system

Abstract

The system according to the embodiment aims to generate and provide videos based on viewer input. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input from a viewer. The generating unit generates a video based on the information received by the receiving unit. The providing unit provides the video generated by the generating unit to the viewer.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately generate and present video based on viewer input, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and provide videos based on viewer input. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input from a viewer. The generating unit generates a video based on the information received by the receiving unit. The providing unit provides the video generated by the generating unit to the viewer. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide videos based on viewer input. [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) An advertising system according to an embodiment of the present invention accepts viewer input, generates a video based on the input, and provides the video to the viewer. When a viewer watches an advertisement, the advertising system accepts the viewer's input, and the generation AI generates a video that provides additional information in response to the viewer's request. In this way, an interactive conversation with the viewer is possible. The generation AI then responds appropriately based on the viewer's response. For example, if a viewer inputs, "I want to purchase this product," the generation AI generates a video that provides information about the purchasing process. This provides advertisements tailored to the viewer's needs, thereby increasing the effectiveness of the advertisements. Furthermore, viewers can earn rewards for watching advertisements. Possible rewards include points and game items earned by watching existing video advertisements. Viewers not only receive advertisements tailored to their needs, but also earn greater rewards in proportion to the effort they put in. This system benefits both advertisers and viewers, allowing them to set higher advertising prices and viewers to receive advertisements tailored to their needs. This allows the advertising system to provide interactive advertisements based on viewer input, thereby increasing the effectiveness of the advertisements. For example, when a viewer watches an advertisement, the system accepts the viewer's input, and the generation AI generates a video that provides additional information in response to the viewer's request, thereby attracting the viewer's attention. In addition, viewers can receive rewards for watching ads, which increases viewer satisfaction. This is beneficial for both advertisers, who can set higher ad prices, and viewers, who can receive ads that meet their needs.

[0029] An advertising system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives a viewer's input. The viewer's input may include, but is not limited to, text input, voice input, and gesture input. For example, when a viewer inputs "I want to know more about this product," the receiving unit receives the request. The receiving unit can also receive a viewer's request when the viewer inputs "I want to purchase this product." The generating unit uses a generation AI to generate a video based on the information received by the receiving unit. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates the video based on the viewer's input. For example, when a viewer inputs "I want to know more about this product," the generation AI generates a video that provides additional information in response to the request. When a viewer inputs "I want to purchase this product," the generation AI can also generate a video that provides information about the purchase procedure. The providing unit provides the video generated by the generating unit to the viewer. The providing unit provides the video by, for example, streaming, downloading, real-time distribution, or other methods. For example, the providing unit provides the generated video to the viewer by streaming. The providing unit can also provide the generated video in a downloadable format. The providing unit can also deliver the generated video in real time. This allows the advertising system according to the embodiment to provide interactive advertisements based on viewer input, thereby enhancing the effectiveness of the advertisements. For example, when a viewer watches an advertisement, the generation AI can receive the viewer's input and generate a video that provides additional information in response to the request, thereby attracting the viewer's attention. Furthermore, viewers can receive rewards for watching advertisements, which improves viewer satisfaction. This allows advertisers to set higher advertising prices and allows viewers to receive advertisements that meet their needs, resulting in benefits for both parties.

[0030] The generation unit can generate a video based on viewer input. The generation unit uses a generation AI to generate a video based on viewer input. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a video based on viewer input. For example, when a viewer inputs, "I want to know more about this product," the generation AI generates a video that provides additional information in response to the request. Also, when a viewer inputs, "I want to purchase this product," the generation AI can generate a video that provides information about the purchase procedure. In this way, by generating a video in response to viewer input, the effectiveness of advertising is increased. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input viewer input to the generation AI, which then generates a video.

[0031] The providing unit can provide the generated video to the viewer. The providing unit provides the video generated by the generating unit to the viewer. The providing unit provides the video by a method such as streaming, downloading, or real-time distribution. For example, the providing unit provides the generated video to the viewer by streaming. The providing unit can also provide the generated video in a downloadable format. The providing unit can also distribute the generated video in real time. This enables advertising tailored to the needs of the viewer. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the generated video in an optimal format using AI.

[0032] The reception unit can instantly accept viewer input. The reception unit accepts viewer input in real time. Viewer input includes, but is not limited to, text input, voice input, and gesture input. For example, when a viewer inputs "I want to know more about this product," the reception unit instantly accepts the request. Furthermore, when a viewer inputs "I want to purchase this product," the reception unit can also instantly accept the request. This allows for real-time acceptance of viewer input, enabling an instantaneous response. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can accept viewer input in real time using AI.

[0033] The generation unit can generate videos in response to viewer requests. The generation unit uses a generation AI to generate videos in response to viewer requests. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates videos in response to viewer requests. For example, when a viewer inputs, "I want to know more about this product," the generation AI generates a video that provides additional information in response to the request. Also, when a viewer inputs, "I want to purchase this product," the generation AI can generate a video that provides information about the purchase procedure. This generates videos tailored to the viewer's needs, further increasing the effectiveness of advertising. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the viewer's request into the generation AI, and the generation AI generates a video.

[0034] The provision unit can provide a reward to a viewer when he or she views an advertisement. The provision unit provides a reward to a viewer when he or she views an advertisement. Types of rewards include, but are not limited to, points, coupons, and cash, for example. The provision unit can provide points to a viewer when he or she views an advertisement. The provision unit can also provide a coupon to a viewer when he or she views an advertisement. The provision unit can also provide cash to a viewer when he or she views an advertisement. This allows a viewer to earn a reward by viewing an advertisement, thereby improving the viewing rate of the advertisement. Some or all of the above-described processing in the provision unit can be performed, for example, using AI or without using AI. For example, the provision unit can use AI to optimize the reward that a viewer earns when he or she views an advertisement.

[0035] The reception unit can analyze the viewer's past input history and select an appropriate input reception method. The reception unit analyzes the viewer's past input history and selects the optimal input reception method. The viewer's past input history includes, for example, input content, input frequency, and history storage period, but is not limited to these examples. For example, the reception unit prioritizes and suggests input methods (voice, text, etc.) that the viewer has frequently used in the past. The reception unit can also predict and suggest input methods to be used during specific time periods based on the viewer's past input history. It can also suggest related input methods based on the content the viewer has previously input. In this way, the optimal input reception method can be selected by analyzing the viewer'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 using AI. For example, the reception unit can input the viewer's past input history into a generation AI, which can select the optimal input reception method.

