System

Abstract

An object of a system according to an embodiment is to identify fake content and suppress crime.SOLUTION: A system according to an embodiment includes a fake content determination unit, an alarm issuing unit, and a guard execution unit. The fake content determination unit determines fake content. The alarm issuing unit issues an alarm when the fake is determined by the fake content determination unit. The guard execution unit executes the guard when the fake is determined by the fake content determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately identify fake content using generative AI technology, and there is room for improvement in order to prevent crime.

[0005] The system according to the embodiment aims to identify fake content and prevent crime. [Means for solving the problem]

[0006] The system according to the embodiment includes a fake content discrimination unit, an alarm issuing unit, and a guard execution unit. The fake content discrimination unit discriminates fake content. The alarm issuing unit issues an alarm when the fake content discrimination unit discriminates fake content. The guard execution unit executes guard when the fake content discrimination unit discriminates fake content. [Effects of the Invention]

[0007] The system according to the embodiment can identify fake content and curb crime. [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 fake content guard app according to an embodiment of the present invention is a system that determines whether content such as phone calls and videos is fake, and if so, issues an alarm or activates a guard. As a result, the fake content guard app can effectively prevent crimes such as telephone fraud and impersonation.

[0029] A fake content guard app according to an embodiment includes a fake content discrimination unit, an alarm issuing unit, and a guard execution unit. The fake content discrimination unit discriminates fake content. For example, the generation AI analyzes audio data and image data and compares them with pre-learned features of fake content to determine whether the content is fake. The generation AI performs analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The alarm issuing unit issues an alarm when the fake content discrimination unit determines that the content is fake. For example, if a call is determined to be fake, the app displays a warning message on the user's smartphone. Also, if a video is determined to be fake, playback is stopped and a warning message is displayed. The guard execution unit executes guard when the fake content discrimination unit determines that the content is fake. For example, if a fake call is received, the app automatically blocks the call. Also, if a fake video is attempted to be played, the app blocks the playback. As a result, the fake content guard app according to an embodiment can effectively suppress crimes using fake technology. For example, if a user receives a phone call from a fraudulent identity the app will automatically block the call and warn the user. If a user attempts to watch a spoofed video, the app will block playback and display a warning message.

[0030] The fake content discrimination unit can analyze audio data and identify characteristics of fake audio by analyzing subtle changes in the voice and background noise. For example, when the generation AI analyzes audio data, the fake content discrimination unit detects subtle changes in the voice and identifies characteristics of fake audio. For example, it analyzes the voice waveform and spectrum to find differences from natural audio. In addition, when the generation AI analyzes audio data, it analyzes patterns in background noise to identify characteristics of fake audio. For example, it detects consistency and unnatural changes in background noise. In addition, when the generation AI analyzes audio data, it detects subtle changes in the speaker's tone and pitch to identify characteristics of fake audio. For example, it analyzes unnatural voice intonation and rhythm. This improves the accuracy of fake audio discrimination by analyzing subtle changes in audio data and background noise.

[0031] The fake content discrimination unit analyzes image data and detects unnatural changes and abnormalities at the pixel level, improving the accuracy of discriminating fake images. For example, when the generation AI analyzes image data, the fake content discrimination unit detects unnatural changes at the pixel level and identifies the characteristics of fake images. For example, it analyzes unnatural image edges and textures. Also, when the generation AI analyzes image data, it detects unnatural color changes and abnormalities and identifies the characteristics of fake images. For example, it analyzes inconsistencies in color gradations and shadows. Also, when the generation AI analyzes image data, it detects unnatural object placement and shapes within the image and identifies the characteristics of fake images. For example, it analyzes unnatural object distortions and overlaps. This improves the accuracy of discriminating fake images by detecting unnatural changes and abnormalities in image data at the pixel level.

[0032] The fake content discrimination unit can analyze text data and detect fake news and false information. In the fake content discrimination unit, for example, the generation AI analyzes the text data to identify the characteristics of fake news. For example, it analyzes the consistency of the text and sources of low credibility. The generation AI also analyzes the text data to identify the characteristics of false information. For example, it detects exaggerated expressions and contradictory information. The generation AI also analyzes the text data to learn patterns of fake news and false information and detect them in real time. For example, it analyzes the frequency of specific keywords and phrases. In this way, fake news and false information can be detected by analyzing the text data.

[0033] The fake content discrimination unit can register the characteristics of the identified fake content in a database, allowing other users to respond quickly when they encounter similar fake content. The fake content discrimination unit, for example, registers the characteristics of fake content identified by the generation AI in a database, and issues a warning when other users encounter similar fake content. For example, it creates a database of the characteristics of fake audio and images. It also registers the characteristics of fake content identified by the generation AI in a database, automatically putting a guard on when other users encounter similar fake content. For example, it creates a database of the characteristics of fake news. It also registers the characteristics of fake content identified by the generation AI in a database, allowing other users to respond quickly when they encounter similar fake content. For example, it creates a database of the characteristics of fake videos. By creating a database of the characteristics of fake content, other users can respond quickly to similar fake content.

