System

Abstract

An object of a system according to an embodiment is to confirm the authenticity of information generated by a generation AI and to provide reliability.SOLUTION: A system according to an embodiment includes a generation unit, a verification unit, and a credit unit. The generation unit generates information using the generation AI. The verification unit verifies the information generated by the generation unit using the blockchain. The credit unit gives credit to the information verified by the verification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to verify the authenticity of information generated by AI, posing the risk of secondary damage due to misinformation.

[0005] The system according to the embodiment aims to verify the authenticity of information generated by a generation AI and to grant it credibility. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a verification unit, and a credit unit. The generation unit generates information using a generation AI. The verification unit verifies the information generated by the generation unit using a blockchain. The credit unit grants credit to the information verified by the verification unit. [Effects of the Invention]

[0007] The system according to the embodiment can verify the authenticity of information generated by the generation AI and grant it credibility. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information credit system according to an embodiment of the present invention is a system that verifies the reliability of information generated by a generation AI on a blockchain and credits the information based on the results. As a result, the information credit system can provide highly reliable information to avoid secondary damage that may occur when the authenticity of information becomes unclear as the generation AI becomes more sophisticated.

[0029] An information crediting system according to an embodiment includes a generation unit, a verification unit, and a crediting unit. The generation unit generates information using a generation AI. For example, the generation AI generates text information such as news articles, reports, and reviews based on prompts input by a user. The generation AI also uses a pre-fine-tuned model to generate highly accurate information according to user instructions. The verification unit verifies the information generated by the generation unit using a blockchain. For example, metadata such as the source of the information, the generation process, and the dataset used are recorded on the blockchain. This prevents information tampering and ensures reliability. The crediting unit grants credit to the information verified by the verification unit. For example, a score is assigned to evaluate the reliability of the information, and the information is credited based on the score. As a result, the information crediting system can provide highly reliable information to avoid secondary damage caused by the authenticity of information becoming unclear as the generation AI becomes more sophisticated.

[0030] The generation unit evaluates the diversity of the datasets used in the information generation process, enabling the generation of information from more diverse perspectives. For example, when the generation AI generates a news article, the generation unit evaluates the diversity of the datasets used and generates an article that incorporates different perspectives and opinions. For example, it collects information from multiple news sources and creates an article from a multifaceted perspective. The generation unit also evaluates the diversity of the datasets used when generating reviews and generates reviews that reflect the opinions and ratings of different users. For example, it incorporates the opinions of users from different age groups and regions. The generation unit also evaluates the diversity of the datasets used when generating reports and generates reports that incorporate data from different industries and fields. For example, it creates a report that combines data from the technical field and the economic field. This enables the provision of unbiased information by generating information from diverse perspectives.

[0031] The generation unit can detect bias in the information generation process and introduce a feedback loop to eliminate bias. For example, when the generation AI generates a news article, the generation unit uses a bias detection function to check whether the content of the article is biased and provides feedback to eliminate bias. For example, it adjusts the article to avoid bias toward a particular political position. In addition, when generating reviews, the generation unit uses a bias detection function to check whether the review content is biased and provides feedback to eliminate bias. For example, it eliminates bias against a particular product or brand. In addition, when generating reports, the generation unit uses a bias detection function to check whether the report content is biased and provides feedback to eliminate bias. For example, it adjusts the data selection and interpretation to avoid bias. This eliminates bias, making it possible to provide fair and reliable information.

[0032] The generation unit generates multimodal information including images and audio data for information, thereby increasing reliability through visual and audio information. For example, when the generation AI generates a news article, the generation unit simultaneously generates related image and audio data, providing an article that includes visual and audio information. For example, it adds photos and interview audio related to the news article. In addition, in generating reviews, the generation unit generates reviews that include product images and audio of the product in use, increasing reliability through visual and audio information. For example, it adds videos of the product in use and audio reviews. In addition, in generating reports, the generation unit generates reports that include data graphs and charts and audio commentary, increasing reliability through visual and audio information. For example, it adds data visualization and audio narration. This makes it possible to increase the reliability of the information by including visual and audio information.

[0033] The generation unit can use datasets from different industries and fields for information to generate cross-domain information. For example, when the generation AI generates a news article, the generation unit uses datasets from different industries and fields to generate the article from a cross-domain perspective. For example, an article is created that combines data from the technical and economic fields. In addition, when generating reviews, the generation unit uses datasets from different industries and fields to generate reviews from a cross-domain perspective. For example, a review is created that combines a product's technical evaluation and market evaluation. In addition, when generating reports, the generation unit uses datasets from different industries and fields to generate reports from a cross-domain perspective. For example, a report is created that combines data from the medical and economic fields. This makes it possible to provide more multifaceted information by using data from different industries and fields.

