system

Abstract

The system according to the embodiment aims to naturally realize two-way interaction with users in content exclusive to fan clubs and online salons. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a display unit, a confirmation unit, a prevention unit, and a revenue unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a video based on the information analyzed by the analysis unit. The display unit displays the video generated by the generation unit. The confirmation unit describes a specific method for verifying identity. The prevention unit describes a specific method for preventing fraudulent use. The revenue unit describes a specific method for managing revenue.

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 technology has had the problem that it is difficult to naturally realize two-way interaction with users when it comes to content exclusive to fan clubs or online salons.

[0005] The system according to the embodiment aims to naturally realize two-way interaction with users in content exclusive to fan clubs and online salons. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a display unit, a confirmation unit, a prevention unit, and a revenue unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a video based on the information analyzed by the analysis unit. The display unit displays the video generated by the generation unit. The confirmation unit describes a specific method for verifying identity. The prevention unit describes a specific method for preventing fraudulent use. The revenue unit describes a specific method for managing revenue. [Effects of the Invention]

[0007] The system according to the embodiment can naturally realize two-way interaction with users in content exclusive to fan clubs and online salons. [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 entertainment system according to an embodiment of the present invention is an official app exclusive to fan clubs and online salons. This entertainment system utilizes multimodal AI generation, including character generation and voice bots, to enable two-way interactions, such as conversations, with the "oshi" (favorite) character or character. Specifically, by combining video generation AI technology, voice recognition, and natural language processing, the "oshi" (favorite) appears to be speaking to the user with a natural expression on the app. Users can have their "oshi" listen to them, wake them up with an alarm, or sing a song just for them. This app is provided as a "limited app" from the perspective of privacy and ethics, and requires identity verification upon download to prevent social media outrage. In addition to preventing fraudulent use, tracking is available upon request from the "oshi" or their management company, allowing for tracing of the source in the event of an incident. This system aims to promote positive and healthy content in the entertainment industry, deepening technologies related to AI ethics and privacy protection, and creating a world where swift action can be taken against defamatory and inflammatory fake news. For example, users can enjoy conversations with their "oshi" (favorite idol) through the app. When a user speaks to their "oshi," the AI ​​analyzes the conversation and generates an appropriate response. The AI ​​continues the conversation based on the user's response to questions like, "How was your day today?" Users can also set an alarm. For example, if a user requests, "Wake me up at 7 a.m. tomorrow," the app will sound the alarm in their "oshi"'s voice at the specified time. Users can also request their "oshi" to sing a song. For example, if a user requests, "Sing me my favorite song," the AI ​​will sing in their "oshi"'s voice. This allows users to enjoy special moments with their "oshi." The app also performs identity verification to protect user privacy. For example, when downloading the app, the app performs facial recognition or fingerprint authentication to complete identity verification. This prevents fraudulent use and provides a safe and secure environment. The app also uses anomaly detection algorithms to prevent fraudulent use.For example, if the app detects unusual access patterns, it will automatically issue a warning and suspend the account if necessary. This protects the user's account and ensures safe use. Regarding revenue management, the app collects a flat fee from users. For example, the fee could be included in the monthly membership fee for a fan club or online salon. The app also provides a paid service to companies, government agencies, political organizations, etc. to verify the authenticity of video and audio, and in-app corporate advertising fees also serve as a revenue source. This allows the app to build a sustainable business model and enable long-term operation. This allows the entertainment system to provide users with two-way interaction with their "oshi" (favorite idols), while protecting their privacy and preventing fraudulent use.

[0029] An entertainment system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a display unit, a confirmation unit, a prevention unit, and a revenue unit. The reception unit receives input from a user. The user input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit receives a text message input by the user to an app. The reception unit can also receive voice instructions from the user. For example, the user can issue a voice instruction such as "Wake me up at 7:00 tomorrow morning." The reception unit can also receive image uploads by the user. For example, the user can upload an image of their "favorite" character and display it within the app. The analysis unit analyzes the information received by the reception unit. The analysis unit can analyze the user input using, for example, data mining technology. The analysis unit can also analyze the text input using natural language processing technology. For example, the analysis unit can analyze the content of the text message entered by the user and generate an appropriate response. The analysis unit can also analyze the image input using image analysis technology. For example, the system analyzes the content of an image uploaded by a user and provides related information. The generation unit generates a video based on the information analyzed by the analysis unit. The generation unit generates the video using, for example, a template-based generation technique. The generation unit can also generate the video using a generation AI. For example, the generation AI generates a "favorite" video based on user input. The generation AI can use a text generation AI (e.g., LLM) or a multimodal generation AI. The display unit displays the video generated by the generation unit. The display unit displays the video according to, for example, a screen size or resolution. The display unit can also display the video according to a display device. For example, the video is displayed on a device such as a smartphone, tablet, or PC. The verification unit performs identity verification. The verification unit performs identity verification using, for example, facial recognition technology. The verification unit can also perform identity verification using fingerprint authentication technology. For example, when a user downloads an app, face recognition or fingerprint authentication is performed to complete identity verification. The prevention unit prevents fraudulent use.The prevention unit detects fraudulent use, for example, using an anomaly detection algorithm. The prevention unit can also prevent fraudulent use using access control technology. For example, if an unusual access pattern is detected, the prevention unit automatically issues a warning and suspends the account as necessary. The revenue unit manages revenue. The revenue unit manages revenue, for example, based on a revenue calculation method and distribution method. The revenue unit can also collect usage fees from users. For example, the usage fees can be collected by including them in the monthly membership fees for a fan club or online salon. This allows the entertainment system according to the embodiment to accept and analyze input from users, generate and display videos, and perform identity verification, fraud prevention, and revenue management.

[0030] The reception unit can analyze the user's past input history and select a reception method based on the user's past input history. For example, the reception unit prioritizes suggesting an input method that the user has frequently used in the past. For example, if the user has frequently used text input in the past, the reception unit prioritizes text input. The reception unit can also select the optimal reception method for a specific time period based on the user's past input history. For example, if the user frequently used voice input during a specific time period, the reception unit prioritizes voice input during that time period. The reception unit can also analyze the user's past input patterns and suggest the most efficient reception method. For example, if the user inputs data using a specific pattern, the reception unit suggests the optimal reception method based on that pattern. In this way, the optimal reception method can be provided by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal reception method.

[0031] The reception unit can filter inputs based on the user's current activity status and areas of interest when receiving the inputs. For example, when the user is at work, the reception unit prioritizes receiving work-related inputs. For example, when the user is using an app while at work, the reception unit prioritizes receiving work-related inputs and filters other inputs. Furthermore, when the user is engaged in a hobby-related activity, the reception unit can prioritize receiving inputs related to the hobby. For example, when the user is searching for information about a hobby, the reception unit prioritizes receiving inputs related to the hobby. Furthermore, when the user is taking a break, the reception unit can prioritize receiving inputs related to relaxing content. For example, when the user is using an app during a break, the reception unit prioritizes receiving inputs related to relaxing content. This allows for more appropriate input reception by filtering inputs based on the user's current activity status and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's activity log data to a generation AI and have the generation AI perform filtering.

[0032] When receiving input, the reception unit can prioritize receiving highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving input related to that area. For example, when the user is in a specific area, the reception unit prioritizes receiving event information and service information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving input related to the travel destination. For example, when the user is using an app while traveling, the reception unit prioritizes receiving tourist information and traffic information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving input related to the user's home. For example, when the user is at home, the reception unit prioritizes receiving household chore information and entertainment information related to the user's home. In this way, highly relevant input can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's GPS data to the generation AI and cause the generation AI to select highly relevant input.

[0033] The reception unit can analyze the user's social media activity and receive related inputs when receiving inputs. For example, the reception unit prioritizes receiving inputs related to topics the user is discussing on social media. For example, if the user is discussing a specific topic on social media, the reception unit prioritizes receiving inputs related to that topic. The reception unit can also prioritize receiving inputs related to posts from accounts the user follows. For example, the reception unit prioritizes receiving information related to posts from accounts the user follows. Furthermore, the reception unit can also prioritize receiving inputs related to activities of groups in which the user participates. For example, the reception unit prioritizes receiving information related to activities of groups in which the user participates. In this way, by analyzing the user's social media activity, related inputs can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to a generation AI and cause the generation AI to select related inputs.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, if the information input by the user is of high importance, the analysis unit performs a detailed analysis and provides a detailed report. The analysis unit can also perform a simplified analysis on information of low importance. For example, if the information input by the user is of low importance, the analysis unit performs a simplified analysis and provides a concise summary. The analysis unit can also perform an analysis with a moderate level of detail on information of medium importance. For example, if the information input by the user is of medium importance, the analysis unit performs an analysis with a moderate level of detail and provides a report with a moderate level of detail. In this way, by adjusting the level of detail of the analysis based on the importance of the input information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input importance data of the input information to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input information. For example, the analysis unit applies a natural language processing algorithm to text information. For example, the analysis unit applies a natural language processing algorithm to text information input by a user to analyze the content of the text. The analysis unit can also apply an image recognition algorithm to image information. For example, the analysis unit applies an image recognition algorithm to image information input by a user to analyze the content of the image. The analysis unit can also apply a voice recognition algorithm to audio information. For example, the analysis unit applies a voice recognition algorithm to audio information input by a user to analyze the content of the audio. This allows for applying an appropriate analysis algorithm depending on the category of the input information, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input information to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0036] During analysis, the analysis unit can determine the analysis priority based on the submission time of the input information. The analysis unit, for example, prioritizes analysis of the most recent input information. For example, if the user inputs the most recent information, the analysis unit prioritizes analysis of that information and provides results quickly. The analysis unit can also postpone information submitted earlier. For example, if the user inputs older information, the analysis unit analyzes that information later. The analysis unit can also moderately prioritize information submitted at an intermediate time. For example, information entered by the user at an intermediate time is analyzed at an intermediate time. This allows for determining the analysis priority based on the submission time of the input information, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the submission time of the input information to the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, if information input by a user has a high relevance to other information, the analysis unit prioritizes analysis of that information. The analysis unit can also postpone information with low relevance. For example, if information input by a user has a low relevance to other information, the analysis unit analyzes that information later. Furthermore, the analysis unit can moderately prioritize information with medium relevance. For example, if information input by a user has a medium relevance to other information, the analysis unit analyzes that information with moderate priority. This allows for adjusting the order of analysis based on the relevance of the input information, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input relevance data of the input information to a generation AI and cause the generation AI to adjust the order of analysis.