[0036] The reception unit can filter inputs from a viewer based on the viewer's current areas of interest when the reception unit receives inputs from a viewer. The reception unit can filter inputs from a viewer based on the viewer's current areas of interest when the viewer receives inputs from a viewer. The viewer's current areas of interest can be identified using, for example, survey results, browsing history, or social media activity. For example, only inputs related to topics in which the viewer is currently interested can be accepted. Related inputs can also be preferentially accepted based on the viewer's past search history. Inputs related to content currently being viewed by the viewer can also be filtered and accepted. By filtering based on the viewer's current areas of interest, highly relevant inputs can be accepted. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the viewer's current areas of interest to a generation AI, which can then perform the filtering.

[0037] The reception unit can select an appropriate reception means depending on the viewer's input method. The reception unit selects the optimal reception means depending on the viewer's input method. Viewer input methods include, but are not limited to, voice input, text input, and gesture input. For example, if the viewer uses voice input, the reception unit can accept the input using voice recognition technology. Also, if the viewer uses text input, the reception unit can preferentially accept keyboard input. Also, if the viewer uses gesture input, the reception unit can accept the input using gesture recognition technology. This improves the accuracy of the input by selecting the optimal reception means depending on the viewer's input method. 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 can input the viewer's input method to a generation AI, which can select the optimal reception means.

[0038] The reception unit can preferentially receive highly relevant inputs based on the viewer's geographical location information. The reception unit preferentially receives highly relevant inputs based on the viewer's geographical location information. For example, GPS data, IP address, location information services, etc. are used to acquire the viewer's geographical location information. For example, if the viewer is in a specific area, the reception unit can preferentially receive inputs related to that area. Also, if the viewer is traveling, the reception unit can preferentially receive inputs related to the viewer's travel destination. Also, if the viewer is at home, the reception unit can preferentially receive inputs related to the viewer's home. In this way, by taking the viewer's geographical location information into consideration, highly relevant inputs can be preferentially received. 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 can input the viewer's geographical location information to the generation AI, which can then preferentially receive highly relevant inputs.

[0039] The reception unit can analyze the viewer's social media activity and receive appropriate input. The reception unit analyzes the viewer's social media activity and receives related input. For example, the content of posts, the number of likes, the number of followers, etc. are used to analyze the viewer's social media activity. For example, input related to locations where the viewer checked in on social media can be preferentially received. The content of the viewer's social media posts can also be analyzed and related input can be preferentially received. The activity of the viewer's friends on social media can also be used to preferentially receive related input. In this way, by analyzing the viewer's social media activity, related input can be preferentially received. 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 can input the viewer's social media activity to a generation AI, which can then receive appropriate input.

[0040] The reception unit can provide an appropriate reception method by reflecting the viewer's past feedback. The reception unit provides the optimal reception method by reflecting the viewer's past feedback. The viewer's past feedback includes, but is not limited to, for example, survey results, comments, and ratings. For example, the optimal input method is suggested based on the viewer's past feedback. It is also possible to preferentially suggest a specific input method based on the viewer's past feedback. It is also possible to analyze the viewer's past feedback and customize the input method. In this way, it is possible to provide the optimal reception method by reflecting the viewer's past feedback. Some or all of the above-mentioned processing in 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 viewer's past feedback into a generation AI, which can then provide the optimal reception method.

[0041] The generation unit can adjust the specificity of the video based on the viewer's input. The generation unit adjusts the level of detail of the video based on the viewer's input. The viewer's input includes, but is not limited to, text content, audio content, and gesture content. For example, if the viewer is looking for detailed information, the generation unit can generate a video including a detailed explanation. Alternatively, if the viewer is looking for concise information, the generation unit can generate a short video that covers the main points. Alternatively, if the viewer is interested in a particular topic, the generation unit can generate a video that focuses on that topic. In this way, by adjusting the level of detail of the video based on the viewer's input, it is possible to provide a video that meets the viewer's needs. 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 the viewer's input into a generation AI, which can then adjust the level of detail of the video.

[0042] The generation unit can apply different generation algorithms depending on the viewer category. The generation unit applies different generation algorithms depending on the viewer category. Viewer categories include, but are not limited to, age group, interests, and purchasing history. For example, if the viewer is young, the generation unit can apply an algorithm that generates a visually appealing video. If the viewer is elderly, the generation unit can apply an algorithm that generates a simple, highly visible video. If the viewer has specialized knowledge, the generation unit can apply an algorithm that generates a video including technical terms. In this way, by applying different generation algorithms depending on the viewer category, the optimal video can be provided to the viewer. Some or all of the above-mentioned 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 the viewer category into a generation AI, which can then apply the optimal generation algorithm.

[0043] The generation unit can improve the accuracy of generation based on the viewer's past viewing history. The generation unit improves the accuracy of generation by referring to the viewer's past viewing history. The viewer's past viewing history includes, for example, the type of video viewed, viewing time, viewing frequency, etc., but is not limited to these examples. For example, the generation unit generates related videos based on the content of videos the viewer has previously viewed. The generation unit can also analyze the viewer's preferred style and topic from the viewer's past viewing history and generate optimal videos. The generation unit can also generate videos that the viewer is likely to be interested in by referring to the viewer's past viewing history. In this way, by referring to the viewer's past viewing history, the accuracy of generation is improved. Some or all of the above-mentioned 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 the viewer's past viewing history into the generation AI, which can improve the accuracy of generation.

[0044] The generation unit can determine the generation priority based on the time of viewer input. The generation unit determines the generation priority based on the time of viewer input. The time of viewer input includes, but is not limited to, the timestamp of the input and the frequency of the input. For example, if the viewer inputs early in the morning, the generation unit can prioritize generating videos related to early morning. Also, if the viewer inputs at night, the generation unit can prioritize generating videos related to night. Also, if the viewer inputs during a specific event, the generation unit can prioritize generating videos related to that event. In this way, by determining the generation priority based on the time of viewer input, videos can be provided at an appropriate time. Some or all of the above-mentioned 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 the time of viewer input to a generation AI, which can determine the generation priority.

