system

Abstract

The system according to this embodiment aims to verify the veracity of information disseminated on social media and to correct misinformation. [Solution] The system according to the embodiment comprises a collection unit, a verification unit, a correction unit, and a notification unit. The collection unit collects information disseminated on social media. The verification unit verifies the truthfulness of the information collected by the collection unit. The correction unit provides correct information if the verification unit determines that the information is false. The notification unit notifies the corporation of the information corrected by the correction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the authenticity of information spread on SNS has not been sufficiently verified quickly and accurately, and misinformation has not been corrected.

[0005] The system according to the embodiment aims to verify the authenticity of information spread on SNS and correct misinformation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a verification unit, a correction unit, and a notification unit. The collection unit collects information disseminated on social media. The verification unit verifies the truthfulness of the information collected by the collection unit. The correction unit provides correct information if the verification unit determines that the information is false. The notification unit notifies the corporation of the information corrected by the correction unit. [Effects of the Invention]

[0007] The system according to this embodiment can verify the veracity of information disseminated on social media and correct misinformation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The information verification system according to an embodiment of the present invention is a system that verifies the truthfulness of information about company names and products that has a certain degree of dissemination power on social media, and corrects it if it is misinformation (fiction). The information verification system collects information that is disseminated on social media, and an AI verifies the truthfulness of the collected information. If it is determined to be misinformation, the AI ​​provides the correct information and notifies the company that misinformation is being disseminated. This system allows companies to prevent damage to their reputation. It also prevents the spread of misinformation on social media and promotes the dissemination of correct information. This makes it possible to realize a world where everyone can access correct information. For example, if misinformation about a certain company is disseminated on social media, this system collects that information, and the AI ​​verifies its truthfulness. If it is determined to be misinformation, the AI ​​provides the correct information and notifies the company. The company, upon receiving the notification from the AI, takes action such as issuing an official statement. This prevents the spread of misinformation and protects the company's reputation. This system aims to become a new platform that guarantees the legitimacy of information on social media. For example, just as other platforms have implemented fact-checking functions, this system will verify the validity of information and provide accurate information, thereby creating a highly reliable information platform. In this way, the information verification system can realize a world where everyone can access accurate information.

[0029] The information verification system according to this embodiment comprises a collection unit, a verification unit, a correction unit, and a notification unit. The collection unit collects information disseminated on social networking services (SNS). For example, the collection unit can monitor specific keywords on SNS and collect related information. The collection unit can also monitor specific accounts or hashtags on SNS and collect information. Furthermore, the collection unit can filter information based on specific regions or languages ​​on SNS and collect information according to regional characteristics. The verification unit verifies the truthfulness of the information collected by the collection unit. For example, the verification unit can evaluate the source of the collected information and the reliability of the poster to verify the truthfulness of the information. The verification unit can also analyze the metadata of images to verify the truthfulness of the information. Furthermore, the verification unit can also verify the truthfulness of the information by referring to relevant literature and news articles. The correction unit provides correct information when the verification unit determines that the information is misinformation. For example, the correction unit can automatically post comments containing correct information to posts containing misinformation. Furthermore, if misinformation is being disseminated, the correction unit can also determine the priority of corrections based on the degree of dissemination. Furthermore, the correction unit can estimate the user's emotions and adjust the correction method based on the estimated emotions. The notification unit notifies the corporation of the information corrected by the correction unit. The notification unit can, for example, send an email to the corporation. The notification unit can also select the optimal notification method by referring to the corporation's past response history. Furthermore, the notification unit can also select the optimal notification method by analyzing the corporation's social media activity. As a result, the information verification system according to this embodiment can automatically verify the truthfulness of information disseminated on social media and correct misinformation, thereby enabling the provision of accurate information.

[0030] The data collection unit collects information disseminated on social media. For example, the unit can monitor specific keywords on social media and collect related information. Specifically, the unit uses natural language processing technology to analyze social media posts and automatically extract posts containing specific keywords or phrases. The unit can also monitor specific accounts and hashtags on social media and collect information. This allows for the efficient collection of information related to influential accounts and trending hashtags. Furthermore, the unit can filter information based on specific regions and languages ​​on social media, collecting information tailored to regional characteristics. For example, it can utilize geographic information systems (GIS) to extract posts originating from specific regions and collect region-specific information. It can also use multilingual natural language processing technology to analyze information posted in different languages ​​and classify the information by language. As a result, the unit can efficiently collect a wide range of social media data and quickly obtain necessary information by filtering it based on specific conditions. In addition, the unit can centrally manage the collected information and collaborate with other departments to share and analyze the information. For example, the collected information is stored in a cloud-based database, making it accessible to the verification and correction units. Furthermore, the collection unit can dynamically adjust the collection frequency and target data, enabling flexible information gathering tailored to specific events and situations. This allows the collection unit to efficiently and effectively collect information disseminated on social media, improving the overall system performance.

[0031] The verification unit verifies the authenticity of the information collected by the collection unit. For example, the verification unit can evaluate the source of the collected information and the reliability of the poster to verify its authenticity. Specifically, the verification unit analyzes metadata such as the poster's past posting history, number of followers, and account creation date to evaluate the poster's reliability. The verification unit can also analyze image metadata to verify the authenticity of the information. For example, it analyzes the date and location information of an image to check if it matches the content of the post. Furthermore, the verification unit can also verify the authenticity of the information by referring to relevant literature and news articles. For example, if the collected information concerns a specific incident or event, it will refer to reliable news sources and academic papers to confirm the accuracy of the information. This allows the verification unit to verify the collected information from multiple angles and make quick and accurate judgments about its authenticity. In addition, the verification unit can introduce an automated verification system using AI to efficiently verify the authenticity of information. For example, it can use natural language processing technology to analyze collected text information and evaluate the context and consistency of the content. It can also use image recognition technology to analyze collected image information and detect image tampering or forgery. This allows the verification unit to quickly and accurately verify the collected information, thereby improving the overall reliability of the system.

