System

Abstract

An object of a system according to an embodiment is to check authenticity and bias of information and provide appropriate information.SOLUTION: A system includes an information analysis part, a block part, a provision part, and a diversity feed part. The information analysis unit checks authenticity and bias of the information. The blocking unit blocks a dangerous site or an inaccurate information source based on the information confirmed by the information analysis unit. The provision unit provides information that is highly credible and matches the interest confirmed by the information analysis unit. The diversity feed unit provides various viewpoints and information based on the information provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately verify the authenticity and bias of information, and do not block dangerous sites or inaccurate sources of information, so there is room for improvement.

[0005] The system according to the embodiment aims to check the authenticity and bias of information and provide appropriate information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information analysis unit, a blocking unit, a providing unit, and a diversity feed unit. The information analysis unit checks the authenticity and bias of information. The blocking unit blocks dangerous sites or inaccurate information sources based on the information checked by the information analysis unit. The providing unit provides information that is highly authentic and relevant to the user's interests, as checked by the information analysis unit. The diversity feed unit provides diverse perspectives and information based on the information provided by the providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can check the authenticity and bias of information and provide appropriate information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The InfoGuardian system, an embodiment of the present invention, instantly checks and verifies the authenticity and bias of information, automatically blocks access from dangerous sites and inaccurate sources, and provides highly authentic information that matches users' interests, offering diverse perspectives and information. This allows the InfoGuardian system to protect users from a flood of information and prevent filter bubbles.

[0029] The InfoGuardian system according to the embodiment includes an information analysis unit, a blocking unit, a providing unit, and a diversity feed unit. The information analysis unit verifies the authenticity and bias of information. For example, the generation AI analyzes the content of news articles and websites to verify whether they are from reliable sources. The generation AI also analyzes the URLs and text of information accessed by users to determine whether the information is trustworthy. The generation AI also verifies the authenticity and bias of information using fact-checking techniques and bias detection algorithms. The blocking unit blocks dangerous sites and inaccurate sources based on the information verified by the information analysis unit. For example, the generation AI detects phishing sites and sites containing false information and displays a warning before users access them. The generation AI also blocks sites containing malware or sites with a history of disseminating false information. The generation AI also refers to the site's security incident history and performs risk assessment. The providing unit provides information that is highly authentic and relevant to the user's interests, as verified by the information analysis unit. For example, the generation AI analyzes the user's past browsing history and interests and selects appropriate information based on that information. The generation AI also provides information using reliability scores and the user's interest profile. The generation AI also analyzes the user's geographic location information and provides reliable information related to the region. The diversity feed unit provides diverse perspectives and information based on the information provided by the provision unit. For example, the generation AI suggests different perspectives and new information to broaden the user's interests and beliefs. The generation AI also cross-references information sources from different cultural spheres and languages ​​to provide information from a global perspective. The generation AI also analyzes the user's past browsing history and suggests information in genres that the user does not normally access. This allows the InfoGuardian system according to the embodiment to protect users from information overload and prevent filter bubbles. For example, users can use the Internet safely and efficiently obtain reliable information. Exposure to diverse perspectives also broadens the user's horizons.

[0030] The information analysis unit can refer to the information source's past reliability history and take into account fluctuations in reliability. For example, the generation AI retrieves the information source's past reliability history from a database and calculates a reliability score. For example, an information source that has provided highly reliable information in the past will have a high score, and conversely, an information source that has provided unreliable information will have a low score. The generation AI also analyzes patterns of reliability fluctuations and evaluates the reliability of the information source. This makes it possible to more accurately verify the authenticity of information by referring to the information source's reliability history and taking into account fluctuations in reliability.

[0031] The information analysis unit can cross-reference information sources from different cultures or languages ​​and perform evaluations from a global perspective. For example, the information analysis unit uses the generation AI to automatically collect information sources from different cultures or languages ​​and cross-reference information on the same topic. For example, it compares news articles in English, Chinese, and Spanish to verify bias. The generation AI also verifies information bias using information source cross-referencing methods and evaluation criteria. The generation AI also performs evaluations from a global perspective based on international news sources and opinions from different cultures. In this way, cross-referencing information sources from different cultures and languages ​​reduces information bias and enables evaluations from a global perspective.

[0032] The information analysis unit can analyze the content of images or videos and evaluate their credibility based on visual evidence. For example, the generation AI uses image recognition technology to analyze the content of images included in news articles or websites and evaluate their credibility based on visual evidence. For example, it verifies the origin of the image and whether it has been edited. The generation AI also uses a video analysis algorithm to analyze the content of videos and evaluate their credibility based on visual evidence. The generation AI also clarifies the specific format and type of images or videos and sets evaluation criteria for visual evidence. This allows the generation AI to analyze the content of images and videos and evaluate their credibility based on visual evidence.

[0033] The information analysis unit can add a live feedback function that verifies the authenticity of information in real time, allowing users to instantly confirm its reliability when viewing the information. The information analysis unit, for example, builds a system in which the generation AI verifies the authenticity of information in real time and instantly displays its reliability when the user views the information. For example, it displays a reliability score on the screen. The generation AI also provides reliability information to users using real-time data processing and an instant notification system. The generation AI also sets specific technologies and implementation methods for the live feedback function, allowing users to instantly confirm its reliability. This allows users to instantly confirm its reliability by verifying the authenticity of information in real time.