[0034] The warning issuing unit can add a function to explain in detail the specific characteristics and reasons for fake content, thereby deepening the user's understanding. The warning issuing unit can add a function to explain in detail the specific characteristics and reasons for fake content when an warning is issued, thereby deepening the user's understanding. For example, it can display the characteristics of fake audio and the reason for detection. Also, it can add a function to explain in detail the specific characteristics and reasons for fake content when an warning is issued, thereby building a system that deepens the user's understanding. For example, it can display the characteristics of fake images and the reason for detection. Also, it can add a function to explain in detail the specific characteristics and reasons for fake content when an warning is issued, thereby deepening the user's understanding. For example, it can display the characteristics of fake news and the reason for detection. This can deepen the user's understanding by explaining the specific characteristics and reasons for fake content.

[0035] The warning issuing unit can display a history of fake content that the user has received in the past and provide advice to prevent recurrence. For example, when issuing an warning, the warning issuing unit displays a history of fake content that the user has received in the past and provides advice to prevent recurrence. For example, it displays a history of past fake phone calls and presents points of caution. Furthermore, a system is constructed that displays a history of fake content that the user has received in the past and provides advice to prevent recurrence when issuing an warning. For example, it displays a history of past fake videos and presents points of caution. Furthermore, when issuing an warning, it displays a history of fake content that the user has received in the past and provides advice to prevent recurrence. For example, it displays a history of past fake news and presents points of caution. In this way, by displaying a history of past fake content and providing advice to prevent recurrence, the user's vigilance is increased.

[0036] The alert issuing unit can display the trends in fake content for each region based on the user's location information and issue region-specific warnings. For example, when issuing an alert, the alert issuing unit displays the trends in fake content for each region based on the user's location information and issues region-specific warnings. For example, it displays information about fake phone calls that are occurring frequently in a specific region. Furthermore, a system is constructed that displays the trends in fake content for each region based on the user's location information and issues region-specific warnings when issuing an alert. For example, it displays information about fake news that is occurring frequently in a specific region. Furthermore, when issuing an alert, it displays the trends in fake content for each region based on the user's location information and issues region-specific warnings. For example, it displays information about fake videos that are occurring frequently in a specific region. In this way, by displaying the trends in fake content for each region and issuing region-specific warnings, users' vigilance is increased.

[0037] The alert issuing unit can add a function to share information with other users, thereby increasing vigilance against fake content throughout the community. The alert issuing unit can add a function to share information with other users when an alert is issued, for example, thereby increasing vigilance against fake content throughout the community. For example, information about fake phone calls can be shared. Also, a function to share information with other users when an alert is issued can be added, thereby building a system that increases vigilance against fake content throughout the community. For example, information about fake news can be shared. Also, a function to share information with other users when an alert is issued can be added, thereby increasing vigilance against fake content throughout the community. For example, information about fake videos can be shared. In this way, by sharing information with other users, vigilance against fake content can be increased throughout the community.

[0038] The guard execution unit can select different guard methods depending on the type and content of the fake content. For example, when executing a guard function, the guard execution unit selects different guard methods depending on the type and content of the fake content. For example, in the case of a fake phone call, the call is blocked. Furthermore, a system is constructed that selects different guard methods depending on the type and content of the fake content when executing the guard function. For example, in the case of a fake video, playback is stopped. Furthermore, when executing the guard function, a different guard method is selected depending on the type and content of the fake content. For example, access is restricted in the case of fake news. In this way, the effectiveness of the guard is enhanced by selecting an appropriate guard method depending on the type and content of the fake content.

[0039] The guard execution unit can add a function to explain the reason for guarding and details to the user, thereby encouraging the user's understanding and acceptance. For example, when executing the guard function, the guard execution unit can add a function to explain the reason for guarding and details to the user, thereby encouraging the user's understanding and acceptance. For example, it can display the characteristics of fake content and the reason for detection. Also, when executing the guard function, it can add a function to explain the reason for guarding and details to the user, thereby building a system that encourages the user's understanding and acceptance. For example, it can display the characteristics of fake audio and the reason for detection. Also, when executing the guard function, it can add a function to explain the reason for guarding and details to the user, thereby encouraging the user's understanding and acceptance. For example, it can display the characteristics of fake video and the reason for detection. In this way, by explaining the reason for guarding and details, it can encourage the user's understanding and acceptance.

[0040] The guard execution unit can extend the guard function to other communication means such as email and messaging apps to prevent the spread of fake content. The guard execution unit, for example, extends the guard function to email to prevent the spread of fake content. For example, it detects fake emails and issues a warning to the recipient. The guard function can also be extended to messaging apps to build a system that prevents the spread of fake content. For example, it can detect fake messages and issue a warning to the sender. The guard function can also be extended to other communication means to prevent the spread of fake content. For example, it can detect fake SNS posts and issue a warning to the poster. In this way, the spread of fake content can be prevented by extending the guard function to other communication means.