[0032] The correction unit provides correct information when the verification unit determines that the information is false. For example, the correction unit can automatically post comments containing correct information to posts that contain false information. Specifically, the correction unit identifies posts containing false information and generates appropriate correction comments for those posts. The generated comments correct the content of the false information and provide accurate information. The correction unit can also determine the priority of corrections based on the degree of spread of the false information. For example, if the false information has been retweeted by many users, corrections to that false information will be prioritized. Furthermore, the correction unit can estimate the user's emotions and adjust the method of correction based on the estimated user emotions. For example, if the user is feeling angry or anxious, the content and tone of the correction comment will be adjusted to make corrections that are considerate of the user's emotions. In this way, the correction unit can correct false information quickly and effectively and provide correct information. Furthermore, the correction unit can introduce an AI-powered automatic correction system to streamline the correction process. For example, it can use natural language generation technology to automatically generate appropriate correction comments and respond quickly to posts containing false information. Furthermore, sentiment analysis technology is used to analyze the user's emotions in real time and select the appropriate correction method. This allows the correction unit to perform the correction of misinformation efficiently and effectively, improving the overall reliability of the system.

[0033] The notification unit notifies the corporation of the information corrected by the correction unit. The notification unit can, for example, send an email to the corporation. Specifically, the notification unit automatically generates an email containing the corrected information and sends it to the corporation's contact person. The notification unit can also select the most suitable notification method by referring to the corporation's past response history. For example, if email notification was effective in the past, it will use the same method. The notification unit can also select the most suitable notification method by analyzing the corporation's social media activity. For example, if the corporation actively uses a particular social media platform, it will send a notification through that platform. This allows the notification unit to quickly and effectively communicate corrected information to the corporation. Furthermore, the notification unit can streamline notification work by introducing an AI-powered automated notification system. For example, it can use natural language generation technology to automatically generate appropriate notification content and quickly notify the corporation. It can also maximize the effectiveness of notifications by analyzing past notification history and the corporation's social media activity to select the most suitable notification method. This allows the notification unit to quickly and effectively communicate corrected information to the corporation and improve the overall reliability of the system.

[0034] The data collection unit includes a monitoring unit that monitors specific keywords on social media. For example, the data collection unit can monitor trending words on social media and collect related information. It can also monitor important words on social media and collect information from them. Furthermore, the data collection unit can determine collection priorities based on the frequency of occurrence of specific keywords on social media. This allows for the efficient collection of important information by monitoring specific keywords. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model that monitors specific keywords on social media.

[0035] The verification unit includes an analysis unit that analyzes the metadata of the image. The verification unit can, for example, analyze the date and time the image was taken and verify the authenticity of the information. The verification unit can also analyze the location information of the image and verify the authenticity of the information. Furthermore, the verification unit can analyze the camera settings of the image and verify the authenticity of the information. In this way, by analyzing the metadata of the image, the authenticity of the information can be verified with high accuracy. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can verify the authenticity of the information using an AI model that analyzes the metadata of the image.

[0036] The correction unit includes a posting unit that automatically posts comments containing correct information to posts containing misinformation. For example, the correction unit can automatically post comments containing corrections to posts containing misinformation. The correction unit can also automatically post comments containing citations to posts containing misinformation. Furthermore, the correction unit can automatically post comments containing official information to posts containing misinformation. This prevents the spread of misinformation by automatically providing correct information to posts containing misinformation. Some or all of the above processing in the correction unit may be performed using AI or not. For example, the correction unit can post comments using an AI model that automatically generates comments containing correct information to posts containing misinformation.

[0037] The notification unit includes a sending unit that sends emails to corporations. For example, the notification unit can send emails to corporations notifying them of the correction of misinformation. The notification unit can also send emails to corporations containing the corrections. Furthermore, the notification unit can send emails to corporations urging them to issue an official statement. This prevents the loss of trust in corporations by notifying them of the correction of misinformation. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can send emails using an AI model that automatically generates emails notifying corporations of the correction of misinformation.

[0038] The data collection unit includes a monitoring unit that monitors specific keywords on social media and determines collection priorities based on keyword frequency. For example, if a particular keyword suddenly appears, the data collection unit can prioritize collecting information related to that keyword. Conversely, if a keyword appears infrequently, the data collection unit can postpone collecting information related to that keyword. Furthermore, if a keyword appears at a moderate frequency, the data collection unit can collect information while balancing it with other high-frequency keywords. This allows for the efficient collection of important information by determining collection priorities based on keyword frequency. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can determine collection priorities using an AI model that analyzes keyword frequency.

[0039] The data collection unit evaluates the source of information and the reliability of the poster during collection, prioritizing the collection of highly reliable information. For example, if the source of information is an official account, the collection unit can prioritize collecting that information. The collection unit can also analyze the poster's past posting history and prioritize collecting information from highly reliable posters. Furthermore, if the source of information is an anonymous account, the collection unit can postpone its collection. This allows for the priority collection of highly reliable information by evaluating the source of information and the reliability of the poster. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model that evaluates the source of information and the reliability of the poster.

[0040] The data collection unit filters information based on specific regions and languages ​​on social media, and collects information tailored to the characteristics of each region. For example, the data collection unit can prioritize collecting information from a specific region and provide information tailored to the characteristics of that region. It can also prioritize collecting information in a specific language and provide information corresponding to that language. Furthermore, the data collection unit can adopt different information collection strategies depending on the characteristics of each region. This enables the provision of more appropriate information by collecting information tailored to the characteristics of each region. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model that filters information based on specific regions and languages ​​on social media.