[0034] The blocking unit can refer to the site's past security incident history and perform risk assessment. In the blocking unit, for example, the generation AI retrieves the site's past security incident history from a database and performs risk assessment. For example, it blocks sites where phishing attacks or malware distribution have been confirmed in the past. The generation AI also sets the specific content and reference method of the security incident history to improve the accuracy of risk assessment. The generation AI also evaluates the risk of the site based on the security assessment report and past attack history. In this way, the accuracy of risk assessment is improved by referring to the site's past security incident history.

[0035] The blocking unit analyzes the background information of the source of the information and can block inaccurate sources. For example, the blocking unit uses the generation AI to retrieve the source's past statements and behavioral history from a database and evaluate their credibility. For example, it blocks information from source who has previously disseminated false information. The generation AI also sets the specific content and analysis method of the source's background information and sets the evaluation criteria for credibility. The generation AI also evaluates the credibility of the source based on the credibility of the organization the source belongs to and past evaluation data. In this way, by analyzing the source's background information, inaccurate sources can be effectively blocked.

[0036] The blocking unit can analyze a user's past access history, predict similar dangerous sites, and issue advance warnings. For example, the blocking unit develops an algorithm in which the generation AI analyzes a user's past access history and predicts similar dangerous sites. For example, the blocking unit warns users in advance of sites similar to dangerous sites previously accessed. The generation AI also sets specific definitions and prediction methods for similar dangerous sites and issues warnings to users. The generation AI also performs risk assessments based on similarity assessments based on past access history and the characteristics of dangerous sites. This makes it possible to predict similar dangerous sites and issue advance warnings by analyzing a user's past access history.

[0037] The blocking unit can analyze information diffusion patterns on social media and prevent the spread of false information. For example, the blocking unit develops an algorithm that allows the generation AI to analyze information diffusion patterns on social media and prevent the spread of false information. For example, the generation AI identifies false information based on the speed and range of diffusion. The generation AI also sets specific analysis methods and standards for information diffusion patterns and prevents the spread of false information. The generation AI also detects the spread of false information using social network analysis and information diffusion models. This makes it possible to prevent the spread of false information by analyzing information diffusion patterns on social media.

[0038] The providing unit can analyze a user's past search history or social media activity to make more accurate interest predictions. For example, the providing unit develops an algorithm for interest prediction by having the generation AI analyze the user's past search history. For example, the generation AI prioritizes providing information related to specific keywords or topics. The generation AI also analyzes social media activity to predict the user's interests. The generation AI also sets specific methods and standards for interest prediction and provides appropriate information to the user. This enables more accurate interest predictions by analyzing the user's past search history and social media activity.

[0039] The providing unit can refer to the information source's past reliability score and provide highly reliable information preferentially. For example, the providing unit constructs a system in which the generation AI retrieves the information source's past reliability score from a database and provides highly reliable information preferentially. For example, the generation AI preferentially displays information from information sources with high reliability scores. The generation AI also sets the specific content and reference method of the reliability score and provides highly reliable information to the user. The generation AI also evaluates the reliability of the information source based on past evaluation data and reliability evaluation criteria. In this way, highly reliable information can be provided preferentially by referring to the information source's past reliability score.

[0040] The providing unit can analyze the user's geographical location information and provide reliable information related to the region. The providing unit, for example, constructs a system in which a generation AI analyzes the user's geographical location information and provides reliable information related to the region. For example, the generation AI prioritizes displaying local news and event information. The generation AI also sets specific methods for acquiring and analyzing the geographical location information and provides appropriate information to the user. The generation AI also uses GPS data and location information services to acquire the user's location information and provide information related to the region. In this way, reliable information related to the region can be provided by analyzing the user's geographical location information.

[0041] The provision unit can optimize the provision of information to the user's device or platform, realizing seamless information provision across different devices. The provision unit, for example, builds a system in which the generation AI provides information optimized for the user's device or platform. For example, it displays information compatible with different devices such as smartphones, tablets, and PCs. The generation AI also sets specific methods and standards for seamless information provision and provides appropriate information to the user. The generation AI also synchronizes data between devices and standardizes the user interface, realizing information provision across different devices. This makes it possible to provide information seamlessly across different devices by optimizing the provision of information to the user's device or platform.

[0042] The diversity feed section can cross-reference information sources from different cultures or languages ​​to provide information from a global perspective. In the diversity feed section, for example, the generation AI automatically collects information sources from different cultures and languages ​​and cross-references information on the same topic. For example, it compares news articles in English, Chinese, and Spanish to verify bias. The generation AI also verifies information bias using source cross-referencing methods and evaluation criteria. The generation AI also provides information from a global perspective based on international news sources and opinions from different cultural spheres. This makes it possible to provide information from a global perspective by cross-referencing information sources from different cultures and languages.

[0043] The diversity feed unit can analyze a user's past browsing history and suggest information in genres that the user does not normally access. For example, the diversity feed unit builds a system in which a generation AI analyzes a user's past browsing history and suggests information in genres that the user does not normally access. For example, it displays news articles in new genres that the user may be interested in. The generation AI also sets specific definitions and suggestion methods for genres that the user does not normally access, and provides appropriate information to the user. The generation AI also provides information using genre classifications and suggestion algorithms based on the user's past browsing history. This makes it possible to suggest information in genres that the user does not normally access by analyzing the user's past browsing history.