[0045] The generation unit can adjust the generation order based on the viewer's interests. The generation unit adjusts the generation order based on the viewer's interests. The viewer's interests can be identified using, for example, survey results, browsing history, or social media activity. For example, if a viewer is interested in a particular topic, the generation unit prioritizes generating videos related to that topic. The generation unit can also prioritize generating related videos based on the content of videos the viewer has previously watched. The generation unit can also prioritize generating related videos based on the viewer's current areas of interest. This makes it easier to attract the viewer's interest by adjusting the generation order based on the viewer's interests. 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 viewer interest data into a generation AI, which can then adjust the generation order.

[0046] The generation unit can adjust the use of technical terminology in the generated video according to the viewer's knowledge level. The generation unit adjusts the use of technical terminology in the generated video according to the viewer's knowledge level. The viewer's knowledge level can be determined, for example, using survey results, past learning history, occupation, etc. For example, if the viewer has specialized knowledge, the generation unit generates a video that uses a lot of technical terminology. On the other hand, if the viewer is a beginner, the generation unit can generate a simple video that avoids technical terminology. The generation unit can also generate a video that uses appropriate technical terminology according to the viewer's knowledge level. In this way, by adjusting the use of technical terminology according to the viewer's knowledge level, it is possible to provide a video that is easy for the viewer to understand. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the viewer's knowledge level into the generation AI, which can adjust the use of technical terminology.

[0047] The providing unit can select an appropriate delivery method based on the viewer's past viewing history. The providing unit selects the optimal delivery method by referring to the viewer's past viewing history. The viewer's past viewing history includes, but is not limited to, the type of video viewed, viewing time, and viewing frequency. For example, the providing unit provides related videos based on the content of videos the viewer has previously viewed. The providing unit can also analyze the viewer's preferred style and topic from the viewer's past viewing history and provide optimal videos. The providing unit can also provide videos that the viewer is likely to be interested in by referring to the viewer's past viewing history. In this way, the optimal video can be provided to the viewer by referring to the viewer's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's past viewing history into a generation AI, which can select the optimal delivery method.

[0048] The providing unit can customize the content to be provided according to the viewer's current task. The providing unit customizes the content to be provided according to the viewer's current task. The viewer's current task can be identified using, for example, work content, project progress, etc. For example, if the viewer is at work, the providing unit can provide work-related videos. Also, if the viewer is taking a break, the providing unit can provide relaxing videos. Also, if the viewer is exercising, the providing unit can provide exercise-related videos. In this way, by customizing the content to be provided according to the viewer's current task, it is possible to provide the optimal video for the viewer. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's current task into a generating AI, which can customize the content to be provided.

[0049] The providing unit can improve the providing method by reflecting viewer feedback. The providing unit improves the providing method by reflecting viewer feedback. Viewer feedback includes, but is not limited to, survey results, comments, ratings, etc. For example, if a viewer provides positive feedback on a provided video, the providing unit can provide a similar video. On the other hand, if a viewer provides negative feedback on a provided video, the providing unit can provide a video in a different style. The providing unit can also analyze viewer feedback and customize the providing method. In this way, the providing method can be improved by reflecting viewer feedback, and viewer satisfaction can be increased. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input viewer feedback into a generation AI, which can improve the providing method.

[0050] The providing unit can select an appropriate delivery method based on the viewer's device information. The providing unit selects the optimal delivery method based on the viewer's device information. Acquired viewer device information includes, but is not limited to, the device type, OS, and browser. For example, if the viewer is using a smartphone, the providing unit can provide a video that matches the screen size. Also, if the viewer is using a tablet, the providing unit can provide a video optimized for a large screen. Also, if the viewer is using a smartwatch, the providing unit can provide a concise, highly visible video. This allows optimal video to be provided by taking the viewer's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's device information into a generation AI, which can then select the optimal delivery method.

[0051] The providing unit can make the provided content multilingual according to the viewer's language setting. The providing unit can make the provided content multilingual according to the viewer's language setting. Acquisition of the viewer's language setting includes, but is not limited to, browser language setting, device language setting, etc. For example, the providing unit automatically sets the language of the video based on the viewer's device language setting. Furthermore, if the viewer uses multiple languages, the providing unit can also provide a language switching function. Furthermore, if the viewer selects a specific language, the providing unit can provide the video in that language. This allows the viewer to deepen their understanding by providing multilingual videos according to the viewer's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's language setting into a generating AI, which can then provide multilingual videos.

[0052] The providing unit can customize the provision method based on the viewer's reward history. The providing unit customizes the provision method based on the viewer's reward history. Acquiring the viewer's reward history includes, for example, the type of reward received in the past, the frequency of rewards, etc., but is not limited to such examples. For example, videos related to rewards that the viewer has given high ratings to in the past are provided. The providing unit can also provide optimal videos based on the type of rewards the viewer has received in the past. The providing unit can also analyze the viewer's reward history and provide videos that the viewer is likely to be interested in. In this way, by taking the viewer's reward history into consideration, it is possible to provide optimal videos for the viewer. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the viewer's reward history into a generation AI, which can customize the provision method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When accepting input from a viewer, the accepting unit can estimate the viewer's current activity status and suggest an input method that corresponds to that activity. For example, if it is estimated that the viewer is exercising, it can preferentially suggest voice input. Also, if it is estimated that the viewer is working, it can preferentially suggest text input. Furthermore, if the viewer is relaxing, it can suggest gesture input. This makes it possible to improve convenience for the viewer by providing the optimal input method that corresponds to the viewer's activity status.

[0055] When generating a video based on the viewer's input, the generation unit can customize the content of the video by taking into account the viewer's past viewing history. For example, a video containing related information is generated based on the content of videos the viewer has viewed in the past. The generation unit can also analyze the viewer's preferred style and topics from the viewer's viewing history to generate the most suitable video. Furthermore, the generation unit can also generate videos that the viewer is likely to be interested in by referring to the ratings of videos the viewer has viewed in the past. In this way, the accuracy of the generated video is improved by referring to the viewer's past viewing history.