[0041] The data collection unit analyzes the user's social media activity during collection and prioritizes the collection of relevant information. For example, the data collection unit can prioritize the collection of information related to topics that the user frequently posts about. It can also prioritize the collection of information from accounts that the user follows. Furthermore, it can prioritize the collection of information from communities that the user participates in. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model that analyzes the user's social media activity.

[0042] The verification unit analyzes the source of the information and the poster's past posting history during verification to evaluate its reliability. For example, if the source of the information is an official account, the verification unit can rate the reliability of that information highly. The verification unit can also analyze the poster's past posting history and rate information from a reliable poster highly highly. Furthermore, if the source of the information is an anonymous account, the verification unit can rate the reliability of that information low. In this way, the reliability of information can be highly evaluated by analyzing the source of the information and the poster's past posting history. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can evaluate reliability using an AI model that analyzes the source of the information and the poster's past posting history.

[0043] The verification unit improves the accuracy of the verification by referring to relevant literature and news articles during the verification process. For example, the verification unit can refer to relevant literature to confirm the truthfulness of the information. It can also refer to relevant news articles to confirm the truthfulness of the information. Furthermore, the verification unit can refer to other reliable sources to confirm the truthfulness of the information. This allows for highly accurate verification of the truthfulness of the information by referring to relevant literature and news articles. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit may use an AI model that refers to relevant literature and news articles to confirm the truthfulness of the information.

[0044] The correction unit determines the priority of corrections based on the degree of spread of the misinformation. For example, if the degree of spread of misinformation is high, the correction unit can prioritize correcting that information. Conversely, if the degree of spread of misinformation is low, the correction unit can postpone correcting that information. Furthermore, if the degree of spread of misinformation is moderate, the correction unit can correct it while balancing it with other highly spread misinformation. In this way, by determining the priority of corrections based on the degree of spread of misinformation, important misinformation can be corrected preferentially. Some or all of the above processing in the correction unit may be performed using AI or not. For example, the correction unit can determine the priority of corrections using an AI model that evaluates the degree of spread of misinformation.

[0045] The notification unit selects the optimal notification method by referring to the corporation's past response history when issuing a notification. For example, the notification unit can prioritize methods that the corporation has used to respond quickly in the past. The notification unit can also analyze the corporation's past response history and select the most effective notification method. Furthermore, the notification unit can select the optimal notification method by referring to notification methods the corporation has used in the past. In this way, the optimal notification method can be selected by referring to the corporation's past response history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can select the optimal notification method using an AI model that analyzes the corporation's past response history.

[0046] The notification unit analyzes the corporation's social media activity to select the optimal notification method at the time of notification. For example, the notification unit can analyze the corporation's social media activity and select the most effective notification method. It can also prioritize the social media platforms that the corporation frequently uses. Furthermore, the notification unit can select the optimal notification method based on the corporation's social media activity. In this way, the optimal notification method can be selected by analyzing the corporation's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use an AI model to analyze the corporation's social media activity to select the optimal notification method.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The information verification system may further include a diffusion evaluation unit that evaluates the information diffusion rate and determines the priority of the information based on the diffusion rate. For example, if the information diffusion rate is fast, that information can be verified preferentially. If the information diffusion rate is slow, the verification of that information can be postponed. Furthermore, if the information diffusion rate is moderate, verification can be carried out while balancing it with other information that diffuses at a high rate. In this way, important information can be verified efficiently by determining the priority based on the information diffusion rate. Some or all of the above processing in the diffusion evaluation unit may be performed using AI or not. For example, the diffusion evaluation unit may determine the priority of information using an AI model that evaluates the information diffusion rate.

[0049] The information verification system may further include a reliability evaluation unit that assesses the reliability of information and determines the priority of information based on that reliability. For example, if the reliability of information is high, it can be prioritized for verification. If the reliability of information is low, its verification can be postponed. Furthermore, if the reliability of information is moderate, it can be verified while balancing it with other highly reliable information. This allows for efficient verification of important information by determining priorities based on the reliability of the information. Some or all of the above-described processes in the reliability evaluation unit may be performed using AI or not. For example, the reliability evaluation unit can determine the priority of information using an AI model that evaluates the reliability of information.

[0050] The information verification system may further include a source evaluation unit that evaluates the source of information and determines the priority of information based on its source. For example, if the source of information is an official account, that information can be given priority for verification. If the source of information is an anonymous account, the verification of that information can be postponed. Furthermore, if the source of information is a highly reliable account, that information can be given priority for verification. This allows for efficient verification of important information by determining priority based on the source of information. Some or all of the above processing in the source evaluation unit may be performed using AI or not. For example, the source evaluation unit may determine the priority of information using an AI model that evaluates the source of information.

[0051] The information verification system may further include a content evaluation unit that evaluates the content of information and determines the priority of information based on that content. For example, if the content of the information is important news, that information can be verified preferentially. Conversely, if the content of the information is general news, the verification of that information can be postponed. Furthermore, if the content of the information relates to a specific topic, the priority can be determined based on the importance of that topic. This allows for efficient verification of important information by determining priority based on the content of the information. Some or all of the above processing in the content evaluation unit may be performed using AI or not. For example, the content evaluation unit may determine the priority of information using an AI model that evaluates the content of information.