[0044] The diversity feed section incorporates the opinions of experts from different industries and fields, allowing it to provide information from a specialized perspective. For example, the diversity feed section uses the generation AI to automatically collect the opinions of experts from different industries and fields, and cross-reference information on the same topic. For example, it compares the opinions of experts in technology, medicine, economics, etc., and verifies bias. The generation AI also determines the specific content and delivery method of the specialized perspective, providing appropriate information to users. The generation AI also evaluates the reliability of the information based on the opinions of experts from different industries and fields. This allows it to provide information from a specialized perspective by incorporating the opinions of experts from different industries and fields.

[0045] The discussion space providing unit can analyze the content of user comments and provide highly reliable information. The discussion space providing unit, for example, constructs a system in which a generation AI analyzes the content of user comments in a discussion space in real time and provides highly reliable information. For example, it displays links from highly reliable information sources related to the content of comments. The generation AI also sets specific analysis methods and standards for the content of comments and provides appropriate information to users. The generation AI also uses natural language processing technology and sentiment analysis algorithms to analyze the content of user comments. In this way, highly reliable information can be provided by analyzing the content of user comments.

[0046] The discussion space providing unit can automatically generate related topics and questions and suggest them to users. For example, the discussion space providing unit builds a system in which the generation AI automatically generates related topics and questions to stimulate discussions in the discussion space. For example, it suggests related topics based on the content of users' comments. The generation AI also sets specific methods and standards for automatic generation and provides appropriate information to users. The generation AI also uses topic generation algorithms and question generation algorithms to generate topics and questions to suggest to users. In this way, by automatically generating related topics and questions, it is possible to make suggestions to users that will stimulate discussions.

[0047] The discussion space provider can support different languages ​​and promote discussion from an international perspective. The discussion space provider, for example, constructs a system in which a generation AI supports different languages ​​in the discussion space and promotes discussion from an international perspective. For example, a real-time translation function can be introduced to enable users of different languages ​​to discuss simultaneously. The generation AI also sets the specific content and method of providing the international perspective and provides appropriate information to users. The generation AI also provides information based on news sources from different countries and international opinions. This makes it possible to support different languages ​​and promote discussion from an international perspective.

[0048] The discussion space providing unit can make the discussion space compatible with different devices and platforms, allowing users to participate from anywhere. The discussion space providing unit, for example, constructs a system in which a generation AI makes the discussion space compatible with different devices and platforms, allowing users to participate from anywhere. For example, it provides an interface compatible with different devices such as smartphones, tablets, and PCs. The generation AI also sets specific compatibility methods and standards for different devices and platforms, and provides appropriate information to users. The generation AI also synchronizes data between devices and ensures compatibility between platforms, allowing users to participate from anywhere. This allows users to participate from anywhere by making the discussion space compatible with different devices and platforms.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The InfoGuardian system can also be equipped with a health monitoring unit that monitors the user's health and adjusts the information provided. For example, sensors can measure the user's heart rate and stress level, and if their health condition worsens, the system can provide relaxation information and health advice. The health monitoring unit can also analyze the user's sleep patterns and provide information at appropriate times. Furthermore, the health monitoring unit can analyze the user's exercise history and provide information to help them refresh after exercise. This makes it possible to provide information tailored to the user's health condition, supporting their overall health.

[0051] The InfoGuardian system can also be equipped with a learning support unit that analyzes a user's learning history and provides information to improve learning effectiveness. For example, it can analyze what a user has learned in the past and provide new related information. The learning support unit can also analyze a user's learning style and suggest optimal learning methods. Furthermore, the learning support unit can monitor a user's learning progress and provide review or additional learning materials at appropriate times. This can improve the user's learning effectiveness.

[0052] The InfoGuardian system can also be equipped with a purchasing support section that analyzes users' purchasing history and supports their purchasing behavior. For example, it can analyze the products and services a user has purchased in the past and suggest new related products and services. The purchasing support section can also analyze users' purchasing patterns and provide discount and campaign information at the optimal time. Furthermore, the purchasing support section can provide reliable product reviews and ratings based on the user's purchasing history. This supports users' purchasing behavior and increases their satisfaction.

[0053] The InfoGuardian system can also include a hobby support unit that analyzes a user's hobbies and interests and provides information related to those hobbies. For example, it can analyze information related to hobbies that the user has previously searched for and provide new related information. The hobby support unit can also provide event information and community information based on the user's hobbies. Furthermore, the hobby support unit can analyze the user's hobby history and make new suggestions to broaden the range of hobbies. This makes it possible to provide information tailored to the user's hobbies, thereby increasing user satisfaction.

[0054] The InfoGuardian system can also be equipped with a travel support unit that analyzes a user's travel history and provides travel-related information. For example, it can analyze places the user has visited in the past and tourist spots that interest them, and provide new related travel information. The travel support unit can also analyze the user's travel style and propose optimal travel plans. Furthermore, the travel support unit can analyze the user's travel history and provide information that can refresh the user after a trip. This makes it possible to provide information tailored to the user's trip, increasing user satisfaction.

