System

Abstract

An object of a system according to an embodiment is to detect biased information and reconstruct accurate information.SOLUTION: A system includes an information collection unit, an analysis unit, a detection unit, a reconstruction unit, and a provision unit. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit. The detection unit detects biased information from the information analyzed by the analysis unit. The reconstruction unit reconstructs the biased information detected by the detection unit. The providing unit provides the information reconstructed by the reconstructing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have the risk of spreading biased information, and there is room for improvement in terms of ensuring the reliability of information.

[0005] The system according to the embodiment aims to detect biased information and reconstruct accurate information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a detection unit, a reconstruction unit, and a provision unit. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit. The detection unit detects biased information from the information analyzed by the analysis unit. The reconstruction unit reconstructs the biased information detected by the detection unit. The provision unit provides the information reconstructed by the reconstruction unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect biased information and reconstruct accurate information. [Brief explanation of the drawings]

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

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more 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 cut-and-restore system according to an embodiment of the present invention is a system that verifies the authenticity of information and increases its reliability. This system is provided to all individuals and businesses that are troubled by biased information, and its generative AI reconstructs accurate information and prevents the spread of false information. In this way, the cut-and-restore system can verify the authenticity of information and increase its reliability.

[0029] The clipping and restoration system according to the embodiment includes an information collection unit, an analysis unit, a detection unit, a reconstruction unit, and a provision unit. The information collection unit collects information. For example, it collects news articles, social media posts, blog posts, etc. from the Internet and other sources. The information collection unit can also filter information based on specific keywords or topics. For example, it can collect information related to specific political positions or commercial interests. The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI compares the content of the collected information with other reliable sources to evaluate the reliability of the information. To evaluate the reliability of the information, the generation AI can also analyze the source's past reliability history and calculate a reliability score. For example, a source that has provided reliable information in the past is assigned a high reliability score. The detection unit detects biased information from the information analyzed by the analysis unit. For example, the generation AI compares the collected information with sources with different linguistic or cultural backgrounds to detect cultural bias. When detecting information bias, the generation AI can also analyze the source's political positions or commercial interests to identify the source of bias. The reconstruction unit reconstructs biased information detected by the detection unit. For example, if a biased news article is detected, the generation AI compares the content of the article with other reliable information sources and reconstructs accurate information. The generation AI can also analyze the information source's past reliability history and calculate a reliability score to evaluate the reliability of the reconstructed information. The provision unit provides the information reconstructed by the reconstruction unit. For example, the generation AI provides the reconstructed information in an appropriate format to prevent the spread of false information. The provision unit also provides the reconstructed information to individuals and companies to help them make decisions based on reliable information. In this way, the cut-out and restoration system according to the embodiment can clarify the authenticity of information and increase its reliability. For example, by evaluating the reliability of a news article and providing accurate information, readers can be prevented from making decisions based on incorrect information. Furthermore, providing accurate information helps companies maintain their credibility when dealing with false information about themselves.

[0030] The analysis unit can analyze the information source's past reliability history and calculate a reliability score in order to evaluate the reliability of the collected information. For example, in order to evaluate the reliability of the collected information, the generation AI analyzes the information source's past reliability history. For example, a high reliability score is assigned to an information source that has provided reliable information in the past. The generation AI also calculates a reliability score for the collected information based on the information source's reliability history. For example, a low reliability score is assigned to an information source that has contained a lot of false information in the past. The generation AI also analyzes the information source's reliability history to evaluate the reliability of the information collected by the generation AI. For example, a high score is assigned to information from a reliable information source, and a low score is assigned to information from a less reliable information source. In this way, the reliability of the information can be evaluated.

[0031] The analysis unit can detect cultural bias by comparing the collected information with sources of information from different languages ​​and cultural backgrounds. For example, the analysis unit compares the information collected by the generation AI with sources of information from different languages ​​and cultural backgrounds to detect cultural bias. For example, it analyzes how the same news is reported in different languages. The generation AI can also detect cultural bias by comparing the collected information with sources of information from different cultural backgrounds. For example, it can check whether reporting is biased toward a particular culture. The generation AI can also detect cultural bias by comparing the collected information with sources of information from different languages ​​and cultural backgrounds. For example, it can analyze how the same event is interpreted in different cultures. This makes it possible to detect cultural bias.

[0032] The analysis unit visually analyzes the collected information and can perform comprehensive information analysis, including the content of images and videos. The analysis unit visually analyzes the collected information, for example, using a generative AI. For example, it analyzes the content of images and videos included in news articles and performs comprehensive information analysis. It also visually analyzes the collected information and performs comprehensive information analysis, including the content of images and videos. For example, it analyzes the content of images using image recognition technology. It also visually analyzes the information collected by the generative AI and performs comprehensive information analysis, including the content of images and videos. For example, it analyzes the content of videos using video analysis technology. This makes it possible to perform comprehensive information analysis, including the content of images and videos.