[0052] The information verification system may further include an impact evaluation unit that assesses the impact of information and determines the priority of information based on that impact. For example, if information has a high impact, it can be prioritized for verification. If information has a low impact, its verification can be postponed. Furthermore, if information has a moderate impact, it can be verified while balancing it with other information with a high impact. This allows for efficient verification of important information by determining priorities based on the impact of the information. Some or all of the above-described processes in the impact evaluation unit may be performed using AI or not. For example, the impact evaluation unit can determine the priority of information using an AI model that evaluates the impact of information.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The collection unit collects information that is disseminated on social media. For example, the collection unit can monitor specific keywords on social media and collect related information. It can also monitor specific accounts or hashtags on social media and collect information. Furthermore, the collection unit can filter information based on specific regions or languages ​​on social media, and collect information tailored to the characteristics of each region. Step 2: The verification unit verifies the authenticity of the information collected by the collection unit. For example, the verification unit can evaluate the source of the collected information and the reliability of the poster to verify its authenticity. The verification unit can also analyze the metadata of images to verify the authenticity of the information. Furthermore, the verification unit can verify the authenticity of the information by referring to relevant literature and news articles. Step 3: The correction unit provides correct information if the verification unit determines that the information is false. For example, the correction unit can automatically post comments containing correct information to posts that contain false information. The correction unit can also determine the priority of corrections based on the degree of spread of the false information if it is widespread. Furthermore, the correction unit can estimate user sentiment and adjust the method of correction based on the estimated user sentiment. Step 4: The notification unit notifies the corporation of the information corrected by the correction unit. The notification unit can, for example, send an email to the corporation. Alternatively, the notification unit can select the most suitable notification method by referring to the corporation's past response history. Furthermore, the notification unit can also select the most suitable notification method by analyzing the corporation's social media activity.

[0055] (Example of form 2) The information verification system according to an embodiment of the present invention is a system that verifies the truthfulness of information about company names and products that has a certain degree of dissemination power on social media, and corrects it if it is misinformation (fiction). The information verification system collects information that is disseminated on social media, and an AI verifies the truthfulness of the collected information. If it is determined to be misinformation, the AI ​​provides the correct information and notifies the company that misinformation is being disseminated. This system allows companies to prevent damage to their reputation. It also prevents the spread of misinformation on social media and promotes the dissemination of correct information. This makes it possible to realize a world where everyone can access correct information. For example, if misinformation about a certain company is disseminated on social media, this system collects that information, and the AI ​​verifies its truthfulness. If it is determined to be misinformation, the AI ​​provides the correct information and notifies the company. The company, upon receiving the notification from the AI, takes action such as issuing an official statement. This prevents the spread of misinformation and protects the company's reputation. This system aims to become a new platform that guarantees the legitimacy of information on social media. For example, just as other platforms have implemented fact-checking functions, this system will verify the validity of information and provide accurate information, thereby creating a highly reliable information platform. In this way, the information verification system can realize a world where everyone can access accurate information.

[0056] The information verification system according to this embodiment comprises a collection unit, a verification unit, a correction unit, and a notification unit. The collection unit collects information disseminated on social networking services (SNS). For example, the collection unit can monitor specific keywords on SNS and collect related information. The collection unit can also monitor specific accounts or hashtags on SNS and collect information. Furthermore, the collection unit can filter information based on specific regions or languages ​​on SNS and collect information according to regional characteristics. The verification unit verifies the truthfulness of the information collected by the collection unit. For example, the verification unit can evaluate the source of the collected information and the reliability of the poster to verify the truthfulness of the information. The verification unit can also analyze the metadata of images to verify the truthfulness of the information. Furthermore, the verification unit can also verify the truthfulness of the information by referring to relevant literature and news articles. The correction unit provides correct information when the verification unit determines that the information is misinformation. For example, the correction unit can automatically post comments containing correct information to posts containing misinformation. Furthermore, if misinformation is being disseminated, the correction unit can also determine the priority of corrections based on the degree of dissemination. Furthermore, the correction unit can estimate the user's emotions and adjust the correction method based on the estimated emotions. The notification unit notifies the corporation of the information corrected by the correction unit. The notification unit can, for example, send an email to the corporation. The notification unit can also select the optimal notification method by referring to the corporation's past response history. Furthermore, the notification unit can also select the optimal notification method by analyzing the corporation's social media activity. As a result, the information verification system according to this embodiment can automatically verify the truthfulness of information disseminated on social media and correct misinformation, thereby enabling the provision of accurate information.

[0057] The data collection unit collects information disseminated on social media. For example, the unit can monitor specific keywords on social media and collect related information. Specifically, the unit uses natural language processing technology to analyze social media posts and automatically extract posts containing specific keywords or phrases. The unit can also monitor specific accounts and hashtags on social media and collect information. This allows for the efficient collection of information related to influential accounts and trending hashtags. Furthermore, the unit can filter information based on specific regions and languages ​​on social media, collecting information tailored to regional characteristics. For example, it can utilize geographic information systems (GIS) to extract posts originating from specific regions and collect region-specific information. It can also use multilingual natural language processing technology to analyze information posted in different languages ​​and classify the information by language. As a result, the unit can efficiently collect a wide range of social media data and quickly obtain necessary information by filtering it based on specific conditions. In addition, the unit can centrally manage the collected information and collaborate with other departments to share and analyze the information. For example, the collected information is stored in a cloud-based database, making it accessible to the verification and correction units. Furthermore, the collection unit can dynamically adjust the collection frequency and target data, enabling flexible information gathering tailored to specific events and situations. This allows the collection unit to efficiently and effectively collect information disseminated on social media, improving the overall system performance.