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

[0056] Step 1: The information analysis unit checks the authenticity and bias of the information. For example, the generation AI analyzes the content of news articles and websites to confirm whether they come from reliable sources. The generation AI also analyzes the URL and text of the information accessed by the user to determine whether the information is trustworthy. The generation AI then uses fact-checking techniques and bias detection algorithms to check the authenticity and bias of the information. Step 2: The blocking unit blocks dangerous sites and inaccurate information sources based on the information confirmed by the information analysis unit. For example, the generation AI detects phishing sites and sites containing false information and displays a warning before users access them. The generation AI also blocks sites that contain malware or have a history of disseminating false information. The generation AI also refers to the site's security incident history and performs a risk assessment. Step 3: The provision unit provides information that is highly authentic and matches the user's interests, as confirmed by the information analysis unit. For example, the generation AI analyzes the user's past browsing history and interests and selects appropriate information based on that. The generation AI also provides information using a reliability score and the user's interest profile. Furthermore, the generation AI analyzes the user's geographic location information and provides reliable information related to the area. Step 4: Diversity The feed unit provides diverse perspectives and information based on the information provided by the provider. For example, the generation AI suggests different perspectives and new information to broaden the user's interests and beliefs. The generation AI also cross-references information sources from different cultures and languages ​​to provide information from a global perspective. Furthermore, the generation AI analyzes the user's past browsing history and suggests information in genres that the user does not usually access.

[0057] (Example 2) The InfoGuardian system, an embodiment of the present invention, instantly checks and verifies the authenticity and bias of information, automatically blocks access from dangerous sites and inaccurate sources, and provides highly authentic information that matches users' interests, offering diverse perspectives and information. This allows the InfoGuardian system to protect users from a flood of information and prevent filter bubbles.

[0058] The InfoGuardian system according to the embodiment includes an information analysis unit, a blocking unit, a providing unit, and a diversity feed unit. The information analysis unit verifies the authenticity and bias of information. For example, the generation AI analyzes the content of news articles and websites to verify whether they are from reliable sources. The generation AI also analyzes the URLs and text of information accessed by users to determine whether the information is trustworthy. The generation AI also verifies the authenticity and bias of information using fact-checking techniques and bias detection algorithms. The blocking unit blocks dangerous sites and inaccurate sources based on the information verified by the information analysis unit. For example, the generation AI detects phishing sites and sites containing false information and displays a warning before users access them. The generation AI also blocks sites containing malware or sites with a history of disseminating false information. The generation AI also refers to the site's security incident history and performs risk assessment. The providing unit provides information that is highly authentic and relevant to the user's interests, as verified by the information analysis unit. For example, the generation AI analyzes the user's past browsing history and interests and selects appropriate information based on that information. The generation AI also provides information using reliability scores and the user's interest profile. The generation AI also analyzes the user's geographic location information and provides reliable information related to the region. The diversity feed unit provides diverse perspectives and information based on the information provided by the provision unit. For example, the generation AI suggests different perspectives and new information to broaden the user's interests and beliefs. The generation AI also cross-references information sources from different cultural spheres and languages ​​to provide information from a global perspective. The generation AI also analyzes the user's past browsing history and suggests information in genres that the user does not normally access. This allows the InfoGuardian system according to the embodiment to protect users from information overload and prevent filter bubbles. For example, users can use the Internet safely and efficiently obtain reliable information. Exposure to diverse perspectives also broadens the user's horizons.

[0059] The information analysis unit can refer to the information source's past reliability history and take into account fluctuations in reliability. For example, the generation AI retrieves the information source's past reliability history from a database and calculates a reliability score. For example, an information source that has provided highly reliable information in the past will have a high score, and conversely, an information source that has provided unreliable information will have a low score. The generation AI also analyzes patterns of reliability fluctuations and evaluates the reliability of the information source. This makes it possible to more accurately verify the authenticity of information by referring to the information source's reliability history and taking into account fluctuations in reliability.

[0060] The information analysis unit can cross-reference information sources from different cultures or languages ​​and perform evaluations from a global perspective. For example, the information analysis unit uses the generation AI to automatically collect information sources from different cultures or languages ​​and cross-reference information on the same topic. For example, it compares news articles in English, Chinese, and Spanish to verify bias. The generation AI also verifies information bias using information source cross-referencing methods and evaluation criteria. The generation AI also performs evaluations from a global perspective based on international news sources and opinions from different cultures. In this way, cross-referencing information sources from different cultures and languages ​​reduces information bias and enables evaluations from a global perspective.

[0061] The information analysis unit uses the emotion estimation function to analyze the user's emotional response and prioritize providing information that is likely to be emotionally trustworthy. The information analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response in real time when viewing information. For example, it analyzes the user's facial expressions and voice and prioritizes providing information that has a high percentage of positive emotional responses. The emotion estimation function also detects the user's emotional response using an emotion analysis algorithm and sensor data. The generation AI also sets standards for information that is likely to be emotionally trustworthy and provides information based on the user's emotional history. In this way, by analyzing the user's emotional response, it is possible to prioritize providing information that is likely to be emotionally trustworthy.

[0062] The information analysis unit can analyze the content of images or videos and evaluate their credibility based on visual evidence. For example, the generation AI uses image recognition technology to analyze the content of images included in news articles or websites and evaluate their credibility based on visual evidence. For example, it verifies the origin of the image and whether it has been edited. The generation AI also uses a video analysis algorithm to analyze the content of videos and evaluate their credibility based on visual evidence. The generation AI also clarifies the specific format and type of images or videos and sets evaluation criteria for visual evidence. This allows the generation AI to analyze the content of images and videos and evaluate their credibility based on visual evidence.