[0038] The generation unit can adjust the level of detail of the generated video based on the importance of the analyzed information during generation. For example, the generation unit generates a detailed video for information of high importance. For example, if the information input by the user is of high importance, the generation unit generates a detailed video to provide detailed information. The generation unit can also generate a simplified video for information of low importance. For example, if the information input by the user is of low importance, the generation unit generates a simplified video to provide concise information. Furthermore, the generation unit can also generate a video with a moderate level of detail for information of medium importance. For example, if the information input by the user is of medium importance, the generation unit generates a video with a moderate level of detail to provide moderate information. In this way, by adjusting the level of detail of the generated video based on the importance of the analyzed information, a more appropriate video can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the analyzed information to the generation AI and cause the generation AI to adjust the level of detail of the generated video.

[0039] During generation, the generation unit can apply different generation algorithms depending on the category of the analyzed information. For example, the generation unit applies a natural language processing algorithm to text information. For example, the generation unit applies a natural language processing algorithm to text information input by a user, analyzes the content of the text, and generates a video. The generation unit can also apply an image recognition algorithm to image information. For example, the generation unit applies an image recognition algorithm to image information input by a user, analyzes the content of the image, and generates a video. The generation unit can also apply a voice recognition algorithm to audio information. For example, the generation unit applies a voice recognition algorithm to audio information input by a user, analyzes the content of the audio, and generates a video. In this way, by applying an appropriate generation algorithm depending on the category of the analyzed information, a more accurate video can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the analyzed information to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0040] During generation, the generation unit can determine the generation priority based on the submission time of the analyzed information. The generation unit, for example, generates a video based on the latest analysis information. For example, if a user inputs the latest information, the generation unit generates a video based on that information and quickly provides results. The generation unit can also postpone information that was submitted earlier. For example, if a user inputs older information, the generation unit postpones generating a video based on that information. Furthermore, the generation unit can also moderately prioritize information that was submitted at a medium time. For example, information entered by a user at a medium time is moderately prioritized when generating a video. This allows for the generation of more appropriate videos by determining the generation priority based on the submission time of the analyzed information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the submission time of the analyzed information into a generation AI and have the generation AI determine the generation priority.

[0041] The generation unit can adjust the order of generation based on the relevance of the analyzed information during generation. The generation unit, for example, generates a video based on information with high relevance. For example, if information input by a user has high relevance to other information, the generation unit generates a video based on that information. The generation unit can also postpone information with low relevance. For example, if information input by a user has low relevance to other information, the generation unit generates a video by deferring that information. The generation unit can also moderately prioritize information with medium relevance. For example, if information input by a user has medium relevance to other information, the generation unit generates a video by moderately prioritizing that information. This allows for the generation of a more appropriate video by adjusting the order of generation based on the relevance of the analyzed information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input relevance data of the analyzed information to a generation AI and cause the generation AI to adjust the order of generation.

[0042] The display unit can adjust the level of detail of the display based on the importance of the generated video when displaying the video. For example, the display unit provides a detailed display for videos with high importance. For example, if the video the user is watching is of high importance, the display unit provides a detailed display at high resolution. The display unit can also provide a simplified display for videos with low importance. For example, if the video the user is watching is of low importance, the display unit provides a simplified display at low resolution. Furthermore, the display unit can also provide a moderate level of detail for videos with medium importance. For example, if the video the user is watching is of medium importance, the display unit provides a moderate level of detail at moderate resolution. This allows for a more appropriate display by adjusting the level of detail of the display based on the importance of the generated video. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input importance data of the generated video to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0043] The display unit can apply different display algorithms depending on the category of the generated video when displaying the video. The display unit, for example, applies a natural language processing algorithm to text information. For example, if the video the user is watching contains text information, the display unit applies a natural language processing algorithm to analyze and display the text content. The display unit can also apply an image recognition algorithm to image information. For example, if the video the user is watching contains image information, the display unit applies an image recognition algorithm to analyze and display the image content. The display unit can also apply a voice recognition algorithm to audio information. For example, if the video the user is watching contains audio information, the display unit applies a voice recognition algorithm to analyze and display the audio content. This enables more accurate display by applying an appropriate display algorithm depending on the category of the generated video. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input category data of the generated video to the generation AI and cause the generation AI to apply an appropriate display algorithm.

[0044] When displaying the videos, the display unit can determine the display priority based on the submission date of the generated videos. The display unit, for example, prioritizes displaying the most recent videos. For example, if the video the user is watching is the most recent, the display unit prioritizes displaying that video to quickly provide information. The display unit can also postpone videos that were submitted earlier. For example, if the video the user is watching is older, the display unit displays that video later. The display unit can also moderately prioritize videos that were submitted at an intermediate time. For example, if the video the user is watching was submitted at an intermediate time, the display unit displays that video with moderate priority. This enables more appropriate display by determining the display priority based on the submission date of the generated videos. Some or all of the above-described processing by the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input submission date data of the generated videos to the generation AI and have the generation AI determine the display priority.

[0045] The display unit can adjust the display order based on the relevance of the generated videos when displaying them. The display unit, for example, prioritizes displaying videos with high relevance. For example, if a video being viewed by a user has high relevance to other videos, the display unit prioritizes displaying that video. The display unit can also postpone videos with low relevance. For example, if a video being viewed by a user has low relevance to other videos, the display unit displays that video later. The display unit can also moderately prioritize videos with medium relevance. For example, if a video being viewed by a user has medium relevance to other videos, the display unit displays that video with moderate priority. This allows for more appropriate display by adjusting the display order based on the relevance of the generated videos. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input relevance data of the generated videos to a generation AI and cause the generation AI to adjust the display order.

[0046] During verification, the verification unit can select an appropriate verification method based on the user's past verification history. For example, the verification unit prioritizes suggesting identity verification methods previously used by the user. For example, if the user has previously verified their identity using facial recognition, the verification unit prioritizes facial recognition. The verification unit can also select the optimal verification method for a specific time period based on the user's past verification history. For example, if the user has previously verified their identity using fingerprint authentication during a specific time period, the verification unit prioritizes fingerprint authentication during that time period. Furthermore, the verification unit can analyze the user's past verification patterns and suggest the most efficient verification method. For example, if the user has verified their identity using a specific pattern, the verification unit suggests the optimal verification method based on that pattern. This allows the optimal identity verification method to be provided by referring to the user's past verification history. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without AI. For example, the verification unit can input the user's past verification history data into a generation AI and have the generation AI select an appropriate verification method.

[0047] The verification unit can customize the verification method based on the user's current activity status during verification. For example, if the user is at work, the verification unit provides a work-related verification method. For example, if the user is using an app at work, the verification unit provides a work-related verification method, allowing the user to complete the verification quickly. Furthermore, if the user is engaged in a hobby-related activity, the verification unit can provide a verification method related to the hobby. For example, if the user is searching for information about a hobby, the verification unit can provide a verification method related to the hobby. Furthermore, if the user is on a break, the verification unit can provide a relaxing verification method. For example, if the user is using an app during a break, the verification unit can provide a relaxing verification method, allowing the user to complete the verification in a relaxed state. This enables more appropriate identity verification by customizing the verification method based on the user's current activity status. Some or all of the above-described processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's activity log data into the generation AI and cause the generation AI to customize the verification method.

[0048] The verification unit can select an appropriate verification method based on the user's geographical location information during verification. For example, if the user is in a specific region, the verification unit provides a verification method related to that region. For example, when the user is in a specific region, the verification unit provides a verification method related to that region, allowing the user to complete verification quickly. Furthermore, if the user is traveling, the verification unit can provide a verification method related to the user's travel destination. For example, when the user is using the app while traveling, the verification unit can provide a verification method related to the user's travel destination, allowing the user to complete verification quickly. Furthermore, if the user is at home, the verification unit can provide a verification method related to the user's home. For example, when the user is at home, the verification unit can provide a verification method related to the user's home, allowing the user to complete verification in a relaxed state. This allows the optimal identity verification method to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the verification unit may be performed using AI, for example, or may be performed without using AI. For example, the verification unit can input the user's geographical location information data into the generation AI and cause the generation AI to select an appropriate verification method.

[0049] During verification, the verification unit can analyze the user's social media activity and suggest verification methods. The verification unit, for example, provides verification methods related to topics the user is discussing on social media. For example, if the user is discussing a specific topic on social media, the verification unit can provide verification methods related to that topic. The verification unit can also provide verification methods related to the content posted by accounts the user follows. For example, the verification unit can provide verification methods related to the content posted by accounts the user follows. Furthermore, the verification unit can also provide verification methods related to the activities of groups the user participates in. For example, the verification unit can provide verification methods related to the activities of groups the user participates in. This makes it possible to provide an optimal identity verification method by analyzing the user's social media activity. Some or all of the above-described processing in the verification unit may be performed using, or without, AI. For example, the verification unit can input the user's social media data into a generation AI and have the generation AI suggest verification methods.