[0034] When verifying information on the blockchain, the verification unit can increase transparency by recording a detailed log of the source and generation process of the information. For example, when verifying the reliability of a news article on the blockchain, the verification unit can increase transparency by recording a detailed log of the source and generation process of the article. For example, the verification unit can record which dataset was used to generate the article. Furthermore, when verifying the reliability of a review, the verification unit can increase transparency by recording a detailed log of the source and generation process of the review. For example, the verification unit can record which user's opinion the review was based on when generated. Furthermore, when verifying the reliability of a report, the verification unit can increase transparency by recording a detailed log of the source and generation process of the report. For example, the verification unit can record which data or example the report was based on when generated. In this way, by recording a detailed log of the source and generation process, the transparency of the information can be increased.

[0035] The verification unit can introduce advanced encryption technology to detect information tampering when verifying information on the blockchain. For example, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a news article on the blockchain. For example, it checks whether the source or content of the article has been tampered with. Furthermore, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a review. For example, it checks whether the content of the review has been tampered with. Furthermore, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a report. For example, it checks whether the data or content of the report has been tampered with. This makes it possible to detect information tampering and ensure the reliability of the information.

[0036] When verifying information on the blockchain, the verification unit ensures interoperability between different blockchain networks and can verify the reliability of the information from multiple angles. For example, when verifying the reliability of a news article on the blockchain, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the article using multiple blockchain networks. Furthermore, when verifying the reliability of a review, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the review using multiple blockchain networks. Furthermore, when verifying the reliability of a report, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the report using multiple blockchain networks. In this way, the reliability of the information can be verified from multiple angles by ensuring interoperability between different blockchain networks.

[0037] The credit department sets multifaceted criteria for assigning credit scores to information verified on the blockchain, and can comprehensively evaluate technical reliability, social impact, economic value, etc. The credit department, for example, comprehensively evaluates technical reliability, social impact, economic value, etc. of news articles verified on the blockchain and assigns a credit score. For example, it evaluates the technical accuracy and social influence of the article. The credit department also comprehensively evaluates technical reliability, social impact, economic value, etc. of reviews and assigns a credit score. For example, it evaluates the technical accuracy and market influence of the review. The credit department also comprehensively evaluates technical reliability, social impact, economic value, etc. of reports and assigns a credit score. For example, it evaluates the technical accuracy and economic value of the report. In this way, by evaluating using multifaceted criteria, a more reliable credit score can be assigned.

[0038] The credit department can make the process of assigning a credit score transparent for information verified on the blockchain and introduce a feedback function that explains the process to users. For example, the credit department makes the process of assigning a credit score transparent for news articles verified on the blockchain and introduces a feedback function that explains the process to users. For example, it explains to users the criteria and process for evaluating the credibility of articles. The credit department also makes the process of assigning a credit score transparent for reviews and introduces a feedback function that explains the process to users. For example, it explains to users the criteria and process for evaluating the credibility of reviews. The credit department also makes the process of assigning a credit score transparent for reports and introduces a feedback function that explains the process to users. For example, it explains to users the criteria and process for evaluating the credibility of reports. In this way, making the process of assigning a credit score transparent makes it easier to gain user trust.

[0039] The credit department assigns credit scores to information verified on the blockchain according to different industries and uses, allowing for more specific assessments of trustworthiness. For example, the credit department assigns credit scores to news articles verified on the blockchain according to different industries and uses, allowing for more specific assessments of trustworthiness. For example, different scores are assigned to news articles in the technology field and news articles in the economics field. The credit department also assigns credit scores to reviews according to different industries and uses, allowing for more specific assessments of trustworthiness. For example, different scores are assigned to reviews of home appliances and fashion items. The credit department also assigns credit scores to reports according to different industries and uses, allowing for more specific assessments of trustworthiness. For example, different scores are assigned to reports in the medical field and reports in the education field. In this way, by assigning credit scores according to different industries and uses, more specific assessments of trustworthiness become possible.