[0033] The analysis unit can collect feedback from experts in different industries and fields and evaluate the reliability of information based on that feedback. The analysis unit, for example, collects feedback from experts in different industries and fields and evaluates the reliability of information based on that feedback. For example, the reliability of medical information is evaluated based on feedback from experts in the medical field. The analysis unit also collects feedback from experts and evaluates the reliability of information. For example, the reliability of technical information is evaluated based on feedback from experts in the technical field. The analysis unit also collects feedback from experts in different industries and fields and evaluates the reliability of information based on that feedback. For example, the reliability of economic information is evaluated based on feedback from experts in the economic field. This makes it possible to evaluate the reliability of information based on feedback from experts in different industries and fields.

[0034] When detecting information bias, the detection unit can analyze the political stance and commercial interests of the source and identify the cause of the bias. The detection unit, for example, uses a generation AI to analyze the political stance and commercial interests of the source and identify the cause of the information bias. For example, it detects bias based on a particular political stance. The generation AI can also analyze the commercial interests of the source and identify the cause of the information bias. For example, it can detect bias based on the interests of a particular company. The generation AI can also analyze the political stance and commercial interests of the source and identify the cause of the information bias. For example, it can detect bias based on a particular political stance or commercial interest. This makes it possible to identify the cause of the information bias.

[0035] The detection unit can track changes in information over time and identify when bias occurs. In the detection unit, for example, the generation AI tracks changes in information over time and identifies when information bias occurs. For example, it analyzes whether bias increased during a specific period. The generation AI also tracks changes in information over time and identifies when information bias occurs. For example, it analyzes whether bias increased during a specific event. The generation AI also tracks changes in information over time and identifies when information bias occurs. For example, it analyzes whether bias increased during a specific political event. This makes it possible to identify when information bias occurs.

[0036] The detection unit can compare information from different regions and cultural spheres and identify bias from a global perspective. The detection unit, for example, uses a generation AI to compare information from different regions and cultural spheres and identify bias from a global perspective. For example, it analyzes how the same news is reported in different regions. Also, by comparing information from different cultural spheres, the generation AI can identify bias from a global perspective. For example, it checks whether there is any reporting that is biased toward a particular culture. Also, the generation AI can compare information from different regions and cultural spheres and identify bias from a global perspective. For example, it analyzes how the same event is interpreted in different cultures. This makes it possible to identify bias from a global perspective.

[0037] The detection unit can analyze the visual elements of information and identify visual bias. In the detection unit, for example, the generation AI analyzes the visual elements of information and identifies visual bias. For example, the content of images and videos included in news articles is analyzed to detect bias. In addition, the visual elements of information are analyzed and the generation AI identifies visual bias. For example, the content of images is analyzed using image recognition technology to detect bias. In addition, the generation AI analyzes the visual elements of information and identifies visual bias. For example, the content of videos is analyzed using video analysis technology to detect bias. In this way, visual bias can be identified.

[0038] The reconstruction unit can analyze the past reliability history of the information source and calculate a reliability score to evaluate the reliability of the reconstructed information. The reconstruction unit, for example, uses a generation AI to analyze the past reliability history of the information source to evaluate the reliability of the reconstructed information. For example, a high reliability score is assigned to an information source that has provided reliable information in the past. Furthermore, to evaluate the reliability of the reconstructed information, the generation AI calculates a reliability score based on the reliability history of the information source. For example, a low reliability score is assigned to an information source that has contained a lot of false information in the past. Furthermore, the reliability history of the information source is analyzed, and the generation AI evaluates the reliability of the reconstructed information. For example, a high score is assigned to information from a reliable information source, and a low score is assigned to information from a less reliable information source. In this way, the reliability of the reconstructed information can be evaluated.

[0039] The reconstruction unit can eliminate cultural bias by comparing the reconstructed information with sources of information from different languages ​​and cultural backgrounds. For example, the reconstruction unit compares the information reconstructed by the generation AI with sources of information from different languages ​​and cultural backgrounds to eliminate cultural bias. For example, it analyzes how the same news is reported in different languages. The generation AI can also compare the reconstructed information with sources of information from different cultural backgrounds to eliminate cultural bias. For example, it can check for reporting that is biased toward a particular culture. The generation AI can also compare the reconstructed information with sources of information from different languages ​​and cultural backgrounds to eliminate cultural bias. For example, it can analyze how the same event is interpreted in different cultures. This can eliminate cultural bias.

