System

Abstract

An object of a system according to an embodiment is to naturally embed an advertisement in original content of a moving image.SOLUTION: A system includes a generation AI, an advertisement generation unit, and an advertisement embedding unit. The generation AI analyzes the content of the moving image using the generation AI. The advertisement generation unit automatically generates an advertisement based on the content analyzed by the generation AI. The advertisement embedding unit naturally embeds the advertisement generated by the advertisement generation unit in the moving image.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional technology, advertisements in video streaming can be unpleasant for users, and there is room for improvement.

[0005] The system according to the embodiment aims to embed advertisements naturally within the original content of a video. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an advertisement generation unit, and an advertisement embedding unit. The generation AI analyzes the content of a video using the generation AI. The advertisement generation unit automatically generates an advertisement based on the content analyzed by the generation AI. The advertisement embedding unit naturally embeds the advertisement generated by the advertisement generation unit into the video. [Effects of the Invention]

[0007] The system according to the embodiment can embed advertisements naturally within the original content of a video. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The ad embedding system according to an embodiment of the present invention uses a generative AI to naturally embed ads into the original content of a video, thereby eliminating the compulsion of ads and improving the user experience.

[0029] An advertisement embedding system according to an embodiment includes a generation AI, an advertisement generation unit, and an advertisement embedding unit. The generation AI analyzes the content of a video. For example, the generation AI performs frame analysis of the video to recognize objects in each scene. The generation AI can also perform scene analysis to understand the story development of the video. The generation AI can also identify specific objects in the video using object recognition technology. The advertisement generation unit automatically generates advertisements based on the content analyzed by the generation AI. For example, the advertisement generation unit creates advertisements using a template-based generation method. The advertisement generation unit can also generate advertisements in real time using dynamic content generation technology. The advertisement generation unit can also generate customized advertisements based on data provided by the generation AI. The advertisement embedding unit naturally embeds advertisements generated by the advertisement generation unit into the video. For example, the advertisement embedding unit inserts advertisements using seamless transition technology. The advertisement embedding unit can also adjust the advertisement to match the color tone and brightness of the video to maintain visual consistency. The advertisement embedding unit can also place advertisements in specific scenes of the video to avoid causing discomfort to viewers. As a result, the advertisement embedding system according to the embodiment can eliminate the compulsion of advertisements and improve the user experience. For example, viewers can watch advertisements in a natural way without skipping them. This is expected to improve viewer satisfaction and increase the rate of continued use of video streaming services.

[0030] The ad generation unit can change the style of the ad to match the atmosphere and theme of the scene. For example, the ad generation unit uses a generation AI to generate different ads for each video scene. For example, the ad generation unit generates ads for sports equipment for action scenes and ads for jewelry for romantic scenes, changing the style of the ad to match the atmosphere of the scene. The ad generation unit also generates different ads for each video scene using the generation AI, adjusting the ad design and color to match the theme of the scene. For example, the ad generation unit generates ads with dark colors for horror scenes and ads with bright colors for comedy scenes. The ad generation unit also generates different ads for each video scene using the generation AI, changing the font and layout of the ad to match the atmosphere and theme of the scene. For example, an old-fashioned font is used for historical drama scenes and a modern font is used for futuristic scenes. This allows the ad generation unit to generate ads that match the scene and improve the viewing experience.

[0031] The advertisement generation unit can automatically generate advertisement narration and sound effects that match the audio data. For example, the advertisement generation unit uses a generation AI to analyze the audio data of a video and automatically generate advertisement narration that matches the scene. For example, it generates energetic narration for action scenes and calm narration for moving scenes. The advertisement generation unit also analyzes the audio data of a video and the generation AI automatically generates sound effects that match the scene. For example, it adds crowd cheers to an advertisement for a sports scene and eerie sounds to an advertisement for a horror scene. The advertisement generation unit also uses a generation AI to analyze the audio data of a video and incorporates music that matches the atmosphere of the scene into the advertisement. For example, it uses soft music for romantic scenes and intense music for action scenes. This allows the advertisement to be generated to match the audio, improving the viewing experience.