[0040] The credit department can update the credit score in real time for information verified on the blockchain, and provide a trustworthiness evaluation based on the latest information. The credit department can update the credit score in real time for, for example, news articles verified on the blockchain, and provide a trustworthiness evaluation based on the latest information. For example, the score is updated every time new information is added. The credit department can also update the credit score in real time for reviews, and provide a trustworthiness evaluation based on the latest information. For example, the score is updated every time a new user review is added. The credit department can also update the credit score in real time for reports, and provide a trustworthiness evaluation based on the latest information. For example, the score is updated every time new data or case studies are added. In this way, by updating the credit score in real time, it is possible to provide a trustworthiness evaluation based on the latest information.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The generation unit evaluates the diversity of the datasets used in the information generation process, enabling the generation of information from more diverse perspectives. For example, when the generation AI generates a news article, the generation unit evaluates the diversity of the datasets used and generates an article that incorporates different perspectives and opinions. For example, it collects information from multiple news sources and creates an article from a multifaceted perspective. The generation unit also evaluates the diversity of the datasets used when generating reviews and generates reviews that reflect the opinions and ratings of different users. For example, it incorporates the opinions of users from different age groups and regions. The generation unit also evaluates the diversity of the datasets used when generating reports and generates reports that incorporate data from different industries and fields. For example, it creates a report that combines data from the technical field and the economic field. This enables the provision of unbiased information by generating information from diverse perspectives.

[0043] The generation unit can detect bias in the information generation process and introduce a feedback loop to eliminate bias. For example, when the generation AI generates a news article, the generation unit uses a bias detection function to check whether the content of the article is biased and provides feedback to eliminate bias. For example, it adjusts the article to avoid bias toward a particular political position. In addition, when generating reviews, the generation unit uses a bias detection function to check whether the review content is biased and provides feedback to eliminate bias. For example, it eliminates bias against a particular product or brand. In addition, when generating reports, the generation unit uses a bias detection function to check whether the report content is biased and provides feedback to eliminate bias. For example, it adjusts the data selection and interpretation to avoid bias. This eliminates bias, making it possible to provide fair and reliable information.

[0044] The generation unit generates multimodal information including images and audio data for information, thereby increasing reliability through visual and audio information. For example, when the generation AI generates a news article, the generation unit simultaneously generates related image and audio data, providing an article that includes visual and audio information. For example, it adds photos and interview audio related to the news article. In addition, in generating reviews, the generation unit generates reviews that include product images and audio of the product in use, increasing reliability through visual and audio information. For example, it adds videos of the product in use and audio reviews. In addition, in generating reports, the generation unit generates reports that include data graphs and charts and audio commentary, increasing reliability through visual and audio information. For example, it adds data visualization and audio narration. This makes it possible to increase the reliability of the information by including visual and audio information.

[0045] The generation unit can use datasets from different industries and fields for information to generate cross-domain information. For example, when the generation AI generates a news article, the generation unit uses datasets from different industries and fields to generate the article from a cross-domain perspective. For example, an article is created that combines data from the technical and economic fields. In addition, when generating reviews, the generation unit uses datasets from different industries and fields to generate reviews from a cross-domain perspective. For example, a review is created that combines a product's technical evaluation and market evaluation. In addition, when generating reports, the generation unit uses datasets from different industries and fields to generate reports from a cross-domain perspective. For example, a report is created that combines data from the medical and economic fields. This makes it possible to provide more multifaceted information by using data from different industries and fields.

[0046] When verifying information on the blockchain, the verification unit can increase transparency by recording a detailed log of the source and generation process of the information. For example, when verifying the reliability of a news article on the blockchain, the verification unit can increase transparency by recording a detailed log of the source and generation process of the article. For example, the verification unit can record which dataset was used to generate the article. Furthermore, when verifying the reliability of a review, the verification unit can increase transparency by recording a detailed log of the source and generation process of the review. For example, the verification unit can record which user's opinion the review was based on when generated. Furthermore, when verifying the reliability of a report, the verification unit can increase transparency by recording a detailed log of the source and generation process of the report. For example, the verification unit can record which data or example the report was based on when generated. In this way, by recording a detailed log of the source and generation process, the transparency of the information can be increased.

[0047] The verification unit can introduce advanced encryption technology to detect information tampering when verifying information on the blockchain. For example, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a news article on the blockchain. For example, it checks whether the source or content of the article has been tampered with. Furthermore, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a review. For example, it checks whether the content of the review has been tampered with. Furthermore, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a report. For example, it checks whether the data or content of the report has been tampered with. This makes it possible to detect information tampering and ensure the reliability of the information.