[0050] During prevention, the prevention unit can select the optimal prevention method by referring to the user's past usage history. For example, the prevention unit prioritizes suggesting fraud prevention methods that the user has used in the past. For example, if the user has used a specific fraud prevention method in the past, the prevention unit prioritizes suggesting that method. The prevention unit can also select the optimal prevention method for a specific time period based on the user's past usage history. For example, if the user has used a specific fraud prevention method during a specific time period, the prevention unit prioritizes suggesting that method during that time period. Furthermore, the prevention unit can analyze the user's past usage patterns and suggest the most efficient prevention method. For example, if the user has used a specific pattern of fraud prevention, the prevention unit proposes the optimal prevention method based on that pattern. In this way, the optimal fraud prevention method can be provided by referring to the user's past usage history. Some or all of the above-described processing in the prevention unit may be performed using, for example, AI, or may be performed without AI. For example, the prevention unit can input the user's past usage history data into a generation AI and have the generation AI select an appropriate prevention method.

[0051] During prevention, the prevention unit can customize prevention measures based on the user's current activity status. For example, if the user is at work, the prevention unit provides work-related prevention methods. For example, if the user is using an app while at work, the prevention unit provides work-related prevention methods, allowing for quick implementation of prevention measures. Furthermore, if the user is engaged in a hobby-related activity, the prevention unit can provide a hobby-related prevention method. For example, if the user is searching for information about a hobby, the prevention unit can provide a hobby-related prevention method. Furthermore, if the user is taking a break, the prevention unit can provide a relaxation-enhancing prevention method. For example, if the user is using an app while taking a break, the prevention unit can provide a relaxation-enhancing prevention method, allowing for implementation of prevention measures while the user is relaxed. This enables more appropriate fraud prevention by customizing prevention measures based on the user's current activity status. Some or all of the above-described processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the user's activity log data into the generation AI and have the generation AI customize the prevention measures.

[0052] During prevention, the prevention unit can select an optimal prevention method based on the user's geographical location information. For example, if the user is in a specific area, the prevention unit provides a prevention method related to that area. For example, when the user is in a specific area, the prevention unit provides a prevention method related to that area, allowing the user to quickly implement the prevention method. Furthermore, if the user is traveling, the prevention unit can provide a prevention method related to the user's travel destination. For example, when the user is using the app while traveling, the prevention unit can provide a prevention method related to the user's travel destination, allowing the user to quickly implement the prevention method. Furthermore, if the user is at home, the prevention unit can provide a prevention method related to the user's home. For example, when the user is at home, the prevention unit can provide a prevention method related to the user's home, allowing the user to implement the prevention method in a relaxed state. This allows the optimal fraud prevention method to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the prevention unit may be performed using AI, for example, or may be performed without using AI. For example, the prevention unit can input the user's geographical location information data into the generation AI and cause the generation AI to select an appropriate prevention method.

[0053] During prevention, the prevention unit can analyze the user's social media activity and suggest prevention measures. The prevention unit, for example, provides prevention methods related to topics the user is discussing on social media. For example, if the user is discussing a specific topic on social media, the prevention unit can provide prevention methods related to that topic. The prevention unit can also provide prevention methods related to the content posted by accounts the user follows. For example, it can provide prevention methods related to the content posted by the accounts the user follows. Furthermore, the prevention unit can also provide prevention methods related to the activities of groups the user participates in. For example, it can provide prevention methods related to the activities of groups the user participates in. In this way, by analyzing the user's social media activity, it is possible to provide an optimal fraud prevention method. Some or all of the above-described processing in the prevention unit can be performed using, for example, AI, or can be performed without using AI. For example, the prevention unit can input the user's social media data into a generation AI and have the generation AI execute the suggestion of prevention measures.

[0054] During revenue management, the revenue unit can select the optimal management method by referring to the user's past revenue history. For example, the revenue unit may prioritize the revenue management method the user has used in the past. For example, if the user has used a specific revenue management method in the past, the revenue unit may prioritize that method. The revenue unit can also select the optimal management method for a specific time period based on the user's past revenue history. For example, if the user has used a specific revenue management method during a specific time period, the revenue unit may prioritize that method during that time period. Furthermore, the revenue unit can analyze the user's past revenue patterns and recommend the most efficient management method. For example, if the user has used a specific revenue management pattern, the revenue unit may recommend the optimal management method based on that pattern. This allows the optimal revenue management method to be provided by referring to the user's past revenue history. Some or all of the above-described processing in the revenue unit may be performed using, for example, AI, or may be performed without AI. For example, the revenue unit may input the user's past revenue history data into a generation AI and have the generation AI select an appropriate management method.

[0055] During revenue management, the revenue unit can customize the management method based on the user's current activity status. For example, if the user is at work, the revenue unit provides a work-related revenue management method. For example, if the user is using an app at work, the revenue unit provides a work-related revenue management method, allowing the user to quickly complete revenue management. Furthermore, if the user is engaged in a hobby-related activity, the revenue unit can provide a hobby-related revenue management method. For example, if the user is searching for information about a hobby, the revenue unit can provide a hobby-related revenue management method. Furthermore, if the user is on a break, the revenue unit can provide a relaxing revenue management method. For example, if the user is using an app during a break, the revenue unit can provide a relaxing revenue management method, allowing the user to complete revenue management in a relaxed state. This enables more appropriate revenue management by customizing the management method based on the user's current activity status. Some or all of the above-described processing in the revenue unit may be performed using AI, for example, or without AI. For example, the revenue unit can input the user's activity log data into the generation AI and have the generation AI customize the management method.

[0056] The revenue unit can select the optimal management method by taking into account the user's geographic location information when managing revenue. For example, if the user is in a specific region, the revenue unit provides a revenue management method related to that region. For example, when the user is in a specific region, the revenue unit provides a revenue management method related to that region, allowing the user to quickly complete management. Furthermore, if the user is traveling, the revenue unit can provide a revenue management method related to the travel destination. For example, when the user is using the app while traveling, the revenue unit can provide a revenue management method related to the travel destination, allowing the user to quickly complete management. Furthermore, if the user is at home, the revenue unit can provide a revenue management method related to the user's home. For example, when the user is at home, the revenue unit can provide a revenue management method related to the user's home, allowing the user to complete management in a relaxed state. This allows the optimal revenue management method to be provided by taking the user's geographic location information into account. Some or all of the above-described processing in the revenue unit may be performed using AI, for example, or without AI. For example, the revenue unit can input the user's geographic location information data into the generation AI and have the generation AI select an appropriate management method.

[0057] During revenue management, the revenue unit can analyze the user's social media activity and suggest management measures. The revenue unit, for example, provides a revenue management method related to the content the user is discussing on social media. For example, if the user is discussing a specific topic on social media, the revenue unit can provide a revenue management method related to that topic. The revenue unit can also provide a revenue management method related to the content posted by accounts the user follows. For example, the revenue unit can provide a revenue management method related to the content posted by the accounts the user follows. Furthermore, the revenue unit can also provide a revenue management method related to the activities of groups the user participates in. For example, the revenue unit can provide a revenue management method related to the activities of groups the user participates in. This allows the optimal revenue management method to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the revenue unit may be performed using, for example, AI, or may be performed without AI. For example, the revenue unit can input the user's social media data into a generation AI and have the generation AI suggest management measures.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can monitor the user's health condition and adjust the input reception method based on the health condition. For example, if the user is tired, the reception unit can suggest an easy input method. For example, when the user is tired, the reception unit can preferentially suggest voice input, allowing the user to easily operate the device. Also, if the user is in a healthy state, the reception unit can suggest a detailed input method. For example, when the user is in a healthy state, the reception unit can preferentially suggest text input, allowing the user to input detailed information. Furthermore, if the user is ill, the reception unit can temporarily stop input and wait until the user recovers. This allows for more appropriate input reception by adjusting the input reception method according to the user's health condition.

[0060] The analysis unit can analyze the user's past behavioral patterns and determine analysis priorities based on the behavioral patterns. For example, it prioritizes analysis of information that the user frequently accessed in the past. For example, if the user frequently accessed a specific topic in the past, the analysis unit prioritizes analysis of information related to that topic. It can also prioritize analysis of information that the user accessed during a specific time period in the past. For example, if the user accessed specific information during a specific time period, the analysis unit prioritizes analysis of information related to that time period. Furthermore, if the user used a specific device in the past, it can also prioritize analysis of information related to that device. For example, if the user used a specific device, the analysis unit prioritizes analysis of information related to that device. This enables more appropriate analysis by analyzing the user's past behavioral patterns.

[0061] The generation unit can learn the user's preferences and customize the content of the video to be generated based on the preferences. For example, if the user likes videos of a particular genre, the generation unit generates videos related to that genre. For example, if the user likes action movies, the generation unit generates videos that include many action scenes. Furthermore, if the user likes a particular character, the generation unit can generate videos centered around that character. For example, if the user likes a particular character, the generation unit generates videos in which that character plays the main role. Furthermore, if the user likes particular music, the generation unit can generate videos using that music in the background. For example, if the user likes particular music, the generation unit generates videos using that music in the background. In this way, by customizing the content of the video based on the user's preferences, it is possible to provide videos that provide a higher level of satisfaction.