[0040] The reconstruction unit visually analyzes the reconstructed information and can perform comprehensive information reconstruction including the content of images and videos. The reconstruction unit visually analyzes the reconstructed information using, for example, a generation AI. For example, the reconstruction unit analyzes the content of images and videos included in news articles and performs comprehensive information reconstruction. The reconstruction unit also visually analyzes the reconstructed information and performs comprehensive information reconstruction including the content of images and videos. For example, the reconstruction unit uses image recognition technology to analyze the content of images. The generation AI also visually analyzes the reconstructed information and performs comprehensive information reconstruction including the content of images and videos. For example, the reconstruction unit uses video analysis technology to analyze the content of videos. This makes it possible to perform comprehensive information reconstruction including the content of images and videos.

[0041] The reconstruction unit can collect feedback from experts in different industries and fields and evaluate the reliability of information based on that feedback. The reconstruction unit, for example, collects feedback from experts in different industries and fields and evaluates the reliability of information based on that feedback. For example, the reliability of medical information is evaluated based on feedback from experts in the medical field. The reconstruction unit also collects feedback from experts and evaluates the reliability of information. For example, the reliability of technical information is evaluated based on feedback from experts in the technical field. The reconstruction unit also collects feedback from experts in different industries and fields and evaluates the reliability of information based on that feedback. For example, the reliability of economic information is evaluated based on feedback from experts in the economic field. This makes it possible to evaluate the reliability of information based on feedback from experts in different industries and fields.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can analyze the reliability history of the information source and calculate a reliability score to evaluate the reliability of the collected information. For example, a high reliability score is assigned to an information source that has provided reliable information in the past. The generation AI also calculates a reliability score for the collected information based on the reliability history of the information source. For example, a low reliability score is assigned to an information source that has contained a lot of false information in the past. The reliability history of the information source is also analyzed to evaluate the reliability of the information collected by the generation AI. For example, a high score is assigned to information from a reliable source, and a low score is assigned to information from a less reliable source. This makes it possible to evaluate the reliability of the information.

[0044] The analysis unit can detect cultural bias by comparing the collected information with sources of information from different languages ​​and cultural backgrounds. For example, it analyzes how the same news is reported in different languages. The generation AI can also detect cultural bias by comparing the collected information with sources of information from different cultural backgrounds. For example, it can check whether reporting is biased toward a particular culture. The generation AI can also detect cultural bias by comparing the collected information with sources of information from different languages ​​and cultural backgrounds. For example, it can analyze how the same event is interpreted in different cultures. This makes it possible to detect cultural bias.

[0045] The analysis unit can visually analyze the collected information and perform comprehensive information analysis, including the content of images and videos. For example, the analysis unit uses generative AI to visually analyze the collected information. For example, the analysis unit analyzes the content of images and videos included in news articles and performs comprehensive information analysis. The analysis unit can also visually analyze the collected information and perform comprehensive information analysis, including the content of images and videos. For example, the analysis unit uses image recognition technology to analyze the content of images. The analysis unit can also visually analyze the information collected by generative AI and perform comprehensive information analysis, including the content of images and videos. For example, the analysis unit uses video analysis technology to analyze the content of videos. This makes it possible to perform comprehensive information analysis, including the content of images and videos.

[0046] The analysis unit can collect feedback from experts in different industries and fields and evaluate the reliability of information based on that feedback. For example, the reliability of medical information is evaluated based on feedback from experts in the medical field. Also, feedback from experts is collected and the reliability of information is evaluated. For example, the reliability of technical information is evaluated based on feedback from experts in the technical field. Also, feedback from experts in different industries and fields is collected and the reliability of information is evaluated based on that feedback. For example, the reliability of economic information is evaluated based on feedback from experts in the economic field. This makes it possible to evaluate the reliability of information based on feedback from experts in different industries and fields.

[0047] When detecting information bias, the detection unit can analyze the political stance and commercial interests of the source and identify the cause of the bias. For example, the generation AI can be used to analyze the political stance and commercial interests of the source and identify the cause of the information bias. For example, bias based on a particular political stance can be detected. The generation AI can also analyze the commercial interests of the source and identify the cause of the information bias. For example, bias based on the interests of a particular company can be detected. The generation AI can also analyze the political stance and commercial interests of the source and identify the cause of the information bias. For example, bias based on a particular political stance or commercial interest can be detected. This makes it possible to identify the cause of the information bias.

[0048] The detection unit can track changes in information over time and identify when bias occurs. For example, the generation AI can track changes in information over time and identify when bias occurs. For example, it can analyze whether bias increased during a specific period. The detection unit can also track changes in information over time and identify when bias occurs. For example, it can analyze whether bias increased during a specific event. The detection unit can also track changes in information over time and identify when bias occurs. For example, it can analyze whether bias increased during a specific political event. This makes it possible to identify when bias occurs.