[0048] When verifying information on the blockchain, the verification unit ensures interoperability between different blockchain networks and can verify the reliability of the information from multiple angles. For example, when verifying the reliability of a news article on the blockchain, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the article using multiple blockchain networks. Furthermore, when verifying the reliability of a review, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the review using multiple blockchain networks. Furthermore, when verifying the reliability of a report, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the report using multiple blockchain networks. In this way, the reliability of the information can be verified from multiple angles by ensuring interoperability between different blockchain networks.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The generator uses generative AI to generate information. For example, the generative AI generates text information such as news articles, reports, and reviews based on prompts entered by the user. The generative AI also uses a pre-finished model to generate highly accurate information according to the user's instructions. Step 2: The verification unit verifies the information generated by the generation unit using the blockchain. For example, metadata such as the source of the information, the generation process, and the dataset used are recorded on the blockchain. This prevents information tampering and ensures reliability. Step 3: The credit department assigns credit to the information verified by the verification department. For example, a score is assigned to evaluate the reliability of the information, and the information is credited based on that score. This enables the information credit system to provide highly reliable information in order to avoid secondary damage that may occur when the authenticity of information becomes unclear as the generation AI becomes more sophisticated.

[0051] (Example 2) The information credit system according to an embodiment of the present invention is a system that verifies the reliability of information generated by a generation AI on a blockchain and credits the information based on the results. As a result, the information credit system can provide highly reliable information to avoid secondary damage that may occur when the authenticity of information becomes unclear as the generation AI becomes more sophisticated.

[0052] An information crediting system according to an embodiment includes a generation unit, a verification unit, and a crediting unit. The generation unit generates information using a generation AI. For example, the generation AI generates text information such as news articles, reports, and reviews based on prompts input by a user. The generation AI also uses a pre-fine-tuned model to generate highly accurate information according to user instructions. The verification unit verifies the information generated by the generation unit using a blockchain. For example, metadata such as the source of the information, the generation process, and the dataset used are recorded on the blockchain. This prevents information tampering and ensures reliability. The crediting unit grants credit to the information verified by the verification unit. For example, a score is assigned to evaluate the reliability of the information, and the information is credited based on the score. As a result, the information crediting system can provide highly reliable information to avoid secondary damage caused by the authenticity of information becoming unclear as the generation AI becomes more sophisticated.

[0053] The generation unit can use an emotion estimation function to analyze user emotions and prioritize generating emotionally positive information. For example, the generation unit can use the emotion estimation function to analyze readers' emotional reactions to news articles generated by the generation AI and prioritize generating content that elicits positive emotions. For example, it can generate positive news that makes readers feel joy or surprise. In addition, the generation unit can use the emotion estimation function to monitor users' emotions in real time when generating reviews and prioritize using expressions that elicit positive emotions. For example, it can generate reviews that emphasize the good points of a product. In addition, the generation unit can use the emotion estimation function to analyze readers' emotions and prioritize data and examples that elicit positive emotions when generating reports. For example, it can compose reports that focus on success stories and positive statistical data. This can improve user satisfaction by generating positive information that takes users' emotions into consideration.

[0054] The generation unit evaluates the diversity of the datasets used in the information generation process, enabling the generation of information from more diverse perspectives. For example, when the generation AI generates a news article, the generation unit evaluates the diversity of the datasets used and generates an article that incorporates different perspectives and opinions. For example, it collects information from multiple news sources and creates an article from a multifaceted perspective. The generation unit also evaluates the diversity of the datasets used when generating reviews and generates reviews that reflect the opinions and ratings of different users. For example, it incorporates the opinions of users from different age groups and regions. The generation unit also evaluates the diversity of the datasets used when generating reports and generates reports that incorporate data from different industries and fields. For example, it creates a report that combines data from the technical field and the economic field. This enables the provision of unbiased information by generating information from diverse perspectives.

[0055] The generation unit can detect bias in the information generation process and introduce a feedback loop to eliminate bias. For example, when the generation AI generates a news article, the generation unit uses a bias detection function to check whether the content of the article is biased and provides feedback to eliminate bias. For example, it adjusts the article to avoid bias toward a particular political position. In addition, when generating reviews, the generation unit uses a bias detection function to check whether the review content is biased and provides feedback to eliminate bias. For example, it eliminates bias against a particular product or brand. In addition, when generating reports, the generation unit uses a bias detection function to check whether the report content is biased and provides feedback to eliminate bias. For example, it adjusts the data selection and interpretation to avoid bias. This eliminates bias, making it possible to provide fair and reliable information.

[0056] The generation unit generates multimodal information including images and audio data for information, thereby increasing reliability through visual and audio information. For example, when the generation AI generates a news article, the generation unit simultaneously generates related image and audio data, providing an article that includes visual and audio information. For example, it adds photos and interview audio related to the news article. In addition, in generating reviews, the generation unit generates reviews that include product images and audio of the product in use, increasing reliability through visual and audio information. For example, it adds videos of the product in use and audio reviews. In addition, in generating reports, the generation unit generates reports that include data graphs and charts and audio commentary, increasing reliability through visual and audio information. For example, it adds data visualization and audio narration. This makes it possible to increase the reliability of the information by including visual and audio information.