[0062] The display unit can adjust the display method based on the characteristics of the user's device. For example, when the user is using a smartphone, the display unit provides a display method optimized for the smartphone. For example, when the user is using the smartphone, the display unit displays using a layout and fonts suitable for a small screen. Furthermore, when the user is using a tablet, the display unit can also provide a display method optimized for the tablet. For example, when the user is using a tablet, the display unit displays using a layout and fonts suitable for a large screen. Furthermore, when the user is using a personal computer, the display unit can also provide a display method optimized for the personal computer. For example, when the user is using a personal computer, the display unit displays using a layout and fonts suitable for a high-resolution screen. In this way, by adjusting the display method based on the characteristics of the user's device, more appropriate display is possible.

[0063] The verification unit can verify the identity of the user by using the user's biometric information. For example, the verification unit measures the user's heart rate and body temperature and performs identity verification based on the measured values. For example, when the user is using an app, the verification unit measures the user's heart rate and body temperature and checks whether they match the registered information. The verification unit can also analyze the user's walking pattern and perform identity verification based on the measured values. For example, when the user is using an app, the verification unit analyzes the user's walking pattern and checks whether they match the registered information. The verification unit can also analyze the user's voiceprint and perform identity verification based on the analyzed values. For example, when the user is using an app, the verification unit analyzes the user's voiceprint and checks whether they match the registered information. In this way, higher security can be achieved by verifying the identity of the user by using the user's biometric information.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives input from the user. The input from the user includes text input, voice input, image input, etc. For example, the reception unit receives input from the user such as a text message, voice instructions, or image upload. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's input using data mining technology, natural language processing technology, and image analysis technology. For example, it analyzes the content of a text message and generates an appropriate response, or analyzes the content of an image and provides related information. Step 3: The generation unit generates a video based on the information analyzed by the analysis unit. The generation unit generates the video using template-based generation technology or generation AI. For example, the generation AI generates a video of the "favorite" based on user input. Step 4: The display unit displays the video generated by the generation unit. The display unit displays the video according to the screen size and resolution, and displays the video on a device such as a smartphone, tablet, or PC. Step 5: The verification unit verifies the user's identity. The verification unit verifies the user's identity using facial recognition or fingerprint authentication technology. For example, when a user downloads an app, facial recognition or fingerprint authentication is performed to complete the identity verification. Step 6: The prevention unit prevents fraudulent use. The prevention unit detects and prevents fraudulent use using anomaly detection algorithms and access control technologies. For example, if the prevention unit detects unusual access patterns, it will automatically issue a warning and suspend the account if necessary. Step 7: The revenue department manages revenue. The revenue department manages revenue based on the revenue calculation method and distribution method, and collects usage fees from users. For example, the usage fees may be collected by including them in the monthly membership fees for a fan club or online salon.

[0066] (Example 2) The entertainment system according to an embodiment of the present invention is an official app exclusive to fan clubs and online salons. This entertainment system utilizes multimodal AI generation, including character generation and voice bots, to enable two-way interactions, such as conversations, with the "oshi" (favorite) character or character. Specifically, by combining video generation AI technology, voice recognition, and natural language processing, the "oshi" (favorite) appears to be speaking to the user with a natural expression on the app. Users can have their "oshi" listen to them, wake them up with an alarm, or sing a song just for them. This app is provided as a "limited app" from the perspective of privacy and ethics, and requires identity verification upon download to prevent social media outrage. In addition to preventing fraudulent use, tracking is available upon request from the "oshi" or their management company, allowing for tracing of the source in the event of an incident. This system aims to promote positive and healthy content in the entertainment industry, deepening technologies related to AI ethics and privacy protection, and creating a world where swift action can be taken against defamatory and inflammatory fake news. For example, users can enjoy conversations with their "oshi" (favorite idol) through the app. When a user speaks to their "oshi," the AI ​​analyzes the conversation and generates an appropriate response. The AI ​​continues the conversation based on the user's response to questions like, "How was your day today?" Users can also set an alarm. For example, if a user requests, "Wake me up at 7 a.m. tomorrow," the app will sound the alarm in their "oshi"'s voice at the specified time. Users can also request their "oshi" to sing a song. For example, if a user requests, "Sing me my favorite song," the AI ​​will sing in their "oshi"'s voice. This allows users to enjoy special moments with their "oshi." The app also performs identity verification to protect user privacy. For example, when downloading the app, the app performs facial recognition or fingerprint authentication to complete identity verification. This prevents fraudulent use and provides a safe and secure environment. The app also uses anomaly detection algorithms to prevent fraudulent use.For example, if the app detects unusual access patterns, it will automatically issue a warning and suspend the account if necessary. This protects the user's account and ensures safe use. Regarding revenue management, the app collects a flat fee from users. For example, the fee could be included in the monthly membership fee for a fan club or online salon. The app also provides a paid service to companies, government agencies, political organizations, etc. to verify the authenticity of video and audio, and in-app corporate advertising fees also serve as a revenue source. This allows the app to build a sustainable business model and enable long-term operation. This allows the entertainment system to provide users with two-way interaction with their "oshi" (favorite idols), while protecting their privacy and preventing fraudulent use.

[0067] An entertainment system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a display unit, a confirmation unit, a prevention unit, and a revenue unit. The reception unit receives input from a user. The user input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit receives a text message input by the user to an app. The reception unit can also receive voice instructions from the user. For example, the user can issue a voice instruction such as "Wake me up at 7:00 tomorrow morning." The reception unit can also receive image uploads by the user. For example, the user can upload an image of their "favorite" character and display it within the app. The analysis unit analyzes the information received by the reception unit. The analysis unit can analyze the user input using, for example, data mining technology. The analysis unit can also analyze the text input using natural language processing technology. For example, the analysis unit can analyze the content of the text message entered by the user and generate an appropriate response. The analysis unit can also analyze the image input using image analysis technology. For example, the system analyzes the content of an image uploaded by a user and provides related information. The generation unit generates a video based on the information analyzed by the analysis unit. The generation unit generates the video using, for example, a template-based generation technique. The generation unit can also generate the video using a generation AI. For example, the generation AI generates a "favorite" video based on user input. The generation AI can use a text generation AI (e.g., LLM) or a multimodal generation AI. The display unit displays the video generated by the generation unit. The display unit displays the video according to, for example, a screen size or resolution. The display unit can also display the video according to a display device. For example, the video is displayed on a device such as a smartphone, tablet, or PC. The verification unit performs identity verification. The verification unit performs identity verification using, for example, facial recognition technology. The verification unit can also perform identity verification using fingerprint authentication technology. For example, when a user downloads an app, face recognition or fingerprint authentication is performed to complete identity verification. The prevention unit prevents fraudulent use.The prevention unit detects fraudulent use, for example, using an anomaly detection algorithm. The prevention unit can also prevent fraudulent use using access control technology. For example, if an unusual access pattern is detected, the prevention unit automatically issues a warning and suspends the account as necessary. The revenue unit manages revenue. The revenue unit manages revenue, for example, based on a revenue calculation method and distribution method. The revenue unit can also collect usage fees from users. For example, the usage fees can be collected by including them in the monthly membership fees for a fan club or online salon. This allows the entertainment system according to the embodiment to accept and analyze input from users, generate and display videos, and perform identity verification, fraud prevention, and revenue management.

[0068] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user emotion. For example, when the user is feeling stressed, the reception unit can delay the timing of input reception to allow the user to relax. For example, when the user is feeling stressed, the reception unit can temporarily stop receiving input and wait until the user relaxes. Furthermore, when the user is relaxed, the reception unit can immediately accept input to provide smooth operation. For example, when the user is relaxed, the reception unit can quickly accept input to allow the user to operate smoothly. Furthermore, when the user is in a hurry, the reception unit can accelerate the timing of input reception to respond quickly. For example, when the user is in a hurry, the reception unit can quickly accept input to allow the user to operate quickly. This allows more appropriate input reception by adjusting the timing of input reception according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotion.

[0069] The reception unit can analyze the user's past input history and select a reception method based on the user's past input history. For example, the reception unit prioritizes suggesting an input method that the user has frequently used in the past. For example, if the user has frequently used text input in the past, the reception unit prioritizes text input. The reception unit can also select the optimal reception method for a specific time period based on the user's past input history. For example, if the user frequently used voice input during a specific time period, the reception unit prioritizes voice input during that time period. The reception unit can also analyze the user's past input patterns and suggest the most efficient reception method. For example, if the user inputs data using a specific pattern, the reception unit suggests the optimal reception method based on that pattern. In this way, the optimal reception method can be provided by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal reception method.

[0070] The reception unit can filter inputs based on the user's current activity status and areas of interest when receiving the inputs. For example, when the user is at work, the reception unit prioritizes receiving work-related inputs. For example, when the user is using an app while at work, the reception unit prioritizes receiving work-related inputs and filters other inputs. Furthermore, when the user is engaged in a hobby-related activity, the reception unit can prioritize receiving inputs related to the hobby. For example, when the user is searching for information about a hobby, the reception unit prioritizes receiving inputs related to the hobby. Furthermore, when the user is taking a break, the reception unit can prioritize receiving inputs related to relaxing content. For example, when the user is using an app during a break, the reception unit prioritizes receiving inputs related to relaxing content. This allows for more appropriate input reception by filtering inputs based on the user's current activity status and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's activity log data to a generation AI and have the generation AI perform filtering.

[0071] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit postpones inputs of lower importance. For example, when the user is feeling stressed, the reception unit can temporarily hold inputs of lower importance and wait until the user relaxes. Furthermore, when the user is relaxed, the reception unit can prioritize receiving inputs of higher importance. For example, when the user is relaxed, the reception unit can quickly accept inputs of higher importance, allowing the user to operate smoothly. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving inputs of higher urgency. For example, when the user is in a hurry, the reception unit can quickly accept inputs of higher urgency, allowing the user to operate quickly. This enables more appropriate input reception by determining the priority of inputs according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotion.

