collected information with high accuracy and improve the overall quality of information within the system.

[0068] The supplementary section provides warnings and supplementary information based on the information evaluated by the evaluation section. Specifically, the supplementary section warns against misinformation and provides accurate information. For example, if the evaluation section identifies information with low credibility, the supplementary section will issue a warning about that information and alert the user. The supplementary section can also provide additional information regarding information with low credibility. For example, if a particular news article is determined to be misinformation, the supplementary section will provide data from reliable sources related to that article to help the user make an accurate judgment. The supplementary section uses natural language generation technology to provide information in a way that is easy for users to understand. For example, it uses generation AI to generate and present to the user a concise summary of accurate information regarding misinformation. Furthermore, the supplementary section can customize how information is provided according to the user's interests and needs. For example, users interested in a particular topic will be given priority in receiving reliable information related to that topic. In this way, the supplementary section can support users in obtaining accurate and reliable information and improve the quality of information.

[0069] The information provider provides users with information supplemented by the supplementary information provider. Specifically, the information provider notifies users of the supplementary information. For example, it provides real-time notifications when important information is collected based on notification conditions set by the user. Notifications can be sent via smartphone push notifications, email, SMS, etc. The information provider can also display the supplementary information on a dashboard. The dashboard is an interface that allows users to centrally view collected information, evaluation results, and supplementary information. The dashboard visually displays the credibility score of the information and the content of warnings, allowing users to intuitively grasp the quality of the information. Furthermore, the information provider can also send the supplementary information via email. It periodically sends summaries of information and important notifications to the email address registered by the user, ensuring that the user always has access to the latest information. The information provider can also collect user feedback and use it to improve the delivery method. For example, when users provide evaluations and comments on the information provided, the information provider can use that feedback to improve the way and content of the information provided. This allows the information provider to deliver information to users efficiently and effectively, improving user satisfaction.

[0070] The data collection unit can collect data such as news articles, social media posts, and website content. For example, the data collection unit can collect news articles from online news sites. The data collection unit can also collect social media posts via APIs. The data collection unit can also collect website content using web scraping techniques. This improves the comprehensiveness of information by collecting data from diverse sources. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, when collecting news articles, the data collection unit can use generative AI to analyze the content of the news articles and extract important information.

[0071] The evaluation unit can determine the source and accuracy of the collected data. For example, the evaluation unit can verify that the source of the information is a reliable media. The evaluation unit can also verify that the content is consistent with other reliable sources. The evaluation unit assesses the credibility of the information and identifies misinformation and unreliable information. This allows for the provision of reliable information by determining the source and accuracy of the data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the collected data into a generating AI, which can then evaluate the credibility of the data.

[0072] The supplementary section can alert users to misinformation and provide accurate information. For example, the supplementary section alerts users to misinformation and provides accurate information. If a user is viewing misinformation, the supplementary section notifies them that the information is incorrect. The supplementary section provides accurate information and helps users make accurate judgments. In this way, by alerting users to misinformation, users can obtain accurate information. Some or all of the above processing in the supplementary section may be performed using AI, for example, or without AI. For example, the supplementary section can input misinformation into a generating AI, which can identify the misinformation and provide accurate information.

[0073] The supplementary section can provide additional information for information of low credibility. For example, the supplementary section provides additional information for information of low credibility. If the user is viewing information of low credibility, the supplementary section provides additional information to help the user make an accurate judgment. This enables the user to make an accurate judgment by providing additional information for information of low credibility. Some or all of the processing described above in the supplementary section may be performed using AI, for example, or not using AI. For example, the supplementary section can input information of low credibility into a generating AI, and the generating AI can provide additional information.

[0074] The information provider can provide supplementary information to users, thereby complementing and strengthening their information literacy. For example, the information provider can notify users of the supplementary information. The information provider can also display the supplementary information on a dashboard. The information provider can also send the supplementary information via email. This improves the user's information literacy by providing supplementary information. Some or all of the above-described processes in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the supplementary information into a generating AI, and the generating AI can generate information to provide to the user.

[0075] The service provider can monetize by displaying advertisements or providing APIs. For example, the service provider can display advertisements. The service provider can also provide APIs and earn usage fees. This improves the sustainability of the service by monetizing through advertising and API provision. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the content of advertisements into a generation AI, and the generation AI can generate advertisements.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect only important information. If the user is relaxed, the data collection unit can increase the frequency of information collection and collect a wider range of information. If the user is in a hurry, the data collection unit can prioritize the collection of important information in real time. This allows for appropriate information collection by adjusting the timing of information collection 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of information collection.