[0049] The detection unit can compare information from different regions and cultural spheres and identify bias from a global perspective. For example, the generation AI can be used to compare information from different regions and cultural spheres and identify bias from a global perspective. For example, it can analyze how the same news is reported in different regions. Information from different cultural spheres can also be compared and the generation AI can identify bias from a global perspective. For example, it can check whether there is any reporting that is biased towards a particular culture. The generation AI can also compare information from different regions and cultural spheres and identify bias from a global perspective. For example, it can analyze how the same event is interpreted in different cultures. This makes it possible to identify bias from a global perspective.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The intelligence gathering unit collects information. For example, it gathers news articles, social media posts, blog posts, etc. from the internet and other sources. The intelligence gathering unit may also filter information based on specific keywords or topics. For example, it may collect information related to a particular political position or commercial interest. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI compares the content of the collected information with other reliable information sources and evaluates the reliability of the information. In addition, the generation AI can analyze the information source's past reliability history and calculate a reliability score to evaluate the reliability of the information. For example, a high reliability score is assigned to an information source that has provided reliable information in the past. Step 3: The detection unit detects biased information from the information analyzed by the analysis unit. For example, the generation AI compares the collected information with sources of different languages ​​and cultural backgrounds to detect cultural bias. When detecting bias in information, the generation AI can also analyze the political positions and commercial interests of the sources to identify the causes of bias. Step 4: The reconstruction unit reconstructs the biased information detected by the detection unit. For example, if the generation AI detects a biased news article, it compares the content of the article with other reliable information sources and reconstructs accurate information. The generation AI can also analyze the source's past reliability history and calculate a reliability score to evaluate the reliability of the reconstructed information. Step 5: The provision unit provides the information reconstructed by the reconstruction unit. For example, the generation AI provides the reconstructed information in an appropriate format to prevent the spread of false information. The provision unit also provides the reconstructed information to individuals and companies, helping them make decisions based on reliable information.

[0052] (Example 2) The cut-and-restore system according to an embodiment of the present invention is a system that verifies the authenticity of information and increases its reliability. This system is provided to all individuals and businesses that are troubled by biased information, and its generative AI reconstructs accurate information and prevents the spread of false information. In this way, the cut-and-restore system can verify the authenticity of information and increase its reliability.

[0053] The clipping and restoration system according to the embodiment includes an information collection unit, an analysis unit, a detection unit, a reconstruction unit, and a provision unit. The information collection unit collects information. For example, it collects news articles, social media posts, blog posts, etc. from the Internet and other sources. The information collection unit can also filter information based on specific keywords or topics. For example, it can collect information related to specific political positions or commercial interests. The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI compares the content of the collected information with other reliable sources to evaluate the reliability of the information. To evaluate the reliability of the information, the generation AI can also analyze the source's past reliability history and calculate a reliability score. For example, a source that has provided reliable information in the past is assigned a high reliability score. The detection unit detects biased information from the information analyzed by the analysis unit. For example, the generation AI compares the collected information with sources with different linguistic or cultural backgrounds to detect cultural bias. When detecting information bias, the generation AI can also analyze the source's political positions or commercial interests to identify the source of bias. The reconstruction unit reconstructs biased information detected by the detection unit. For example, if a biased news article is detected, the generation AI compares the content of the article with other reliable information sources and reconstructs accurate information. The generation AI can also analyze the information source's past reliability history and calculate a reliability score to evaluate the reliability of the reconstructed information. The provision unit provides the information reconstructed by the reconstruction unit. For example, the generation AI provides the reconstructed information in an appropriate format to prevent the spread of false information. The provision unit also provides the reconstructed information to individuals and companies to help them make decisions based on reliable information. In this way, the cut-out and restoration system according to the embodiment can clarify the authenticity of information and increase its reliability. For example, by evaluating the reliability of a news article and providing accurate information, readers can be prevented from making decisions based on incorrect information. Furthermore, providing accurate information helps companies maintain their credibility when dealing with false information about themselves.

[0054] The analysis unit can analyze the information source's past reliability history and calculate a reliability score in order to evaluate the reliability of the collected information. For example, in order to evaluate the reliability of the collected information, the generation AI analyzes the information source's past reliability history. For example, a high reliability score is assigned to an information source that has provided reliable information in the past. The generation AI also calculates a reliability score for the collected information based on the information source's reliability history. For example, a low reliability score is assigned to an information source that has contained a lot of false information in the past. The generation AI also analyzes the information source's reliability history to evaluate the reliability of the information collected by the generation AI. For example, a high score is assigned to information from a reliable information source, and a low score is assigned to information from a less reliable information source. In this way, the reliability of the information can be evaluated.