[0072] When receiving input, the reception unit can prioritize receiving highly relevant input by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving input related to that area. For example, when the user is in a specific area, the reception unit prioritizes receiving event information and service information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving input related to the travel destination. For example, when the user is using an app while traveling, the reception unit prioritizes receiving tourist information and traffic information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving input related to the user's home. For example, when the user is at home, the reception unit prioritizes receiving household chore information and entertainment information related to the user's home. In this way, highly relevant input can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's GPS data to the generation AI and cause the generation AI to select highly relevant input.

[0073] The reception unit can analyze the user's social media activity and receive related inputs when receiving inputs. For example, the reception unit prioritizes receiving inputs related to topics the user is discussing on social media. For example, if the user is discussing a specific topic on social media, the reception unit prioritizes receiving inputs related to that topic. The reception unit can also prioritize receiving inputs related to posts from accounts the user follows. For example, the reception unit prioritizes receiving information related to posts from accounts the user follows. Furthermore, the reception unit can also prioritize receiving inputs related to activities of groups in which the user participates. For example, the reception unit prioritizes receiving information related to activities of groups in which the user participates. In this way, by analyzing the user's social media activity, related inputs can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to a generation AI and cause the generation AI to select related inputs.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, when the user is stressed, the analysis unit uses a simple and easy-to-understand presentation method. For example, when the user is stressed, the analysis unit displays the analysis results using simple graphs or charts. Furthermore, when the user is relaxed, the analysis unit can use a presentation method that includes detailed information. For example, when the user is relaxed, the analysis unit displays the analysis results using a detailed text report or complex graphs. Furthermore, when the user is in a hurry, the analysis unit can use a concise presentation method that focuses on the main points. For example, when the user is in a hurry, the analysis unit displays the analysis results using a concise summary that focuses on the main points. This allows for adjusting the presentation method of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, if the information input by the user is of high importance, the analysis unit performs a detailed analysis and provides a detailed report. The analysis unit can also perform a simplified analysis on information of low importance. For example, if the information input by the user is of low importance, the analysis unit performs a simplified analysis and provides a concise summary. The analysis unit can also perform an analysis with a moderate level of detail on information of medium importance. For example, if the information input by the user is of medium importance, the analysis unit performs an analysis with a moderate level of detail and provides a report with a moderate level of detail. In this way, by adjusting the level of detail of the analysis based on the importance of the input information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input importance data of the input information to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input information. For example, the analysis unit applies a natural language processing algorithm to text information. For example, the analysis unit applies a natural language processing algorithm to text information input by a user to analyze the content of the text. The analysis unit can also apply an image recognition algorithm to image information. For example, the analysis unit applies an image recognition algorithm to image information input by a user to analyze the content of the image. The analysis unit can also apply a voice recognition algorithm to audio information. For example, the analysis unit applies a voice recognition algorithm to audio information input by a user to analyze the content of the audio. This allows for applying an appropriate analysis algorithm depending on the category of the input information, thereby providing more accurate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input information to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, when the user is in a hurry, the analysis unit performs a short and concise analysis. For example, when the user is in a hurry, the analysis unit provides a short and concise summary. The analysis unit can also perform a detailed analysis when the user is relaxed. For example, when the user is relaxed, the analysis unit provides a detailed report. Furthermore, when the user is excited, the analysis unit can perform a visually stimulating analysis. For example, when the user is excited, the analysis unit displays the analysis results using visually stimulating graphs and charts. This allows for adjusting the length of the analysis according to the user's emotions and providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0078] During analysis, the analysis unit can determine the analysis priority based on the submission time of the input information. The analysis unit, for example, prioritizes analysis of the most recent input information. For example, if the user inputs the most recent information, the analysis unit prioritizes analysis of that information and provides results quickly. The analysis unit can also postpone information submitted earlier. For example, if the user inputs older information, the analysis unit analyzes that information later. The analysis unit can also moderately prioritize information submitted at an intermediate time. For example, information entered by the user at an intermediate time is analyzed at an intermediate time. This allows for determining the analysis priority based on the submission time of the input information, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the submission time of the input information to the generation AI and have the generation AI determine the analysis priority.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. For example, the analysis unit prioritizes analysis of information with high relevance. For example, if information input by a user has a high relevance to other information, the analysis unit prioritizes analysis of that information. The analysis unit can also postpone information with low relevance. For example, if information input by a user has a low relevance to other information, the analysis unit analyzes that information later. Furthermore, the analysis unit can moderately prioritize information with medium relevance. For example, if information input by a user has a medium relevance to other information, the analysis unit analyzes that information with moderate priority. This allows for adjusting the order of analysis based on the relevance of the input information, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input relevance data of the input information to a generation AI and cause the generation AI to adjust the order of analysis.

[0080] The generation unit can estimate the user's emotions and adjust the expression method of the generated video based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates a video that progresses at a leisurely pace. For example, when the user is relaxed, the generation unit generates a video that progresses at a leisurely pace, allowing the user to watch in a relaxed state. Furthermore, when the user is in a hurry, the generation unit can generate a video that emphasizes the shortest route. For example, when the user is in a hurry, the generation unit generates a video that emphasizes the shortest route, allowing the user to quickly obtain information. Furthermore, when the user is excited, the generation unit can generate a video that adds visually stimulating effects. For example, when the user is excited, the generation unit generates a video that adds visually stimulating effects, allowing the user to watch in an excited state. This allows the generation of more appropriate videos by adjusting the expression method of the video according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0081] The generation unit can adjust the level of detail of the generated video based on the importance of the analyzed information during generation. For example, the generation unit generates a detailed video for information of high importance. For example, if the information input by the user is of high importance, the generation unit generates a detailed video to provide detailed information. The generation unit can also generate a simplified video for information of low importance. For example, if the information input by the user is of low importance, the generation unit generates a simplified video to provide concise information. Furthermore, the generation unit can also generate a video with a moderate level of detail for information of medium importance. For example, if the information input by the user is of medium importance, the generation unit generates a video with a moderate level of detail to provide moderate information. In this way, by adjusting the level of detail of the generated video based on the importance of the analyzed information, a more appropriate video can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the analyzed information to the generation AI and cause the generation AI to adjust the level of detail of the generated video.

[0082] During generation, the generation unit can apply different generation algorithms depending on the category of the analyzed information. For example, the generation unit applies a natural language processing algorithm to text information. For example, the generation unit applies a natural language processing algorithm to text information input by a user, analyzes the content of the text, and generates a video. The generation unit can also apply an image recognition algorithm to image information. For example, the generation unit applies an image recognition algorithm to image information input by a user, analyzes the content of the image, and generates a video. The generation unit can also apply a voice recognition algorithm to audio information. For example, the generation unit applies a voice recognition algorithm to audio information input by a user, analyzes the content of the audio, and generates a video. In this way, by applying an appropriate generation algorithm depending on the category of the analyzed information, a more accurate video can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the analyzed information to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the video to be generated based on the estimated user emotions. For example, when the user is in a hurry, the generation unit generates a short, to-the-point video. For example, when the user is in a hurry, the generation unit generates a short, to-the-point video, allowing the user to quickly obtain information. The generation unit can also generate a longer video with detailed explanations when the user is relaxed. For example, when the user is relaxed, the generation unit generates a longer video with detailed explanations, allowing the user to watch in a relaxed state. Furthermore, when the user is excited, the generation unit can generate a video with visually stimulating effects. For example, when the user is excited, the generation unit generates a video with visually stimulating effects, allowing the user to watch in an excited state. This allows the generation of more appropriate videos by adjusting the length of the video according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0084] During generation, the generation unit can determine the generation priority based on the submission time of the analyzed information. The generation unit, for example, generates a video based on the latest analysis information. For example, if a user inputs the latest information, the generation unit generates a video based on that information and quickly provides results. The generation unit can also postpone information that was submitted earlier. For example, if a user inputs older information, the generation unit postpones generating a video based on that information. Furthermore, the generation unit can also moderately prioritize information that was submitted at a medium time. For example, information entered by a user at a medium time is moderately prioritized when generating a video. This allows for the generation of more appropriate videos by determining the generation priority based on the submission time of the analyzed information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the submission time of the analyzed information into a generation AI and have the generation AI determine the generation priority.

[0085] The generation unit can adjust the order of generation based on the relevance of the analyzed information during generation. The generation unit, for example, generates a video based on information with high relevance. For example, if information input by a user has high relevance to other information, the generation unit generates a video based on that information. The generation unit can also postpone information with low relevance. For example, if information input by a user has low relevance to other information, the generation unit generates a video by deferring that information. The generation unit can also moderately prioritize information with medium relevance. For example, if information input by a user has medium relevance to other information, the generation unit generates a video by moderately prioritizing that information. This allows for the generation of a more appropriate video by adjusting the order of generation based on the relevance of the analyzed information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input relevance data of the analyzed information to a generation AI and cause the generation AI to adjust the order of generation.

