system

Abstract

The system according to this embodiment aims to evaluate the reliability of information sources and prevent the spread of misinformation. [Solution] The system according to the embodiment comprises an analysis unit, a scoring unit, a visualization unit, a setting unit, and a notification unit. The analysis unit analyzes information sources. The scoring unit assigns reliability scores based on the information sources analyzed by the analysis unit. The visualization unit visualizes the reliability of the information based on the reliability scores assigned by the scoring unit. The setting unit allows the user to set specific information. The notification unit automatically monitors related information based on the information set by the setting unit and notifies if there are any changes or updates.

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, the reliability of information sources has not been sufficiently evaluated to prevent the spread of misinformation, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the reliability of information sources and prevent the spread of misinformation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a scoring unit, a visualization unit, a configuration unit, and a notification unit. The analysis unit analyzes information sources. The scoring unit assigns reliability scores based on the information sources analyzed by the analysis unit. The visualization unit visualizes the reliability of the information based on the reliability scores assigned by the scoring unit. The configuration unit allows users to configure specific information. The notification unit automatically monitors related information based on the information configured by the configuration unit and notifies of any changes or updates. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the reliability of information sources and prevent the spread of misinformation. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The RootFinder Enhanced system according to an embodiment of the present invention is a system that uses generative AI to evaluate the reliability of information sources and prevent the spread of misinformation. Specifically, it consists of the following steps: First, the generative AI analyzes the information source and assigns a reliability score. Based on this score, the reliability of the information is visualized. Next, when the user sets specific information, the generative AI automatically monitors the related information and notifies the user of any changes or updates. Furthermore, the generative AI traces back the information to reveal its origin and visualizes how the information has changed over time. This allows the user to access highly reliable information and prevent the spread of misinformation. Specifically, the RootFinder Enhanced system allows the user to access highly reliable information and prevent the spread of misinformation.

[0029] The RootFinder Enhanced system according to this embodiment comprises an analysis unit, a scoring unit, a visualization unit, a configuration unit, and a notification unit. The analysis unit analyzes information sources. The analysis unit analyzes information sources such as news articles, social media posts, and academic papers. The analysis unit can analyze information sources using generative AI. The scoring unit assigns reliability scores based on the information sources analyzed by the analysis unit. The scoring unit assigns reliability scores based on reliability indicators or score ranges, for example. The scoring unit can assign reliability scores using generative AI. The visualization unit visualizes the reliability of information based on the reliability scores assigned by the scoring unit. The visualization unit visualizes the reliability of information in the form of graphs, charts, heatmaps, etc. The visualization unit can visualize the reliability of information using generative AI. The configuration unit allows users to configure specific information. The configuration unit allows users to configure information such as specific topics, keywords, and datasets, for example. The configuration unit can configure information using generative AI. The notification unit automatically monitors relevant information based on the information configured by the configuration unit and notifies if there are any changes or updates. The notification unit automatically monitors information such as relevant news articles and database entries, and notifies the user of any changes or updates. The notification unit can use generational AI to automatically monitor relevant information and notify users of any changes or updates. This allows the RootFinder Enhanced system to evaluate the reliability of information sources and prevent the spread of misinformation.

[0030] The analysis department analyzes information sources. Specifically, it targets a wide range of sources, including news articles, social media posts, and academic papers. These sources are collected as text data and analyzed in detail using generative AI. The generative AI utilizes natural language processing techniques to understand the content of the text and extract important information. For example, in news articles, it analyzes the article's subject, the people involved, and the timeline of events; in social media posts, it performs sentiment analysis and trend detection. In academic papers, it analyzes the paper's summary and citation relationships to evaluate the reliability and impact of the research. The generative AI integrates this information and provides foundational data for understanding the overall picture of the information sources. Furthermore, the generative AI can perform initial filtering to evaluate the reliability of the information sources and eliminate unreliable information. As a result, the analysis department can efficiently extract useful data from a vast amount of information sources and provide reliable information.

[0031] The scoring unit assigns reliability scores based on the information sources analyzed by the analysis unit. Specifically, it performs a numerical evaluation of each information source based on reliability indicators and score ranges. The generating AI utilizes historical data and statistical information to execute algorithms for evaluating the reliability of information sources. For example, in the case of news articles, the score is assigned considering factors such as the publisher, the reliability of the author, and the reliability of the sources cited. For social media posts, evaluation criteria include the number of followers of the poster, the content of past posts, and the degree of spread of the posts. For academic papers, the reliability score is calculated based on the number of citations of the paper, the impact factor of the publishing journal, and the author's research history. The generating AI comprehensively judges these evaluation criteria and assigns an appropriate reliability score to each information source. The scoring unit can update reliability scores in real time, providing evaluations based on the latest information. In this way, the scoring unit provides important indicators for users to quickly and accurately judge the reliability of information sources.

[0032] The visualization unit visualizes the reliability of information based on the reliability score assigned by the scoring unit. Specifically, it visually represents the reliability of information in the form of graphs, charts, heatmaps, etc. The generation AI also plays an important role in data visualization, organizing and displaying data in a way that users can intuitively understand. For example, it displays the reliability score of news articles as a bar graph, allowing users to compare the reliability of each article. It displays the reliability of social media posts as a heatmap, showing highly reliable and low-reliability posts in different colors. It displays the reliability of academic papers as charts, visualizing the distribution of reliability based on the number of citations and the impact factor of the publishing journal. The generation AI can customize the visualization format and display content according to the user's settings and preferences. This allows the visualization unit to enable users to grasp the reliability of information at a glance and support rapid decision-making.

[0033] The settings section allows users to configure specific information. Specifically, users can set information such as specific topics, keywords, and datasets. The generating AI analyzes the user's settings and filters the information to provide the most relevant information. For example, if a user is interested in a particular news topic, news articles related to that topic will be displayed preferentially. Information is collected based on specific keywords, and information that the user is interested in is efficiently provided. In the dataset settings, information is analyzed based on the dataset specified by the user, and relevant information is extracted. The generating AI learns the user's settings and can perform personalized filtering to provide information that matches the user's preferences and interests. As a result, the settings section provides the information the user needs quickly and accurately, and the reliability of the information is enhanced.