[0056] The providing unit can optimize the method of providing videos based on the viewer's device information. For example, if the viewer is using a smartphone, the providing unit can provide videos that fit the screen size. Also, if the viewer is using a tablet, the providing unit can provide videos that are optimized for a large screen. Furthermore, if the viewer is using a smartwatch, the providing unit can provide videos that are concise and highly visible. In this way, the optimal video can be provided by taking into account the viewer's device information.

[0057] When accepting input from a viewer, the acceptance unit can provide the optimal acceptance method by reflecting the viewer's past feedback. For example, the acceptance unit can suggest the optimal input method based on feedback previously provided by the viewer. It can also preferentially suggest a specific input method based on the viewer's past feedback. Furthermore, it can analyze the viewer's past feedback and customize the input method. In this way, the optimal acceptance method can be provided by reflecting the viewer's past feedback.

[0058] The generation unit can adjust the use of technical terms in the generated video depending on the viewer's knowledge level. For example, if the viewer has specialized knowledge, the generation unit can generate a video that uses a lot of technical terms. On the other hand, if the viewer is a beginner, the generation unit can generate a simple video that avoids technical terms. Furthermore, the generation unit can generate a video that uses appropriate technical terms depending on the viewer's knowledge level. In this way, by adjusting the use of technical terms depending on the viewer's knowledge level, it is possible to provide a video that is easy for the viewer to understand.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives viewer input. Viewer input includes text input, voice input, gesture input, and so on. For example, if a viewer inputs "I want to know more about this product," the request is accepted. Also, if a viewer inputs "I want to purchase this product," the request is also accepted. Step 2: The generation unit uses a generation AI to generate a video based on the information received by the reception unit. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a video based on the viewer's input. For example, if a viewer inputs, "I want to know more about this product," a video providing additional information in response to that request can be generated. Alternatively, if a viewer inputs, "I want to purchase this product," a video providing information on the purchasing procedure can be generated. Step 3: The providing unit provides the video generated by the generating unit to the viewer. The providing unit provides the video by a method such as streaming, downloading, or real-time distribution. For example, the generated video is provided to the viewer by streaming. The generated video can also be provided in a downloadable format. Furthermore, the generated video can also be distributed in real time.

[0061] (Example 2) An advertising system according to an embodiment of the present invention accepts viewer input, generates a video based on the input, and provides the video to the viewer. When a viewer watches an advertisement, the advertising system accepts the viewer's input, and the generation AI generates a video that provides additional information in response to the viewer's request. In this way, an interactive conversation with the viewer is possible. The generation AI then responds appropriately based on the viewer's response. For example, if a viewer inputs, "I want to purchase this product," the generation AI generates a video that provides information about the purchasing process. This provides advertisements tailored to the viewer's needs, thereby increasing the effectiveness of the advertisements. Furthermore, viewers can earn rewards for watching advertisements. Possible rewards include points and game items earned by watching existing video advertisements. Viewers not only receive advertisements tailored to their needs, but also earn greater rewards in proportion to the effort they put in. This system benefits both advertisers and viewers, allowing them to set higher advertising prices and viewers to receive advertisements tailored to their needs. This allows the advertising system to provide interactive advertisements based on viewer input, thereby increasing the effectiveness of the advertisements. For example, when a viewer watches an advertisement, the system accepts the viewer's input, and the generation AI generates a video that provides additional information in response to the viewer's request, thereby attracting the viewer's attention. In addition, viewers can receive rewards for watching ads, which increases viewer satisfaction. This is beneficial for both advertisers, who can set higher ad prices, and viewers, who can receive ads that meet their needs.

[0062] An advertising system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives a viewer's input. The viewer's input may include, but is not limited to, text input, voice input, and gesture input. For example, when a viewer inputs "I want to know more about this product," the receiving unit receives the request. The receiving unit can also receive a viewer's request when the viewer inputs "I want to purchase this product." The generating unit uses a generation AI to generate a video based on the information received by the receiving unit. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates the video based on the viewer's input. For example, when a viewer inputs "I want to know more about this product," the generation AI generates a video that provides additional information in response to the request. When a viewer inputs "I want to purchase this product," the generation AI can also generate a video that provides information about the purchase procedure. The providing unit provides the video generated by the generating unit to the viewer. The providing unit provides the video by, for example, streaming, downloading, real-time distribution, or other methods. For example, the providing unit provides the generated video to the viewer by streaming. The providing unit can also provide the generated video in a downloadable format. The providing unit can also deliver the generated video in real time. This allows the advertising system according to the embodiment to provide interactive advertisements based on viewer input, thereby enhancing the effectiveness of the advertisements. For example, when a viewer watches an advertisement, the generation AI can receive the viewer's input and generate a video that provides additional information in response to the request, thereby attracting the viewer's attention. Furthermore, viewers can receive rewards for watching advertisements, which improves viewer satisfaction. This allows advertisers to set higher advertising prices and allows viewers to receive advertisements that meet their needs, resulting in benefits for both parties.

[0063] The generation unit can generate a video based on viewer input. The generation unit uses a generation AI to generate a video based on viewer input. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a video based on viewer input. For example, when a viewer inputs, "I want to know more about this product," the generation AI generates a video that provides additional information in response to the request. Also, when a viewer inputs, "I want to purchase this product," the generation AI can generate a video that provides information about the purchase procedure. In this way, by generating a video in response to viewer input, the effectiveness of advertising is increased. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input viewer input to the generation AI, which then generates a video.

[0064] The providing unit can provide the generated video to the viewer. The providing unit provides the video generated by the generating unit to the viewer. The providing unit provides the video by a method such as streaming, downloading, or real-time distribution. For example, the providing unit provides the generated video to the viewer by streaming. The providing unit can also provide the generated video in a downloadable format. The providing unit can also distribute the generated video in real time. This enables advertising tailored to the needs of the viewer. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the generated video in an optimal format using AI.

[0065] The reception unit can instantly accept viewer input. The reception unit accepts viewer input in real time. Viewer input includes, but is not limited to, text input, voice input, and gesture input. For example, when a viewer inputs "I want to know more about this product," the reception unit instantly accepts the request. Furthermore, when a viewer inputs "I want to purchase this product," the reception unit can also instantly accept the request. This allows for real-time acceptance of viewer input, enabling an instantaneous response. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can accept viewer input in real time using AI.