[0057] The generation unit can use datasets from different industries and fields for information to generate cross-domain information. For example, when the generation AI generates a news article, the generation unit uses datasets from different industries and fields to generate the article from a cross-domain perspective. For example, an article is created that combines data from the technical and economic fields. In addition, when generating reviews, the generation unit uses datasets from different industries and fields to generate reviews from a cross-domain perspective. For example, a review is created that combines a product's technical evaluation and market evaluation. In addition, when generating reports, the generation unit uses datasets from different industries and fields to generate reports from a cross-domain perspective. For example, a report is created that combines data from the medical and economic fields. This makes it possible to provide more multifaceted information by using data from different industries and fields.

[0058] The generation unit can use an emotion estimation function to monitor user emotions in real time and generate information that elicits positive emotions. For example, when the generation AI generates a news article, the generation unit uses the emotion estimation function to monitor reader emotions in real time and prioritizes generating content that elicits positive emotions. For example, the generation unit generates positive news that makes readers feel joy or surprise. In addition, when generating reviews, the generation unit uses the emotion estimation function to monitor user emotions in real time and prioritizes using expressions that elicit positive emotions. For example, it generates reviews that emphasize the good points of a product. In addition, when generating reports, the generation unit uses the emotion estimation function to monitor reader emotions in real time and prioritizes data and examples that elicit positive emotions. For example, it composes reports centered on success stories and positive statistical data. In this way, user satisfaction can be improved by monitoring user emotions in real time and providing positive information.

[0059] When verifying information on the blockchain, the verification unit can use the emotion estimation function to analyze users' emotional reactions to the reliability of the information and prioritize verification of emotionally positive information. For example, when verifying the reliability of a news article on the blockchain, the verification unit uses the emotion estimation function to analyze readers' emotional reactions and prioritize verification of articles that elicit positive emotions. For example, it prioritizes verification of articles that make readers feel happy or surprised. Furthermore, when verifying the reliability of a review, the verification unit uses the emotion estimation function to analyze users' emotional reactions and prioritize verification of reviews that elicit positive emotions. For example, it prioritizes verification of reviews that emphasize the good points of a product. Furthermore, when verifying the reliability of a report, the verification unit uses the emotion estimation function to analyze readers' emotional reactions and prioritize verification of reports that elicit positive emotions. For example, it prioritizes verification of reports that focus on success stories and positive statistical data. In this way, by prioritizing verification of emotionally positive information, it becomes easier to gain users' trust.

[0060] When verifying information on the blockchain, the verification unit can increase transparency by recording a detailed log of the source and generation process of the information. For example, when verifying the reliability of a news article on the blockchain, the verification unit can increase transparency by recording a detailed log of the source and generation process of the article. For example, the verification unit can record which dataset was used to generate the article. Furthermore, when verifying the reliability of a review, the verification unit can increase transparency by recording a detailed log of the source and generation process of the review. For example, the verification unit can record which user's opinion the review was based on when generated. Furthermore, when verifying the reliability of a report, the verification unit can increase transparency by recording a detailed log of the source and generation process of the report. For example, the verification unit can record which data or example the report was based on when generated. In this way, by recording a detailed log of the source and generation process, the transparency of the information can be increased.

[0061] The verification unit can introduce advanced encryption technology to detect information tampering when verifying information on the blockchain. For example, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a news article on the blockchain. For example, it checks whether the source or content of the article has been tampered with. Furthermore, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a review. For example, it checks whether the content of the review has been tampered with. Furthermore, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a report. For example, it checks whether the data or content of the report has been tampered with. This makes it possible to detect information tampering and ensure the reliability of the information.

[0062] When verifying information on the blockchain, the verification unit ensures interoperability between different blockchain networks and can verify the reliability of the information from multiple angles. For example, when verifying the reliability of a news article on the blockchain, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the article using multiple blockchain networks. Furthermore, when verifying the reliability of a review, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the review using multiple blockchain networks. Furthermore, when verifying the reliability of a report, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the report using multiple blockchain networks. In this way, the reliability of the information can be verified from multiple angles by ensuring interoperability between different blockchain networks.