[0034] The notification unit automatically monitors relevant information based on the settings configured by the configuration unit and notifies users of any changes or updates. Specifically, it automatically monitors information such as relevant news articles and database entries, and notifies users of any changes or updates. The generation AI plays a crucial role in information monitoring and notification, detecting changes in information in real time. For example, if a new article on a specific news topic is published, the generation AI automatically detects the article and notifies the user. If a database entry is updated, it analyzes the update and notifies the user. The generation AI can quickly detect changes in information and notify users at the appropriate time. Furthermore, the notification unit can customize the frequency and format of notifications according to the user's notification settings. This allows the notification unit to ensure that users are always up-to-date with the latest information and maintain the reliability of the information.

[0035] The analysis unit can analyze information sources using generative AI. For example, the analysis unit can use generative AI to analyze information sources such as news articles, social media posts, and academic papers. The generative AI can analyze the content of information sources and evaluate their reliability using, for example, natural language processing technology. The generative AI can analyze the content of information sources using, for example, text generation AI (e.g., LLM). The generative AI can summarize the content of information sources and extract important information. As a result, the accuracy of information source analysis is improved by using generative AI.

[0036] The scoring unit can assign reliability scores using a generative AI. For example, the scoring unit can assign reliability scores to information sources such as news articles, social media posts, and academic papers using a generative AI. The generative AI can analyze the content of the information source using, for example, natural language processing technology and calculate the reliability score. The generative AI can analyze the content of the information source using, for example, a text generation AI (e.g., LLM) and assign a reliability score. The generative AI can summarize the content of the information source, extract important information, and assign a reliability score. This improves the accuracy of reliability score assignment by using a generative AI.

[0037] The visualization unit can visualize the reliability of information based on reliability scores. For example, the visualization unit can visualize the reliability of information such as news articles, social media posts, and academic papers based on reliability scores. The visualization unit can visualize the reliability of information in formats such as graphs, charts, and heatmaps. The visualization unit can also visualize the reliability of information using generative AI. For example, the generative AI can take reliability scores as input and output visualization data. This allows users to intuitively understand the reliability of information by visualizing it based on reliability scores.

[0038] The settings section allows users to configure specific information. For example, users can configure information such as specific topics, keywords, or datasets. The settings section can also use generative AI to configure information. For example, the generative AI can analyze user input and suggest appropriate configuration options. For example, the generative AI can refer to the user's past configuration history and suggest the optimal settings. This allows the system to provide information tailored to the user's needs by enabling users to configure specific information.

[0039] The notification unit can automatically monitor relevant information using generative AI and notify users of any changes or updates. For example, the notification unit can automatically monitor relevant information such as news articles, social media posts, and academic papers using generative AI. The generative AI can analyze the content of relevant information using natural language processing technology to detect changes and updates. The generative AI can analyze the content of relevant information using text generation AI (e.g., LLM) to detect changes and updates. The generative AI can summarize the content of relevant information, extract important information, and notify users. As a result, by using generative AI, changes and updates to relevant information can be quickly detected and notified to the user.

[0040] The analysis unit can optimize its analysis algorithm by referring to past analysis results when analyzing information sources. For example, the analysis unit can use generative AI to refer to past analysis results and re-evaluate the reliability of a particular information source. Generative AI can, for example, refer to past datasets and analysis reports and apply similar analysis methods to similar information sources. Generative AI can, for example, adjust the parameters of the analysis algorithm based on past analysis results to improve accuracy. In this way, the accuracy of the analysis algorithm is improved by referring to past analysis results.

[0041] The analysis department can apply different analytical methods depending on the category of information when analyzing information sources. For example, it can use generative AI to analyze news articles and academic papers using different methods and evaluate their respective reliability. Generative AI can, for example, analyze social media posts and official announcements using different methods and compare their reliability. Generative AI can, for example, analyze entertainment information and business information using different methods and assign appropriate reliability scores. By applying different analytical methods depending on the category of information, it is possible to provide more appropriate analysis results.

[0042] The analysis unit can perform analysis while considering the geographical distribution of information sources. For example, the analysis unit can use generative AI to prioritize the analysis of information sources in a specific region and evaluate the reliability of that region. Generative AI can, for example, compare information sources in different regions and detect geographical biases. Generative AI can, for example, analyze information sources related to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0043] The analysis department can improve the accuracy of its analysis by referring to relevant literature and databases when analyzing information sources. For example, the analysis department can use generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature to supplement the background information of the information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of the analysis is improved by referring to relevant literature and databases.

[0044] The scoring unit can optimize the scoring algorithm by referring to past scoring results when assigning reliability scores. For example, the scoring unit can use a generative AI to refer to past scoring results and re-evaluate the reliability of a particular information source. The generative AI can, for example, refer to past datasets and scoring reports and apply similar scoring methods to similar information sources. For example, the generative AI can adjust the parameters of the scoring algorithm based on past scoring results to improve accuracy. In this way, the accuracy of the scoring algorithm is improved by referring to past scoring results.

[0045] The scoring unit can apply different scoring methods depending on the information category when assigning reliability scores. For example, the scoring unit can use a generative AI to score news articles and academic papers using different methods and evaluate their reliability. The generative AI can, for example, score social media posts and official announcements using different methods and compare their reliability. The generative AI can, for example, score entertainment information and business information using different methods and assign appropriate reliability scores. This allows for the provision of more appropriate scores by applying different scoring methods depending on the information category.

[0046] The scoring unit can perform scoring while considering the geographical distribution of information when assigning reliability scores. For example, the scoring unit can use generative AI to prioritize scoring information sources in a specific region and evaluate the reliability of that region. For example, the generative AI can compare information sources in different regions and detect geographical biases. For example, the generative AI can score information sources related to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0047] The scoring unit can improve the accuracy of its scoring by referring to relevant literature and databases when assigning reliability scores. For example, the scoring unit uses generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature and supplement background information on information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of scoring is improved by referring to relevant literature and databases.

[0048] The visualization unit can optimize the visualization algorithm by referring to past visualization results when visualizing reliability. For example, the visualization unit can use generative AI to refer to past visualization results and re-evaluate the reliability of a specific information source. For example, the generative AI can refer to past datasets and visualization reports and apply similar visualization methods to similar information sources. For example, the generative AI can adjust the parameters of the visualization algorithm based on past visualization results to improve accuracy. In this way, the accuracy of the visualization algorithm is improved by referring to past visualization results.