[0055] The analysis unit can detect cultural bias by comparing the collected information with sources of information from different languages ​​and cultural backgrounds. For example, the analysis unit compares the information collected by the generation AI with sources of information from different languages ​​and cultural backgrounds to detect cultural bias. For example, it analyzes how the same news is reported in different languages. The generation AI can also detect cultural bias by comparing the collected information with sources of information from different cultural backgrounds. For example, it can check whether reporting is biased toward a particular culture. The generation AI can also detect cultural bias by comparing the collected information with sources of information from different languages ​​and cultural backgrounds. For example, it can analyze how the same event is interpreted in different cultures. This makes it possible to detect cultural bias.

[0056] The analysis unit can analyze the user's emotional response to the collected information and preferentially collect emotionally neutral information. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotional response to the collected information. For example, it preferentially collects information with a large number of positive emotional responses. The analysis unit also analyzes the user's emotional response to the collected information and preferentially collects emotionally neutral information. For example, it selects information with fewer emotionally extreme responses. The emotion estimation function also analyzes the user's emotional response to the collected information and preferentially collects emotionally neutral information. For example, it selects information with an emotion score close to neutral. This makes it possible to preferentially collect emotionally neutral information.

[0057] The analysis unit visually analyzes the collected information and can perform comprehensive information analysis, including the content of images and videos. The analysis unit visually analyzes the collected information, for example, using a generative AI. For example, it analyzes the content of images and videos included in news articles and performs comprehensive information analysis. It also visually analyzes the collected information and performs comprehensive information analysis, including the content of images and videos. For example, it analyzes the content of images using image recognition technology. It also visually analyzes the information collected by the generative AI and performs comprehensive information analysis, including the content of images and videos. For example, it analyzes the content of videos using video analysis technology. This makes it possible to perform comprehensive information analysis, including the content of images and videos.

[0058] The analysis unit can collect feedback from experts in different industries and fields and evaluate the reliability of information based on that feedback. The analysis unit, for example, collects feedback from experts in different industries and fields and evaluates the reliability of information based on that feedback. For example, the reliability of medical information is evaluated based on feedback from experts in the medical field. The analysis unit also collects feedback from experts and evaluates the reliability of information. For example, the reliability of technical information is evaluated based on feedback from experts in the technical field. The analysis unit also collects feedback from experts in different industries and fields and evaluates the reliability of information based on that feedback. For example, the reliability of economic information is evaluated based on feedback from experts in the economic field. This makes it possible to evaluate the reliability of information based on feedback from experts in different industries and fields.

[0059] The analysis unit can monitor the user's emotional response to the collected information in real time and prioritize analysis of emotionally positive information. The analysis unit, for example, uses an emotion estimation function to monitor the user's emotional response to the collected information in real time. For example, it prioritizes analysis of information with a large number of positive emotional responses. The analysis unit also monitors the user's emotional response to the collected information in real time and prioritizes analysis of emotionally positive information. For example, it selects information with a high emotion score. The analysis unit also uses the emotion estimation function to monitor the user's emotional response to the collected information in real time and prioritize analysis of emotionally positive information. For example, it selects information with a strong positive emotion. This makes it possible to prioritize analysis of emotionally positive information.

[0060] When detecting information bias, the detection unit can analyze the political stance and commercial interests of the source and identify the cause of the bias. The detection unit, for example, uses a generation AI to analyze the political stance and commercial interests of the source and identify the cause of the information bias. For example, it detects bias based on a particular political stance. The generation AI can also analyze the commercial interests of the source and identify the cause of the information bias. For example, it can detect bias based on the interests of a particular company. The generation AI can also analyze the political stance and commercial interests of the source and identify the cause of the information bias. For example, it can detect bias based on a particular political stance or commercial interest. This makes it possible to identify the cause of the information bias.

[0061] The detection unit can track changes in information over time and identify when bias occurs. In the detection unit, for example, the generation AI tracks changes in information over time and identifies when information bias occurs. For example, it analyzes whether bias increased during a specific period. The generation AI also tracks changes in information over time and identifies when information bias occurs. For example, it analyzes whether bias increased during a specific event. The generation AI also tracks changes in information over time and identifies when information bias occurs. For example, it analyzes whether bias increased during a specific political event. This makes it possible to identify when information bias occurs.

[0062] The detection unit can analyze the user's emotional response to information bias and preferentially detect emotionally negative information. The detection unit, for example, uses an emotion estimation function to analyze the user's emotional response to information bias. For example, it preferentially detects information with a large number of negative emotional responses. The detection unit also analyzes the user's emotional response to information bias and preferentially detects emotionally negative information. For example, it selects information with a low emotion score. The detection unit also uses the emotion estimation function to analyze the user's emotional response to information bias and preferentially detects emotionally negative information. For example, it selects information with a strong negative emotion. This makes it possible to preferentially detect emotionally negative information.