[0086] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. For example, when the user is nervous, the display unit provides a display method using subdued colors. For example, when the user is nervous, the display unit displays using subdued backgrounds and fonts. The display unit can also provide a display method using bright colors when the user is having fun. For example, when the user is having fun, the display unit displays using bright backgrounds and fonts. Furthermore, when the user is tired, the display unit can provide a display method that is simple and highly visible. For example, when the user is tired, the display unit displays using simple and highly visible layouts and fonts. This enables more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0087] The display unit can adjust the level of detail of the display based on the importance of the generated video when displaying the video. For example, the display unit provides a detailed display for videos with high importance. For example, if the video the user is watching is of high importance, the display unit provides a detailed display at high resolution. The display unit can also provide a simplified display for videos with low importance. For example, if the video the user is watching is of low importance, the display unit provides a simplified display at low resolution. Furthermore, the display unit can also provide a moderate level of detail for videos with medium importance. For example, if the video the user is watching is of medium importance, the display unit provides a moderate level of detail at moderate resolution. This allows for a more appropriate display by adjusting the level of detail of the display based on the importance of the generated video. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input importance data of the generated video to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0088] The display unit can apply different display algorithms depending on the category of the generated video when displaying the video. The display unit, for example, applies a natural language processing algorithm to text information. For example, if the video the user is watching contains text information, the display unit applies a natural language processing algorithm to analyze and display the text content. The display unit can also apply an image recognition algorithm to image information. For example, if the video the user is watching contains image information, the display unit applies an image recognition algorithm to analyze and display the image content. The display unit can also apply a voice recognition algorithm to audio information. For example, if the video the user is watching contains audio information, the display unit applies a voice recognition algorithm to analyze and display the audio content. This enables more accurate display by applying an appropriate display algorithm depending on the category of the generated video. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input category data of the generated video to the generation AI and cause the generation AI to apply an appropriate display algorithm.

[0089] The display unit can estimate the user's emotions and adjust the display order based on the estimated user emotions. For example, when the user is nervous, the display unit displays information of higher importance first. For example, when the user is nervous, the display unit prioritizes displaying information of higher importance, allowing the user to quickly obtain important information. The display unit can also display detailed information later when the user is relaxed. For example, when the user is relaxed, the display unit displays detailed information later, allowing the user to obtain information in a relaxed state. Furthermore, when the user is in a hurry, the display unit can also display information that emphasizes the main points first. For example, when the user is in a hurry, the display unit prioritizes displaying information that emphasizes the main points, allowing the user to quickly obtain information. This allows for more appropriate display by adjusting the display order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the user's emotions.

[0090] When displaying the videos, the display unit can determine the display priority based on the submission date of the generated videos. The display unit, for example, prioritizes displaying the most recent videos. For example, if the video the user is watching is the most recent, the display unit prioritizes displaying that video to quickly provide information. The display unit can also postpone videos that were submitted earlier. For example, if the video the user is watching is older, the display unit displays that video later. The display unit can also moderately prioritize videos that were submitted at an intermediate time. For example, if the video the user is watching was submitted at an intermediate time, the display unit displays that video with moderate priority. This enables more appropriate display by determining the display priority based on the submission date of the generated videos. Some or all of the above-described processing by the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input submission date data of the generated videos to the generation AI and have the generation AI determine the display priority.

[0091] The display unit can adjust the display order based on the relevance of the generated videos when displaying them. The display unit, for example, prioritizes displaying videos with high relevance. For example, if a video being viewed by a user has high relevance to other videos, the display unit prioritizes displaying that video. The display unit can also postpone videos with low relevance. For example, if a video being viewed by a user has low relevance to other videos, the display unit displays that video later. The display unit can also moderately prioritize videos with medium relevance. For example, if a video being viewed by a user has medium relevance to other videos, the display unit displays that video with moderate priority. This allows for more appropriate display by adjusting the display order based on the relevance of the generated videos. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input relevance data of the generated videos to a generation AI and cause the generation AI to adjust the display order.

[0092] The verification unit can estimate the user's emotions and adjust the identity verification method based on the estimated user emotions. For example, when the user is nervous, the verification unit provides a simple and easy-to-understand identity verification method. For example, when the user is nervous, the verification unit performs identity verification using simple steps, allowing the user to quickly complete the verification. The verification unit can also provide a detailed identity verification method when the user is relaxed. For example, when the user is relaxed, the verification unit performs identity verification using detailed steps, allowing the user to complete the verification with peace of mind. The verification unit can also provide a quick identity verification method when the user is in a hurry. For example, when the user is in a hurry, the verification unit performs identity verification using quick steps, allowing the user to quickly complete the verification. This enables more appropriate identity verification by adjusting the identity verification method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the confirmation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0093] During verification, the verification unit can select an appropriate verification method based on the user's past verification history. For example, the verification unit prioritizes suggesting identity verification methods previously used by the user. For example, if the user has previously verified their identity using facial recognition, the verification unit prioritizes facial recognition. The verification unit can also select the optimal verification method for a specific time period based on the user's past verification history. For example, if the user has previously verified their identity using fingerprint authentication during a specific time period, the verification unit prioritizes fingerprint authentication during that time period. Furthermore, the verification unit can analyze the user's past verification patterns and suggest the most efficient verification method. For example, if the user has verified their identity using a specific pattern, the verification unit suggests the optimal verification method based on that pattern. This allows the optimal identity verification method to be provided by referring to the user's past verification history. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without AI. For example, the verification unit can input the user's past verification history data into a generation AI and have the generation AI select an appropriate verification method.

[0094] The verification unit can customize the verification method based on the user's current activity status during verification. For example, if the user is at work, the verification unit provides a work-related verification method. For example, if the user is using an app at work, the verification unit provides a work-related verification method, allowing the user to complete the verification quickly. Furthermore, if the user is engaged in a hobby-related activity, the verification unit can provide a verification method related to the hobby. For example, if the user is searching for information about a hobby, the verification unit can provide a verification method related to the hobby. Furthermore, if the user is on a break, the verification unit can provide a relaxing verification method. For example, if the user is using an app during a break, the verification unit can provide a relaxing verification method, allowing the user to complete the verification in a relaxed state. This enables more appropriate identity verification by customizing the verification method based on the user's current activity status. Some or all of the above-described processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's activity log data into the generation AI and cause the generation AI to customize the verification method.

[0095] The confirmation unit can estimate the user's emotions and determine the priority of confirmation based on the estimated user emotions. For example, when the user is nervous, the confirmation unit performs more important confirmations first. For example, when the user is nervous, the confirmation unit prioritizes more important confirmations, allowing the user to quickly complete important confirmations. Furthermore, when the user is relaxed, the confirmation unit can perform more detailed confirmations later. For example, when the user is relaxed, the confirmation unit can perform more detailed confirmations later, allowing the user to complete confirmations in a relaxed state. Furthermore, when the user is in a hurry, the confirmation unit can perform quick confirmations first. For example, when the user is in a hurry, the confirmation unit prioritizes quick confirmations, allowing the user to quickly complete confirmations. This enables more appropriate identity verification by determining the priority of confirmations according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the confirmation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0096] The verification unit can select an appropriate verification method based on the user's geographical location information during verification. For example, if the user is in a specific region, the verification unit provides a verification method related to that region. For example, when the user is in a specific region, the verification unit provides a verification method related to that region, allowing the user to complete verification quickly. Furthermore, if the user is traveling, the verification unit can provide a verification method related to the user's travel destination. For example, when the user is using the app while traveling, the verification unit can provide a verification method related to the user's travel destination, allowing the user to complete verification quickly. Furthermore, if the user is at home, the verification unit can provide a verification method related to the user's home. For example, when the user is at home, the verification unit can provide a verification method related to the user's home, allowing the user to complete verification in a relaxed state. This allows the optimal identity verification method to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the verification unit may be performed using AI, for example, or may be performed without using AI. For example, the verification unit can input the user's geographical location information data into the generation AI and cause the generation AI to select an appropriate verification method.

[0097] During verification, the verification unit can analyze the user's social media activity and suggest verification methods. The verification unit, for example, provides verification methods related to topics the user is discussing on social media. For example, if the user is discussing a specific topic on social media, the verification unit can provide verification methods related to that topic. The verification unit can also provide verification methods related to the content posted by accounts the user follows. For example, the verification unit can provide verification methods related to the content posted by accounts the user follows. Furthermore, the verification unit can also provide verification methods related to the activities of groups the user participates in. For example, the verification unit can provide verification methods related to the activities of groups the user participates in. This makes it possible to provide an optimal identity verification method by analyzing the user's social media activity. Some or all of the above-described processing in the verification unit may be performed using, or without, AI. For example, the verification unit can input the user's social media data into a generation AI and have the generation AI suggest verification methods.

[0098] The prevention unit can estimate the user's emotions and adjust the fraud prevention method based on the estimated user emotions. For example, when the user is stressed, the prevention unit provides a simple and easy-to-understand fraud prevention method. For example, when the user is stressed, the prevention unit performs fraud prevention using simple steps, allowing the user to quickly implement prevention measures. The prevention unit can also provide a detailed fraud prevention method when the user is relaxed. For example, when the user is relaxed, the prevention unit performs fraud prevention using detailed steps, allowing the user to implement prevention measures with peace of mind. Furthermore, the prevention unit can also provide a quick fraud prevention method when the user is in a hurry. For example, when the user is in a hurry, the prevention unit performs fraud prevention using quick steps, allowing the user to quickly implement prevention measures. This enables more appropriate fraud prevention by adjusting the fraud prevention method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prevention unit may be performed using, for example, AI, or may be performed without using AI. For example, the prevention unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0099] During prevention, the prevention unit can select the optimal prevention method by referring to the user's past usage history. For example, the prevention unit prioritizes suggesting fraud prevention methods that the user has used in the past. For example, if the user has used a specific fraud prevention method in the past, the prevention unit prioritizes suggesting that method. The prevention unit can also select the optimal prevention method for a specific time period based on the user's past usage history. For example, if the user has used a specific fraud prevention method during a specific time period, the prevention unit prioritizes suggesting that method during that time period. Furthermore, the prevention unit can analyze the user's past usage patterns and suggest the most efficient prevention method. For example, if the user has used a specific pattern of fraud prevention, the prevention unit proposes the optimal prevention method based on that pattern. In this way, the optimal fraud prevention method can be provided by referring to the user's past usage history. Some or all of the above-described processing in the prevention unit may be performed using, for example, AI, or may be performed without AI. For example, the prevention unit can input the user's past usage history data into a generation AI and have the generation AI select an appropriate prevention method.