[0066] The generation unit can generate videos in response to viewer requests. The generation unit uses a generation AI to generate videos in response to viewer requests. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, and generates videos in response to viewer requests. For example, when a viewer inputs, "I want to know more about this product," the generation AI generates a video that provides additional information in response to the request. Also, when a viewer inputs, "I want to purchase this product," the generation AI can generate a video that provides information about the purchase procedure. This generates videos tailored to the viewer's needs, further increasing the effectiveness of advertising. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the viewer's request into the generation AI, and the generation AI generates a video.

[0067] The provision unit can provide a reward to a viewer when he or she views an advertisement. The provision unit provides a reward to a viewer when he or she views an advertisement. Types of rewards include, but are not limited to, points, coupons, and cash, for example. The provision unit can provide points to a viewer when he or she views an advertisement. The provision unit can also provide a coupon to a viewer when he or she views an advertisement. The provision unit can also provide cash to a viewer when he or she views an advertisement. This allows a viewer to earn a reward by viewing an advertisement, thereby improving the viewing rate of the advertisement. Some or all of the above-described processing in the provision unit can be performed, for example, using AI or without using AI. For example, the provision unit can use AI to optimize the reward that a viewer earns when he or she views an advertisement.

[0068] The reception unit can estimate the viewer's emotions and adjust the timing of input reception based on the emotions. The reception unit estimates the viewer's emotions and adjusts the timing of input reception based on the estimated viewer's emotions. Technologies such as facial expression recognition, voice analysis, and biometrics are used to estimate the viewer's emotions. For example, if the viewer is excited, the reception unit can accelerate the timing of input reception to request an immediate response. Alternatively, if the viewer is relaxed, the reception unit can delay the timing of input reception to provide the viewer with time to think. Alternatively, if the viewer is stressed, the reception unit can adjust the timing of input reception to wait until the viewer calms down. In this way, by adjusting the timing of input reception according to the viewer's emotions, input can be received at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, 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-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the viewer's emotional data into the generation AI, and the generation AI can adjust the timing of receiving the input based on the emotion.

[0069] The reception unit can analyze the viewer's past input history and select an appropriate input reception method. The reception unit analyzes the viewer's past input history and selects the optimal input reception method. The viewer's past input history includes, for example, input content, input frequency, and history storage period, but is not limited to these examples. For example, the reception unit prioritizes and suggests input methods (voice, text, etc.) that the viewer has frequently used in the past. The reception unit can also predict and suggest input methods to be used during specific time periods based on the viewer's past input history. It can also suggest related input methods based on the content the viewer has previously input. In this way, the optimal input reception method can be selected by analyzing the viewer'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 using AI. For example, the reception unit can input the viewer's past input history into a generation AI, which can select the optimal input reception method.

[0070] The reception unit can filter inputs from a viewer based on the viewer's current areas of interest when the reception unit receives inputs from a viewer. The reception unit can filter inputs from a viewer based on the viewer's current areas of interest when the viewer receives inputs from a viewer. The viewer's current areas of interest can be identified using, for example, survey results, browsing history, or social media activity. For example, only inputs related to topics in which the viewer is currently interested can be accepted. Related inputs can also be preferentially accepted based on the viewer's past search history. Inputs related to content currently being viewed by the viewer can also be filtered and accepted. By filtering based on the viewer's current areas of interest, highly relevant inputs can be accepted. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the viewer's current areas of interest to a generation AI, which can then perform the filtering.

[0071] The reception unit can select an appropriate reception means depending on the viewer's input method. The reception unit selects the optimal reception means depending on the viewer's input method. Viewer input methods include, but are not limited to, voice input, text input, and gesture input. For example, if the viewer uses voice input, the reception unit can accept the input using voice recognition technology. Also, if the viewer uses text input, the reception unit can preferentially accept keyboard input. Also, if the viewer uses gesture input, the reception unit can accept the input using gesture recognition technology. This improves the accuracy of the input by selecting the optimal reception means depending on the viewer's input method. 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 can input the viewer's input method to a generation AI, which can select the optimal reception means.

[0072] The reception unit can estimate the viewer's emotions and determine the priority of inputs to be received based on the emotions. The reception unit estimates the viewer's emotions and determines the priority of inputs to be received based on the estimated viewer's emotions. Technologies such as facial expression recognition, voice analysis, and biometrics are used to estimate the viewer's emotions. For example, if the viewer is excited, the reception unit can prioritize receiving important inputs. Also, if the viewer is relaxed, the reception unit can prioritize receiving detailed inputs. Also, if the viewer is stressed, the reception unit can prioritize receiving simple inputs. In this way, by determining the priority of inputs according to the viewer's emotions, important inputs can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input viewer emotion data into the generation AI, and the generation AI can determine the priority of the input based on the emotion.

[0073] The reception unit can preferentially receive highly relevant inputs based on the viewer's geographical location information. The reception unit preferentially receives highly relevant inputs based on the viewer's geographical location information. For example, GPS data, IP address, location information services, etc. are used to acquire the viewer's geographical location information. For example, if the viewer is in a specific area, the reception unit can preferentially receive inputs related to that area. Also, if the viewer is traveling, the reception unit can preferentially receive inputs related to the viewer's travel destination. Also, if the viewer is at home, the reception unit can preferentially receive inputs related to the viewer's home. In this way, by taking the viewer's geographical location information into consideration, highly relevant inputs can be preferentially received. 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 can input the viewer's geographical location information to the generation AI, which can then preferentially receive highly relevant inputs.

[0074] The reception unit can analyze the viewer's social media activity and receive appropriate input. The reception unit analyzes the viewer's social media activity and receives related input. For example, the content of posts, the number of likes, the number of followers, etc. are used to analyze the viewer's social media activity. For example, input related to locations where the viewer checked in on social media can be preferentially received. The content of the viewer's social media posts can also be analyzed and related input can be preferentially received. The activity of the viewer's friends on social media can also be used to preferentially receive related input. In this way, by analyzing the viewer's social media activity, related input can be preferentially received. 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 can input the viewer's social media activity to a generation AI, which can then receive appropriate input.