[0058] The verification unit verifies the authenticity of the information collected by the collection unit. For example, the verification unit can evaluate the source of the collected information and the reliability of the poster to verify its authenticity. Specifically, the verification unit analyzes metadata such as the poster's past posting history, number of followers, and account creation date to evaluate the poster's reliability. The verification unit can also analyze image metadata to verify the authenticity of the information. For example, it analyzes the date and location information of an image to check if it matches the content of the post. Furthermore, the verification unit can also verify the authenticity of the information by referring to relevant literature and news articles. For example, if the collected information concerns a specific incident or event, it will refer to reliable news sources and academic papers to confirm the accuracy of the information. This allows the verification unit to verify the collected information from multiple angles and make quick and accurate judgments about its authenticity. In addition, the verification unit can introduce an automated verification system using AI to efficiently verify the authenticity of information. For example, it can use natural language processing technology to analyze collected text information and evaluate the context and consistency of the content. It can also use image recognition technology to analyze collected image information and detect image tampering or forgery. This allows the verification unit to quickly and accurately verify the collected information, thereby improving the overall reliability of the system.

[0059] The correction unit provides correct information when the verification unit determines that the information is false. For example, the correction unit can automatically post comments containing correct information to posts that contain false information. Specifically, the correction unit identifies posts containing false information and generates appropriate correction comments for those posts. The generated comments correct the content of the false information and provide accurate information. The correction unit can also determine the priority of corrections based on the degree of spread of the false information. For example, if the false information has been retweeted by many users, corrections to that false information will be prioritized. Furthermore, the correction unit can estimate the user's emotions and adjust the method of correction based on the estimated user emotions. For example, if the user is feeling angry or anxious, the content and tone of the correction comment will be adjusted to make corrections that are considerate of the user's emotions. In this way, the correction unit can correct false information quickly and effectively and provide correct information. Furthermore, the correction unit can introduce an AI-powered automatic correction system to streamline the correction process. For example, it can use natural language generation technology to automatically generate appropriate correction comments and respond quickly to posts containing false information. Furthermore, sentiment analysis technology is used to analyze the user's emotions in real time and select the appropriate correction method. This allows the correction unit to perform the correction of misinformation efficiently and effectively, improving the overall reliability of the system.

[0060] The notification unit notifies the corporation of the information corrected by the correction unit. The notification unit can, for example, send an email to the corporation. Specifically, the notification unit automatically generates an email containing the corrected information and sends it to the corporation's contact person. The notification unit can also select the most suitable notification method by referring to the corporation's past response history. For example, if email notification was effective in the past, it will use the same method. The notification unit can also select the most suitable notification method by analyzing the corporation's social media activity. For example, if the corporation actively uses a particular social media platform, it will send a notification through that platform. This allows the notification unit to quickly and effectively communicate corrected information to the corporation. Furthermore, the notification unit can streamline notification work by introducing an AI-powered automated notification system. For example, it can use natural language generation technology to automatically generate appropriate notification content and quickly notify the corporation. It can also maximize the effectiveness of notifications by analyzing past notification history and the corporation's social media activity to select the most suitable notification method. This allows the notification unit to quickly and effectively communicate corrected information to the corporation and improve the overall reliability of the system.

[0061] The data collection unit includes a monitoring unit that monitors specific keywords on social media. For example, the data collection unit can monitor trending words on social media and collect related information. It can also monitor important words on social media and collect information from them. Furthermore, the data collection unit can determine collection priorities based on the frequency of occurrence of specific keywords on social media. This allows for the efficient collection of important information by monitoring specific keywords. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model that monitors specific keywords on social media.

[0062] The verification unit includes an analysis unit that analyzes the metadata of the image. The verification unit can, for example, analyze the date and time the image was taken and verify the authenticity of the information. The verification unit can also analyze the location information of the image and verify the authenticity of the information. Furthermore, the verification unit can analyze the camera settings of the image and verify the authenticity of the information. In this way, by analyzing the metadata of the image, the authenticity of the information can be verified with high accuracy. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can verify the authenticity of the information using an AI model that analyzes the metadata of the image.

[0063] The correction unit includes a posting unit that automatically posts comments containing correct information to posts containing misinformation. For example, the correction unit can automatically post comments containing corrections to posts containing misinformation. The correction unit can also automatically post comments containing citations to posts containing misinformation. Furthermore, the correction unit can automatically post comments containing official information to posts containing misinformation. This prevents the spread of misinformation by automatically providing correct information to posts containing misinformation. Some or all of the above processing in the correction unit may be performed using AI or not. For example, the correction unit can post comments using an AI model that automatically generates comments containing correct information to posts containing misinformation.

[0064] The notification unit includes a sending unit that sends emails to corporations. For example, the notification unit can send emails to corporations notifying them of the correction of misinformation. The notification unit can also send emails to corporations containing the corrections. Furthermore, the notification unit can send emails to corporations urging them to issue an official statement. This prevents the loss of trust in corporations by notifying them of the correction of misinformation. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can send emails using an AI model that automatically generates emails notifying corporations of the correction of misinformation.

[0065] The data collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions. For example, if the user is excited, the data collection unit can increase the frequency of information collection to collect information in real time. Conversely, if the user is relaxed, the data collection unit can decrease the frequency of information collection and collect information periodically. Furthermore, if the user is stressed, the data collection unit can adjust the timing of information collection to times when the user is less active. By adjusting the timing of information collection according to the user's emotions, more appropriate information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can adjust the timing of information collection using an AI model that estimates the user's emotions.

[0066] The data collection unit includes a monitoring unit that monitors specific keywords on social media and determines collection priorities based on keyword frequency. For example, if a particular keyword suddenly appears, the data collection unit can prioritize collecting information related to that keyword. Conversely, if a keyword appears infrequently, the data collection unit can postpone collecting information related to that keyword. Furthermore, if a keyword appears at a moderate frequency, the data collection unit can collect information while balancing it with other high-frequency keywords. This allows for the efficient collection of important information by determining collection priorities based on keyword frequency. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can determine collection priorities using an AI model that analyzes keyword frequency.