[0032] The advertisement generation unit can generate advertisements related to the subtitle data and embed them in the subtitles. In the advertisement generation unit, for example, a generation AI analyzes the subtitle data of a video and generates advertisements related to the content of the subtitles. For example, the advertisement generation unit generates advertisements based on the names of products or services that appear in the subtitles and embeds them in the subtitles. In addition, the advertisement generation unit analyzes the subtitle data of a video and the generation AI generates advertisements related to the content of the subtitles and displays them as part of the subtitles. For example, advertisements related to places or events that appear in the subtitles are generated. In addition, the advertisement generation unit analyzes the subtitle data of a video and the generation AI generates advertisement text based on the content of the subtitles. For example, an advertisement related to the lines of a character that appears in the subtitles is generated and embedded in the subtitles. This generates advertisements related to the subtitles and improves the viewing experience.

[0033] The advertisement generation unit can analyze interactive elements and generate interactive advertisements that allow viewers to access the advertisements by clicking or tapping. In the advertisement generation unit, for example, a generation AI analyzes interactive elements of a video and generates interactive advertisements that allow viewers to access the advertisements by clicking or tapping. For example, an advertisement is generated in which detailed information is displayed when a specific object in a video is clicked. The advertisement generation unit also analyzes interactive elements of a video and a generation AI generates interactive advertisements that allow viewers to access the advertisements by tapping. For example, when a character in a video is tapped, an advertisement for a related product is displayed. The advertisement generation unit also analyzes interactive elements of a video and a generation AI generates interactive advertisements that allow viewers to access the advertisements by clicking or tapping. For example, when a specific scene in a video is clicked, an advertisement for a related service is displayed. This generates interactive advertisements and improves the viewing experience.

[0034] The ad embedding unit can analyze gaze data and embed advertisements in places where gazes are concentrated. For example, the generation AI in the ad embedding unit analyzes viewer gaze data for each video scene and embeds advertisements in places where gazes are concentrated. For example, advertisements are displayed near characters that viewers are paying attention to. The ad embedding unit also analyzes viewer gaze data and embeds advertisements in places where gazes are concentrated. For example, advertisements are displayed in the background of scenes that viewers are paying attention to. The ad embedding unit also analyzes viewer gaze data and embeds advertisements in places where gazes are concentrated. For example, advertisements are displayed near objects that viewers are paying attention to. In this way, the viewing experience can be improved by embedding advertisements in places where gazes are concentrated.

[0035] The ad embedding unit can analyze the color and brightness of the video and adjust the color and brightness of the ad to suit the video. For example, the generation AI in the ad embedding unit analyzes the color and brightness of the video and adjusts the color and brightness of the ad to suit the video. For example, in dark scenes, the brightness of the ad is adjusted to improve visibility. The ad embedding unit also analyzes the color and brightness of the video and the generation AI adjusts the color and brightness of the ad to suit the video. For example, in colorful scenes, the color of the ad is adjusted to make it look natural. The ad embedding unit also analyzes the color and brightness of the video and adjusts the color and brightness of the ad to suit the video. For example, in monochrome scenes, the color of the ad is suppressed to make it blend into the scene. This adjusts the color and brightness of the ad to suit the video, improving the viewing experience.

[0036] The ad embedding unit can analyze 3D data and embed advertisements as 3D objects. In the ad embedding unit, for example, the generation AI analyzes the 3D data of a video and embeds advertisements as 3D objects. For example, a 3D advertising billboard is added to a building in the video. The ad embedding unit also analyzes the 3D data of a video and the generation AI embeds advertisements as 3D objects. For example, a 3D advertisement is displayed in an item held by a character in the video. The ad embedding unit also analyzes the 3D data of a video and embeds advertisements as 3D objects. For example, a 3D advertisement is placed naturally in the scenery in the video. This allows advertisements to be embedded as 3D objects, improving the viewing experience.