[0063] The information analysis unit can add a live feedback function that verifies the authenticity of information in real time, allowing users to instantly confirm its reliability when viewing the information. The information analysis unit, for example, builds a system in which the generation AI verifies the authenticity of information in real time and instantly displays its reliability when the user views the information. For example, it displays a reliability score on the screen. The generation AI also provides reliability information to users using real-time data processing and an instant notification system. The generation AI also sets specific technologies and implementation methods for the live feedback function, allowing users to instantly confirm its reliability. This allows users to instantly confirm its reliability by verifying the authenticity of information in real time.

[0064] The information analysis unit can use the emotion estimation function to monitor the user's emotions in real time and provide additional reliability information if the user feels anxious. The information analysis unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when viewing information and build a system that provides additional reliability information if the user feels anxious. For example, it displays supplementary information from a reliable source. The emotion estimation function also uses an emotion analysis algorithm and the user's behavioral patterns to detect when the user feels emotionally anxious. The generation AI also sets methods and standards for providing additional reliability information, increasing the user's sense of security. In this way, by providing additional reliability information when the user feels anxious, the user's sense of security is increased.

[0065] The blocking unit can refer to the site's past security incident history and perform risk assessment. In the blocking unit, for example, the generation AI retrieves the site's past security incident history from a database and performs risk assessment. For example, it blocks sites where phishing attacks or malware distribution have been confirmed in the past. The generation AI also sets the specific content and reference method of the security incident history to improve the accuracy of risk assessment. The generation AI also evaluates the risk of the site based on the security assessment report and past attack history. In this way, the accuracy of risk assessment is improved by referring to the site's past security incident history.

[0066] The blocking unit analyzes the background information of the source of the information and can block inaccurate sources. For example, the blocking unit uses the generation AI to retrieve the source's past statements and behavioral history from a database and evaluate their credibility. For example, it blocks information from source who has previously disseminated false information. The generation AI also sets the specific content and analysis method of the source's background information and sets the evaluation criteria for credibility. The generation AI also evaluates the credibility of the source based on the credibility of the organization the source belongs to and past evaluation data. In this way, by analyzing the source's background information, inaccurate sources can be effectively blocked.

[0067] The blocking unit can use the emotion estimation function to display an emotional warning to alert the user when the user attempts to access a dangerous site. The blocking unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when the user attempts to access a dangerous site and build a system to display an emotional warning. For example, the blocking unit highlights the warning message. The emotion estimation function also detects the user's emotional reaction using an emotion analysis algorithm and sensor data. The generation AI also sets the specific content and display method of the emotional warning to alert the user. As a result, by displaying an emotional warning when the user attempts to access a dangerous site, the user can be alerted and risks can be avoided.

[0068] The blocking unit can analyze a user's past access history, predict similar dangerous sites, and issue advance warnings. For example, the blocking unit develops an algorithm in which the generation AI analyzes a user's past access history and predicts similar dangerous sites. For example, the blocking unit warns users in advance of sites similar to dangerous sites previously accessed. The generation AI also sets specific definitions and prediction methods for similar dangerous sites and issues warnings to users. The generation AI also performs risk assessments based on similarity assessments based on past access history and the characteristics of dangerous sites. This makes it possible to predict similar dangerous sites and issue advance warnings by analyzing a user's past access history.

[0069] The blocking unit can analyze information diffusion patterns on social media and prevent the spread of false information. For example, the blocking unit develops an algorithm that allows the generation AI to analyze information diffusion patterns on social media and prevent the spread of false information. For example, the generation AI identifies false information based on the speed and range of diffusion. The generation AI also sets specific analysis methods and standards for information diffusion patterns and prevents the spread of false information. The generation AI also detects the spread of false information using social network analysis and information diffusion models. This makes it possible to prevent the spread of false information by analyzing information diffusion patterns on social media.

[0070] The blocking unit can use the emotion estimation function to monitor a user's emotional reactions in real time when they attempt to access a dangerous site and customize an appropriate warning. For example, the blocking unit uses the emotion estimation function to build a system that monitors a user's emotions in real time when they attempt to access a dangerous site and customizes an appropriate warning. For example, the blocking unit adjusts the content and display method of the warning message according to the emotion score. The emotion estimation function also detects the user's emotional reactions using an emotion analysis algorithm and sensor data. The generation AI also sets specific methods and criteria for customizing an appropriate warning and alerts the user. This allows the system to monitor a user's emotional reactions when they attempt to access a dangerous site and customize an appropriate warning to alert the user and avoid risks.

[0071] The providing unit can analyze a user's past search history or social media activity to make more accurate interest predictions. For example, the providing unit develops an algorithm for interest prediction by having the generation AI analyze the user's past search history. For example, the generation AI prioritizes providing information related to specific keywords or topics. The generation AI also analyzes social media activity to predict the user's interests. The generation AI also sets specific methods and standards for interest prediction and provides appropriate information to the user. This enables more accurate interest predictions by analyzing the user's past search history and social media activity.

[0072] The providing unit can refer to the information source's past reliability score and provide highly reliable information preferentially. For example, the providing unit constructs a system in which the generation AI retrieves the information source's past reliability score from a database and provides highly reliable information preferentially. For example, the generation AI preferentially displays information from information sources with high reliability scores. The generation AI also sets the specific content and reference method of the reliability score and provides highly reliable information to the user. The generation AI also evaluates the reliability of the information source based on past evaluation data and reliability evaluation criteria. In this way, highly reliable information can be provided preferentially by referring to the information source's past reliability score.