[0063] The detection unit can compare information from different regions and cultural spheres and identify bias from a global perspective. The detection unit, for example, uses a generation AI to compare information from different regions and cultural spheres and identify bias from a global perspective. For example, it analyzes how the same news is reported in different regions. Also, by comparing information from different cultural spheres, the generation AI can identify bias from a global perspective. For example, it checks whether there is any reporting that is biased toward a particular culture. Also, the generation AI can compare information from different regions and cultural spheres and identify bias from a global perspective. For example, it analyzes how the same event is interpreted in different cultures. This makes it possible to identify bias from a global perspective.

[0064] The detection unit can analyze the visual elements of information and identify visual bias. In the detection unit, for example, the generation AI analyzes the visual elements of information and identifies visual bias. For example, the content of images and videos included in news articles is analyzed to detect bias. In addition, the visual elements of information are analyzed and the generation AI identifies visual bias. For example, the content of images is analyzed using image recognition technology to detect bias. In addition, the generation AI analyzes the visual elements of information and identifies visual bias. For example, the content of videos is analyzed using video analysis technology to detect bias. In this way, visual bias can be identified.

[0065] The detection unit can monitor the user's emotional response to information bias in real time and preferentially detect emotionally positive information. The detection unit, for example, uses an emotion estimation function to monitor the user's emotional response to information bias in real time. For example, it preferentially detects information with a high number of positive emotional responses. The detection unit also monitors the user's emotional response to information bias in real time and preferentially detects emotionally positive information. For example, it selects information with a high emotion score. The detection unit also uses the emotion estimation function to monitor the user's emotional response to information bias in real time and preferentially detects emotionally positive information. For example, it selects information with a strong positive emotion. This makes it possible to preferentially detect emotionally positive information.

[0066] The reconstruction unit can analyze the past reliability history of the information source and calculate a reliability score to evaluate the reliability of the reconstructed information. The reconstruction unit, for example, uses a generation AI to analyze the past reliability history of the information source to evaluate the reliability of the reconstructed information. For example, a high reliability score is assigned to an information source that has provided reliable information in the past. Furthermore, to evaluate the reliability of the reconstructed information, the generation AI calculates a reliability score based on the reliability history of the information source. For example, a low reliability score is assigned to an information source that has contained a lot of false information in the past. Furthermore, the reliability history of the information source is analyzed, and the generation AI evaluates the reliability of the reconstructed information. For example, a high score is assigned to information from a reliable information source, and a low score is assigned to information from a less reliable information source. In this way, the reliability of the reconstructed information can be evaluated.

[0067] The reconstruction unit can eliminate cultural bias by comparing the reconstructed information with sources of information from different languages ​​and cultural backgrounds. For example, the reconstruction unit compares the information reconstructed by the generation AI with sources of information from different languages ​​and cultural backgrounds to eliminate cultural bias. For example, it analyzes how the same news is reported in different languages. The generation AI can also compare the reconstructed information with sources of information from different cultural backgrounds to eliminate cultural bias. For example, it can check for reporting that is biased toward a particular culture. The generation AI can also compare the reconstructed information with sources of information from different languages ​​and cultural backgrounds to eliminate cultural bias. For example, it can analyze how the same event is interpreted in different cultures. This can eliminate cultural bias.

[0068] The reconstruction unit can analyze the user's emotional response to the reconstructed information and preferentially reconstruct emotionally neutral information. The reconstruction unit, for example, uses an emotion estimation function to analyze the user's emotional response to the reconstructed information. For example, it preferentially reconstructs information with a large number of positive emotional responses. The reconstruction unit also analyzes the user's emotional response to the reconstructed information and preferentially reconstructs emotionally neutral information. For example, it selects information with a small number of extreme emotional responses. The reconstruction unit also uses the emotion estimation function to analyze the user's emotional response to the reconstructed information and preferentially reconstructs emotionally neutral information. For example, it selects information with an emotion score close to neutral. This makes it possible to preferentially reconstruct emotionally neutral information.

[0069] The reconstruction unit visually analyzes the reconstructed information and can perform comprehensive information reconstruction including the content of images and videos. The reconstruction unit visually analyzes the reconstructed information using, for example, a generation AI. For example, the reconstruction unit analyzes the content of images and videos included in news articles and performs comprehensive information reconstruction. The reconstruction unit also visually analyzes the reconstructed information and performs comprehensive information reconstruction including the content of images and videos. For example, the reconstruction unit uses image recognition technology to analyze the content of images. The generation AI also visually analyzes the reconstructed information and performs comprehensive information reconstruction including the content of images and videos. For example, the reconstruction unit uses video analysis technology to analyze the content of videos. This makes it possible to perform comprehensive information reconstruction including the content of images and videos.