[0037] The ad embedding unit can analyze music data and generate advertisements that match the rhythm and tempo of the music. In the ad embedding unit, for example, a generation AI analyzes the music data of a video and generates advertisements that match the rhythm and tempo of the music. For example, an advertisement animation is displayed in time with the beat of the music. In addition, the ad embedding unit analyzes the music data of a video and a generation AI generates advertisements that match the rhythm and tempo of the music. For example, the timing of advertisement display is adjusted to match the tempo of the music. In addition, the ad embedding unit analyzes the music data of a video and a generation AI generates advertisements that match the rhythm and tempo of the music. For example, the content of the advertisement is changed to match the melody of the music. This makes it possible to generate advertisements that match the rhythm and tempo of the music, improving the viewing experience.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The advertisement generation unit can analyze the viewer's past viewing history and generate advertisements based on the viewer's interests. For example, if the viewer has watched many sports-related videos in the past, an advertisement for sports equipment can be generated. If the viewer has watched many cooking videos in the past, an advertisement for cooking utensils or ingredients can be generated. Furthermore, if the viewer has watched many travel-related videos in the past, an advertisement promoting travel destinations can be generated. This makes it possible to provide advertisements based on the viewer's interests and increase the effectiveness of the advertisements.

[0040] The ad embedding unit can analyze the viewer's device information and generate ads optimized for the device. For example, if the viewer is watching on a smartphone, a vertical ad can be generated. If the viewer is watching on a tablet, a horizontal ad can be generated. Furthermore, if the viewer is watching on a desktop computer, a high-resolution ad suitable for a large screen can be generated. This makes it possible to provide ads optimized for the viewing device and improve the viewing experience.

[0041] The advertisement generation unit can analyze the viewer's geographic location information and generate advertisements specific to the region. For example, if the viewer is in a specific city, an advertisement for a local business in that city can be generated. Also, if the viewer is in a specific country, an advertisement tailored to the language and culture of that country can be generated. Furthermore, if the viewer is in a specific region, an advertisement for an event or promotion in that region can be generated. This makes it possible to provide advertisements specific to the region and more easily attract the viewer's attention.

[0042] The ad generation unit can analyze the viewer's social media activity and generate ads based on their interests on social media. For example, if the viewer posts frequently about a particular brand or product, it can generate ads for that brand or product. Also, if the viewer frequently uses a particular hashtag, it can generate ads related to that hashtag. Furthermore, if the viewer follows a particular influencer, it can generate ads for products or services recommended by that influencer. This makes it possible to provide ads based on the viewer's social media interests and increase the effectiveness of the ads.

[0043] The advertisement generation unit can analyze the purchase history of the viewer and generate advertisements based on past purchases. For example, if the viewer has previously purchased products from a specific brand, an advertisement for a new product from that brand can be generated. Also, if the viewer has previously purchased products from a specific category, an advertisement related to that category can be generated. Furthermore, if the viewer has previously used a specific service, an advertisement for an upgrade to that service or a related service can be generated. This makes it possible to provide advertisements based on the viewer's purchase history and increase the effectiveness of the advertisements.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The generative AI analyzes the content of the video. For example, the generative AI performs frame analysis of the video and recognizes objects in each scene. The generative AI can also perform scene analysis to understand the story development of the video. Furthermore, the generative AI can use object recognition technology to identify specific objects within the video. Step 2: The advertisement generation unit automatically generates advertisements based on the content analyzed by the generation AI. For example, the advertisement generation unit creates advertisements using a template-based generation method. The advertisement generation unit can also generate advertisements in real time using dynamic content generation technology. Furthermore, the advertisement generation unit can generate customized advertisements based on data provided by the generation AI. Step 3: The ad embedding unit embeds the advertisements generated by the ad generation unit into the video in a natural way. For example, the ad embedding unit inserts the advertisements using seamless transition technology. The ad embedding unit can also adjust the advertisements to match the color tone and brightness of the video to maintain visual consistency. Furthermore, the ad embedding unit can place the advertisements in specific scenes of the video to avoid causing discomfort to the viewer.

[0046] (Example 2) The ad embedding system according to an embodiment of the present invention uses a generative AI to naturally embed ads into the original content of a video, thereby eliminating the compulsion of ads and improving the user experience.