[0073] The provision unit can use the emotion estimation function to analyze the user's emotional response and provide emotionally positive information preferentially. The provision unit, for example, uses the emotion estimation function to analyze the user's emotional response in real time when viewing information, and builds a system that preferentially provides information with a high number of positive emotional responses. For example, information with a high emotional score is displayed at the top. The emotion estimation function also detects the user's emotional response using an emotion analysis algorithm and sensor data. The generation AI also sets specific criteria and delivery methods for emotionally positive information and provides appropriate information to the user. In this way, by analyzing the user's emotional response, emotionally positive information can be provided preferentially.

[0074] The providing unit can analyze the user's geographical location information and provide reliable information related to the region. The providing unit, for example, constructs a system in which a generation AI analyzes the user's geographical location information and provides reliable information related to the region. For example, the generation AI prioritizes displaying local news and event information. The generation AI also sets specific methods for acquiring and analyzing the geographical location information and provides appropriate information to the user. The generation AI also uses GPS data and location information services to acquire the user's location information and provide information related to the region. In this way, reliable information related to the region can be provided by analyzing the user's geographical location information.

[0075] The provision unit can optimize the provision of information to the user's device or platform, realizing seamless information provision across different devices. The provision unit, for example, builds a system in which the generation AI provides information optimized for the user's device or platform. For example, it displays information compatible with different devices such as smartphones, tablets, and PCs. The generation AI also sets specific methods and standards for seamless information provision and provides appropriate information to the user. The generation AI also synchronizes data between devices and standardizes the user interface, realizing information provision across different devices. This makes it possible to provide information seamlessly across different devices by optimizing the provision of information to the user's device or platform.

[0076] The provision unit can use the emotion estimation function to monitor the user's emotions in real time and continuously provide emotionally positive information. The provision unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when viewing information, and builds a system that continuously provides information with a high positive emotional response. For example, information with a high emotional score is preferentially displayed. The emotion estimation function also detects the user's emotional response using an emotion analysis algorithm and sensor data. The generation AI also sets specific methods and standards for continuously providing emotionally positive information and provides appropriate information to the user. In this way, emotionally positive information can be continuously provided by monitoring the user's emotions in real time.

[0077] The diversity feed section can cross-reference information sources from different cultures or languages ​​to provide information from a global perspective. In the diversity feed section, for example, the generation AI automatically collects information sources from different cultures and languages ​​and cross-references information on the same topic. For example, it compares news articles in English, Chinese, and Spanish to verify bias. The generation AI also verifies information bias using source cross-referencing methods and evaluation criteria. The generation AI also provides information from a global perspective based on international news sources and opinions from different cultural spheres. This makes it possible to provide information from a global perspective by cross-referencing information sources from different cultures and languages.

[0078] The diversity feed unit can analyze a user's past browsing history and suggest information in genres that the user does not normally access. For example, the diversity feed unit builds a system in which a generation AI analyzes a user's past browsing history and suggests information in genres that the user does not normally access. For example, it displays news articles in new genres that the user may be interested in. The generation AI also sets specific definitions and suggestion methods for genres that the user does not normally access, and provides appropriate information to the user. The generation AI also provides information using genre classifications and suggestion algorithms based on the user's past browsing history. This makes it possible to suggest information in genres that the user does not normally access by analyzing the user's past browsing history.

[0079] The diversity feed unit can use the emotion estimation function to analyze the user's emotional reactions and provide emotionally positive and diverse perspectives. For example, the diversity feed unit uses the emotion estimation function to analyze the user's emotional reactions in real time when viewing information, and builds a system that provides diverse perspectives with a high number of positive emotional reactions. For example, it prioritizes displaying information with a high emotion score. The emotion estimation function also detects the user's emotional reactions using an emotion analysis algorithm and sensor data. The generation AI also sets specific criteria and provision methods for emotionally positive and diverse perspectives, and provides appropriate information to the user. In this way, it is possible to provide emotionally positive and diverse perspectives by analyzing the user's emotional reactions.

[0080] The diversity feed section incorporates the opinions of experts from different industries and fields, allowing it to provide information from a specialized perspective. For example, the diversity feed section uses the generation AI to automatically collect the opinions of experts from different industries and fields, and cross-reference information on the same topic. For example, it compares the opinions of experts in technology, medicine, economics, etc., and verifies bias. The generation AI also determines the specific content and delivery method of the specialized perspective, providing appropriate information to users. The generation AI also evaluates the reliability of the information based on the opinions of experts from different industries and fields. This allows it to provide information from a specialized perspective by incorporating the opinions of experts from different industries and fields.

[0081] The diversity feed unit uses the emotion estimation function to monitor the user's emotions in real time and continuously provide emotionally positive and diverse perspectives. For example, the diversity feed unit uses the emotion estimation function to monitor the user's emotions in real time when viewing information, and builds a system that continuously provides diverse perspectives with a high number of positive emotional responses. For example, it prioritizes displaying information with a high emotion score. The emotion estimation function also detects the user's emotional response using an emotion analysis algorithm and sensor data. The generation AI also sets specific methods and standards for continuously providing emotionally positive and diverse perspectives and provides appropriate information to the user. In this way, by monitoring the user's emotions in real time, it is possible to continuously provide emotionally positive and diverse perspectives.