[0049] The visualization unit can apply different visualization methods depending on the category of information when visualizing reliability. For example, the visualization unit can use generative AI to visualize news articles and academic papers using different methods and evaluate their reliability. For example, the generative AI can visualize social media posts and official announcements using different methods and compare their reliability. For example, the generative AI can visualize entertainment information and business information using different methods and assign appropriate reliability scores. By applying different visualization methods depending on the category of information, more appropriate visualization results can be provided.

[0050] The visualization unit can visualize information while considering its geographical distribution when visualizing reliability. For example, the visualization unit can use generative AI to prioritize the visualization of information sources in a specific region and evaluate the reliability of that region. For example, the generative AI can compare information sources in different regions and detect geographical biases. For example, the generative AI can visualize information sources related to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0051] The visualization unit can improve the accuracy of its visualizations by referring to relevant literature and databases when visualizing reliability. For example, the visualization unit uses generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature and supplement the background information of the information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of visualizations is improved by referring to relevant literature and databases.

[0052] The configuration unit can optimize its configuration algorithm by referring to past configuration history when configuring information. For example, the configuration unit can use generative AI to refer to past configuration history and automatically suggest configuration options that the user frequently uses. For example, the generative AI can refer to past datasets and configuration reports and apply similar configuration methods to similar configurations. For example, the generative AI can adjust the parameters of the configuration algorithm based on past configuration history to improve accuracy. In this way, the accuracy of the configuration algorithm is improved by referring to past configuration history.

[0053] The configuration unit can apply different configuration methods depending on the information category when configuring information. For example, the configuration unit can use a generative AI to configure news articles and academic papers using different methods and evaluate their respective reliability. The generative AI can, for example, configure social media posts and official announcements using different methods and compare their reliability. The generative AI can, for example, configure entertainment information and business information using different methods and assign appropriate reliability scores. This allows for more appropriate configuration by applying different configuration methods depending on the information category.

[0054] The configuration unit can configure information while considering its geographical distribution. For example, the configuration unit can use a generative AI to prioritize information sources in a specific region and evaluate the reliability of that region. The generative AI can, for example, compare information sources in different regions and detect geographical biases. The generative AI can, for example, configure information sources relevant to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0055] The configuration unit can improve the accuracy of its settings by referring to relevant literature and databases when configuring information. For example, the configuration unit uses generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature and supplement background information on information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of the settings is improved by referring to relevant literature and databases.

[0056] The notification unit can optimize its notification algorithm by referring to past notification history when issuing a notification. For example, the notification unit can use a generative AI to refer to past notification history and automatically suggest notification content that the user frequently receives. The generative AI can, for example, refer to past datasets and notification reports and apply similar notification methods to similar notifications. For example, the generative AI can adjust the parameters of the notification algorithm based on past notification history to improve accuracy. In this way, the accuracy of the notification algorithm is improved by referring to past notification history.

[0057] The notification unit can apply different notification methods depending on the category of information at the time of notification. For example, the notification unit can use generative AI to notify about news articles and academic papers using different methods and evaluate their reliability. For example, the generative AI can notify about social media posts and official announcements using different methods and compare their reliability. For example, the generative AI can notify about entertainment information and business information using different methods and assign appropriate reliability scores. This makes it possible to provide more appropriate notifications by applying different notification methods depending on the category of information.

[0058] The notification unit can consider the geographical distribution of information when issuing notifications. For example, the notification unit can use generative AI to prioritize notifications of information sources in a specific region and evaluate the reliability of that region. For example, the generative AI can compare information sources in different regions and detect geographical biases. For example, the generative AI can notify users of information sources relevant to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0059] The notification unit can improve the accuracy of notifications by referring to relevant literature and databases at the time of notification. For example, the notification unit can use generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature and supplement background information on information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of notifications is improved by referring to relevant literature and databases.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The RootFinder Enhanced system can also include a behavioral analysis unit that analyzes the user's browsing history. This unit records and analyzes what information the user has viewed and what actions they have taken in the past. For example, if a user frequently views a particular news article, it can prioritize providing information related to that news article. Similarly, if a user frequently searches for information on a specific topic, it can notify them of the latest information related to that topic. Furthermore, if a user has a tendency to avoid unreliable sources in the past, it can prioritize displaying reliable sources. This enables more personalized information delivery based on the user's browsing history.

[0062] The RootFinder Enhanced system can further include a reliability history section that references the past reliability history of the information source's sender when evaluating the reliability of the information source. For example, the reliability history section prioritizes evaluating senders who have provided reliable information in the past. It can also carefully evaluate information from senders who have provided misinformation in the past. Furthermore, it can evaluate reliability based on the sender's area of ​​expertise and past performance. This enables more accurate reliability evaluation based on the sender's past reliability history.

[0063] The RootFinder Enhanced system can further include a content evaluation unit that applies different evaluation criteria based on the content of the information when assessing the reliability of information sources. For example, the content evaluation unit can evaluate news articles and academic papers using different criteria and assess their respective reliability. It can also evaluate social media posts and official announcements using different criteria and compare their reliability. Furthermore, it can evaluate entertainment information and business information using different criteria and assign appropriate reliability scores. This allows for more accurate reliability assessment by applying different evaluation criteria depending on the content of the information.

[0064] The RootFinder Enhanced system can further include a geographic evaluation unit that considers the geographical distribution of information when evaluating the reliability of information sources. The geographic evaluation unit can, for example, prioritize the evaluation of information sources in a specific region and assess the reliability of that region. It can also compare information sources in different regions and detect geographical biases. Furthermore, it can evaluate information sources relevant to a specific region based on their geographical distribution. This enables region-specific reliability evaluation by considering the geographical distribution of information.

[0065] The RootFinder Enhanced system can also include a reference section that consults relevant literature and databases when evaluating the reliability of information sources. This reference section can, for example, refer to academic databases to assess the reliability of information sources. It can also refer to relevant literature to supplement background information on information sources. Furthermore, it can leverage databases to evaluate the reliability of information sources from multiple perspectives. This improves the accuracy of reliability assessments by referencing relevant literature and databases.