[0067] The data collection unit evaluates the source of information and the reliability of the poster during collection, prioritizing the collection of highly reliable information. For example, if the source of information is an official account, the collection unit can prioritize collecting that information. The collection unit can also analyze the poster's past posting history and prioritize collecting information from highly reliable posters. Furthermore, if the source of information is an anonymous account, the collection unit can postpone its collection. This allows for the priority collection of highly reliable information by evaluating the source of information and the reliability of the poster. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model that evaluates the source of information and the reliability of the poster.

[0068] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting information related to those emotions. If the user is relaxed, the data collection unit can prioritize collecting general information. Furthermore, if the user is stressed, the data collection unit can prioritize collecting information that helps reduce stress. This allows for more appropriate information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can determine the priority of information using an AI model that estimates the user's emotions.

[0069] The data collection unit filters information based on specific regions and languages ​​on social media, and collects information tailored to the characteristics of each region. For example, the data collection unit can prioritize collecting information from a specific region and provide information tailored to the characteristics of that region. It can also prioritize collecting information in a specific language and provide information corresponding to that language. Furthermore, the data collection unit can adopt different information collection strategies depending on the characteristics of each region. This enables the provision of more appropriate information by collecting information tailored to the characteristics of each region. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model that filters information based on specific regions and languages ​​on social media.

[0070] The data collection unit analyzes the user's social media activity during collection and prioritizes the collection of relevant information. For example, the data collection unit can prioritize the collection of information related to topics that the user frequently posts about. It can also prioritize the collection of information from accounts that the user follows. Furthermore, it can prioritize the collection of information from communities that the user participates in. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can collect information using an AI model that analyzes the user's social media activity.

[0071] The verification unit estimates the user's emotions and adjusts the verification criteria based on the estimated emotions. For example, if the user is excited, the verification unit can apply strict verification criteria to confirm the truthfulness of the information. If the user is relaxed, the verification unit can apply flexible verification criteria to confirm the truthfulness of the information. Furthermore, if the user is stressed, the verification unit can perform rapid verification to confirm the truthfulness of the information. This allows for more appropriate information verification by adjusting the verification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can adjust the verification criteria using an AI model that estimates the user's emotions.

[0072] The verification unit analyzes the source of the information and the poster's past posting history during verification to evaluate its reliability. For example, if the source of the information is an official account, the verification unit can rate the reliability of that information highly. The verification unit can also analyze the poster's past posting history and rate information from a reliable poster highly highly. Furthermore, if the source of the information is an anonymous account, the verification unit can rate the reliability of that information low. In this way, the reliability of information can be highly evaluated by analyzing the source of the information and the poster's past posting history. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can evaluate reliability using an AI model that analyzes the source of the information and the poster's past posting history.

[0073] The verification unit improves the accuracy of the verification by referring to relevant literature and news articles during the verification process. For example, the verification unit can refer to relevant literature to confirm the truthfulness of the information. It can also refer to relevant news articles to confirm the truthfulness of the information. Furthermore, the verification unit can refer to other reliable sources to confirm the truthfulness of the information. This allows for highly accurate verification of the truthfulness of the information by referring to relevant literature and news articles. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit may use an AI model that refers to relevant literature and news articles to confirm the truthfulness of the information.

[0074] The correction unit estimates the user's emotions and adjusts the correction method based on the estimated emotions. For example, if the user is excited, the correction unit can make quick and concise corrections. If the user is relaxed, the correction unit can also make corrections that include detailed explanations. Furthermore, if the user is stressed, the correction unit can make corrections that help reduce stress. This allows for more appropriate information correction by adjusting the correction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the correction unit may be performed using AI or not. For example, the correction unit can adjust the correction method using an AI model that estimates the user's emotions.

[0075] The correction unit determines the priority of corrections based on the degree of spread of the misinformation. For example, if the degree of spread of misinformation is high, the correction unit can prioritize correcting that information. Conversely, if the degree of spread of misinformation is low, the correction unit can postpone correcting that information. Furthermore, if the degree of spread of misinformation is moderate, the correction unit can correct it while balancing it with other highly spread misinformation. In this way, by determining the priority of corrections based on the degree of spread of misinformation, important misinformation can be corrected preferentially. Some or all of the above processing in the correction unit may be performed using AI or not. For example, the correction unit can determine the priority of corrections using an AI model that evaluates the degree of spread of misinformation.

[0076] The notification unit estimates the user's emotions and adjusts the notification method based on the estimated emotions. For example, if the user is excited, the notification unit can provide a quick and concise notification. If the user is relaxed, the notification unit can provide a notification with a detailed explanation. Furthermore, if the user is stressed, the notification unit can provide a notification that helps reduce stress. By adjusting the notification method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can adjust the notification method using an AI model that estimates the user's emotions.

[0077] The notification unit selects the optimal notification method by referring to the corporation's past response history when issuing a notification. For example, the notification unit can prioritize methods that the corporation has used to respond quickly in the past. The notification unit can also analyze the corporation's past response history and select the most effective notification method. Furthermore, the notification unit can select the optimal notification method by referring to notification methods the corporation has used in the past. In this way, the optimal notification method can be selected by referring to the corporation's past response history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can select the optimal notification method using an AI model that analyzes the corporation's past response history.