[0075] The reception unit can provide an appropriate reception method by reflecting the viewer's past feedback. The reception unit provides the optimal reception method by reflecting the viewer's past feedback. The viewer's past feedback includes, but is not limited to, for example, survey results, comments, and ratings. For example, the optimal input method is suggested based on the viewer's past feedback. It is also possible to preferentially suggest a specific input method based on the viewer's past feedback. It is also possible to analyze the viewer's past feedback and customize the input method. In this way, it is possible to provide the optimal reception method by reflecting the viewer's past feedback. Some or all of the above-mentioned processing in 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 viewer's past feedback into a generation AI, which can then provide the optimal reception method.

[0076] The generation unit can estimate the viewer's emotions and adjust the video presentation method based on the emotions. The generation unit estimates the viewer's emotions and adjusts the video presentation method based on the estimated viewer's emotions. Technologies such as facial expression recognition, voice analysis, and biometrics are used to estimate the viewer's emotions. For example, if the viewer is excited, the generation unit can generate a video with visually stimulating effects. If the viewer is relaxed, the generation unit can generate a video with a calm tone. If the viewer is stressed, the generation unit can generate a simple, highly visible video. This makes it easier to attract the viewer's interest by adjusting the video presentation method according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 these examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the viewer's emotional data into the generation AI, which can then adjust the way the video is expressed based on the emotion.

[0077] The generation unit can adjust the specificity of the video based on the viewer's input. The generation unit adjusts the level of detail of the video based on the viewer's input. The viewer's input includes, but is not limited to, text content, audio content, and gesture content. For example, if the viewer is looking for detailed information, the generation unit can generate a video including a detailed explanation. Alternatively, if the viewer is looking for concise information, the generation unit can generate a short video that covers the main points. Alternatively, if the viewer is interested in a particular topic, the generation unit can generate a video that focuses on that topic. In this way, by adjusting the level of detail of the video based on the viewer's input, it is possible to provide a video that meets the viewer's needs. 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 the viewer's input into a generation AI, which can then adjust the level of detail of the video.

[0078] The generation unit can apply different generation algorithms depending on the viewer category. The generation unit applies different generation algorithms depending on the viewer category. Viewer categories include, but are not limited to, age group, interests, and purchasing history. For example, if the viewer is young, the generation unit can apply an algorithm that generates a visually appealing video. If the viewer is elderly, the generation unit can apply an algorithm that generates a simple, highly visible video. If the viewer has specialized knowledge, the generation unit can apply an algorithm that generates a video including technical terms. In this way, by applying different generation algorithms depending on the viewer category, the optimal video can be provided to the viewer. Some or all of the above-mentioned 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 the viewer category into a generation AI, which can then apply the optimal generation algorithm.

[0079] The generation unit can improve the accuracy of generation based on the viewer's past viewing history. The generation unit improves the accuracy of generation by referring to the viewer's past viewing history. The viewer's past viewing history includes, for example, the type of video viewed, viewing time, viewing frequency, etc., but is not limited to these examples. For example, the generation unit generates related videos based on the content of videos the viewer has previously viewed. The generation unit can also analyze the viewer's preferred style and topic from the viewer's past viewing history and generate optimal videos. The generation unit can also generate videos that the viewer is likely to be interested in by referring to the viewer's past viewing history. In this way, by referring to the viewer's past viewing history, the accuracy of generation is improved. Some or all of the above-mentioned 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 the viewer's past viewing history into the generation AI, which can improve the accuracy of generation.

[0080] The generation unit can estimate the viewer's emotions and adjust the length of the video based on the emotions. The generation unit estimates the viewer's emotions and adjusts the length of the video based on the estimated viewer's emotions. Technologies such as facial expression recognition, voice analysis, and biometrics are used to estimate the viewer's emotions. For example, if the viewer is in a hurry, the generation unit can generate a short, to-the-point video. If the viewer is relaxed, the generation unit can generate a longer video with detailed explanations. If the viewer is excited, the generation unit can generate a video with visually stimulating effects. This makes it easier to attract the viewer's interest by adjusting the length of the video based on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 can be performed using AI, for example, or without AI. For example, the generation unit can input viewer emotion data into the generation AI, which can then adjust the length of the video based on the emotion.

[0081] The generation unit can determine the generation priority based on the time of viewer input. The generation unit determines the generation priority based on the time of viewer input. The time of viewer input includes, but is not limited to, the timestamp of the input and the frequency of the input. For example, if the viewer inputs early in the morning, the generation unit can prioritize generating videos related to early morning. Also, if the viewer inputs at night, the generation unit can prioritize generating videos related to night. Also, if the viewer inputs during a specific event, the generation unit can prioritize generating videos related to that event. In this way, by determining the generation priority based on the time of viewer input, videos can be provided at an appropriate time. Some or all of the above-mentioned 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 the time of viewer input to a generation AI, which can determine the generation priority.

[0082] The generation unit can adjust the generation order based on the viewer's interests. The generation unit adjusts the generation order based on the viewer's interests. The viewer's interests can be identified using, for example, survey results, browsing history, or social media activity. For example, if a viewer is interested in a particular topic, the generation unit prioritizes generating videos related to that topic. The generation unit can also prioritize generating related videos based on the content of videos the viewer has previously watched. The generation unit can also prioritize generating related videos based on the viewer's current areas of interest. This makes it easier to attract the viewer's interest by adjusting the generation order based on the viewer's interests. 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 viewer interest data into a generation AI, which can then adjust the generation order.

[0083] The generation unit can adjust the use of technical terminology in the generated video according to the viewer's knowledge level. The generation unit adjusts the use of technical terminology in the generated video according to the viewer's knowledge level. The viewer's knowledge level can be determined, for example, using survey results, past learning history, occupation, etc. For example, if the viewer has specialized knowledge, the generation unit generates a video that uses a lot of technical terminology. On the other hand, if the viewer is a beginner, the generation unit can generate a simple video that avoids technical terminology. The generation unit can also generate a video that uses appropriate technical terminology according to the viewer's knowledge level. In this way, by adjusting the use of technical terminology according to the viewer's knowledge level, it is possible to provide a video that is easy for the viewer to understand. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the viewer's knowledge level into the generation AI, which can adjust the use of technical terminology.