[0066] The RootFinder Enhanced system can further include an evaluation history unit that references past evaluation results when assessing the reliability of information sources. For example, the evaluation history unit can refer to past evaluation results to re-evaluate the reliability of a particular information source. It can also refer to past datasets and evaluation reports to apply similar evaluation methods to similar information sources. Furthermore, it can adjust the parameters of the evaluation algorithm based on past evaluation results to improve accuracy. This means that the accuracy of the evaluation algorithm is improved by referring to past evaluation results.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The analysis unit analyzes information sources. For example, it analyzes information sources such as news articles, social media posts, and academic papers. The analysis unit can use generative AI to analyze information sources. Step 2: The scoring unit assigns a reliability score based on the information sources analyzed by the analysis unit. For example, it assigns a reliability score based on reliability indicators or score ranges. The scoring unit can assign reliability scores using a generation AI. Step 3: The visualization unit visualizes the reliability of the information based on the reliability score assigned by the scoring unit. For example, it visualizes the reliability of the information in the form of graphs, charts, heatmaps, etc. The visualization unit can visualize the reliability of the information using generating AI. Step 4: The settings section allows the user to configure specific information. For example, information such as specific topics, keywords, and datasets can be configured. The settings section can use generative AI to configure the information. Step 5: The notification unit automatically monitors relevant information based on the information set by the settings unit and notifies the user if there are any changes or updates. For example, it automatically monitors information such as relevant news articles and database entries and notifies the user if there are any changes or updates. The notification unit can use generation AI to automatically monitor relevant information and notify the user if there are any changes or updates.

[0069] (Example of form 2) The RootFinder Enhanced system according to an embodiment of the present invention is a system that uses generative AI to evaluate the reliability of information sources and prevent the spread of misinformation. Specifically, it consists of the following steps: First, the generative AI analyzes the information source and assigns a reliability score. Based on this score, the reliability of the information is visualized. Next, when the user sets specific information, the generative AI automatically monitors the related information and notifies the user of any changes or updates. Furthermore, the generative AI traces back the information to reveal its origin and visualizes how the information has changed over time. This allows the user to access highly reliable information and prevent the spread of misinformation. Specifically, the RootFinder Enhanced system allows the user to access highly reliable information and prevent the spread of misinformation.

[0070] The RootFinder Enhanced system according to this embodiment comprises an analysis unit, a scoring unit, a visualization unit, a configuration unit, and a notification unit. The analysis unit analyzes information sources. The analysis unit analyzes information sources such as news articles, social media posts, and academic papers. The analysis unit can analyze information sources using generative AI. The scoring unit assigns reliability scores based on the information sources analyzed by the analysis unit. The scoring unit assigns reliability scores based on reliability indicators or score ranges, for example. The scoring unit can assign reliability scores using generative AI. The visualization unit visualizes the reliability of information based on the reliability scores assigned by the scoring unit. The visualization unit visualizes the reliability of information in the form of graphs, charts, heatmaps, etc. The visualization unit can visualize the reliability of information using generative AI. The configuration unit allows users to configure specific information. The configuration unit allows users to configure information such as specific topics, keywords, and datasets, for example. The configuration unit can configure information using generative AI. The notification unit automatically monitors relevant information based on the information configured by the configuration unit and notifies if there are any changes or updates. The notification unit automatically monitors information such as relevant news articles and database entries, and notifies the user of any changes or updates. The notification unit can use generational AI to automatically monitor relevant information and notify users of any changes or updates. This allows the RootFinder Enhanced system to evaluate the reliability of information sources and prevent the spread of misinformation.

[0071] The analysis department analyzes information sources. Specifically, it targets a wide range of sources, including news articles, social media posts, and academic papers. These sources are collected as text data and analyzed in detail using generative AI. The generative AI utilizes natural language processing techniques to understand the content of the text and extract important information. For example, in news articles, it analyzes the article's subject, the people involved, and the timeline of events; in social media posts, it performs sentiment analysis and trend detection. In academic papers, it analyzes the paper's summary and citation relationships to evaluate the reliability and impact of the research. The generative AI integrates this information and provides foundational data for understanding the overall picture of the information sources. Furthermore, the generative AI can perform initial filtering to evaluate the reliability of the information sources and eliminate unreliable information. As a result, the analysis department can efficiently extract useful data from a vast amount of information sources and provide reliable information.

[0072] The scoring unit assigns reliability scores based on the information sources analyzed by the analysis unit. Specifically, it performs a numerical evaluation of each information source based on reliability indicators and score ranges. The generating AI utilizes historical data and statistical information to execute algorithms for evaluating the reliability of information sources. For example, in the case of news articles, the score is assigned considering factors such as the publisher, the reliability of the author, and the reliability of the sources cited. For social media posts, evaluation criteria include the number of followers of the poster, the content of past posts, and the degree of spread of the posts. For academic papers, the reliability score is calculated based on the number of citations of the paper, the impact factor of the publishing journal, and the author's research history. The generating AI comprehensively judges these evaluation criteria and assigns an appropriate reliability score to each information source. The scoring unit can update reliability scores in real time, providing evaluations based on the latest information. In this way, the scoring unit provides important indicators for users to quickly and accurately judge the reliability of information sources.

[0073] The visualization unit visualizes the reliability of information based on the reliability score assigned by the scoring unit. Specifically, it visually represents the reliability of information in the form of graphs, charts, heatmaps, etc. The generation AI also plays an important role in data visualization, organizing and displaying data in a way that users can intuitively understand. For example, it displays the reliability score of news articles as a bar graph, allowing users to compare the reliability of each article. It displays the reliability of social media posts as a heatmap, showing highly reliable and low-reliability posts in different colors. It displays the reliability of academic papers as charts, visualizing the distribution of reliability based on the number of citations and the impact factor of the publishing journal. The generation AI can customize the visualization format and display content according to the user's settings and preferences. This allows the visualization unit to enable users to grasp the reliability of information at a glance and support rapid decision-making.

[0074] The settings section allows users to configure specific information. Specifically, users can set information such as specific topics, keywords, and datasets. The generating AI analyzes the user's settings and filters the information to provide the most relevant information. For example, if a user is interested in a particular news topic, news articles related to that topic will be displayed preferentially. Information is collected based on specific keywords, and information that the user is interested in is efficiently provided. In the dataset settings, information is analyzed based on the dataset specified by the user, and relevant information is extracted. The generating AI learns the user's settings and can perform personalized filtering to provide information that matches the user's preferences and interests. As a result, the settings section provides the information the user needs quickly and accurately, and the reliability of the information is enhanced.