[0077] The data collection unit can analyze the user's past information browsing history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information sources that the user has frequently viewed in the past. The data collection unit can also prioritize collecting information of a specific genre from the user's past browsing history. The data collection unit can also analyze the user's past browsing history and select and collect highly reliable information sources. In this way, the optimal information collection method can be selected by analyzing the user's past information browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information browsing history into a generating AI, which can then select the optimal information collection method.

[0078] The data collection unit can filter information based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to topics the user is currently interested in. The data collection unit can also filter information containing relevant keywords based on the user's current areas of interest. The data collection unit can also prioritize collecting information from specific categories based on the user's current areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into a generating AI, which can then filter the information.

[0079] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit may prioritize collecting information that provides a sense of security. If the user is excited, the data collection unit may also prioritize collecting information that encourages calm judgment. If the user is tired, the data collection unit may also prioritize collecting concise and easy-to-understand information. This allows for the provision of appropriate information by prioritizing information 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 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of information.

[0080] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during information gathering. For example, the data collection unit can prioritize the collection of news and event information related to the user's current location. The data collection unit can also collect region-specific information based on the user's geographical location. The data collection unit can also prioritize the collection of urgent information by considering the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

[0081] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also prioritize collecting information shared by the user's followers and friends on social media. The data collection unit can also analyze the user's social media activity and collect information related to trends. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into a generating AI, and the generating AI can collect relevant information.

[0082] The evaluation unit can estimate the user's emotions and adjust the credibility evaluation criteria based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit may apply strict credibility evaluation criteria. If the user is relaxed, the evaluation unit may also apply flexible credibility evaluation criteria. If the user is in a hurry, the evaluation unit may also apply criteria for quickly evaluating credibility. This allows for appropriate credibility evaluation by adjusting the credibility evaluation criteria 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 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 evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI, which can then adjust the credibility evaluation criteria.

[0083] The evaluation unit can determine the reliability of the information source by comparing it with a past database during the evaluation process. For example, the evaluation unit can check whether the information source is a media outlet that has been evaluated as highly reliable in the past. The evaluation unit can also check with the database whether the information source has ever provided misinformation in the past. The evaluation unit can also check whether the information source has ever provided unreliable information in the past. By doing so, reliable information can be provided by comparing the reliability of the information source with a past database. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the information source into a generating AI, and the generating AI can determine the reliability by comparing it with a past database.

[0084] The evaluation unit can verify during the evaluation whether the content of the information is consistent with other reliable sources. For example, the evaluation unit can verify whether the content of the information is consistent with other reliable news media. The evaluation unit can also verify whether the content of the information is consistent with expert opinions. The evaluation unit can also verify whether the content of the information is consistent with announcements from public institutions. This ensures that reliable information is provided by verifying that the content of the information is consistent with other reliable sources. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the content of the information into a generating AI and verify that the generating AI is consistent with other reliable sources.

[0085] The evaluation unit can estimate the user's emotions and adjust how the evaluation results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the evaluation unit can display detailed evaluation results. If the user is relaxed, the evaluation unit can also display concise evaluation results. If the user is in a hurry, the evaluation unit can also display concise evaluation results. This allows for the provision of appropriate information by adjusting how the evaluation results are displayed 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 evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into the generative AI, and the generative AI can adjust how the evaluation results are displayed.

[0086] The evaluation unit can assess the credibility of information by considering its geographical distribution during the evaluation process. For example, the evaluation unit can verify whether the source of the information is a highly reliable region. The evaluation unit can also verify whether the source of the information is a region that has previously provided misinformation. The evaluation unit can also verify whether the source of the information is a less reliable region. By considering the geographical distribution of the information, it is possible to provide highly credible information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical distribution of the information into a generating AI, which can then evaluate its credibility.

[0087] The evaluation unit can improve the accuracy of its credibility assessment by referring to relevant literature during the evaluation process. For example, the evaluation unit can verify whether the content of the information is consistent with relevant academic papers. The evaluation unit can also verify whether the content of the information is consistent with relevant books. The evaluation unit can also verify whether the content of the information is consistent with relevant reports. This improves the accuracy of credibility assessment by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the content of the information into a generating AI, which can then evaluate its credibility by referring to relevant literature.

[0088] The supplementary section can estimate the user's emotions and adjust the method of alerting based on the estimated emotions. For example, if the user is feeling anxious, the supplementary section can provide a detailed alert. If the user is relaxed, the supplementary section can provide a concise alert. If the user is in a hurry, the supplementary section can provide a rapid alert. This allows for appropriate alerting by adjusting the method of alerting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the supplementary section may be performed using AI, for example, or not using AI. For example, the supplementary section can input user emotion data into the generative AI, which can then adjust the method of alerting.