[0100] During prevention, the prevention unit can customize prevention measures based on the user's current activity status. For example, if the user is at work, the prevention unit provides work-related prevention methods. For example, if the user is using an app while at work, the prevention unit provides work-related prevention methods, allowing for quick implementation of prevention measures. Furthermore, if the user is engaged in a hobby-related activity, the prevention unit can provide a hobby-related prevention method. For example, if the user is searching for information about a hobby, the prevention unit can provide a hobby-related prevention method. Furthermore, if the user is taking a break, the prevention unit can provide a relaxation-enhancing prevention method. For example, if the user is using an app while taking a break, the prevention unit can provide a relaxation-enhancing prevention method, allowing for implementation of prevention measures while the user is relaxed. This enables more appropriate fraud prevention by customizing prevention measures based on the user's current activity status. Some or all of the above-described processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit can input the user's activity log data into the generation AI and have the generation AI customize the prevention measures.

[0101] The prevention unit can estimate the user's emotions and determine the priority of prevention measures based on the estimated user emotions. For example, when the user is nervous, the prevention unit performs more important prevention measures first. For example, when the user is nervous, the prevention unit prioritizes more important prevention measures, allowing the user to quickly implement important prevention measures. Furthermore, when the user is relaxed, the prevention unit can perform more detailed prevention measures later. For example, when the user is relaxed, the prevention unit can perform more detailed prevention measures later, allowing the user to implement prevention measures in a relaxed state. Furthermore, when the user is in a hurry, the prevention unit can perform quick prevention measures first. For example, when the user is in a hurry, the prevention unit prioritizes quick prevention measures, allowing the user to quickly implement prevention measures. This enables more appropriate fraud prevention by determining the priority of prevention measures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prevention unit may be performed using, for example, AI, or may be performed without using AI. For example, the prevention unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0102] During prevention, the prevention unit can select an optimal prevention method based on the user's geographical location information. For example, if the user is in a specific area, the prevention unit provides a prevention method related to that area. For example, when the user is in a specific area, the prevention unit provides a prevention method related to that area, allowing the user to quickly implement the prevention method. Furthermore, if the user is traveling, the prevention unit can provide a prevention method related to the user's travel destination. For example, when the user is using the app while traveling, the prevention unit can provide a prevention method related to the user's travel destination, allowing the user to quickly implement the prevention method. Furthermore, if the user is at home, the prevention unit can provide a prevention method related to the user's home. For example, when the user is at home, the prevention unit can provide a prevention method related to the user's home, allowing the user to implement the prevention method in a relaxed state. This allows the optimal fraud prevention method to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the prevention unit may be performed using AI, for example, or may be performed without using AI. For example, the prevention unit can input the user's geographical location information data into the generation AI and cause the generation AI to select an appropriate prevention method.

[0103] During prevention, the prevention unit can analyze the user's social media activity and suggest prevention measures. The prevention unit, for example, provides prevention methods related to topics the user is discussing on social media. For example, if the user is discussing a specific topic on social media, the prevention unit can provide prevention methods related to that topic. The prevention unit can also provide prevention methods related to the content posted by accounts the user follows. For example, it can provide prevention methods related to the content posted by the accounts the user follows. Furthermore, the prevention unit can also provide prevention methods related to the activities of groups the user participates in. For example, it can provide prevention methods related to the activities of groups the user participates in. In this way, by analyzing the user's social media activity, it is possible to provide an optimal fraud prevention method. Some or all of the above-described processing in the prevention unit can be performed using, for example, AI, or can be performed without using AI. For example, the prevention unit can input the user's social media data into a generation AI and have the generation AI execute the suggestion of prevention measures.

[0104] The revenue unit can estimate the user's emotions and adjust the revenue management method based on the estimated user emotions. For example, when the user is stressed, the revenue unit provides a simple and easy-to-understand revenue management method. For example, when the user is stressed, the revenue unit performs revenue management using simple steps, allowing the user to quickly complete the management. The revenue unit can also provide a detailed revenue management method when the user is relaxed. For example, when the user is relaxed, the revenue unit performs revenue management using detailed steps, allowing the user to complete the management with peace of mind. The revenue unit can also provide a quick revenue management method when the user is in a hurry. For example, when the user is in a hurry, the revenue unit performs revenue management using quick steps, allowing the user to quickly complete the management. This enables more appropriate revenue management by adjusting the revenue management method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the revenue unit can be performed using, for example, AI, or without AI. For example, the revenue department can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0105] During revenue management, the revenue unit can select the optimal management method by referring to the user's past revenue history. For example, the revenue unit may prioritize the revenue management method the user has used in the past. For example, if the user has used a specific revenue management method in the past, the revenue unit may prioritize that method. The revenue unit can also select the optimal management method for a specific time period based on the user's past revenue history. For example, if the user has used a specific revenue management method during a specific time period, the revenue unit may prioritize that method during that time period. Furthermore, the revenue unit can analyze the user's past revenue patterns and recommend the most efficient management method. For example, if the user has used a specific revenue management pattern, the revenue unit may recommend the optimal management method based on that pattern. This allows the optimal revenue management method to be provided by referring to the user's past revenue history. Some or all of the above-described processing in the revenue unit may be performed using, for example, AI, or may be performed without AI. For example, the revenue unit may input the user's past revenue history data into a generation AI and have the generation AI select an appropriate management method.

[0106] During revenue management, the revenue unit can customize the management method based on the user's current activity status. For example, if the user is at work, the revenue unit provides a work-related revenue management method. For example, if the user is using an app at work, the revenue unit provides a work-related revenue management method, allowing the user to quickly complete revenue management. Furthermore, if the user is engaged in a hobby-related activity, the revenue unit can provide a hobby-related revenue management method. For example, if the user is searching for information about a hobby, the revenue unit can provide a hobby-related revenue management method. Furthermore, if the user is on a break, the revenue unit can provide a relaxing revenue management method. For example, if the user is using an app during a break, the revenue unit can provide a relaxing revenue management method, allowing the user to complete revenue management in a relaxed state. This enables more appropriate revenue management by customizing the management method based on the user's current activity status. Some or all of the above-described processing in the revenue unit may be performed using AI, for example, or without AI. For example, the revenue unit can input the user's activity log data into the generation AI and have the generation AI customize the management method.

[0107] The revenue unit can estimate the user's emotions and determine the priority of revenue management based on the estimated user emotions. For example, if the user is nervous, the revenue unit can perform revenue management with higher importance first. For example, when the user is nervous, the revenue unit can prioritize revenue management with higher importance, allowing the user to quickly complete important management. Furthermore, if the user is relaxed, the revenue unit can perform detailed revenue management later. For example, when the user is relaxed, the revenue unit can perform detailed management later, allowing the user to complete management in a relaxed state. Furthermore, if the user is in a hurry, the revenue unit can perform quick revenue management first. For example, when the user is in a hurry, the revenue unit can prioritize quick management, allowing the user to quickly complete management. This enables more appropriate revenue management by determining the priority of revenue management according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the profit unit may be performed using, for example, AI, or may be performed without using AI. For example, the profit unit may input user facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0108] The revenue unit can select the optimal management method by taking into account the user's geographic location information when managing revenue. For example, if the user is in a specific region, the revenue unit provides a revenue management method related to that region. For example, when the user is in a specific region, the revenue unit provides a revenue management method related to that region, allowing the user to quickly complete management. Furthermore, if the user is traveling, the revenue unit can provide a revenue management method related to the travel destination. For example, when the user is using the app while traveling, the revenue unit can provide a revenue management method related to the travel destination, allowing the user to quickly complete management. Furthermore, if the user is at home, the revenue unit can provide a revenue management method related to the user's home. For example, when the user is at home, the revenue unit can provide a revenue management method related to the user's home, allowing the user to complete management in a relaxed state. This allows the optimal revenue management method to be provided by taking the user's geographic location information into account. Some or all of the above-described processing in the revenue unit may be performed using AI, for example, or without AI. For example, the revenue unit can input the user's geographic location information data into the generation AI and have the generation AI select an appropriate management method.