[0075] The notification unit automatically monitors relevant information based on the settings configured by the configuration unit and notifies users of any changes or updates. Specifically, it automatically monitors information such as relevant news articles and database entries, and notifies users of any changes or updates. The generation AI plays a crucial role in information monitoring and notification, detecting changes in information in real time. For example, if a new article on a specific news topic is published, the generation AI automatically detects the article and notifies the user. If a database entry is updated, it analyzes the update and notifies the user. The generation AI can quickly detect changes in information and notify users at the appropriate time. Furthermore, the notification unit can customize the frequency and format of notifications according to the user's notification settings. This allows the notification unit to ensure that users are always up-to-date with the latest information and maintain the reliability of the information.

[0076] The analysis unit can analyze information sources using generative AI. For example, the analysis unit can use generative AI to analyze information sources such as news articles, social media posts, and academic papers. The generative AI can analyze the content of information sources and evaluate their reliability using, for example, natural language processing technology. The generative AI can analyze the content of information sources using, for example, text generation AI (e.g., LLM). The generative AI can summarize the content of information sources and extract important information. As a result, the accuracy of information source analysis is improved by using generative AI.

[0077] The scoring unit can assign reliability scores using a generative AI. For example, the scoring unit can assign reliability scores to information sources such as news articles, social media posts, and academic papers using a generative AI. The generative AI can analyze the content of the information source using, for example, natural language processing technology and calculate the reliability score. The generative AI can analyze the content of the information source using, for example, a text generation AI (e.g., LLM) and assign a reliability score. The generative AI can summarize the content of the information source, extract important information, and assign a reliability score. This improves the accuracy of reliability score assignment by using a generative AI.

[0078] The visualization unit can visualize the reliability of information based on reliability scores. For example, the visualization unit can visualize the reliability of information such as news articles, social media posts, and academic papers based on reliability scores. The visualization unit can visualize the reliability of information in formats such as graphs, charts, and heatmaps. The visualization unit can also visualize the reliability of information using generative AI. For example, the generative AI can take reliability scores as input and output visualization data. This allows users to intuitively understand the reliability of information by visualizing it based on reliability scores.

[0079] The settings section allows users to configure specific information. For example, users can configure information such as specific topics, keywords, or datasets. The settings section can also use generative AI to configure information. For example, the generative AI can analyze user input and suggest appropriate configuration options. For example, the generative AI can refer to the user's past configuration history and suggest the optimal settings. This allows the system to provide information tailored to the user's needs by enabling users to configure specific information.

[0080] The notification unit can automatically monitor relevant information using generative AI and notify users of any changes or updates. For example, the notification unit can automatically monitor relevant information such as news articles, social media posts, and academic papers using generative AI. The generative AI can analyze the content of relevant information using natural language processing technology to detect changes and updates. The generative AI can analyze the content of relevant information using text generation AI (e.g., LLM) to detect changes and updates. The generative AI can summarize the content of relevant information, extract important information, and notify users. As a result, by using generative AI, changes and updates to relevant information can be quickly detected and notified to the user.

[0081] The analysis unit can estimate the user's emotions and adjust the method of analyzing information sources based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the generative AI will perform a detailed analysis and prioritize the analysis of reliable information sources. If the user is relaxed, the generative AI can analyze a wide range of information sources and provide diverse perspectives. Furthermore, if the user is in a hurry, the generative AI can perform a rapid analysis and focus on the most important information sources. This allows for more appropriate analysis results by adjusting the method of analyzing information sources according to the user's emotions.

[0082] The analysis unit can optimize its analysis algorithm by referring to past analysis results when analyzing information sources. For example, the analysis unit can use generative AI to refer to past analysis results and re-evaluate the reliability of a particular information source. Generative AI can, for example, refer to past datasets and analysis reports and apply similar analysis methods to similar information sources. Generative AI can, for example, adjust the parameters of the analysis algorithm based on past analysis results to improve accuracy. In this way, the accuracy of the analysis algorithm is improved by referring to past analysis results.

[0083] The analysis department can apply different analytical methods depending on the category of information when analyzing information sources. For example, it can use generative AI to analyze news articles and academic papers using different methods and evaluate their respective reliability. Generative AI can, for example, analyze social media posts and official announcements using different methods and compare their reliability. Generative AI can, for example, analyze entertainment information and business information using different methods and assign appropriate reliability scores. By applying different analytical methods depending on the category of information, it is possible to provide more appropriate analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the generative AI can provide detailed analysis results and highlight reliable sources. If the user is relaxed, the generative AI can provide analysis results that include diverse perspectives. Furthermore, if the user is in a hurry, the generative AI can provide concise analysis results that get straight to the point. This allows for the provision of more relevant information by adjusting how the analysis results are displayed according to the user's emotions.

[0085] The analysis unit can perform analysis while considering the geographical distribution of information sources. For example, the analysis unit can use generative AI to prioritize the analysis of information sources in a specific region and evaluate the reliability of that region. Generative AI can, for example, compare information sources in different regions and detect geographical biases. Generative AI can, for example, analyze information sources related to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0086] The analysis department can improve the accuracy of its analysis by referring to relevant literature and databases when analyzing information sources. For example, the analysis department can use generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature to supplement the background information of the information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of the analysis is improved by referring to relevant literature and databases.

[0087] The scoring unit can estimate the user's emotions and adjust the criteria for assigning reliability scores based on the estimated emotions. For example, the scoring unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the scoring unit can assign a reliability score based on strict criteria using the generative AI. Conversely, if the user is relaxed, the scoring unit can assign a reliability score based on flexible criteria using the generative AI. Furthermore, if the user is in a hurry, the scoring unit can assign a reliability score quickly using the generative AI. This allows for the provision of more appropriate scores by adjusting the reliability score assignment criteria according to the user's emotions.

[0088] The scoring unit can optimize the scoring algorithm by referring to past scoring results when assigning reliability scores. For example, the scoring unit can use a generative AI to refer to past scoring results and re-evaluate the reliability of a particular information source. The generative AI can, for example, refer to past datasets and scoring reports and apply similar scoring methods to similar information sources. For example, the generative AI can adjust the parameters of the scoring algorithm based on past scoring results to improve accuracy. In this way, the accuracy of the scoring algorithm is improved by referring to past scoring results.