[0063] When verifying information on the blockchain, the verification unit can use the emotion estimation function to monitor users' emotional reactions in real time and prioritize verification of highly reliable information. For example, when verifying the reliability of news articles on the blockchain, the verification unit can use the emotion estimation function to monitor readers' emotional reactions in real time and prioritize verification of articles that elicit positive emotions. For example, it prioritizes verification of articles that make readers feel joy or surprise. Furthermore, when verifying the reliability of reviews, the verification unit can use the emotion estimation function to monitor users' emotional reactions in real time and prioritize verification of reviews that elicit positive emotions. For example, it prioritizes verification of reviews that emphasize the good points of a product. Furthermore, when verifying the reliability of reports, the verification unit can use the emotion estimation function to monitor readers' emotional reactions in real time and prioritize verification of reports that elicit positive emotions. For example, it prioritizes verification of reports that focus on success stories and positive statistical data. In this way, by monitoring users' emotional reactions in real time, it is possible to prioritize verification of highly reliable information.

[0064] The credit department can use the emotion estimation function to analyze users' emotional reactions to information verified on the blockchain and assign a high credit score to emotionally positive information. For example, the credit department uses the emotion estimation function to analyze readers' emotional reactions to news articles verified on the blockchain and assigns a high credit score to articles that elicit positive emotions. For example, a high score is assigned to an article that makes readers feel joy or surprise. The credit department also uses the emotion estimation function to analyze users' emotional reactions to reviews and assigns a high credit score to reviews that elicit positive emotions. For example, a high score is assigned to a review that emphasizes the good points of a product. The credit department also uses the emotion estimation function to analyze readers' emotional reactions to reports and assigns a high credit score to reports that elicit positive emotions. For example, a high score is assigned to a report that focuses on success stories and positive statistical data. In this way, assigning a high credit score to emotionally positive information makes it easier to gain users' trust.

[0065] The credit department sets multifaceted criteria for assigning credit scores to information verified on the blockchain, and can comprehensively evaluate technical reliability, social impact, economic value, etc. The credit department, for example, comprehensively evaluates technical reliability, social impact, economic value, etc. of news articles verified on the blockchain and assigns a credit score. For example, it evaluates the technical accuracy and social influence of the article. The credit department also comprehensively evaluates technical reliability, social impact, economic value, etc. of reviews and assigns a credit score. For example, it evaluates the technical accuracy and market influence of the review. The credit department also comprehensively evaluates technical reliability, social impact, economic value, etc. of reports and assigns a credit score. For example, it evaluates the technical accuracy and economic value of the report. In this way, by evaluating using multifaceted criteria, a more reliable credit score can be assigned.

[0066] The credit department can make the process of assigning a credit score transparent for information verified on the blockchain and introduce a feedback function that explains the process to users. For example, the credit department makes the process of assigning a credit score transparent for news articles verified on the blockchain and introduces a feedback function that explains the process to users. For example, it explains to users the criteria and process for evaluating the credibility of articles. The credit department also makes the process of assigning a credit score transparent for reviews and introduces a feedback function that explains the process to users. For example, it explains to users the criteria and process for evaluating the credibility of reviews. The credit department also makes the process of assigning a credit score transparent for reports and introduces a feedback function that explains the process to users. For example, it explains to users the criteria and process for evaluating the credibility of reports. In this way, making the process of assigning a credit score transparent makes it easier to gain user trust.

[0067] The credit department assigns credit scores to information verified on the blockchain according to different industries and uses, allowing for more specific assessments of trustworthiness. For example, the credit department assigns credit scores to news articles verified on the blockchain according to different industries and uses, allowing for more specific assessments of trustworthiness. For example, different scores are assigned to news articles in the technology field and news articles in the economics field. The credit department also assigns credit scores to reviews according to different industries and uses, allowing for more specific assessments of trustworthiness. For example, different scores are assigned to reviews of home appliances and fashion items. The credit department also assigns credit scores to reports according to different industries and uses, allowing for more specific assessments of trustworthiness. For example, different scores are assigned to reports in the medical field and reports in the education field. In this way, by assigning credit scores according to different industries and uses, more specific assessments of trustworthiness become possible.

[0068] The credit department can update the credit score in real time for information verified on the blockchain, and provide a trustworthiness evaluation based on the latest information. The credit department can update the credit score in real time for, for example, news articles verified on the blockchain, and provide a trustworthiness evaluation based on the latest information. For example, the score is updated every time new information is added. The credit department can also update the credit score in real time for reviews, and provide a trustworthiness evaluation based on the latest information. For example, the score is updated every time a new user review is added. The credit department can also update the credit score in real time for reports, and provide a trustworthiness evaluation based on the latest information. For example, the score is updated every time new data or case studies are added. In this way, by updating the credit score in real time, it is possible to provide a trustworthiness evaluation based on the latest information.