[0047] An advertisement embedding system according to an embodiment includes a generation AI, an advertisement generation unit, and an advertisement embedding unit. The generation AI analyzes the content of a video. For example, the generation AI performs frame analysis of the video to recognize objects in each scene. The generation AI can also perform scene analysis to understand the story development of the video. The generation AI can also identify specific objects in the video using object recognition technology. The advertisement generation unit automatically generates advertisements based on the content analyzed by the generation AI. For example, the advertisement generation unit creates advertisements using a template-based generation method. The advertisement generation unit can also generate advertisements in real time using dynamic content generation technology. The advertisement generation unit can also generate customized advertisements based on data provided by the generation AI. The advertisement embedding unit naturally embeds advertisements generated by the advertisement generation unit into the video. For example, the advertisement embedding unit inserts advertisements using seamless transition technology. The advertisement embedding unit can also adjust the advertisement to match the color tone and brightness of the video to maintain visual consistency. The advertisement embedding unit can also place advertisements in specific scenes of the video to avoid causing discomfort to viewers. As a result, the advertisement embedding system according to the embodiment can eliminate the compulsion of advertisements and improve the user experience. For example, viewers can watch advertisements in a natural way without skipping them. This is expected to improve viewer satisfaction and increase the rate of continued use of video streaming services.

[0048] The ad generation unit can change the style of the ad to match the atmosphere and theme of the scene. For example, the ad generation unit uses a generation AI to generate different ads for each video scene. For example, the ad generation unit generates ads for sports equipment for action scenes and ads for jewelry for romantic scenes, changing the style of the ad to match the atmosphere of the scene. The ad generation unit also generates different ads for each video scene using the generation AI, adjusting the ad design and color to match the theme of the scene. For example, the ad generation unit generates ads with dark colors for horror scenes and ads with bright colors for comedy scenes. The ad generation unit also generates different ads for each video scene using the generation AI, changing the font and layout of the ad to match the atmosphere and theme of the scene. For example, an old-fashioned font is used for historical drama scenes and a modern font is used for futuristic scenes. This allows the ad generation unit to generate ads that match the scene and improve the viewing experience.

[0049] The advertisement generation unit can automatically generate advertisement narration and sound effects that match the audio data. For example, the advertisement generation unit uses a generation AI to analyze the audio data of a video and automatically generate advertisement narration that matches the scene. For example, it generates energetic narration for action scenes and calm narration for moving scenes. The advertisement generation unit also analyzes the audio data of a video and the generation AI automatically generates sound effects that match the scene. For example, it adds crowd cheers to an advertisement for a sports scene and eerie sounds to an advertisement for a horror scene. The advertisement generation unit also uses a generation AI to analyze the audio data of a video and incorporates music that matches the atmosphere of the scene into the advertisement. For example, it uses soft music for romantic scenes and intense music for action scenes. This allows the advertisement to be generated to match the audio, improving the viewing experience.

[0050] The advertisement generation unit uses the emotion estimation function to generate advertisements that correspond to the viewer's emotional state and adjust them so that the viewer feels positive emotions. The advertisement generation unit, for example, uses the emotion estimation function to analyze the viewer's emotional state in real time and generate advertisements that elicit positive emotions. For example, when the viewer is smiling, it displays a humorous advertisement. The advertisement generation unit also uses the generation AI to adjust the content and tone of the advertisement according to the viewer's emotional state. For example, when the viewer is relaxed, it generates a calm advertisement, and when the viewer is excited, it generates an energetic advertisement. The advertisement generation unit also uses the emotion estimation function to adjust the timing of advertisement display based on the viewer's emotional state. For example, when the viewer is moved, it displays an emotional advertisement, and when the viewer is enjoying themselves, it displays a fun advertisement. This allows advertisements to be generated that correspond to the viewer's emotions, improving the viewing experience.

[0051] The advertisement generation unit can generate advertisements related to the subtitle data and embed them in the subtitles. In the advertisement generation unit, for example, a generation AI analyzes the subtitle data of a video and generates advertisements related to the content of the subtitles. For example, the advertisement generation unit generates advertisements based on the names of products or services that appear in the subtitles and embeds them in the subtitles. In addition, the advertisement generation unit analyzes the subtitle data of a video and the generation AI generates advertisements related to the content of the subtitles and displays them as part of the subtitles. For example, advertisements related to places or events that appear in the subtitles are generated. In addition, the advertisement generation unit analyzes the subtitle data of a video and the generation AI generates advertisement text based on the content of the subtitles. For example, an advertisement related to the lines of a character that appears in the subtitles is generated and embedded in the subtitles. This generates advertisements related to the subtitles and improves the viewing experience.