[0089] The scoring unit can apply different scoring methods depending on the information category when assigning reliability scores. For example, the scoring unit can use a generative AI to score news articles and academic papers using different methods and evaluate their reliability. The generative AI can, for example, score social media posts and official announcements using different methods and compare their reliability. The generative AI can, for example, score entertainment information and business information using different methods and assign appropriate reliability scores. This allows for the provision of more appropriate scores by applying different scoring methods depending on the information category.

[0090] The scoring unit can estimate the user's emotions and adjust the display method of the score based on the estimated emotions. For example, the scoring unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the generative AI can provide detailed score information and highlight reliable sources. If the user is relaxed, the generative AI can also provide score information that includes diverse perspectives. Furthermore, if the user is in a hurry, the generative AI can provide concise score information that gets straight to the point. This allows for the provision of more appropriate information by adjusting the display method of the score according to the user's emotions.

[0091] The scoring unit can perform scoring while considering the geographical distribution of information when assigning reliability scores. For example, the scoring unit can use generative AI to prioritize scoring information sources in a specific region and evaluate the reliability of that region. For example, the generative AI can compare information sources in different regions and detect geographical biases. For example, the generative AI can score information sources related to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0092] The scoring unit can improve the accuracy of its scoring by referring to relevant literature and databases when assigning reliability scores. For example, the scoring unit uses generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature and supplement background information on information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of scoring is improved by referring to relevant literature and databases.

[0093] The visualization unit can estimate the user's emotions and adjust the reliability visualization method based on the estimated user emotions. For example, the visualization unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the visualization unit can use the generative AI to provide detailed visualization information and highlight reliable sources. Furthermore, if the user is relaxed, the visualization unit can use the generative AI to provide visualization information that includes diverse perspectives. Additionally, if the user is in a hurry, the visualization unit can use the generative AI to provide concise visualization information that gets straight to the point. This allows for the provision of more appropriate information by adjusting the reliability visualization method according to the user's emotions.

[0094] The visualization unit can optimize the visualization algorithm by referring to past visualization results when visualizing reliability. For example, the visualization unit can use generative AI to refer to past visualization results and re-evaluate the reliability of a specific information source. For example, the generative AI can refer to past datasets and visualization reports and apply similar visualization methods to similar information sources. For example, the generative AI can adjust the parameters of the visualization algorithm based on past visualization results to improve accuracy. In this way, the accuracy of the visualization algorithm is improved by referring to past visualization results.

[0095] The visualization unit can apply different visualization methods depending on the category of information when visualizing reliability. For example, the visualization unit can use generative AI to visualize news articles and academic papers using different methods and evaluate their reliability. For example, the generative AI can visualize social media posts and official announcements using different methods and compare their reliability. For example, the generative AI can visualize entertainment information and business information using different methods and assign appropriate reliability scores. By applying different visualization methods depending on the category of information, more appropriate visualization results can be provided.

[0096] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. The visualization unit estimates the user's emotions, for example, using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, if the user is feeling anxious, the visualization unit can have the generative AI provide detailed visualization information and highlight reliable sources. Also, if the user is relaxed, the visualization unit can have the generative AI provide visualization information that includes diverse perspectives. Furthermore, if the user is in a hurry, the visualization unit can have the generative AI provide concise visualization information that gets straight to the point. In this way, by adjusting the display method of the visualization according to the user's emotions, more appropriate information can be provided.

[0097] The visualization unit can visualize information while considering its geographical distribution when visualizing reliability. For example, the visualization unit can use generative AI to prioritize the visualization of information sources in a specific region and evaluate the reliability of that region. For example, the generative AI can compare information sources in different regions and detect geographical biases. For example, the generative AI can visualize information sources related to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0098] The visualization unit can improve the accuracy of its visualizations by referring to relevant literature and databases when visualizing reliability. For example, the visualization unit uses generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature and supplement the background information of the information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of visualizations is improved by referring to relevant literature and databases.

[0099] The settings unit can estimate the user's emotions and adjust the information settings method based on the estimated emotions. For example, the settings unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the generative AI may provide simple settings options to minimize the setup process. Alternatively, if the user is relaxed, the generative AI may provide detailed settings options and suggest a customizable setup method. Furthermore, if the user is in a hurry, the generative AI may prioritize voice input to allow for quick information setup. This allows for more appropriate settings by adjusting the information settings method according to the user's emotions.

[0100] The configuration unit can optimize its configuration algorithm by referring to past configuration history when configuring information. For example, the configuration unit can use generative AI to refer to past configuration history and automatically suggest configuration options that the user frequently uses. For example, the generative AI can refer to past datasets and configuration reports and apply similar configuration methods to similar configurations. For example, the generative AI can adjust the parameters of the configuration algorithm based on past configuration history to improve accuracy. In this way, the accuracy of the configuration algorithm is improved by referring to past configuration history.

[0101] The configuration unit can apply different configuration methods depending on the information category when configuring information. For example, the configuration unit can use a generative AI to configure news articles and academic papers using different methods and evaluate their respective reliability. The generative AI can, for example, configure social media posts and official announcements using different methods and compare their reliability. The generative AI can, for example, configure entertainment information and business information using different methods and assign appropriate reliability scores. This allows for more appropriate configuration by applying different configuration methods depending on the information category.

[0102] The settings unit can estimate the user's emotions and adjust how the settings are displayed based on those emotions. For example, the settings unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the generative AI can provide a simple and highly visible display method. If the user is relaxed, the generative AI can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generative AI can provide a concise display method that gets straight to the point. This allows for the provision of more appropriate information by adjusting the display method of settings according to the user's emotions.

[0103] The configuration unit can configure information while considering its geographical distribution. For example, the configuration unit can use a generative AI to prioritize information sources in a specific region and evaluate the reliability of that region. The generative AI can, for example, compare information sources in different regions and detect geographical biases. The generative AI can, for example, configure information sources relevant to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0104] The configuration unit can improve the accuracy of its settings by referring to relevant literature and databases when configuring information. For example, the configuration unit uses generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature and supplement background information on information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of the settings is improved by referring to relevant literature and databases.