[0078] The notification unit analyzes the corporation's social media activity to select the optimal notification method at the time of notification. For example, the notification unit can analyze the corporation's social media activity and select the most effective notification method. It can also prioritize the social media platforms that the corporation frequently uses. Furthermore, the notification unit can select the optimal notification method based on the corporation's social media activity. In this way, the optimal notification method can be selected by analyzing the corporation's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use an AI model to analyze the corporation's social media activity to select the optimal notification method.

[0079] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0080] The information verification system may further include an evaluation unit that estimates the user's emotions and evaluates the reliability of the information based on the estimated emotions. For example, if the user is excited, the reliability of the information may be evaluated as low. Conversely, if the user is relaxed, the reliability of the information may be evaluated as high. Furthermore, if the user is stressed, the reliability of the information may be evaluated as moderate. This allows for more appropriate information verification by evaluating the reliability of the information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit may evaluate the reliability of the information using an AI model that estimates the user's emotions.

[0081] The information verification system may further include an importance evaluation unit that estimates the user's emotions and evaluates the importance of the information based on the estimated emotions. For example, if the user is excited, the importance of the information may be highly evaluated. If the user is relaxed, the importance of the information may be low. Furthermore, if the user is stressed, the importance of the information may be evaluated as moderate. This makes it possible to provide more appropriate information by evaluating the importance of the information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the importance evaluation unit may be performed using AI or not. For example, the importance evaluation unit may evaluate the importance of the information using an AI model that estimates the user's emotions.

[0082] The information verification system may further include a display unit that estimates the user's emotions and adjusts the way information is displayed based on the estimated emotions. For example, if the user is excited, the information can be displayed concisely. If the user is relaxed, the information can be displayed in detail. Furthermore, if the user is stressed, the information can be displayed in a visually easy-to-understand manner. This allows for more appropriate information to be provided by adjusting the way information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can adjust the way information is displayed using an AI model that estimates the user's emotions.

[0083] The information verification system may further include a distribution unit that estimates the user's emotions and adjusts the timing of information delivery based on the estimated emotions. For example, if the user is excited, information can be delivered immediately. If the user is relaxed, information can be delivered periodically. Furthermore, if the user is stressed, information can be delivered during times when the user is less active. This allows for more appropriate information provision by adjusting the timing of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can adjust the timing of information delivery using an AI model that estimates the user's emotions.

[0084] The information verification system may further include a filtering unit that estimates the user's emotions and adjusts the information filtering criteria based on the estimated emotions. For example, if the user is excited, strict filtering criteria can be applied to select information. If the user is relaxed, flexible filtering criteria can be applied to select information. Furthermore, if the user is stressed, rapid filtering can be performed to select information. This allows for the provision of more appropriate information by adjusting the filtering criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the filtering unit may be performed using AI or not. For example, the filtering unit can adjust the filtering criteria using an AI model that estimates the user's emotions.

[0085] The information verification system may further include a diffusion evaluation unit that evaluates the information diffusion rate and determines the priority of the information based on the diffusion rate. For example, if the information diffusion rate is fast, that information can be verified preferentially. If the information diffusion rate is slow, the verification of that information can be postponed. Furthermore, if the information diffusion rate is moderate, verification can be carried out while balancing it with other information that diffuses at a high rate. In this way, important information can be verified efficiently by determining the priority based on the information diffusion rate. Some or all of the above processing in the diffusion evaluation unit may be performed using AI or not. For example, the diffusion evaluation unit may determine the priority of information using an AI model that evaluates the information diffusion rate.

[0086] The information verification system may further include a reliability evaluation unit that assesses the reliability of information and determines the priority of information based on that reliability. For example, if the reliability of information is high, it can be prioritized for verification. If the reliability of information is low, its verification can be postponed. Furthermore, if the reliability of information is moderate, it can be verified while balancing it with other highly reliable information. This allows for efficient verification of important information by determining priorities based on the reliability of the information. Some or all of the above-described processes in the reliability evaluation unit may be performed using AI or not. For example, the reliability evaluation unit can determine the priority of information using an AI model that evaluates the reliability of information.

[0087] The information verification system may further include a source evaluation unit that evaluates the source of information and determines the priority of information based on its source. For example, if the source of information is an official account, that information can be given priority for verification. If the source of information is an anonymous account, the verification of that information can be postponed. Furthermore, if the source of information is a highly reliable account, that information can be given priority for verification. This allows for efficient verification of important information by determining priority based on the source of information. Some or all of the above processing in the source evaluation unit may be performed using AI or not. For example, the source evaluation unit may determine the priority of information using an AI model that evaluates the source of information.

[0088] The information verification system may further include a content evaluation unit that evaluates the content of information and determines the priority of information based on that content. For example, if the content of the information is important news, that information can be verified preferentially. Conversely, if the content of the information is general news, the verification of that information can be postponed. Furthermore, if the content of the information relates to a specific topic, the priority can be determined based on the importance of that topic. This allows for efficient verification of important information by determining priority based on the content of the information. Some or all of the above processing in the content evaluation unit may be performed using AI or not. For example, the content evaluation unit may determine the priority of information using an AI model that evaluates the content of information.

[0089] The information verification system may further include an impact evaluation unit that assesses the impact of information and determines the priority of information based on that impact. For example, if information has a high impact, it can be prioritized for verification. If information has a low impact, its verification can be postponed. Furthermore, if information has a moderate impact, it can be verified while balancing it with other information with a high impact. This allows for efficient verification of important information by determining priorities based on the impact of the information. Some or all of the above-described processes in the impact evaluation unit may be performed using AI or not. For example, the impact evaluation unit can determine the priority of information using an AI model that evaluates the impact of information.

[0090] The following briefly describes the processing flow for example form 2.