[0082] The discussion space providing unit can analyze the content of user comments and provide highly reliable information. The discussion space providing unit, for example, constructs a system in which a generation AI analyzes the content of user comments in a discussion space in real time and provides highly reliable information. For example, it displays links from highly reliable information sources related to the content of comments. The generation AI also sets specific analysis methods and standards for the content of comments and provides appropriate information to users. The generation AI also uses natural language processing technology and sentiment analysis algorithms to analyze the content of user comments. In this way, highly reliable information can be provided by analyzing the content of user comments.

[0083] The discussion space providing unit can automatically generate related topics and questions and suggest them to users. For example, the discussion space providing unit builds a system in which the generation AI automatically generates related topics and questions to stimulate discussions in the discussion space. For example, it suggests related topics based on the content of users' comments. The generation AI also sets specific methods and standards for automatic generation and provides appropriate information to users. The generation AI also uses topic generation algorithms and question generation algorithms to generate topics and questions to suggest to users. In this way, by automatically generating related topics and questions, it is possible to make suggestions to users that will stimulate discussions.

[0084] The discussion space providing unit can use the emotion estimation function to analyze users' emotional reactions and promote emotionally positive discussions. The discussion space providing unit, for example, uses the emotion estimation function to analyze users' emotional reactions in the discussion space in real time and build a system that promotes emotionally positive discussions. For example, it highlights statements that have a high number of positive emotional reactions. The emotion estimation function also detects users' emotional reactions using emotion analysis algorithms and sensor data. The generation AI also sets specific criteria and promotion methods for emotionally positive discussions and provides appropriate information to users. In this way, emotionally positive discussions can be promoted by analyzing users' emotional reactions.

[0085] The discussion space provider can support different languages ​​and promote discussion from an international perspective. The discussion space provider, for example, constructs a system in which a generation AI supports different languages ​​in the discussion space and promotes discussion from an international perspective. For example, a real-time translation function can be introduced to enable users of different languages ​​to discuss simultaneously. The generation AI also sets the specific content and method of providing the international perspective and provides appropriate information to users. The generation AI also provides information based on news sources from different countries and international opinions. This makes it possible to support different languages ​​and promote discussion from an international perspective.

[0086] The discussion space providing unit can make the discussion space compatible with different devices and platforms, allowing users to participate from anywhere. The discussion space providing unit, for example, constructs a system in which a generation AI makes the discussion space compatible with different devices and platforms, allowing users to participate from anywhere. For example, it provides an interface compatible with different devices such as smartphones, tablets, and PCs. The generation AI also sets specific compatibility methods and standards for different devices and platforms, and provides appropriate information to users. The generation AI also synchronizes data between devices and ensures compatibility between platforms, allowing users to participate from anywhere. This allows users to participate from anywhere by making the discussion space compatible with different devices and platforms.

[0087] The discussion space providing unit uses the emotion estimation function to monitor users' emotions in real time and continuously promote emotionally positive discussions. The discussion space providing unit, for example, uses the emotion estimation function to monitor users' emotions in the discussion space in real time and build a system that continuously promotes emotionally positive discussions. For example, it highlights statements that have a high number of positive emotional responses. The emotion estimation function also detects users' emotional responses using emotion analysis algorithms and sensor data. The generation AI also sets specific methods and standards for continuously promoting emotionally positive discussions and provides appropriate information to users. In this way, by monitoring users' emotions in real time, it is possible to continuously promote emotionally positive discussions.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The InfoGuardian system can also be equipped with a health monitoring unit that monitors the user's health and adjusts the information provided. For example, sensors can measure the user's heart rate and stress level, and if their health condition worsens, the system can provide relaxation information and health advice. The health monitoring unit can also analyze the user's sleep patterns and provide information at appropriate times. Furthermore, the health monitoring unit can analyze the user's exercise history and provide information to help them refresh after exercise. This makes it possible to provide information tailored to the user's health condition, supporting their overall health.

[0090] The InfoGuardian system can also be equipped with a learning support unit that analyzes a user's learning history and provides information to improve learning effectiveness. For example, it can analyze what a user has learned in the past and provide new related information. The learning support unit can also analyze a user's learning style and suggest optimal learning methods. Furthermore, the learning support unit can monitor a user's learning progress and provide review or additional learning materials at appropriate times. This can improve the user's learning effectiveness.

[0091] The InfoGuardian system can also be equipped with a purchasing support section that analyzes users' purchasing history and supports their purchasing behavior. For example, it can analyze the products and services a user has purchased in the past and suggest new related products and services. The purchasing support section can also analyze users' purchasing patterns and provide discount and campaign information at the optimal time. Furthermore, the purchasing support section can provide reliable product reviews and ratings based on the user's purchasing history. This supports users' purchasing behavior and increases their satisfaction.

[0092] The InfoGuardian system can also include an emotion adjustment unit that estimates a user's emotions and adjusts the information provided based on those emotions. For example, if a user is feeling stressed, the emotion adjustment unit can provide relaxing or entertaining information. The emotion adjustment unit can also provide challenging information or learning materials if the user is feeling positive. Furthermore, the emotion adjustment unit can analyze the user's emotion history and provide information according to emotional fluctuations. This makes it possible to provide information according to the user's emotions, thereby increasing user satisfaction.