[0105] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, the notification unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the generative AI can provide a detailed notification and highlight reliable sources. If the user is relaxed, the generative AI can provide a notification that includes diverse perspectives. Furthermore, if the user is in a hurry, the generative AI can provide a concise notification that gets straight to the point. This allows for the provision of more relevant information by adjusting the notification method according to the user's emotions.

[0106] The notification unit can optimize its notification algorithm by referring to past notification history when issuing a notification. For example, the notification unit can use a generative AI to refer to past notification history and automatically suggest notification content that the user frequently receives. The generative AI can, for example, refer to past datasets and notification reports and apply similar notification methods to similar notifications. For example, the generative AI can adjust the parameters of the notification algorithm based on past notification history to improve accuracy. In this way, the accuracy of the notification algorithm is improved by referring to past notification history.

[0107] The notification unit can apply different notification methods depending on the category of information at the time of notification. For example, the notification unit can use generative AI to notify about news articles and academic papers using different methods and evaluate their reliability. For example, the generative AI can notify about social media posts and official announcements using different methods and compare their reliability. For example, the generative AI can notify about entertainment information and business information using different methods and assign appropriate reliability scores. This makes it possible to provide more appropriate notifications by applying different notification methods depending on the category of information.

[0108] The notification unit can estimate the user's emotions and adjust how notifications are displayed based on those emotions. For example, the notification unit estimates the user's emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is feeling anxious, the generative AI can provide detailed notification information and highlight reliable sources. If the user is relaxed, the generative AI can provide notification information that includes diverse perspectives. Furthermore, if the user is in a hurry, the generative AI can provide concise notification information that gets straight to the point. This allows for the provision of more appropriate information by adjusting how notifications are displayed according to the user's emotions.

[0109] The notification unit can consider the geographical distribution of information when issuing notifications. For example, the notification unit can use generative AI to prioritize notifications of information sources in a specific region and evaluate the reliability of that region. For example, the generative AI can compare information sources in different regions and detect geographical biases. For example, the generative AI can notify users of information sources relevant to a specific region based on their geographical distribution. This allows for the evaluation of reliability for each region by considering the geographical distribution of information.

[0110] The notification unit can improve the accuracy of notifications by referring to relevant literature and databases at the time of notification. For example, the notification unit can use generative AI to refer to academic databases and evaluate the reliability of information sources. The generative AI can, for example, refer to relevant literature and supplement background information on information sources. The generative AI can, for example, utilize databases to evaluate the reliability of information sources from multiple perspectives. As a result, the accuracy of notifications is improved by referring to relevant literature and databases.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The RootFinder Enhanced system can also include a behavioral analysis unit that analyzes the user's browsing history. This unit records and analyzes what information the user has viewed and what actions they have taken in the past. For example, if a user frequently views a particular news article, it can prioritize providing information related to that news article. Similarly, if a user frequently searches for information on a specific topic, it can notify them of the latest information related to that topic. Furthermore, if a user has a tendency to avoid unreliable sources in the past, it can prioritize displaying reliable sources. This enables more personalized information delivery based on the user's browsing history.

[0113] The RootFinder Enhanced system can further include an emotion assessment unit that estimates the user's emotions and evaluates the reliability of information based on those emotions. For example, if the user is feeling anxious, the emotion assessment unit will prioritize evaluating and displaying reliable information sources. If the user is relaxed, it can evaluate a wide range of information sources and provide diverse perspectives. Furthermore, if the user is in a hurry, it can perform a rapid evaluation and focus on the most important information sources. This allows for more appropriate information delivery by adjusting the reliability evaluation of information according to the user's emotions.

[0114] The RootFinder Enhanced system can further include a reliability history section that references the past reliability history of the information source's sender when evaluating the reliability of the information source. For example, the reliability history section prioritizes evaluating senders who have provided reliable information in the past. It can also carefully evaluate information from senders who have provided misinformation in the past. Furthermore, it can evaluate reliability based on the sender's area of ​​expertise and past performance. This enables more accurate reliability evaluation based on the sender's past reliability history.

[0115] The RootFinder Enhanced system can further include a content evaluation unit that applies different evaluation criteria based on the content of the information when assessing the reliability of information sources. For example, the content evaluation unit can evaluate news articles and academic papers using different criteria and assess their respective reliability. It can also evaluate social media posts and official announcements using different criteria and compare their reliability. Furthermore, it can evaluate entertainment information and business information using different criteria and assign appropriate reliability scores. This allows for more accurate reliability assessment by applying different evaluation criteria depending on the content of the information.

[0116] The RootFinder Enhanced system can also include a notification timing adjustment unit that estimates the user's emotions and adjusts the timing of notifications based on those emotions. For example, if the user is feeling anxious, the notification timing adjustment unit can quickly notify them of important information. If the user is relaxed, it can reduce the frequency of notifications and provide only the necessary information. Furthermore, if the user is in a hurry, it can prioritize notifying them of the most important information. This allows for more appropriate information delivery by adjusting the timing of notifications according to the user's emotions.

[0117] The RootFinder Enhanced system can further include a geographic evaluation unit that considers the geographical distribution of information when evaluating the reliability of information sources. The geographic evaluation unit can, for example, prioritize the evaluation of information sources in a specific region and assess the reliability of that region. It can also compare information sources in different regions and detect geographical biases. Furthermore, it can evaluate information sources relevant to a specific region based on their geographical distribution. This enables region-specific reliability evaluation by considering the geographical distribution of information.

[0118] The RootFinder Enhanced system can further include a display adjustment unit that estimates the user's emotions and adjusts how information is displayed based on those emotions. For example, if the user is feeling anxious, the display adjustment unit can provide detailed information and highlight reliable sources. If the user is relaxed, it can also provide information with diverse perspectives. Furthermore, if the user is in a hurry, it can provide concise information that gets straight to the point. This allows for more appropriate information delivery by adjusting how information is displayed according to the user's emotions.

[0119] The RootFinder Enhanced system can also include a reference section that consults relevant literature and databases when evaluating the reliability of information sources. This reference section can, for example, refer to academic databases to assess the reliability of information sources. It can also refer to relevant literature to supplement background information on information sources. Furthermore, it can leverage databases to evaluate the reliability of information sources from multiple perspectives. This improves the accuracy of reliability assessments by referencing relevant literature and databases.