[0091] Step 1: The collection unit collects information that is disseminated on social media. For example, the collection unit can monitor specific keywords on social media and collect related information. It can also monitor specific accounts or hashtags on social media and collect information. Furthermore, the collection unit can filter information based on specific regions or languages ​​on social media, and collect information tailored to the characteristics of each region. Step 2: The verification unit verifies the authenticity of the information collected by the collection unit. For example, the verification unit can evaluate the source of the collected information and the reliability of the poster to verify its authenticity. The verification unit can also analyze the metadata of images to verify the authenticity of the information. Furthermore, the verification unit can verify the authenticity of the information by referring to relevant literature and news articles. Step 3: The correction unit provides correct information if the verification unit determines that the information is false. For example, the correction unit can automatically post comments containing correct information to posts that contain false information. The correction unit can also determine the priority of corrections based on the degree of spread of the false information if it is widespread. Furthermore, the correction unit can estimate user sentiment and adjust the method of correction based on the estimated user sentiment. Step 4: The notification unit notifies the corporation of the information corrected by the correction unit. The notification unit can, for example, send an email to the corporation. Alternatively, the notification unit can select the most suitable notification method by referring to the corporation's past response history. Furthermore, the notification unit can also select the most suitable notification method by analyzing the corporation's social media activity.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0094] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] Each of the multiple elements described above, including the collection unit, verification unit, correction unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information on social networking services (SNS). The verification unit is implemented by the identification processing unit 290 of the data processing device 12 and verifies the truthfulness of the collected information. The correction unit is implemented by the control unit 46A of the smart device 14 and provides correct information in case of misinformation. The notification unit is implemented by the identification processing unit 290 of the data processing device 12 and notifies the corporation of the correction of misinformation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0096] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0097] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0102] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0103] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0104] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0105] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0106] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the collection unit, verification unit, correction unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from social networking services (SNS). The verification unit is implemented by the identification processing unit 290 of the data processing device 12 and verifies the truthfulness of the collected information. The correction unit is implemented by the control unit 46A of the smart glasses 214 and provides correct information in case of misinformation. The notification unit is implemented by the identification processing unit 290 of the data processing device 12 and notifies the corporation of the correction of misinformation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0112] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0113] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the collection unit, verification unit, correction unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information on social networking services (SNS). The verification unit is implemented by the identification processing unit 290 of the data processing unit 12 and verifies the truthfulness of the collected information. The correction unit is implemented by the control unit 46A of the headset terminal 314 and provides correct information in case of misinformation. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and notifies the corporation of the correction of misinformation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0129] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0136] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, verification unit, correction unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information on social networking services (SNS). The verification unit is implemented by the identification processing unit 290 of the data processing unit 12 and verifies the truthfulness of the collected information. The correction unit is implemented by the control unit 46A of the robot 414 and provides correct information in case of misinformation. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and notifies the corporation of the correction of misinformation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0147] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0148] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0149] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0153] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0154] 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.

[0155] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0156] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0157] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0158] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0160] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0161] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0162] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0163] (Note 1) A collection department that collects information spread on social media, A verification unit that verifies the truthfulness of the information collected by the aforementioned collection unit, A correction unit that provides correct information when the verification unit determines that the information is incorrect, The system includes a notification unit that notifies the corporation of the information corrected by the correction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It has a monitoring unit that monitors specific keywords on social media. The system described in Appendix 1, characterized by the features described herein. (Note 3) The verification unit, It includes an analysis unit that analyzes image metadata. The system described in Appendix 1, characterized by the features described herein. (Note 4) The correction section is, It includes a posting section that automatically posts comments containing correct information in response to posts that contain misinformation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Equipped with a sending function to send emails to corporations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It includes a monitoring unit that tracks specific keywords on social media and determines the priority of data collection based on the frequency of keyword occurrence. The system described in Appendix 2, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, we evaluate the source of the information and the reliability of the poster, prioritizing the collection of highly reliable information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Filter information on social media based on specific regions and languages, and collect information tailored to the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and prioritizes collecting relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The verification unit, We estimate the user's emotions and adjust the validation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The verification unit, During verification, the source of the information and the poster's past posting history are analyzed to assess its reliability. The system described in Appendix 1, characterized by the features described herein. (Note 14) The verification unit, During verification, we refer to relevant literature and news articles to improve the accuracy of the verification. The system described in Appendix 1, characterized by the features described herein. (Note 15) The correction section is, It estimates the user's emotions and adjusts the correction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The correction section is, When making corrections, the priority of corrections is determined based on the degree to which the misinformation has spread. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When sending a notification, the most suitable notification method will be selected by referring to the corporation's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending notifications, we analyze the company's social media activity to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information spread on social media, A verification unit that verifies the truthfulness of the information collected by the aforementioned collection unit, A correction unit that provides correct information when the verification unit determines that the information is incorrect, The system includes a notification unit that notifies the corporation of the information corrected by the correction unit. A system characterized by the following features.

2. The aforementioned collection unit is It has a monitoring unit that monitors specific keywords on social media. The system according to feature 1.

3. The verification unit, It includes an analysis unit that analyzes image metadata. The system according to feature 1.

4. The correction section is, It includes a posting section that automatically posts comments containing correct information in response to posts that contain misinformation. The system according to feature 1.

5. The aforementioned notification unit, Equipped with a sending function to send emails to corporations. The system according to feature 1.

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

7. The aforementioned collection unit is It includes a monitoring unit that tracks specific keywords on social media and determines the priority of data collection based on the frequency of keyword occurrence. The system according to feature 2.

8. The aforementioned collection unit is During data collection, we evaluate the source of the information and the reliability of the poster, prioritizing the collection of highly reliable information. The system according to feature 1.

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