[0052] The advertisement generation unit can analyze interactive elements and generate interactive advertisements that allow viewers to access the advertisements by clicking or tapping. In the advertisement generation unit, for example, a generation AI analyzes interactive elements of a video and generates interactive advertisements that allow viewers to access the advertisements by clicking or tapping. For example, an advertisement is generated in which detailed information is displayed when a specific object in a video is clicked. The advertisement generation unit also analyzes interactive elements of a video and a generation AI generates interactive advertisements that allow viewers to access the advertisements by tapping. For example, when a character in a video is tapped, an advertisement for a related product is displayed. The advertisement generation unit also analyzes interactive elements of a video and a generation AI generates interactive advertisements that allow viewers to access the advertisements by clicking or tapping. For example, when a specific scene in a video is clicked, an advertisement for a related service is displayed. This generates interactive advertisements and improves the viewing experience.

[0053] The advertisement generation unit can use the emotion estimation function to monitor the emotional response of the viewer when viewing the advertisement in real time and dynamically change the content of the advertisement. For example, the advertisement generation unit uses the emotion estimation function to monitor the emotional response of the viewer when viewing the advertisement in real time and dynamically change the content of the advertisement. For example, the advertisement content is changed if the viewer shows no interest. The advertisement generation unit also monitors the viewer's emotional response in real time, and the generation AI dynamically changes the content of the advertisement. For example, the advertisement content is emphasized if the viewer shows positive emotions. The advertisement generation unit also uses the emotion estimation function to analyze the viewer's emotional response in real time and dynamically change the content of the advertisement. For example, the tone of the advertisement is adjusted if the viewer shows negative emotions. This allows the content of the advertisement to be dynamically changed according to the viewer's emotional response, improving the viewing experience.

[0054] The ad embedding unit can analyze gaze data and embed advertisements in places where gazes are concentrated. For example, the generation AI in the ad embedding unit analyzes viewer gaze data for each video scene and embeds advertisements in places where gazes are concentrated. For example, advertisements are displayed near characters that viewers are paying attention to. The ad embedding unit also analyzes viewer gaze data and embeds advertisements in places where gazes are concentrated. For example, advertisements are displayed in the background of scenes that viewers are paying attention to. The ad embedding unit also analyzes viewer gaze data and embeds advertisements in places where gazes are concentrated. For example, advertisements are displayed near objects that viewers are paying attention to. In this way, the viewing experience can be improved by embedding advertisements in places where gazes are concentrated.

[0055] The ad embedding unit can analyze the color and brightness of the video and adjust the color and brightness of the ad to suit the video. For example, the generation AI in the ad embedding unit analyzes the color and brightness of the video and adjusts the color and brightness of the ad to suit the video. For example, in dark scenes, the brightness of the ad is adjusted to improve visibility. The ad embedding unit also analyzes the color and brightness of the video and the generation AI adjusts the color and brightness of the ad to suit the video. For example, in colorful scenes, the color of the ad is adjusted to make it look natural. The ad embedding unit also analyzes the color and brightness of the video and adjusts the color and brightness of the ad to suit the video. For example, in monochrome scenes, the color of the ad is suppressed to make it blend into the scene. This adjusts the color and brightness of the ad to suit the video, improving the viewing experience.

[0056] The ad embedding unit can analyze 3D data and embed advertisements as 3D objects. In the ad embedding unit, for example, the generation AI analyzes the 3D data of a video and embeds advertisements as 3D objects. For example, a 3D advertising billboard is added to a building in the video. The ad embedding unit also analyzes the 3D data of a video and the generation AI embeds advertisements as 3D objects. For example, a 3D advertisement is displayed in an item held by a character in the video. The ad embedding unit also analyzes the 3D data of a video and embeds advertisements as 3D objects. For example, a 3D advertisement is placed naturally in the scenery in the video. This allows advertisements to be embedded as 3D objects, improving the viewing experience.