[0070] The reconstruction unit can collect feedback from experts in different industries and fields and evaluate the reliability of information based on that feedback. The reconstruction unit, for example, collects feedback from experts in different industries and fields and evaluates the reliability of information based on that feedback. For example, the reliability of medical information is evaluated based on feedback from experts in the medical field. The reconstruction unit also collects feedback from experts and evaluates the reliability of information. For example, the reliability of technical information is evaluated based on feedback from experts in the technical field. The reconstruction unit also collects feedback from experts in different industries and fields and evaluates the reliability of information based on that feedback. For example, the reliability of economic information is evaluated based on feedback from experts in the economic field. This makes it possible to evaluate the reliability of information based on feedback from experts in different industries and fields.

[0071] The reconstruction unit monitors the user's emotional response to the reconstructed information in real time, and is able to preferentially reconstruct emotionally positive information. The reconstruction unit, for example, uses an emotion estimation function to monitor the user's emotional response to the reconstructed information in real time. For example, it preferentially reconstructs information with a large number of positive emotional responses. The reconstruction unit also monitors the user's emotional response to the reconstructed information in real time, and preferentially reconstructs emotionally positive information. For example, it selects information with a high emotion score. The reconstruction unit also uses the emotion estimation function to monitor the user's emotional response to the reconstructed information in real time, and preferentially reconstructs emotionally positive information. For example, it selects information with a strong positive emotion. This makes it possible to preferentially reconstruct emotionally positive information.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The analysis unit can analyze the reliability history of the information source and calculate a reliability score to evaluate the reliability of the collected information. For example, a high reliability score is assigned to an information source that has provided reliable information in the past. The generation AI also calculates a reliability score for the collected information based on the reliability history of the information source. For example, a low reliability score is assigned to an information source that has contained a lot of false information in the past. The reliability history of the information source is also analyzed to evaluate the reliability of the information collected by the generation AI. For example, a high score is assigned to information from a reliable source, and a low score is assigned to information from a less reliable source. This makes it possible to evaluate the reliability of the information.

[0074] The analysis unit can detect cultural bias by comparing the collected information with sources of information from different languages ​​and cultural backgrounds. For example, it analyzes how the same news is reported in different languages. The generation AI can also detect cultural bias by comparing the collected information with sources of information from different cultural backgrounds. For example, it can check whether reporting is biased toward a particular culture. The generation AI can also detect cultural bias by comparing the collected information with sources of information from different languages ​​and cultural backgrounds. For example, it can analyze how the same event is interpreted in different cultures. This makes it possible to detect cultural bias.

[0075] The analysis unit can analyze the user's emotional response to the collected information and prioritize collecting emotionally neutral information. For example, it prioritizes collecting information with a large number of positive emotional responses. It can also analyze the user's emotional response to the collected information and prioritize collecting emotionally neutral information. For example, it selects information with fewer emotionally extreme responses. It can also use an emotion estimation function to analyze the user's emotional response to the collected information and prioritize collecting emotionally neutral information. For example, it selects information with an emotion score close to neutral. This makes it possible to prioritize collecting emotionally neutral information.

[0076] The analysis unit can visually analyze the collected information and perform comprehensive information analysis, including the content of images and videos. For example, the analysis unit uses generative AI to visually analyze the collected information. For example, the analysis unit analyzes the content of images and videos included in news articles and performs comprehensive information analysis. The analysis unit can also visually analyze the collected information and perform comprehensive information analysis, including the content of images and videos. For example, the analysis unit uses image recognition technology to analyze the content of images. The analysis unit can also visually analyze the information collected by generative AI and perform comprehensive information analysis, including the content of images and videos. For example, the analysis unit uses video analysis technology to analyze the content of videos. This makes it possible to perform comprehensive information analysis, including the content of images and videos.

[0077] The analysis unit can collect feedback from experts in different industries and fields and evaluate the reliability of information based on that feedback. For example, the reliability of medical information is evaluated based on feedback from experts in the medical field. Also, feedback from experts is collected and the reliability of information is evaluated. For example, the reliability of technical information is evaluated based on feedback from experts in the technical field. Also, feedback from experts in different industries and fields is collected and the reliability of information is evaluated based on that feedback. For example, the reliability of economic information is evaluated based on feedback from experts in the economic field. This makes it possible to evaluate the reliability of information based on feedback from experts in different industries and fields.

[0078] The analysis unit can monitor the user's emotional response to the collected information in real time and prioritize analysis of emotionally positive information. For example, the emotion estimation function is used to monitor the user's emotional response to the collected information in real time. For example, information with a large number of positive emotional responses is prioritized for analysis. The analysis unit can also monitor the user's emotional response to the collected information in real time and prioritize analysis of emotionally positive information. For example, information with a high emotion score is selected. The emotion estimation function is also used to monitor the user's emotional response to the collected information in real time and prioritize analysis of emotionally positive information. For example, information with a strong positive emotion is selected. This makes it possible to prioritize analysis of emotionally positive information.