[0109] During revenue management, the revenue unit can analyze the user's social media activity and suggest management measures. The revenue unit, for example, provides a revenue management method related to the content the user is discussing on social media. For example, if the user is discussing a specific topic on social media, the revenue unit can provide a revenue management method related to that topic. The revenue unit can also provide a revenue management method related to the content posted by accounts the user follows. For example, the revenue unit can provide a revenue management method related to the content posted by the accounts the user follows. Furthermore, the revenue unit can also provide a revenue management method related to the activities of groups the user participates in. For example, the revenue unit can provide a revenue management method related to the activities of groups the user participates in. This allows the optimal revenue management method to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the revenue unit may be performed using, for example, AI, or may be performed without AI. For example, the revenue unit can input the user's social media data into a generation AI and have the generation AI suggest management measures. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, display unit, confirmation unit, prevention unit, and revenue unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text input or voice input from the user. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the user's input. The generation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates a video based on the analyzed information. The display unit is realized by the output device 40 of the smart device 14 and displays the generated video. The confirmation unit is realized by, for example, the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing device 12 and performs facial recognition and fingerprint authentication. The prevention unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and prevents fraudulent use using an anomaly detection algorithm. The revenue unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and manages revenue. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, display unit, verification unit, prevention unit, and revenue unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a video based on the analyzed information. The display unit is realized by the speaker 240 of the smart glasses 214 and displays the generated video. The verification unit is realized by the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12 and performs facial recognition and fingerprint authentication. The prevention unit is realized by the specific processing unit 290 of the data processing device 12 and prevents fraudulent use using an anomaly detection algorithm. The profit unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and manages profits. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, display unit, confirmation unit, prevention unit, and revenue unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a video based on the analyzed information. The display unit is realized by the display 343 of the headset-type terminal 314 and displays the generated video. The confirmation unit is realized by the camera 42 of the headset-type terminal 314 and the specific processing unit 290 of the data processing device 12 and performs facial recognition and fingerprint authentication. The prevention unit is realized by the specific processing unit 290 of the data processing device 12 and prevents fraudulent use using an anomaly detection algorithm. The profit unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and manages profits. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, display unit, verification unit, prevention unit, and revenue unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user input. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a video based on the analyzed information. The display unit is realized by the speaker 240 of the robot 414 and displays the generated video. The verification unit is realized by the camera 42 of the robot 414 and the specific processing unit 290 of the data processing device 12 and performs facial recognition and fingerprint authentication. The prevention unit is realized by the specific processing unit 290 of the data processing device 12 and prevents fraudulent use using an anomaly detection algorithm. The profit unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and manages profits.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The reception unit can monitor the user's health condition and adjust the input reception method based on the health condition. For example, if the user is tired, the reception unit can suggest an easy input method. For example, when the user is tired, the reception unit can preferentially suggest voice input, allowing the user to easily operate the device. Also, if the user is in a healthy state, the reception unit can suggest a detailed input method. For example, when the user is in a healthy state, the reception unit can preferentially suggest text input, allowing the user to input detailed information. Furthermore, if the user is ill, the reception unit can temporarily stop input and wait until the user recovers. This allows for more appropriate input reception by adjusting the input reception method according to the user's health condition.

[0112] The analysis unit can analyze the user's past behavioral patterns and determine analysis priorities based on the behavioral patterns. For example, it prioritizes analysis of information that the user frequently accessed in the past. For example, if the user frequently accessed a specific topic in the past, the analysis unit prioritizes analysis of information related to that topic. It can also prioritize analysis of information that the user accessed during a specific time period in the past. For example, if the user accessed specific information during a specific time period, the analysis unit prioritizes analysis of information related to that time period. Furthermore, if the user used a specific device in the past, it can also prioritize analysis of information related to that device. For example, if the user used a specific device, the analysis unit prioritizes analysis of information related to that device. This enables more appropriate analysis by analyzing the user's past behavioral patterns.

[0113] The generation unit can learn the user's preferences and customize the content of the video to be generated based on the preferences. For example, if the user likes videos of a particular genre, the generation unit generates videos related to that genre. For example, if the user likes action movies, the generation unit generates videos that include many action scenes. Furthermore, if the user likes a particular character, the generation unit can generate videos centered around that character. For example, if the user likes a particular character, the generation unit generates videos in which that character plays the main role. Furthermore, if the user likes particular music, the generation unit can generate videos using that music in the background. For example, if the user likes particular music, the generation unit generates videos using that music in the background. In this way, by customizing the content of the video based on the user's preferences, it is possible to provide videos that provide a higher level of satisfaction.

[0114] The display unit can adjust the display method based on the characteristics of the user's device. For example, when the user is using a smartphone, the display unit provides a display method optimized for the smartphone. For example, when the user is using the smartphone, the display unit displays using a layout and fonts suitable for a small screen. Furthermore, when the user is using a tablet, the display unit can also provide a display method optimized for the tablet. For example, when the user is using a tablet, the display unit displays using a layout and fonts suitable for a large screen. Furthermore, when the user is using a personal computer, the display unit can also provide a display method optimized for the personal computer. For example, when the user is using a personal computer, the display unit displays using a layout and fonts suitable for a high-resolution screen. In this way, by adjusting the display method based on the characteristics of the user's device, more appropriate display is possible.

[0115] The verification unit can verify the identity of the user by using the user's biometric information. For example, the verification unit measures the user's heart rate and body temperature and performs identity verification based on the measured values. For example, when the user is using an app, the verification unit measures the user's heart rate and body temperature and checks whether they match the registered information. The verification unit can also analyze the user's walking pattern and perform identity verification based on the measured values. For example, when the user is using an app, the verification unit analyzes the user's walking pattern and checks whether they match the registered information. The verification unit can also analyze the user's voiceprint and perform identity verification based on the analyzed values. For example, when the user is using an app, the verification unit analyzes the user's voiceprint and checks whether they match the registered information. In this way, higher security can be achieved by verifying the identity of the user by using the user's biometric information.

[0116] The reception unit can estimate the user's emotions and customize the input reception interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface is provided. For example, when the user is feeling stressed, the reception unit accepts input using simple buttons and icons. Also, if the user is relaxed, a detailed interface can be provided. For example, when the user is relaxed, the reception unit accepts input by displaying detailed menus and options. Furthermore, if the user is in a hurry, an interface that allows quick input can be provided. For example, when the user is in a hurry, the reception unit accepts input using quick access buttons and shortcuts. In this way, by customizing the input reception interface according to the user's emotions, more appropriate input reception is possible.

[0117] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user's emotions. For example, if the user is feeling stressed, simple and easy-to-understand analysis results can be provided. For example, when the user is feeling stressed, the analysis unit displays the analysis results using simple graphs and charts. Furthermore, if the user is relaxed, detailed analysis results can be provided. For example, when the user is relaxed, the analysis unit displays the analysis results using detailed text reports and complex graphs. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be provided. For example, when the user is in a hurry, the analysis unit displays the analysis results using a concise summary that focuses on the main points. In this way, by adjusting the way in which the analysis results are presented according to the user's emotions, more appropriate analysis results can be provided.

[0118] The generation unit can estimate the user's emotions and adjust the tone of the generated content based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates content with a calm tone. For example, when the user is relaxed, the generation unit generates content using calm music and visuals with soft colors. Also, when the user is excited, the generation unit can generate content with an energetic tone. For example, when the user is excited, the generation unit generates content using up-tempo music and visuals with bright colors. Furthermore, when the user is sad, the generation unit can generate content with a comforting tone. For example, when the user is sad, the generation unit generates content using calm music and visuals with calm colors. In this way, more appropriate content can be provided by adjusting the tone of the content according to the user's emotions.

[0119] The display unit can estimate the user's emotions and adjust the display layout based on the estimated user's emotions. For example, if the user is nervous, a simple and calm layout is provided. For example, when the user is nervous, the display unit displays using a simple layout and a background with calm colors. Also, if the user is having fun, a bright and cheerful layout can be provided. For example, when the user is having fun, the display unit displays using bright colors and cheerful icons. Furthermore, if the user is tired, a highly visible layout can be provided. For example, when the user is tired, the display unit displays using large fonts and high-contrast colors. In this way, a more appropriate display can be achieved by adjusting the display layout according to the user's emotions.

[0120] The verification unit can estimate the user's emotions and adjust the identity verification procedure based on the estimated user's emotions. For example, if the user is nervous, a simple and easy-to-understand procedure can be provided. For example, when the user is nervous, the verification unit performs identity verification using simple procedures, allowing the user to complete the verification quickly. Furthermore, if the user is relaxed, detailed procedures can be provided. For example, when the user is relaxed, the verification unit performs identity verification using detailed procedures, allowing the user to complete the verification with peace of mind. Furthermore, if the user is in a hurry, quick procedures can be provided. For example, when the user is in a hurry, the verification unit performs identity verification using quick procedures, allowing the user to complete the verification quickly. This allows more appropriate identity verification by adjusting the identity verification procedure according to the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit receives input from the user. The input from the user includes text input, voice input, image input, etc. For example, the reception unit receives input from the user such as a text message, voice instructions, or image upload. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's input using data mining technology, natural language processing technology, and image analysis technology. For example, it analyzes the content of a text message and generates an appropriate response, or analyzes the content of an image and provides related information. Step 3: The generation unit generates a video based on the information analyzed by the analysis unit. The generation unit generates the video using template-based generation technology or generation AI. For example, the generation AI generates a video of the "favorite" based on user input. Step 4: The display unit displays the video generated by the generation unit. The display unit displays the video according to the screen size and resolution, and displays the video on a device such as a smartphone, tablet, or PC. Step 5: The verification unit verifies the user's identity. The verification unit verifies the user's identity using facial recognition or fingerprint authentication technology. For example, when a user downloads an app, facial recognition or fingerprint authentication is performed to complete the identity verification. Step 6: The prevention unit prevents fraudulent use. The prevention unit detects and prevents fraudulent use using anomaly detection algorithms and access control technologies. For example, if the prevention unit detects unusual access patterns, it will automatically issue a warning and suspend the account if necessary. Step 7: The revenue department manages revenue. The revenue department manages revenue based on the revenue calculation method and distribution method, and collects usage fees from users. For example, the usage fees may be collected by including them in the monthly membership fees for a fan club or online salon.

[0123] 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.

[0124] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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.

[0152] 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.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0171] 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.

[0172] 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.

[0173] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit; a generating unit that generates a video based on the information analyzed by the analyzing unit; a display unit that displays the moving image generated by the generation unit; A verification section that describes the specific method for verifying your identity; a prevention section that describes specific methods for preventing fraudulent use; A revenue section that describes a specific method for managing revenue. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past input history and select a reception method based on the user's past input history.

2. The system of claim 1.

4. The reception unit As input is received, it filters based on the user's current activity and interests.

2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When accepting input, prioritize relevant input based on the user's geographic location information.

2. The system of claim 1.

7. The reception unit When receiving input, analyze the user's social media activity and receive relevant input 2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

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