[0041] The guard execution unit can provide a guard function to companies and organizations and use it as a tool for employees to reduce the risk of fake content. The guard execution unit, for example, provides a guard function to companies and uses it as a tool for employees to reduce the risk of fake content. For example, the guard function is introduced into an in-house email system. The guard function is also provided to organizations and a system is built that employees can use as a tool to reduce the risk of fake content. For example, the guard function is introduced into an in-house messaging app. The guard function is also provided to companies and organizations and used as a tool for employees to reduce the risk of fake content. For example, the guard function is introduced into an in-house social media platform. In this way, by providing the guard function to companies and organizations, employees can reduce the risk of fake content.

[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 fake content discrimination unit can analyze a user's past behavioral history to assess their vulnerability to fake content. For example, it can analyze the number and type of times a user has fallen victim to fake content in the past to calculate the user's vulnerability score. It can also analyze the user's behavioral patterns to assess their level of vigilance against fake content. For example, it can analyze behavior during specific times or situations. It can also provide individually customized warning messages based on the user's vulnerability score. This makes it possible to assess a user's vulnerability to fake content and respond individually by analyzing the user's past behavioral history.

[0044] The fake content detection unit can analyze audio data and identify characteristics of fake audio by analyzing subtle changes in the voice and background noise. For example, when the generation AI analyzes audio data, it detects subtle changes in the voice and identifies characteristics of fake audio. For example, it analyzes the voice waveform and spectrum to find differences from natural audio. When the generation AI analyzes audio data, it also analyzes patterns in background noise to identify characteristics of fake audio. For example, it detects consistency and unnatural changes in background noise. When the generation AI analyzes audio data, it also detects subtle changes in the speaker's tone and pitch to identify characteristics of fake audio. For example, it analyzes unnatural voice intonation and rhythm. This improves the accuracy of fake audio detection by analyzing subtle changes in audio data and background noise.

[0045] The fake content discrimination unit analyzes image data and detects unnatural changes and abnormalities at the pixel level, improving the accuracy of identifying fake images. For example, when the generation AI analyzes image data, it detects unnatural changes at the pixel level and identifies the characteristics of fake images. For example, it analyzes unnatural image edges and textures. Furthermore, when the generation AI analyzes image data, it detects unnatural color changes and abnormalities and identifies the characteristics of fake images. For example, it analyzes inconsistencies in color gradations and shadows. Furthermore, when the generation AI analyzes image data, it detects unnatural object placement and shapes within the image and identifies the characteristics of fake images. For example, it analyzes unnatural object distortions and overlaps. This improves the accuracy of identifying fake images by detecting unnatural changes and abnormalities in image data at the pixel level.

[0046] The fake content detection unit can analyze text data to detect fake news and false information. For example, the generation AI analyzes text data to identify characteristics of fake news. For example, it analyzes inconsistent writing and sources of low credibility. The generation AI also analyzes text data to identify characteristics of false information. For example, it detects exaggerated expressions and contradictory information. The generation AI also analyzes text data to learn patterns of fake news and false information and detect them in real time. For example, it analyzes the frequency of specific keywords and phrases. In this way, fake news and false information can be detected by analyzing text data.

[0047] The fake content discrimination unit can register the characteristics of the identified fake content in a database, allowing other users to respond quickly when they encounter similar fake content. For example, the characteristics of fake content identified by the generation AI can be registered in a database, and a warning can be issued when other users encounter similar fake content. For example, the characteristics of fake audio and images can be databased. Also, the characteristics of fake content identified by the generation AI can be registered in a database, automatically putting a guard on when other users encounter similar fake content. For example, the characteristics of fake news can be databased. Also, the characteristics of fake content identified by the generation AI can be registered in a database, allowing other users to respond quickly when they encounter similar fake content. For example, the characteristics of fake videos can be databased. By databased, the characteristics of fake content can be databased, allowing other users to respond quickly to similar fake content.

[0048] The warning issuing unit can add a function to explain in detail the specific characteristics and reasons for fake content, thereby deepening the user's understanding. For example, when a warning is issued, a function to explain in detail the specific characteristics and reasons for fake content can be added to deepen the user's understanding. For example, the characteristics of fake audio and the reason for detection can be displayed. Also, when a warning is issued, a function to explain in detail the specific characteristics and reasons for fake content can be added to build a system that deepens the user's understanding. For example, the characteristics of fake images and the reason for detection can be displayed. Also, when a warning is issued, a function to explain in detail the specific characteristics and reasons for fake content can be added to deepen the user's understanding. For example, the characteristics of fake news and the reason for detection can be displayed. This deepens the user's understanding by explaining the specific characteristics and reasons for fake content.