[0069] The credit department can use the emotion estimation function to monitor users' emotional reactions to information verified on the blockchain in real time and continuously assign optimal credit scores. The credit department can use the emotion estimation function to monitor readers' emotional reactions to news articles verified on the blockchain in real time and continuously assign optimal credit scores. For example, the score can be adjusted based on the reader's emotional score. The credit department can also use the emotion estimation function to monitor users' emotional reactions to reviews in real time and continuously assign optimal credit scores. For example, the score can be adjusted based on the user's emotional score. The credit department can also use the emotion estimation function to monitor readers' emotional reactions to reports in real time and continuously assign optimal credit scores. For example, the score can be adjusted based on the reader's emotional score. In this way, the credit department can continuously assign optimal credit scores by monitoring users' emotional reactions in real time.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The generation unit evaluates the diversity of the datasets used in the information generation process, enabling the generation of information from more diverse perspectives. For example, when the generation AI generates a news article, the generation unit evaluates the diversity of the datasets used and generates an article that incorporates different perspectives and opinions. For example, it collects information from multiple news sources and creates an article from a multifaceted perspective. The generation unit also evaluates the diversity of the datasets used when generating reviews and generates reviews that reflect the opinions and ratings of different users. For example, it incorporates the opinions of users from different age groups and regions. The generation unit also evaluates the diversity of the datasets used when generating reports and generates reports that incorporate data from different industries and fields. For example, it creates a report that combines data from the technical field and the economic field. This enables the provision of unbiased information by generating information from diverse perspectives.

[0072] The generation unit can detect bias in the information generation process and introduce a feedback loop to eliminate bias. For example, when the generation AI generates a news article, the generation unit uses a bias detection function to check whether the content of the article is biased and provides feedback to eliminate bias. For example, it adjusts the article to avoid bias toward a particular political position. In addition, when generating reviews, the generation unit uses a bias detection function to check whether the review content is biased and provides feedback to eliminate bias. For example, it eliminates bias against a particular product or brand. In addition, when generating reports, the generation unit uses a bias detection function to check whether the report content is biased and provides feedback to eliminate bias. For example, it adjusts the data selection and interpretation to avoid bias. This eliminates bias, making it possible to provide fair and reliable information.

[0073] The generation unit generates multimodal information including images and audio data for information, thereby increasing reliability through visual and audio information. For example, when the generation AI generates a news article, the generation unit simultaneously generates related image and audio data, providing an article that includes visual and audio information. For example, it adds photos and interview audio related to the news article. In addition, in generating reviews, the generation unit generates reviews that include product images and audio of the product in use, increasing reliability through visual and audio information. For example, it adds videos of the product in use and audio reviews. In addition, in generating reports, the generation unit generates reports that include data graphs and charts and audio commentary, increasing reliability through visual and audio information. For example, it adds data visualization and audio narration. This makes it possible to increase the reliability of the information by including visual and audio information.

[0074] The generation unit can use datasets from different industries and fields for information to generate cross-domain information. For example, when the generation AI generates a news article, the generation unit uses datasets from different industries and fields to generate the article from a cross-domain perspective. For example, an article is created that combines data from the technical and economic fields. In addition, when generating reviews, the generation unit uses datasets from different industries and fields to generate reviews from a cross-domain perspective. For example, a review is created that combines a product's technical evaluation and market evaluation. In addition, when generating reports, the generation unit uses datasets from different industries and fields to generate reports from a cross-domain perspective. For example, a report is created that combines data from the medical and economic fields. This makes it possible to provide more multifaceted information by using data from different industries and fields.

[0075] The generation unit can use an emotion estimation function to monitor user emotions in real time and generate information that elicits positive emotions. For example, when the generation AI generates a news article, the generation unit uses the emotion estimation function to monitor reader emotions in real time and prioritizes generating content that elicits positive emotions. For example, the generation unit generates positive news that makes readers feel joy or surprise. In addition, when generating reviews, the generation unit uses the emotion estimation function to monitor user emotions in real time and prioritizes using expressions that elicit positive emotions. For example, it generates reviews that emphasize the good points of a product. In addition, when generating reports, the generation unit uses the emotion estimation function to monitor reader emotions in real time and prioritizes data and examples that elicit positive emotions. For example, it composes reports centered on success stories and positive statistical data. In this way, user satisfaction can be improved by monitoring user emotions in real time and providing positive information.

[0076] When verifying information on the blockchain, the verification unit can use the emotion estimation function to analyze users' emotional reactions to the reliability of the information and prioritize verification of emotionally positive information. For example, when verifying the reliability of a news article on the blockchain, the verification unit uses the emotion estimation function to analyze readers' emotional reactions and prioritize verification of articles that elicit positive emotions. For example, it prioritizes verification of articles that make readers feel happy or surprised. Furthermore, when verifying the reliability of a review, the verification unit uses the emotion estimation function to analyze users' emotional reactions and prioritize verification of reviews that elicit positive emotions. For example, it prioritizes verification of reviews that emphasize the good points of a product. Furthermore, when verifying the reliability of a report, the verification unit uses the emotion estimation function to analyze readers' emotional reactions and prioritize verification of reports that elicit positive emotions. For example, it prioritizes verification of reports that focus on success stories and positive statistical data. In this way, by prioritizing verification of emotionally positive information, it becomes easier to gain users' trust.