[0089] The supplementary section can adjust the level of detail of the warning based on the impact of the misinformation when supplementing it. For example, if the impact of the misinformation is high, the supplementary section will provide a detailed warning. If the impact of the misinformation is moderate, the supplementary section may provide a concise warning. If the impact of the misinformation is low, the supplementary section may provide a simple warning. This allows for appropriate warnings by adjusting the level of detail of the warning according to the impact of the misinformation. Some or all of the above processing in the supplementary section may be performed using AI, for example, or without AI. For example, the supplementary section can input the impact of the misinformation into a generating AI, which can then adjust the level of detail of the warning.

[0090] The supplementary component can apply different supplementary algorithms depending on the category of information during the supplementation process. For example, for news articles, the supplementary component supplements information by referring to reliable sources. For social media posts, the supplementary component can also supplement information by referring to the opinions of relevant experts. For website content, the supplementary component can also supplement information by referring to announcements from relevant public institutions. This enables appropriate supplementation according to the category of information. Some or all of the above processing in the supplementary component may be performed using AI, for example, or without AI. For example, the supplementary component can input the category of information into a generating AI, and the generating AI can apply different supplementary algorithms.

[0091] The supplementary section can estimate the user's emotions and adjust how supplementary information is displayed based on the estimated emotions. For example, if the user is feeling anxious, the supplementary section can display detailed supplementary information. If the user is relaxed, the supplementary section can also display concise supplementary information. If the user is in a hurry, the supplementary section can also display concise supplementary information. This allows for the provision of appropriate information by adjusting how supplementary information is displayed 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 supplementary section may be performed using AI, or not using AI. For example, the supplementary section can input user emotion data into the generative AI, which can then adjust how supplementary information is displayed.

[0092] The supplementary unit can determine the priority of supplementation based on the information submission date. For example, the supplementary unit can prioritize supplementing the most recent information. It can also lower the priority of supplementing older information. For information whose submission date is unknown, the supplementary unit can set the priority to a medium level. This allows for the provision of appropriate information by determining the priority of supplementation based on the information submission date. Some or all of the above processing in the supplementary unit may be performed using AI, for example, or not using AI. For example, the supplementary unit can input the information submission date into a generating AI, which can then determine the priority of supplementation.

[0093] The supplementary unit can adjust the order of supplementation based on the relevance of the information. For example, the supplementary unit can prioritize supplementing information with high relevance. For information with moderate relevance, the supplementary unit can set the supplementation order to a moderate level. For information with low relevance, the supplementary unit can postpone the supplementation order. This allows for the provision of appropriate information by adjusting the order of supplementation based on the relevance of the information. Some or all of the above processing in the supplementary unit may be performed using AI, for example, or without AI. For example, the supplementary unit can input the relevance of the information into a generating AI, which can then adjust the order of supplementation.

[0094] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is feeling anxious, the information provider can provide detailed information. If the user is relaxed, the information provider can provide concise information. If the user is in a hurry, the information provider can provide information quickly. This allows for appropriate information delivery by adjusting the method of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into the generative AI, and the generative AI can adjust the method of information delivery.

[0095] The information provider can select the optimal method of information delivery by referring to the user's past information browsing history at the time of delivery. For example, the provider may prioritize providing information sources that the user has frequently viewed in the past. The provider may also prioritize providing information of a specific genre based on the user's past browsing history. The provider may also analyze the user's past browsing history and select and provide highly reliable information sources. This allows the provider to select the optimal method of information delivery by referring to the user's past information browsing history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider may input the user's past information browsing history into a generating AI, which can then select the optimal method of information delivery.

[0096] The information provider can customize the means of providing information based on the user's current areas of interest at the time of delivery. For example, the provider may prioritize providing information related to topics the user is currently interested in. The provider may also provide information that includes relevant keywords based on the user's current areas of interest. The provider may also prioritize providing information from specific categories based on the user's current areas of interest. This makes it possible to provide appropriate information by customizing the means of providing information based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider may input the user's current areas of interest into a generating AI, which can then customize the means of providing information.

[0097] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is feeling anxious, the information provider will prioritize providing information that provides reassurance. If the user is excited, the information provider may also prioritize providing information that encourages calm judgment. If the user is tired, the information provider may also prioritize providing concise and easy-to-understand information. This allows for appropriate information provision by determining the priority of information provision 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 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 information provider may be performed using AI, or not using AI. For example, the information provider can input user emotion data into a generative AI, which can then determine the priority of information provision.