[0049] The warning issuing unit can display a history of fake content that the user has received in the past and provide advice to prevent recurrence. For example, when a warning is issued, the unit displays a history of fake content that the user has received in the past and provides advice to prevent recurrence. For example, the unit displays a history of past fake phone calls and provides points to be careful about. Furthermore, a system is constructed that displays a history of fake content that the user has received in the past and provides advice to prevent recurrence when a warning is issued. For example, the unit displays a history of past fake videos and provides points to be careful about. Furthermore, when a warning is issued, the unit displays a history of fake content that the user has received in the past and provides advice to prevent recurrence. For example, the unit displays a history of past fake news and provides points to be careful about. In this way, by displaying a history of past fake content and providing advice to prevent recurrence, the user's vigilance is increased.

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

[0051] Step 1: The fake content discrimination unit discriminates fake content. For example, the generation AI analyzes audio and image data and compares them with pre-trained features of fake content to determine whether they are fake. The generation AI performs its analysis using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The warning issuing unit issues a warning if the fake content is determined to be fake by the fake content determination unit. For example, if the call is determined to be fake, the app displays a warning message on the user's smartphone. Also, if the video is determined to be fake, playback is stopped and a warning message is displayed. Step 3: The guard execution unit executes the guard when the fake content determination unit determines that the content is fake. For example, if a fake call is received, the app will automatically block the call. Also, if a fake video is about to be played, the app will block the playback.

[0052] (Example 2) The fake content guard app according to an embodiment of the present invention is a system that determines whether content such as phone calls and videos is fake, and if so, issues an alarm or activates a guard. As a result, the fake content guard app can effectively prevent crimes such as telephone fraud and impersonation.

[0053] A fake content guard app according to an embodiment includes a fake content discrimination unit, an alarm issuing unit, and a guard execution unit. The fake content discrimination unit discriminates fake content. For example, the generation AI analyzes audio data and image data and compares them with pre-learned features of fake content to determine whether the content is fake. The generation AI performs analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The alarm issuing unit issues an alarm when the fake content discrimination unit determines that the content is fake. For example, if a call is determined to be fake, the app displays a warning message on the user's smartphone. Also, if a video is determined to be fake, playback is stopped and a warning message is displayed. The guard execution unit executes guard when the fake content discrimination unit determines that the content is fake. For example, if a fake call is received, the app automatically blocks the call. Also, if a fake video is attempted to be played, the app blocks the playback. As a result, the fake content guard app according to an embodiment can effectively suppress crimes using fake technology. For example, if a user receives a phone call from a fraudulent identity the app will automatically block the call and warn the user. If a user attempts to watch a spoofed video, the app will block playback and display a warning message.

[0054] The fake content discrimination unit uses the emotion estimation function to detect unnatural emotions in content, increasing the likelihood of it being fake. For example, when the generation AI analyzes audio data, the fake content discrimination unit detects changes in the speaker's emotions and determines the likelihood of fake content based on the unnaturalness of the emotions. For example, it detects sudden changes in the speaker's tone or pitch. Also, when the generation AI analyzes text data, it detects unnatural emotional expressions in the text, increasing the likelihood of fake content. For example, it analyzes the consistency of the text and the flow of emotions. Also, when the generation AI analyzes image data, it detects unnatural facial expressions and postures of people in the image and determines the likelihood of fake content being fake. For example, it detects unnatural smiles and inconsistent eye movements. This detects unnatural emotions and improves the accuracy of fake content discrimination.

[0055] The fake content discrimination unit can analyze audio data and identify characteristics of fake audio by analyzing subtle changes in the voice and background noise. For example, when the generation AI analyzes audio data, the fake content discrimination unit detects subtle changes in the voice and identifies characteristics of fake audio. For example, it analyzes the voice waveform and spectrum to find differences from natural audio. In addition, when the generation AI analyzes audio data, it analyzes patterns in background noise to identify characteristics of fake audio. For example, it detects consistency and unnatural changes in background noise. In addition, when the generation AI analyzes audio data, it detects subtle changes in the speaker's tone and pitch to identify characteristics of fake audio. For example, it analyzes unnatural voice intonation and rhythm. This improves the accuracy of fake audio discrimination by analyzing subtle changes in audio data and background noise.

[0056] The fake content discrimination unit analyzes image data and detects unnatural changes and abnormalities at the pixel level, improving the accuracy of discriminating fake images. For example, when the generation AI analyzes image data, the fake content discrimination unit detects unnatural changes at the pixel level and identifies the characteristics of fake images. For example, it analyzes unnatural image edges and textures. Also, when the generation AI analyzes image data, it detects unnatural color changes and abnormalities and identifies the characteristics of fake images. For example, it analyzes inconsistencies in color gradations and shadows. Also, when the generation AI analyzes image data, it detects unnatural object placement and shapes within the image and identifies the characteristics of fake images. For example, it analyzes unnatural object distortions and overlaps. This improves the accuracy of discriminating fake images by detecting unnatural changes and abnormalities in image data at the pixel level.