[0084] The providing unit can estimate the viewer's emotions and adjust the video presentation method based on the emotions. The providing unit estimates the viewer's emotions and adjusts the video presentation method based on the estimated viewer's emotions. Technologies such as facial expression recognition, voice analysis, and biometrics are used to estimate the viewer's emotions. For example, if the viewer is excited, the providing unit can provide a video with visually stimulating effects. If the viewer is relaxed, the providing unit can provide a video with a calm tone. If the viewer is stressed, the providing unit can provide a simple, highly visible video. This makes it easier to attract the viewer's interest by adjusting the video presentation method according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input viewer emotion data into a generation AI, which can then adjust the video presentation method based on the emotion.

[0085] The providing unit can select an appropriate delivery method based on the viewer's past viewing history. The providing unit selects the optimal delivery method by referring to the viewer's past viewing history. The viewer's past viewing history includes, but is not limited to, the type of video viewed, viewing time, and viewing frequency. For example, the providing unit provides related videos based on the content of videos the viewer has previously viewed. The providing unit can also analyze the viewer's preferred style and topic from the viewer's past viewing history and provide optimal videos. The providing unit can also provide videos that the viewer is likely to be interested in by referring to the viewer's past viewing history. In this way, the optimal video can be provided to the viewer by referring to the viewer's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's past viewing history into a generation AI, which can select the optimal delivery method.

[0086] The providing unit can customize the content to be provided according to the viewer's current task. The providing unit customizes the content to be provided according to the viewer's current task. The viewer's current task can be identified using, for example, work content, project progress, etc. For example, if the viewer is at work, the providing unit can provide work-related videos. Also, if the viewer is taking a break, the providing unit can provide relaxing videos. Also, if the viewer is exercising, the providing unit can provide exercise-related videos. In this way, by customizing the content to be provided according to the viewer's current task, it is possible to provide the optimal video for the viewer. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's current task into a generating AI, which can customize the content to be provided.

[0087] The providing unit can improve the providing method by reflecting viewer feedback. The providing unit improves the providing method by reflecting viewer feedback. Viewer feedback includes, but is not limited to, survey results, comments, ratings, etc. For example, if a viewer provides positive feedback on a provided video, the providing unit can provide a similar video. On the other hand, if a viewer provides negative feedback on a provided video, the providing unit can provide a video in a different style. The providing unit can also analyze viewer feedback and customize the providing method. In this way, the providing method can be improved by reflecting viewer feedback, and viewer satisfaction can be increased. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input viewer feedback into a generation AI, which can improve the providing method.

[0088] The providing unit can estimate the viewer's emotions and adjust the order in which videos are presented based on the emotions. The providing unit estimates the viewer's emotions and adjusts the order in which videos are presented based on the estimated viewer's emotions. Technologies such as facial expression recognition, voice analysis, and biometrics are used to estimate the viewer's emotions. For example, if the viewer is excited, the providing unit can prioritize providing visually stimulating videos. Also, if the viewer is relaxed, the providing unit can prioritize providing videos with a calm tone. Also, if the viewer is stressed, the providing unit can prioritize providing videos with simple, high visibility. This adjusts the order in which videos are presented based on the viewer's emotions, making it easier to attract the viewer's interest. Emotion estimation is achieved using an emotion estimation function, such as 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 providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input viewer emotional data into the generation AI, which can then adjust the order in which videos are provided based on the emotions.

[0089] The providing unit can select an appropriate delivery method based on the viewer's device information. The providing unit selects the optimal delivery method based on the viewer's device information. Acquired viewer device information includes, but is not limited to, the device type, OS, and browser. For example, if the viewer is using a smartphone, the providing unit can provide a video that matches the screen size. Also, if the viewer is using a tablet, the providing unit can provide a video optimized for a large screen. Also, if the viewer is using a smartwatch, the providing unit can provide a concise, highly visible video. This allows optimal video to be provided by taking the viewer's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's device information into a generation AI, which can then select the optimal delivery method.

[0090] The providing unit can make the provided content multilingual according to the viewer's language setting. The providing unit can make the provided content multilingual according to the viewer's language setting. Acquisition of the viewer's language setting includes, but is not limited to, browser language setting, device language setting, etc. For example, the providing unit automatically sets the language of the video based on the viewer's device language setting. Furthermore, if the viewer uses multiple languages, the providing unit can also provide a language switching function. Furthermore, if the viewer selects a specific language, the providing unit can provide the video in that language. This allows the viewer to deepen their understanding by providing multilingual videos according to the viewer's language setting. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the viewer's language setting into a generating AI, which can then provide multilingual videos.

[0091] The providing unit can customize the provision method based on the viewer's reward history. The providing unit customizes the provision method based on the viewer's reward history. Acquiring the viewer's reward history includes, for example, the type of reward received in the past, the frequency of rewards, etc., but is not limited to such examples. For example, videos related to rewards that the viewer has given high ratings to in the past are provided. The providing unit can also provide optimal videos based on the type of rewards the viewer has received in the past. The providing unit can also analyze the viewer's reward history and provide videos that the viewer is likely to be interested in. In this way, by taking the viewer's reward history into consideration, it is possible to provide optimal videos for the viewer. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the viewer's reward history into a generation AI, which can customize the provision method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 receives viewer input using a touch panel 38A or a microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a video based on the viewer input using a generation AI. The provision unit provides the generated video to the viewer using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and provision 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 receives viewer input using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a video based on the viewer input using a generation AI. The provision unit provides the generated video to the viewer using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 receives viewer input using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a video based on the viewer input using a generation AI. The provision unit provides the generated video to the viewer using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives viewer input using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a video based on the viewer input using a generation AI. The provision unit provides the generated video to the viewer using the speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] When accepting input from a viewer, the accepting unit can estimate the viewer's current activity status and suggest an input method that corresponds to that activity. For example, if it is estimated that the viewer is exercising, it can preferentially suggest voice input. Also, if it is estimated that the viewer is working, it can preferentially suggest text input. Furthermore, if the viewer is relaxing, it can suggest gesture input. This makes it possible to improve convenience for the viewer by providing the optimal input method that corresponds to the viewer's activity status.