[0120] The RootFinder Enhanced system can further include a filtering adjustment unit that estimates the user's emotions and adjusts the information filtering method based on those emotions. For example, if the user is feeling anxious, the filtering adjustment unit will prioritize filtering reliable sources. If the user is relaxed, it can filter a wide range of sources to provide diverse perspectives. Furthermore, if the user is in a hurry, it can quickly filter and focus on the most important sources. This allows for more appropriate information delivery by adjusting the information filtering method according to the user's emotions.

[0121] The RootFinder Enhanced system can further include an evaluation history unit that references past evaluation results when assessing the reliability of information sources. For example, the evaluation history unit can refer to past evaluation results to re-evaluate the reliability of a particular information source. It can also refer to past datasets and evaluation reports to apply similar evaluation methods to similar information sources. Furthermore, it can adjust the parameters of the evaluation algorithm based on past evaluation results to improve accuracy. This means that the accuracy of the evaluation algorithm is improved by referring to past evaluation results.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The analysis unit analyzes information sources. For example, it analyzes information sources such as news articles, social media posts, and academic papers. The analysis unit can use generative AI to analyze information sources. Step 2: The scoring unit assigns a reliability score based on the information sources analyzed by the analysis unit. For example, it assigns a reliability score based on reliability indicators or score ranges. The scoring unit can assign reliability scores using a generation AI. Step 3: The visualization unit visualizes the reliability of the information based on the reliability score assigned by the scoring unit. For example, it visualizes the reliability of the information in the form of graphs, charts, heatmaps, etc. The visualization unit can visualize the reliability of the information using generating AI. Step 4: The settings section allows the user to configure specific information. For example, information such as specific topics, keywords, and datasets can be configured. The settings section can use generative AI to configure the information. Step 5: The notification unit automatically monitors relevant information based on the information set by the settings unit and notifies the user if there are any changes or updates. For example, it automatically monitors information such as relevant news articles and database entries and notifies the user if there are any changes or updates. The notification unit can use generation AI to automatically monitor relevant information and notify the user if there are any changes or updates.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the analysis unit, scoring unit, visualization unit, setting unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The scoring unit is implemented by the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The setting unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the analysis unit, scoring unit, visualization unit, setting unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The scoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The visualization unit is implemented, for example, by the display of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The setting unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the analysis unit, scoring unit, visualization unit, setting unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The scoring unit is implemented by the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The setting unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the analysis unit, scoring unit, visualization unit, setting unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The scoring unit is implemented by the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the display of the robot 414 or the specific processing unit 290 of the data processing unit 12. The setting unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The notification unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The analysis department analyzes the information sources, A scoring unit assigns a reliability score based on the information source analyzed by the aforementioned analysis unit, A visualization unit that visualizes the reliability of information based on the reliability score assigned by the scoring unit, A settings section where the user sets specific information, The system includes a notification unit that automatically monitors related information based on the information set by the aforementioned setting unit and notifies the user if there are any changes or updates. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Using generative AI to analyze information sources The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned score assignment unit, A reliability score is assigned using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, Visualize the reliability of information based on reliability scores. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned setting unit is, The user sets specific information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, It uses a generation AI to automatically monitor relevant information and notifies you if there are any changes or updates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate the user's sentiment and adjust the information source analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is When analyzing information sources, we optimize the analysis algorithm by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is When analyzing information sources, different analytical methods are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is When analyzing information sources, the geographical distribution of the information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing information sources, referencing relevant literature and databases improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned score assignment unit, The system estimates user sentiment and adjusts the criteria for assigning reliability scores based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned score assignment unit, When assigning reliability scores, the scoring algorithm is optimized by referring to past scoring results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned score assignment unit, When assigning reliability scores, different scoring methods are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned score assignment unit, The system estimates the user's emotions and adjusts how the score is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned score assignment unit, When assigning reliability scores, the geographical distribution of the information is taken into consideration during the scoring process. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned score assignment unit, When assigning reliability scores, we improve the accuracy of the scoring by referring to relevant literature and databases. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, We estimate user sentiment and adjust the reliability visualization method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, When visualizing reliability, the visualization algorithm is optimized by referring to past visualization results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, When visualizing reliability, different visualization methods are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, When visualizing reliability, the geographical distribution of information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, When visualizing reliability, refer to relevant literature and databases to improve the accuracy of the visualization. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned setting unit is, It estimates the user's emotions and adjusts how information is set based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned setting unit is, When setting information, the setting algorithm is optimized by referring to past setting history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned setting unit is, When configuring information, different configuration methods are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned setting unit is, It estimates the user's emotions and adjusts how settings are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned setting unit is, When setting up information, the geographical distribution of the information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned setting unit is, When setting up information, refer to relevant literature and databases to improve the accuracy of the settings. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the notification algorithm is optimized by referring to past notification history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, different notification methods will be applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, It estimates the user's emotions and adjusts how notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When sending notifications, the geographical distribution of the information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, we refer to relevant literature and databases to improve the accuracy of the notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 analysis department analyzes the information sources, A scoring unit assigns a reliability score based on the information source analyzed by the aforementioned analysis unit, A visualization unit that visualizes the reliability of information based on the reliability score assigned by the scoring unit, A settings section where the user sets specific information, The system includes a notification unit that automatically monitors related information based on the information set by the aforementioned setting unit and notifies the user if there are any changes or updates. A system characterized by the following features.

2. The aforementioned analysis unit is Using generative AI to analyze information sources The system according to feature 1.

3. The aforementioned score assignment unit, A reliability score is assigned using generative AI. The system according to feature 1.

4. The aforementioned visualization unit, Visualize the reliability of information based on reliability scores. The system according to feature 1.

5. The setting unit is, The user sets specific information. The system according to feature 1.

6. The aforementioned notification unit, It uses AI generation to automatically monitor relevant information and notifies users of any changes or updates. The system according to feature 1.

7. The aforementioned analysis unit is We estimate the user's sentiment and adjust the information source analysis method based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned analysis unit is When analyzing information sources, we optimize the analysis algorithm by referring to past analysis results. The system according to feature 1.

9. The aforementioned analysis unit is When analyzing information sources, different analytical methods are applied depending on the category of information. The system according to feature 1.

10. The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

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