[0057] The fake content discrimination unit can analyze text data and detect fake news and false information. In the fake content discrimination unit, for example, the generation AI analyzes the text data to identify the characteristics of fake news. For example, it analyzes the consistency of the text and sources of low credibility. The generation AI also analyzes the text data to identify the characteristics of false information. For example, it detects exaggerated expressions and contradictory information. The generation AI also analyzes the text data to learn patterns of fake news and false information and detect them in real time. For example, it analyzes the frequency of specific keywords and phrases. In this way, fake news and false information can be detected by analyzing the text data.

[0058] The fake content discrimination unit can register the characteristics of the identified fake content in a database, allowing other users to respond quickly when they encounter similar fake content. The fake content discrimination unit, for example, registers the characteristics of fake content identified by the generation AI in a database, and issues a warning when other users encounter similar fake content. For example, it creates a database of the characteristics of fake audio and images. It also registers the characteristics of fake content identified by the generation AI in a database, automatically putting a guard on when other users encounter similar fake content. For example, it creates a database of the characteristics of fake news. It also registers the characteristics of fake content identified by the generation AI in a database, allowing other users to respond quickly when they encounter similar fake content. For example, it creates a database of the characteristics of fake videos. By creating a database of the characteristics of fake content, other users can respond quickly to similar fake content.

[0059] The fake content discrimination unit can use the emotion estimation function to analyze the emotional response of a user to the content received and evaluate the impact of fake content. The fake content discrimination unit, for example, uses the emotion estimation function to analyze the emotional response of a user to the content received and evaluate the impact of fake content. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also uses the emotion estimation function to analyze the emotional response of a user to the content received in real time and evaluate the impact of fake content. For example, it monitors changes in the user's emotions. It also uses the emotion estimation function to analyze the emotional response of a user to the content received and builds a system to evaluate the impact of fake content. For example, it evaluates the impact of fake content based on the emotion score. In this way, it is possible to evaluate the impact of fake content by analyzing the user's emotional response.

[0060] The warning issuing unit can analyze the user's emotional state using the emotion estimation function and customize an appropriate warning message. For example, when issuing an alarm, the warning issuing unit uses the emotion estimation function to analyze the user's emotional state and customize an appropriate warning message. For example, if the user is surprised, a message urging the user to remain calm is displayed. Also, when issuing an alarm, the emotion estimation function is used to analyze the user's emotional state in real time and customize an appropriate warning message. For example, if the user is feeling anxious, a reassuring message is displayed. Also, a system is constructed that uses the emotion estimation function to analyze the user's emotional state when issuing an alarm and customize an appropriate warning message. For example, the warning message is adjusted based on the user's emotion score. In this way, the warning message is customized according to the user's emotional state, thereby improving the effectiveness of the warning.

[0061] The warning issuing unit can add a function to explain in detail the specific characteristics and reasons for fake content, thereby deepening the user's understanding. The warning issuing unit can add a function to explain in detail the specific characteristics and reasons for fake content when an warning is issued, thereby deepening the user's understanding. For example, it can display the characteristics of fake audio and the reason for detection. Also, it can add a function to explain in detail the specific characteristics and reasons for fake content when an warning is issued, thereby building a system that deepens the user's understanding. For example, it can display the characteristics of fake images and the reason for detection. Also, it can add a function to explain in detail the specific characteristics and reasons for fake content when an warning is issued, thereby deepening the user's understanding. For example, it can display the characteristics of fake news and the reason for detection. This can deepen the user's understanding by explaining the specific characteristics and reasons for fake content.

[0062] The warning issuing unit can display a history of fake content that the user has received in the past and provide advice to prevent recurrence. For example, when issuing an warning, the warning issuing unit displays a history of fake content that the user has received in the past and provides advice to prevent recurrence. For example, it displays a history of past fake phone calls and presents points of caution. Furthermore, a system is constructed that displays a history of fake content that the user has received in the past and provides advice to prevent recurrence when issuing an warning. For example, it displays a history of past fake videos and presents points of caution. Furthermore, when issuing an warning, it displays a history of fake content that the user has received in the past and provides advice to prevent recurrence. For example, it displays a history of past fake news and presents points of caution. In this way, by displaying a history of past fake content and providing advice to prevent recurrence, the user's vigilance is increased.