[0098] The information delivery unit can select the optimal information delivery method by considering the user's geographical location information at the time of delivery. For example, the information delivery unit may prioritize providing news and event information related to the user's current location. The information delivery unit may also provide region-specific information based on the user's geographical location information. The information delivery unit may also prioritize providing information of high urgency by considering the user's geographical location information. In this way, the optimal information delivery method can be selected by considering the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit may input the user's geographical location information into a generating AI, and the generating AI may select the optimal information delivery method.

[0099] The information provider can analyze the user's social media activity and propose means of information provision at the time of provision. For example, the information provider can provide information related to topics the user has shown interest in on social media. The information provider can also prioritize providing information shared by the user's followers and friends on social media. The information provider can also analyze the user's social media activity and provide information related to trends. In this way, by analyzing the user's social media activity, it is possible to propose appropriate means of information provision. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the user's social media activity into a generating AI, and the generating AI can propose means of information provision.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The information delivery system can estimate the user's emotions and adjust the timing of information delivery based on those estimates. For example, if the user is stressed, the system will reduce the frequency of information delivery and provide only essential information. If the user is relaxed, the system can increase the frequency of information delivery and provide a wider range of information. If the user is in a hurry, the system can prioritize providing important information in real time. This allows for appropriate information delivery by adjusting the timing of information delivery according to the user's emotions.

[0102] The evaluation unit can estimate the user's emotions and adjust the credibility evaluation criteria based on those emotions. For example, if the user is feeling anxious, the evaluation unit will apply strict credibility evaluation criteria. If the user is relaxed, the evaluation unit can also apply flexible credibility evaluation criteria. If the user is in a hurry, the evaluation unit can also apply criteria for quickly assessing credibility. This allows for appropriate credibility evaluation by adjusting the credibility evaluation criteria according to the user's emotions.

[0103] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, if the user is stressed, the unit reduces the frequency of information collection and collects only essential information. If the user is relaxed, the unit increases the frequency of information collection and can also collect a wider range of information. If the user is in a hurry, the unit can prioritize collecting important information in real time. This allows for appropriate information collection by adjusting the timing of information collection according to the user's emotions.

[0104] The supplementary section can estimate the user's emotions and adjust the method of alerting based on those emotions. For example, if the user is feeling anxious, the supplementary section will provide a detailed alert. If the user is relaxed, the supplementary section may provide a concise alert. If the user is in a hurry, the supplementary section may provide a quick alert. This allows for appropriate alerting by adjusting the method of alerting according to the user's emotions.

[0105] The information delivery system can estimate the user's emotions and prioritize information delivery based on those emotions. For example, if a user is feeling anxious, the system will prioritize providing information that provides reassurance. If a user is excited, the system may also prioritize providing information that encourages calm judgment. If a user is tired, the system may also prioritize providing concise and easy-to-understand information. By prioritizing information delivery according to the user's emotions, appropriate information delivery becomes possible.

[0106] The data collection unit can analyze a user's past information browsing history and select the most suitable information collection method. For example, it can prioritize collecting information from sources that the user has frequently viewed in the past. The data collection unit can also prioritize collecting information of a specific genre based on the user's past browsing history. The data collection unit can also analyze a user's past browsing history and select and collect highly reliable information sources. In this way, by analyzing a user's past information browsing history, the optimal information collection method can be selected.

[0107] The evaluation unit can determine the reliability of the information source by comparing it with past databases during the evaluation process. For example, it can check whether the information source is a media outlet that has been previously evaluated as highly reliable. The evaluation unit can also check whether the information source has previously provided misinformation with the database. The evaluation unit can also check whether the information source has previously provided unreliable information. By comparing the reliability of the information source with past databases, the evaluation unit can provide highly reliable information.

[0108] The supplementary section can adjust the level of detail in its warnings based on the impact of the misinformation. For example, if the impact of the misinformation is high, the supplementary section will provide a detailed warning. If the impact is moderate, the supplementary section can provide a concise warning. If the impact is low, the supplementary section can provide a simple warning. This allows for appropriate warnings by adjusting the level of detail in the warnings according to the impact of the misinformation.

[0109] The information provider can select the most appropriate method of information delivery by considering the user's geographical location. For example, it can prioritize providing news and event information relevant to the user's current location. The information provider can also provide region-specific information based on the user's geographical location. Furthermore, it can prioritize providing information of high urgency by considering the user's geographical location. This allows for the selection of the most appropriate method of information delivery by considering the user's geographical location.