[0079] When detecting information bias, the detection unit can analyze the political stance and commercial interests of the source and identify the cause of the bias. For example, the generation AI can be used to analyze the political stance and commercial interests of the source and identify the cause of the information bias. For example, bias based on a particular political stance can be detected. The generation AI can also analyze the commercial interests of the source and identify the cause of the information bias. For example, bias based on the interests of a particular company can be detected. The generation AI can also analyze the political stance and commercial interests of the source and identify the cause of the information bias. For example, bias based on a particular political stance or commercial interest can be detected. This makes it possible to identify the cause of the information bias.

[0080] The detection unit can track changes in information over time and identify when bias occurs. For example, the generation AI can track changes in information over time and identify when bias occurs. For example, it can analyze whether bias increased during a specific period. The detection unit can also track changes in information over time and identify when bias occurs. For example, it can analyze whether bias increased during a specific event. The detection unit can also track changes in information over time and identify when bias occurs. For example, it can analyze whether bias increased during a specific political event. This makes it possible to identify when bias occurs.

[0081] The detection unit can analyze the user's emotional response to information bias and preferentially detect emotionally negative information. For example, the emotion estimation function is used to analyze the user's emotional response to information bias. For example, information with a high number of negative emotional responses is preferentially detected. The detection unit can also analyze the user's emotional response to information bias and preferentially detect emotionally negative information. For example, information with a low emotion score is selected. The emotion estimation function can also analyze the user's emotional response to information bias and preferentially detect emotionally negative information. For example, information with a strong negative emotion is selected. This makes it possible to preferentially detect emotionally negative information.

[0082] The detection unit can compare information from different regions and cultural spheres and identify bias from a global perspective. For example, the generation AI can be used to compare information from different regions and cultural spheres and identify bias from a global perspective. For example, it can analyze how the same news is reported in different regions. Information from different cultural spheres can also be compared and the generation AI can identify bias from a global perspective. For example, it can check whether there is any reporting that is biased towards a particular culture. The generation AI can also compare information from different regions and cultural spheres and identify bias from a global perspective. For example, it can analyze how the same event is interpreted in different cultures. This makes it possible to identify bias from a global perspective.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The intelligence gathering unit collects information. For example, it gathers news articles, social media posts, blog posts, etc. from the internet and other sources. The intelligence gathering unit may also filter information based on specific keywords or topics. For example, it may collect information related to a particular political position or commercial interest. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI compares the content of the collected information with other reliable information sources and evaluates the reliability of the information. In addition, the generation AI can analyze the information source's past reliability history and calculate a reliability score to evaluate the reliability of the information. For example, a high reliability score is assigned to an information source that has provided reliable information in the past. Step 3: The detection unit detects biased information from the information analyzed by the analysis unit. For example, the generation AI compares the collected information with sources of different languages ​​and cultural backgrounds to detect cultural bias. When detecting bias in information, the generation AI can also analyze the political positions and commercial interests of the sources to identify the causes of bias. Step 4: The reconstruction unit reconstructs the biased information detected by the detection unit. For example, if the generation AI detects a biased news article, it compares the content of the article with other reliable information sources and reconstructs accurate information. The generation AI can also analyze the source's past reliability history and calculate a reliability score to evaluate the reliability of the reconstructed information. Step 5: The provision unit provides the information reconstructed by the reconstruction unit. For example, the generation AI provides the reconstructed information in an appropriate format to prevent the spread of false information. The provision unit also provides the reconstructed information to individuals and companies, helping them make decisions based on reliable information.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

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

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

[0129] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0133] The data processing system 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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, in order to avoid confusion and to 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects information; an analysis unit that analyzes the information collected by the information collection unit; a detection unit that detects biased information from the information analyzed by the analysis unit; a reconstruction unit that reconstructs the biased information detected by the detection unit; a providing unit that provides the information reconstructed by the reconstruction unit. A system characterized by:

2. The analysis unit To assess the reliability of collected information, we analyze the source's past reliability history and calculate a reliability score.

2. The system of claim 1.

3. The analysis unit Compare collected information with sources in different languages ​​and cultural backgrounds to detect cultural biases 2. The system of claim 1.

4. The analysis unit Analyze users' emotional reactions to collected information and prioritize collection of emotionally neutral information.

2. The system of claim 1.

5. The analysis unit Visually analyze the collected information and conduct a comprehensive analysis of the information, including the content of images and videos.

2. The system of claim 1.

6. The analysis unit Gathering feedback from experts in different industries and fields and using it to assess the reliability of said information 2. The system of claim 1.

Images