[0063] The alert issuing unit can display the trends in fake content for each region based on the user's location information and issue region-specific warnings. For example, when issuing an alert, the alert issuing unit displays the trends in fake content for each region based on the user's location information and issues region-specific warnings. For example, it displays information about fake phone calls that are occurring frequently in a specific region. Furthermore, a system is constructed that displays the trends in fake content for each region based on the user's location information and issues region-specific warnings when issuing an alert. For example, it displays information about fake news that is occurring frequently in a specific region. Furthermore, when issuing an alert, it displays the trends in fake content for each region based on the user's location information and issues region-specific warnings. For example, it displays information about fake videos that are occurring frequently in a specific region. In this way, by displaying the trends in fake content for each region and issuing region-specific warnings, users' vigilance is increased.

[0064] The alert issuing unit can add a function to share information with other users, thereby increasing vigilance against fake content throughout the community. The alert issuing unit can add a function to share information with other users when an alert is issued, for example, thereby increasing vigilance against fake content throughout the community. For example, information about fake phone calls can be shared. Also, a function to share information with other users when an alert is issued can be added, thereby building a system that increases vigilance against fake content throughout the community. For example, information about fake news can be shared. Also, a function to share information with other users when an alert is issued can be added, thereby increasing vigilance against fake content throughout the community. For example, information about fake videos can be shared. In this way, by sharing information with other users, vigilance against fake content can be increased throughout the community.

[0065] The warning issuing unit can use the emotion estimation function to monitor the user's emotional reaction after the warning is issued and evaluate the effectiveness of the warning message. The warning issuing unit, for example, uses the emotion estimation function to monitor the user's emotional reaction after the warning is issued and evaluate the effectiveness of the warning message. For example, the warning issuing unit analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function is also used to monitor the user's emotional reaction after the warning is issued in real time and evaluate the effectiveness of the warning message. For example, changes in the user's emotions are monitored. The emotion estimation function is also used to build a system that monitors the user's emotional reaction after the warning is issued and evaluates the effectiveness of the warning message. For example, the effectiveness of the warning message is evaluated based on the emotion score. In this way, the effectiveness of the warning message can be evaluated by monitoring the user's emotional reaction after the warning is issued.

[0066] The guard execution unit can analyze the emotional state of the user using the emotion estimation function and execute guard at an appropriate timing. For example, when executing the guard function, the guard execution unit analyzes the emotional state of the user using the emotion estimation function and executes guard at an appropriate timing. For example, the guard execution unit executes guard when the user is calm. Furthermore, when executing the guard function, the emotion estimation function is used to analyze the emotional state of the user in real time and execute guard at an appropriate timing. For example, if the user is feeling anxious, the guard is delayed. Furthermore, a system is constructed in which the emotion estimation function is used to analyze the emotional state of the user when executing the guard function and execute guard at an appropriate timing. For example, the timing of the guard is adjusted based on the user's emotion score. In this way, the guard is executed at an appropriate timing according to the user's emotional state, thereby enhancing the effectiveness of the guard.

[0067] The guard execution unit can select different guard methods depending on the type and content of the fake content. For example, when executing a guard function, the guard execution unit selects different guard methods depending on the type and content of the fake content. For example, in the case of a fake phone call, the call is blocked. Furthermore, a system is constructed that selects different guard methods depending on the type and content of the fake content when executing the guard function. For example, in the case of a fake video, playback is stopped. Furthermore, when executing the guard function, a different guard method is selected depending on the type and content of the fake content. For example, access is restricted in the case of fake news. In this way, the effectiveness of the guard is enhanced by selecting an appropriate guard method depending on the type and content of the fake content.

[0068] The guard execution unit can add a function to explain the reason for guarding and details to the user, thereby encouraging the user's understanding and acceptance. For example, when executing the guard function, the guard execution unit can add a function to explain the reason for guarding and details to the user, thereby encouraging the user's understanding and acceptance. For example, it can display the characteristics of fake content and the reason for detection. Also, when executing the guard function, it can add a function to explain the reason for guarding and details to the user, thereby building a system that encourages the user's understanding and acceptance. For example, it can display the characteristics of fake audio and the reason for detection. Also, when executing the guard function, it can add a function to explain the reason for guarding and details to the user, thereby encouraging the user's understanding and acceptance. For example, it can display the characteristics of fake video and the reason for detection. In this way, by explaining the reason for guarding and details, it can encourage the user's understanding and acceptance.

[0069] The guard execution unit can extend the guard function to other communication means such as email and messaging apps to prevent the spread of fake content. The guard execution unit, for example, extends the guard function to email to prevent the spread of fake content. For example, it detects fake emails and issues a warning to the recipient. The guard function can also be extended to messaging apps to build a system that prevents the spread of fake content. For example, it can detect fake messages and issue a warning to the sender. The guard function can also be extended to other communication means to prevent the spread of fake content. For example, it can detect fake SNS posts and issue a warning to the poster. In this way, the spread of fake content can be prevented by extending the guard function to other communication means.