[0094] When generating a video based on the viewer's input, the generation unit can customize the content of the video by taking into account the viewer's past viewing history. For example, a video containing related information is generated based on the content of videos the viewer has viewed in the past. The generation unit can also analyze the viewer's preferred style and topics from the viewer's viewing history to generate the most suitable video. Furthermore, the generation unit can also generate videos that the viewer is likely to be interested in by referring to the ratings of videos the viewer has viewed in the past. In this way, the accuracy of the generated video is improved by referring to the viewer's past viewing history.

[0095] The providing unit can optimize the method of providing videos based on the viewer's device information. For example, if the viewer is using a smartphone, the providing unit can provide videos that fit the screen size. Also, if the viewer is using a tablet, the providing unit can provide videos that are optimized for a large screen. Furthermore, if the viewer is using a smartwatch, the providing unit can provide videos that are concise and highly visible. In this way, the optimal video can be provided by taking into account the viewer's device information.

[0096] When accepting input from a viewer, the acceptance unit can provide the optimal acceptance method by reflecting the viewer's past feedback. For example, the acceptance unit can suggest the optimal input method based on feedback previously provided by the viewer. It can also preferentially suggest a specific input method based on the viewer's past feedback. Furthermore, it can analyze the viewer's past feedback and customize the input method. In this way, the optimal acceptance method can be provided by reflecting the viewer's past feedback.

[0097] The generation unit can adjust the use of technical terms in the generated video depending on the viewer's knowledge level. For example, if the viewer has specialized knowledge, the generation unit can generate a video that uses a lot of technical terms. On the other hand, if the viewer is a beginner, the generation unit can generate a simple video that avoids technical terms. Furthermore, the generation unit can generate a video that uses appropriate technical terms depending on the viewer's knowledge level. In this way, by adjusting the use of technical terms depending on the viewer's knowledge level, it is possible to provide a video that is easy for the viewer to understand.

[0098] The reception unit can estimate the viewer's emotions and adjust the timing of input reception based on the estimated emotions. For example, if the viewer is excited, the reception unit can accelerate the timing of input reception to request an immediate response. Alternatively, if the viewer is relaxed, the reception unit can delay the timing of input reception to provide the viewer with time to think. Furthermore, if the viewer is feeling stressed, the reception unit can adjust the timing of input reception to wait until the viewer has calmed down. In this way, by adjusting the timing of input reception according to the viewer's emotions, it is possible to receive input at a more appropriate timing.

[0099] The generation unit can estimate the viewer's emotions and adjust the way the video is presented based on the estimated emotions. For example, if the viewer is excited, the generation unit can generate a video with visually stimulating effects. If the viewer is relaxed, the generation unit can also generate a video with a calm tone. Furthermore, if the viewer is feeling stressed, the generation unit can also generate a simple, highly visible video. In this way, adjusting the way the video is presented according to the viewer's emotions makes it easier to attract the viewer's interest.

[0100] The providing unit can estimate the viewer's emotions and adjust the way the video is provided based on the estimated emotions. For example, if the viewer is excited, the providing unit can provide a video with visually stimulating effects. If the viewer is relaxed, the providing unit can also provide a video with a calm tone. Furthermore, if the viewer is feeling stressed, the providing unit can also provide a simple, highly visible video. In this way, adjusting the way the video is provided according to the viewer's emotions makes it easier to attract the viewer's interest.

[0101] The providing unit can estimate the viewer's emotions and adjust the order in which videos are provided based on the estimated emotions. For example, if the viewer is excited, the providing unit can provide visually stimulating videos with priority. Also, if the viewer is relaxed, the providing unit can provide videos with a calm tone with priority. Furthermore, if the viewer is feeling stressed, the providing unit can provide videos with a simple, highly visible priority. In this way, adjusting the order in which videos are provided based on the viewer's emotions makes it easier to attract the viewer's interest.

[0102] The generator can estimate the viewer's emotions and adjust the length of the video based on the estimated emotions. For example, if the viewer is in a hurry, the generator can generate a short, to-the-point video. If the viewer is relaxed, the generator can generate a longer video with detailed explanations. Furthermore, if the viewer is excited, the generator can generate a video with visually stimulating effects. In this way, adjusting the length of the video according to the viewer's emotions makes it easier to attract the viewer's attention.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives viewer input. Viewer input includes text input, voice input, gesture input, and so on. For example, if a viewer inputs "I want to know more about this product," the request is accepted. Also, if a viewer inputs "I want to purchase this product," the request is also accepted. Step 2: The generation unit uses a generation AI to generate a video based on the information received by the reception unit. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates a video based on the viewer's input. For example, if a viewer inputs, "I want to know more about this product," a video providing additional information in response to that request can be generated. Alternatively, if a viewer inputs, "I want to purchase this product," a video providing information on the purchasing procedure can be generated. Step 3: The providing unit provides the video generated by the generating unit to the viewer. The providing unit provides the video by a method such as streaming, downloading, or real-time distribution. For example, the generated video is provided to the viewer by streaming. The generated video can also be provided in a downloadable format. Furthermore, the generated video can also be distributed in real time.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 viewers; a generating unit that generates a video based on the information received by the receiving unit; a providing unit that provides the video generated by the generating unit to a viewer; A system comprising:

2. The generation unit Generate videos based on viewer input 2. The system of claim 1.

3. The providing unit Providing the generated video to viewers 2. The system of claim 1.

4. The reception unit Accepts viewer input immediately 2. The system of claim 1.

5. The generation unit Generate videos based on viewer requests 2. The system of claim 1.

6. The providing unit Reward viewers for watching ads 2. The system of claim 1.

7. The reception unit Estimate the viewer's emotions and adjust the timing of input acceptance based on those emotions 2. The system of claim 1.

8. The reception unit Analyze the viewer's past input history and select the appropriate input reception method 2. The system of claim 1.

9. The reception unit As viewers type, filtering based on their current interests 2. The system of claim 1.

10. The reception unit Select the appropriate reception method depending on the viewer's input method 2. The system of claim 1.

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