[0057] The ad embedding unit can analyze music data and generate advertisements that match the rhythm and tempo of the music. In the ad embedding unit, for example, a generation AI analyzes the music data of a video and generates advertisements that match the rhythm and tempo of the music. For example, an advertisement animation is displayed in time with the beat of the music. In addition, the ad embedding unit analyzes the music data of a video and a generation AI generates advertisements that match the rhythm and tempo of the music. For example, the timing of advertisement display is adjusted to match the tempo of the music. In addition, the ad embedding unit analyzes the music data of a video and a generation AI generates advertisements that match the rhythm and tempo of the music. For example, the content of the advertisement is changed to match the melody of the music. This makes it possible to generate advertisements that match the rhythm and tempo of the music, improving the viewing experience.

[0058] The advertisement embedding unit can dynamically adjust the timing of advertisement display based on the emotional state of the viewer using the emotion estimation function. The advertisement embedding unit, for example, uses the emotion estimation function to dynamically adjust the timing of advertisement display based on the emotional state of the viewer. For example, an advertisement is displayed when the viewer is relaxed. The advertisement embedding unit also analyzes the emotional state of the viewer in real time, and the generation AI dynamically adjusts the timing of advertisement display. For example, an advertisement is displayed when the viewer is excited. The advertisement embedding unit also uses the emotion estimation function to dynamically adjust the timing of advertisement display based on the emotional state of the viewer. For example, an advertisement is displayed when the viewer is moved. This makes it possible to dynamically adjust the timing of advertisement display based on the emotional state of the viewer, improving the viewing experience.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The advertisement generation unit can analyze the viewer's past viewing history and generate advertisements based on the viewer's interests. For example, if the viewer has watched many sports-related videos in the past, an advertisement for sports equipment can be generated. If the viewer has watched many cooking videos in the past, an advertisement for cooking utensils or ingredients can be generated. Furthermore, if the viewer has watched many travel-related videos in the past, an advertisement promoting travel destinations can be generated. This makes it possible to provide advertisements based on the viewer's interests and increase the effectiveness of the advertisements.

[0061] The ad embedding unit can analyze the viewer's device information and generate ads optimized for the device. For example, if the viewer is watching on a smartphone, a vertical ad can be generated. If the viewer is watching on a tablet, a horizontal ad can be generated. Furthermore, if the viewer is watching on a desktop computer, a high-resolution ad suitable for a large screen can be generated. This makes it possible to provide ads optimized for the viewing device and improve the viewing experience.

[0062] The advertisement generation unit can analyze the viewer's geographic location information and generate advertisements specific to the region. For example, if the viewer is in a specific city, an advertisement for a local business in that city can be generated. Also, if the viewer is in a specific country, an advertisement tailored to the language and culture of that country can be generated. Furthermore, if the viewer is in a specific region, an advertisement for an event or promotion in that region can be generated. This makes it possible to provide advertisements specific to the region and more easily attract the viewer's attention.

[0063] The ad generation unit can analyze the viewer's social media activity and generate ads based on their interests on social media. For example, if the viewer posts frequently about a particular brand or product, it can generate ads for that brand or product. Also, if the viewer frequently uses a particular hashtag, it can generate ads related to that hashtag. Furthermore, if the viewer follows a particular influencer, it can generate ads for products or services recommended by that influencer. This makes it possible to provide ads based on the viewer's social media interests and increase the effectiveness of the ads.

[0064] The advertisement generation unit can analyze the purchase history of the viewer and generate advertisements based on past purchases. For example, if the viewer has previously purchased products from a specific brand, an advertisement for a new product from that brand can be generated. Also, if the viewer has previously purchased products from a specific category, an advertisement related to that category can be generated. Furthermore, if the viewer has previously used a specific service, an advertisement for an upgrade to that service or a related service can be generated. This makes it possible to provide advertisements based on the viewer's purchase history and increase the effectiveness of the advertisements.