[0070] The guard execution unit can provide a guard function to companies and organizations and use it as a tool for employees to reduce the risk of fake content. The guard execution unit, for example, provides a guard function to companies and uses it as a tool for employees to reduce the risk of fake content. For example, the guard function is introduced into an in-house email system. The guard function is also provided to organizations and a system is built that employees can use as a tool to reduce the risk of fake content. For example, the guard function is introduced into an in-house messaging app. The guard function is also provided to companies and organizations and used as a tool for employees to reduce the risk of fake content. For example, the guard function is introduced into an in-house social media platform. In this way, by providing the guard function to companies and organizations, employees can reduce the risk of fake content.

[0071] The guard execution unit can use the emotion estimation function to monitor the user's emotional reaction after the guard function is executed, and use the result to improve the guard method. The guard execution unit, for example, uses the emotion estimation function to monitor the user's emotional reaction after the guard function is executed, and use the result to improve the guard method. For example, the guard execution unit analyzes the user's facial expressions and voice and calculates an emotion score. The emotion estimation function is also used to monitor the user's emotional reaction after the guard function is executed in real time, and use the result to improve the guard method. For example, the emotion estimation function is used to monitor the user's emotional reaction after the guard function is executed, and use the result to improve the guard method. For example, the guard execution unit adjusts the guard method based on the emotion score. In this way, monitoring the user's emotional reaction after the guard function is executed can be used to improve the guard method.

[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 fake content discrimination unit can analyze a user's past behavioral history to assess their vulnerability to fake content. For example, it can analyze the number and type of times a user has fallen victim to fake content in the past to calculate the user's vulnerability score. It can also analyze the user's behavioral patterns to assess their level of vigilance against fake content. For example, it can analyze behavior during specific times or situations. It can also provide individually customized warning messages based on the user's vulnerability score. This makes it possible to assess a user's vulnerability to fake content and respond individually by analyzing the user's past behavioral history.

[0074] The fake content discrimination unit can use the emotion estimation function to analyze the emotional response of a user to the content received and evaluate the impact of fake content. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. It can also use the emotion estimation function to analyze the emotional response of a user to the content received in real time and evaluate the impact of fake content. For example, it can monitor changes in the user's emotions. It can also use the emotion estimation function to analyze the emotional response of a user to the content received and build a system to evaluate the impact of fake content. For example, it can evaluate the impact of fake content based on the emotion score. This makes it possible to evaluate the impact of fake content by analyzing the user's emotional response.

[0075] The fake content detection unit can analyze audio data and identify characteristics of fake audio by analyzing subtle changes in the voice and background noise. For example, when the generation AI analyzes audio data, it detects subtle changes in the voice and identifies characteristics of fake audio. For example, it analyzes the voice waveform and spectrum to find differences from natural audio. When the generation AI analyzes audio data, it also analyzes patterns in background noise to identify characteristics of fake audio. For example, it detects consistency and unnatural changes in background noise. When the generation AI analyzes audio data, it also detects subtle changes in the speaker's tone and pitch to identify characteristics of fake audio. For example, it analyzes unnatural voice intonation and rhythm. This improves the accuracy of fake audio detection by analyzing subtle changes in audio data and background noise.

[0076] The fake content discrimination unit analyzes image data and detects unnatural changes and abnormalities at the pixel level, improving the accuracy of identifying fake images. For example, when the generation AI analyzes image data, it detects unnatural changes at the pixel level and identifies the characteristics of fake images. For example, it analyzes unnatural image edges and textures. Furthermore, when the generation AI analyzes image data, it detects unnatural color changes and abnormalities and identifies the characteristics of fake images. For example, it analyzes inconsistencies in color gradations and shadows. Furthermore, when the generation AI analyzes image data, it detects unnatural object placement and shapes within the image and identifies the characteristics of fake images. For example, it analyzes unnatural object distortions and overlaps. This improves the accuracy of identifying fake images by detecting unnatural changes and abnormalities in image data at the pixel level.

[0077] The fake content detection unit can analyze text data to detect fake news and false information. For example, the generation AI analyzes text data to identify characteristics of fake news. For example, it analyzes inconsistent writing and sources of low credibility. The generation AI also analyzes text data to identify characteristics of false information. For example, it detects exaggerated expressions and contradictory information. The generation AI also analyzes text data to learn patterns of fake news and false information and detect them in real time. For example, it analyzes the frequency of specific keywords and phrases. In this way, fake news and false information can be detected by analyzing text data.

[0078] The fake content discrimination unit can register the characteristics of the identified fake content in a database, allowing other users to respond quickly when they encounter similar fake content. For example, the characteristics of fake content identified by the generation AI can be registered in a database, and a warning can be issued when other users encounter similar fake content. For example, the characteristics of fake audio and images can be databased. Also, the characteristics of fake content identified by the generation AI can be registered in a database, automatically putting a guard on when other users encounter similar fake content. For example, the characteristics of fake news can be databased. Also, the characteristics of fake content identified by the generation AI can be registered in a database, allowing other users to respond quickly when they encounter similar fake content. For example, the characteristics of fake videos can be databased. By databased, the characteristics of fake content can be databased, allowing other users to respond quickly to similar fake content.