[0077] When verifying information on the blockchain, the verification unit can increase transparency by recording a detailed log of the source and generation process of the information. For example, when verifying the reliability of a news article on the blockchain, the verification unit can increase transparency by recording a detailed log of the source and generation process of the article. For example, the verification unit can record which dataset was used to generate the article. Furthermore, when verifying the reliability of a review, the verification unit can increase transparency by recording a detailed log of the source and generation process of the review. For example, the verification unit can record which user's opinion the review was based on when generated. Furthermore, when verifying the reliability of a report, the verification unit can increase transparency by recording a detailed log of the source and generation process of the report. For example, the verification unit can record which data or example the report was based on when generated. In this way, by recording a detailed log of the source and generation process, the transparency of the information can be increased.

[0078] The verification unit can introduce advanced encryption technology to detect information tampering when verifying information on the blockchain. For example, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a news article on the blockchain. For example, it checks whether the source or content of the article has been tampered with. Furthermore, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a review. For example, it checks whether the content of the review has been tampered with. Furthermore, the verification unit introduces advanced encryption technology to detect information tampering when verifying the reliability of a report. For example, it checks whether the data or content of the report has been tampered with. This makes it possible to detect information tampering and ensure the reliability of the information.

[0079] When verifying information on the blockchain, the verification unit ensures interoperability between different blockchain networks and can verify the reliability of the information from multiple angles. For example, when verifying the reliability of a news article on the blockchain, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the article using multiple blockchain networks. Furthermore, when verifying the reliability of a review, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the review using multiple blockchain networks. Furthermore, when verifying the reliability of a report, the verification unit ensures interoperability between different blockchain networks and verifies the reliability of the information from multiple angles. For example, the verification unit checks the reliability of the report using multiple blockchain networks. In this way, the reliability of the information can be verified from multiple angles by ensuring interoperability between different blockchain networks.

[0080] The credit department can use the emotion estimation function to analyze users' emotional reactions to information verified on the blockchain and assign a high credit score to emotionally positive information. For example, the credit department uses the emotion estimation function to analyze readers' emotional reactions to news articles verified on the blockchain and assigns a high credit score to articles that elicit positive emotions. For example, a high score is assigned to an article that makes readers feel joy or surprise. The credit department also uses the emotion estimation function to analyze users' emotional reactions to reviews and assigns a high credit score to reviews that elicit positive emotions. For example, a high score is assigned to a review that emphasizes the good points of a product. The credit department also uses the emotion estimation function to analyze readers' emotional reactions to reports and assigns a high credit score to reports that elicit positive emotions. For example, a high score is assigned to a report that focuses on success stories and positive statistical data. In this way, assigning a high credit score to emotionally positive information makes it easier to gain users' trust.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The generator uses generative AI to generate information. For example, the generative AI generates text information such as news articles, reports, and reviews based on prompts entered by the user. The generative AI also uses a pre-finished model to generate highly accurate information according to the user's instructions. Step 2: The verification unit verifies the information generated by the generation unit using the blockchain. For example, metadata such as the source of the information, the generation process, and the dataset used are recorded on the blockchain. This prevents information tampering and ensures reliability. Step 3: The credit department assigns credit to the information verified by the verification department. For example, a score is assigned to evaluate the reliability of the information, and the information is credited based on that score. This enables the information credit system to provide highly reliable information in order to avoid secondary damage that may occur when the authenticity of information becomes unclear as the generation AI becomes more sophisticated.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

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

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that generates information using a generation AI; a verification unit that verifies the information generated by the generation unit using a blockchain; a credit unit that grants credit to the information verified by the verification unit. A system characterized by:

2. The generation unit Analyzing the user's feelings about the information and generating emotionally positive information preferentially 2. The system of claim 1.

3. The generation unit Evaluate the diversity of the datasets used in the process of generating the information, and generate information from more diverse perspectives.

2. The system of claim 1.

4. The generation unit Detect bias in the information generation process and introduce a feedback loop to eliminate the bias.

2. The system of claim 1.

5. The generation unit Multimodal information generation including images and audio data is performed on the information, increasing reliability from visual and audio information as well.

2. The system of claim 1.

Images