[0093] The InfoGuardian system can also be equipped with an emotion evaluation unit that estimates a user's emotions and evaluates the reliability of information based on those emotions. For example, if a user is feeling anxious, information from a more reliable source is provided preferentially. The emotion evaluation unit can also provide information from a different perspective if the user is feeling positive. Furthermore, the emotion evaluation unit can analyze the user's emotion history and evaluate reliability according to emotional fluctuations. This enables reliability evaluation according to the user's emotions, increasing the user's sense of security.

[0094] The InfoGuardian system can also include an emotional diversity unit that estimates a user's emotions and adjusts the diversity of information based on the user's emotions. For example, if a user is feeling stressed, the emotional diversity unit can provide relaxing information from diverse perspectives. The emotional diversity unit can also provide challenging information from diverse perspectives if the user is feeling positive. Furthermore, the emotional diversity unit can analyze the user's emotional history and adjust the diversity according to emotional fluctuations. This allows for diversity adjustment according to the user's emotions, broadening the user's perspective.

[0095] The InfoGuardian system can also be equipped with an emotion customization unit that estimates a user's emotions and customizes the information provided based on those emotions. For example, if a user is feeling anxious, it can provide information that gives a sense of security. The emotion customization unit can also provide challenging information or a new perspective if the user is feeling positive. Furthermore, the emotion customization unit can analyze the user's emotion history and provide information according to emotional fluctuations. This makes it possible to provide information that is tailored to the user's emotions, thereby increasing user satisfaction.

[0096] The InfoGuardian system can also include a hobby support unit that analyzes a user's hobbies and interests and provides information related to those hobbies. For example, it can analyze information related to hobbies that the user has previously searched for and provide new related information. The hobby support unit can also provide event information and community information based on the user's hobbies. Furthermore, the hobby support unit can analyze the user's hobby history and make new suggestions to broaden the range of hobbies. This makes it possible to provide information tailored to the user's hobbies, thereby increasing user satisfaction.

[0097] The InfoGuardian system can also be equipped with a travel support unit that analyzes a user's travel history and provides travel-related information. For example, it can analyze places the user has visited in the past and tourist spots that interest them, and provide new related travel information. The travel support unit can also analyze the user's travel style and propose optimal travel plans. Furthermore, the travel support unit can analyze the user's travel history and provide information that can refresh the user after a trip. This makes it possible to provide information tailored to the user's trip, increasing user satisfaction.

[0098] The InfoGuardian system can also be equipped with an emotion optimization unit that estimates a user's emotions and optimizes the information provided based on those emotions. For example, if a user is feeling stressed, it can provide relaxing or entertaining information. The emotion optimization unit can also provide challenging information or learning materials if the user is feeling positive. Furthermore, the emotion optimization unit can analyze the user's emotion history and provide information according to emotional fluctuations. This makes it possible to provide information according to the user's emotions, thereby increasing user satisfaction.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The information analysis unit checks the authenticity and bias of the information. For example, the generation AI analyzes the content of news articles and websites to confirm whether they come from reliable sources. The generation AI also analyzes the URL and text of the information accessed by the user to determine whether the information is trustworthy. The generation AI then uses fact-checking techniques and bias detection algorithms to check the authenticity and bias of the information. Step 2: The blocking unit blocks dangerous sites and inaccurate information sources based on the information confirmed by the information analysis unit. For example, the generation AI detects phishing sites and sites containing false information and displays a warning before users access them. The generation AI also blocks sites that contain malware or have a history of disseminating false information. The generation AI also refers to the site's security incident history and performs a risk assessment. Step 3: The provision unit provides information that is highly authentic and matches the user's interests, as confirmed by the information analysis unit. For example, the generation AI analyzes the user's past browsing history and interests and selects appropriate information based on that. The generation AI also provides information using a reliability score and the user's interest profile. Furthermore, the generation AI analyzes the user's geographic location information and provides reliable information related to the area. Step 4: Diversity The feed unit provides diverse perspectives and information based on the information provided by the provider. For example, the generation AI suggests different perspectives and new information to broaden the user's interests and beliefs. The generation AI also cross-references information sources from different cultures and languages ​​to provide information from a global perspective. Furthermore, the generation AI analyzes the user's past browsing history and suggests information in genres that the user does not usually access.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. An information analysis department that checks the authenticity and bias of information, a blocking unit that blocks dangerous sites or inaccurate information sources based on the information confirmed by the information analysis unit; a providing unit that provides information that is highly authentic and matches the user's interests, as confirmed by the information analysis unit; a diversity feed unit that provides diverse viewpoints and information based on the information provided by the providing unit; A system characterized by:

2. The information analysis unit Analyze the content of an image or video and assess its credibility based on visual evidence 2. The system of claim 1.

3. The block portion is Conduct risk assessment by looking at the site's past security incident history 2. The system of claim 1.

4. The providing unit Analyzing your search history or social media activity to better predict your interests 2. The system of claim 1.

5. The diversity feed section includes: Cross-reference sources from different cultures or languages ​​to provide a global perspective 2. The system of claim 1.

6. The information analysis unit Using emotion estimation function, we analyze the user's emotional response and provide information that is likely to be emotionally trustworthy.

2. The system of claim 1.

7. The block portion is Using the emotion estimation function, when a user attempts to access the dangerous site, an emotional warning is displayed to draw the user's attention.

2. The system of claim 1.

8. The providing unit Using emotion estimation function, the system analyzes the user's emotional response and prioritizes providing emotionally positive information.

2. The system of claim 1.

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