[0079] The fake content discrimination unit can use the emotion estimation function to analyze the emotional response of a user to the content received and evaluate the impact of fake content. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. It can also use the emotion estimation function to analyze the emotional response of a user to the content received in real time and evaluate the impact of fake content. For example, it can monitor changes in the user's emotions. It can also use the emotion estimation function to analyze the emotional response of a user to the content received and build a system to evaluate the impact of fake content. For example, it can evaluate the impact of fake content based on the emotion score. This makes it possible to evaluate the impact of fake content by analyzing the user's emotional response.

[0080] The warning issuing unit can use the emotion estimation function to analyze the user's emotional state and customize an appropriate warning message. For example, when a warning is issued, the emotion estimation function is used to analyze the user's emotional state and customize an appropriate warning message. For example, if the user is surprised, a message urging the user to remain calm is displayed. Also, when a warning is issued, the emotion estimation function is used to analyze the user's emotional state in real time and customize an appropriate warning message. For example, if the user is feeling anxious, a reassuring message is displayed. Also, a system is constructed that uses the emotion estimation function to analyze the user's emotional state when a warning is issued and customize an appropriate warning message. For example, the warning message is adjusted based on the user's emotion score. In this way, the warning message can be customized according to the user's emotional state, thereby improving the effectiveness of the warning.

[0081] The warning issuing unit can add a function to explain in detail the specific characteristics and reasons for fake content, thereby deepening the user's understanding. For example, when a warning is issued, a function to explain in detail the specific characteristics and reasons for fake content can be added to deepen the user's understanding. For example, the characteristics of fake audio and the reason for detection can be displayed. Also, when a warning is issued, a function to explain in detail the specific characteristics and reasons for fake content can be added to build a system that deepens the user's understanding. For example, the characteristics of fake images and the reason for detection can be displayed. Also, when a warning is issued, a function to explain in detail the specific characteristics and reasons for fake content can be added to deepen the user's understanding. For example, the characteristics of fake news and the reason for detection can be displayed. This deepens the user's understanding by explaining the specific characteristics and reasons for fake content.

[0082] The warning issuing unit can display a history of fake content that the user has received in the past and provide advice to prevent recurrence. For example, when a warning is issued, the unit displays a history of fake content that the user has received in the past and provides advice to prevent recurrence. For example, the unit displays a history of past fake phone calls and provides points to be careful about. Furthermore, a system is constructed that displays a history of fake content that the user has received in the past and provides advice to prevent recurrence when a warning is issued. For example, the unit displays a history of past fake videos and provides points to be careful about. Furthermore, when a warning is issued, the unit displays a history of fake content that the user has received in the past and provides advice to prevent recurrence. For example, the unit displays a history of past fake news and provides points to be careful about. In this way, by displaying a history of past fake content and providing advice to prevent recurrence, the user's vigilance is increased.

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

[0084] Step 1: The fake content discrimination unit discriminates fake content. For example, the generation AI analyzes audio and image data and compares them with pre-trained features of fake content to determine whether they are fake. The generation AI performs its analysis using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The warning issuing unit issues a warning if the fake content is determined to be fake by the fake content determination unit. For example, if the call is determined to be fake, the app displays a warning message on the user's smartphone. Also, if the video is determined to be fake, playback is stopped and a warning message is displayed. Step 3: The guard execution unit executes the guard when the fake content determination unit determines that the content is fake. For example, if a fake call is received, the app will automatically block the call. Also, if a fake video is about to be played, the app will block the playback.

[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. a fake content discrimination unit that discriminates fake content; an alarm issuing unit that issues an alarm when the content is determined to be fake by the fake content determining unit; a guard execution unit that executes guard when the content is determined to be fake by the fake content determination unit. A system characterized by:

2. The fake content determination unit Detects unnatural emotions in content, increasing the likelihood of it being fake 2. The system of claim 1.

3. The fake content determination unit Analyzes audio data and identifies subtle variations in voice and background noise to identify the characteristics of fake voices 2. The system of claim 1.

4. The fake content determination unit Analyzes image data and detects unnatural changes and anomalies at the pixel level to improve accuracy in identifying fake images 2. The system of claim 1.

5. The fake content determination unit Analyzing text data to detect fake news and disinformation 2. The system of claim 1.

6. The fake content determination unit The characteristics of the identified fake content are registered in a database, enabling other users to respond quickly when they encounter similar fake content.

2. The system of claim 1.

7. The fake content determination unit Analyzing users' emotional responses to the content they receive and assessing the impact of said fake content 2. The system of claim 1.

8. The alarm issuing unit Analyze the user's emotional state and customize appropriate warning messages 2. The system of claim 1.

Images