[0065] The advertisement generation unit can dynamically change the color and design of the advertisement based on the viewer's emotional state using the emotion estimation function. For example, when the viewer is relaxed, an advertisement with calm colors and design can be generated. When the viewer is excited, an advertisement with vivid colors and an energetic design can be generated. Furthermore, when the viewer is moved, an advertisement with emotional colors and design can be generated. This makes it possible to provide advertisements based on the viewer's emotional state and improve the viewing experience.

[0066] The advertisement generation unit can dynamically change the audio and music of the advertisement based on the viewer's emotional state using the emotion estimation function. For example, when the viewer is relaxed, an advertisement with calm audio and music can be generated. When the viewer is excited, an advertisement with energetic audio and music can be generated. Furthermore, when the viewer is moved, an advertisement with emotional audio and music can be generated. This makes it possible to provide advertisements based on the viewer's emotional state and improve the viewing experience.

[0067] The advertisement generation unit can dynamically change the advertisement display format based on the viewer's emotional state using the emotion estimation function. For example, a static advertisement can be generated when the viewer is relaxed. A dynamic animated advertisement can be generated when the viewer is excited. Furthermore, an emotional story-style advertisement can be generated when the viewer is moved. This makes it possible to provide advertisements based on the viewer's emotional state and improve the viewing experience.

[0068] The advertisement generation unit can dynamically change the length of advertisements based on the viewer's emotional state using the emotion estimation function. For example, a short advertisement can be generated when the viewer is relaxed. A longer advertisement can be generated when the viewer is excited. Furthermore, an emotional long advertisement can be generated when the viewer is moved. This makes it possible to provide advertisements based on the viewer's emotional state and improve the viewing experience.

[0069] The advertisement generation unit can dynamically change the content of the advertisement based on the emotional state of the viewer using the emotion estimation function. For example, when the viewer is relaxed, an advertisement with calm content can be generated. When the viewer is excited, an advertisement with energetic content can be generated. Furthermore, when the viewer is moved, an advertisement with emotional content can be generated. This makes it possible to provide advertisements based on the viewer's emotional state and improve the viewing experience.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The generative AI analyzes the content of the video. For example, the generative AI performs frame analysis of the video and recognizes objects in each scene. The generative AI can also perform scene analysis to understand the story development of the video. Furthermore, the generative AI can use object recognition technology to identify specific objects within the video. Step 2: The advertisement generation unit automatically generates advertisements based on the content analyzed by the generation AI. For example, the advertisement generation unit creates advertisements using a template-based generation method. The advertisement generation unit can also generate advertisements in real time using dynamic content generation technology. Furthermore, the advertisement generation unit can generate customized advertisements based on data provided by the generation AI. Step 3: The ad embedding unit embeds the advertisements generated by the ad generation unit into the video in a natural way. For example, the ad embedding unit inserts the advertisements using seamless transition technology. The ad embedding unit can also adjust the advertisements to match the color tone and brightness of the video to maintain visual consistency. Furthermore, the ad embedding unit can place the advertisements in specific scenes of the video to avoid causing discomfort to the viewer.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0139] 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. Generative AI that analyzes the content of videos using generative AI, an advertisement generation unit that automatically generates advertisements based on the content analyzed by the generation AI; an advertisement embedding unit that embeds the advertisement generated by the advertisement generation unit into the video in a natural way; A system characterized by:

2. The advertisement generation unit Ad style to match the mood and theme of the scene 2. The system of claim 1.

3. The advertisement generation unit Automatically generate advertising narration and sound effects that match audio data 2. The system of claim 1.

4. The advertisement generation unit To generate advertisements according to the emotional state of a viewer and adjust the advertisements so that the viewer feels positive emotions.

2. The system of claim 1.

5. The advertisement generation unit Generate advertisements related to the subtitle data and embed them in the subtitles 2. The system of claim 1.

6. The advertisement generation unit Analyze interactive elements and generate interactive ads that allow viewers to click or tap to access the ad.

2. The system of claim 1.

7. The advertisement generation unit Monitor viewers' emotional responses to ads in real time and dynamically change the content of the ads.

2. The system of claim 1.

8. The advertisement embedding unit Analyze gaze data and embed advertisements in areas where gazes are concentrated 2. The system of claim 1.

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