[0110] The information provider can analyze the user's social media activity at the time of delivery and propose methods for providing information. For example, it can provide information related to topics the user has shown interest in on social media. The information provider can also prioritize providing information shared by the user's followers and friends on social media. The information provider can also analyze the user's social media activity and provide information related to trends. In this way, by analyzing the user's social media activity, it can propose appropriate methods for providing information.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects information. The collection unit collects data such as news articles, social media posts, and website content. The collection unit can collect news articles from online news sites, social media posts via APIs, and website content using web scraping technology. Step 2: The evaluation unit assesses the credibility of the information collected by the collection unit. The evaluation unit determines the source and accuracy of the information, for example, verifying that news articles originate from reliable media outlets and that their content is consistent with other reliable sources. The evaluation unit assesses the credibility of the information and identifies misinformation and unreliable information. Step 3: The supplementary section provides warnings and supplementary information based on the information evaluated by the evaluation section. The supplementary section warns against misinformation, provides accurate information, and provides additional information for information with low credibility. The supplementary section helps users make accurate judgments. Step 4: The provider unit provides the user with the information supplemented by the supplementary unit. The provider unit can notify the user of the supplementary information, display it on the dashboard, or send it via email.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, evaluation unit, supplementation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects news articles and social media posts. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the credibility of the collected information. The supplementation unit is implemented by the control unit 46A of the smart device 14 and warns against misinformation. The provision unit is implemented by the output device 40 of the smart device 14 and notifies the user of the supplemented information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, evaluation unit, supplementation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects news articles and social media posts. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the credibility of the collected information. The supplementation unit is implemented by the control unit 46A of the smart glasses 214 and warns against misinformation. The provision unit is implemented by the speaker 240 of the smart glasses 214 and notifies the user of the supplemented information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[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 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.

[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 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.

[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 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.

[0148] Each of the multiple elements described above, including the collection unit, evaluation unit, supplementation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects news articles and social media posts. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the credibility of the collected information. The supplementation unit is implemented by the control unit 46A of the headset terminal 314 and warns against misinformation. The provision unit is implemented by the display 343 of the headset terminal 314 and notifies the user of the supplemented information. 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, evaluation unit, supplementation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects news articles and social media posts. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the credibility of the collected information. The supplementation unit is implemented by the control unit 46A of the robot 414 and warns against misinformation. The provision unit is implemented by the speaker 240 of the robot 414 and notifies the user of the supplemented information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The information collection unit, An evaluation unit that evaluates the credibility of the information collected by the collection unit, A supplementary unit provides warnings and supplementary information based on the information evaluated by the aforementioned evaluation unit, The system comprises a providing unit that provides the user with the information supplemented by the supplementing unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as news articles, social media posts, and website content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, Determining the source and accuracy of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supplementary section is, We will raise awareness about misinformation and provide accurate information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supplementary section is, Provide additional information for information with low credibility. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide users with supplementary information to complement and enhance their information literacy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We will monetize through advertising and API provision. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past information browsing history to select the optimal information collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, We estimate the user's emotions and adjust the credibility evaluation criteria for the information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, During the evaluation, the reliability of the information source is determined by comparing it with past databases. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, During the evaluation, verify that the information is consistent with other reliable sources. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, When evaluating, the geographical distribution of the information is taken into consideration to assess its credibility. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During evaluation, we improve the accuracy of credibility assessments by referring to relevant literature on the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supplementary section is, The system estimates the user's emotions and adjusts the method of alerting users based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supplementary section is, When supplementing information, adjust the level of detail in the warning based on the impact of the misinformation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supplementary section is, During supplementation, different supplementation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supplementary section is, It estimates the user's emotions and adjusts how supplementary information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supplementary section is, When supplementing information, prioritize the supplements based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supplementary section is, When supplementing information, adjust the order of supplementation based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, the system will select the most suitable method of delivery by referring to the user's past information browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the means of delivery will be customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and propose methods for providing information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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. The information collection unit, An evaluation unit that evaluates the credibility of the information collected by the collection unit, A supplementary unit provides warnings and supplementary information based on the information evaluated by the aforementioned evaluation unit, The system comprises a providing unit that provides the user with the information supplemented by the supplementing unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as news articles, social media posts, and website content. The system according to feature 1.

3. The evaluation unit, Determining the source and accuracy of the collected data. The system according to feature 1.

4. The aforementioned supplementary section is, We will raise awareness about misinformation and provide accurate information. The system according to feature 1.

5. The aforementioned supplementary section is, Provide additional information for information with low credibility. The system according to feature 1.

6. The aforementioned supply unit is, Provide users with supplementary information to complement and enhance their information literacy. The system according to feature 1.

7. The aforementioned supply unit is, We will monetize through advertising and API provision. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past information browsing history to select the optimal information collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current areas of interest. The system according to feature 1.

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