System

Abstract

An object of a system according to an embodiment is to improve the quality while suppressing the production cost of a video advertisement.SOLUTION: A system according to an embodiment includes a generation part, an analysis part and a distribution part. The generation unit generates a video advertisement using the generation AI. The analysis unit analyzes an effect of the video advertisement generated by the generation unit based on the big data. The distribution unit distributes the video advertisement to the target audience based on a result of the analysis by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, video ad production costs were high and it was difficult to improve quality.

[0005] The system according to the embodiment aims to improve the quality of video advertisements while reducing production costs. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, an analysis unit, and a distribution unit. The generation unit generates a video advertisement using a generation AI. The analysis unit analyzes the effectiveness of the video advertisement generated by the generation unit based on big data. The distribution unit distributes the video advertisement to a target audience based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the quality of video advertisements while reducing production costs. [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) A video advertisement creation system according to an embodiment of the present invention is a system that creates video advertisements using generation AI and big data. This system uses generation AI to reduce video advertisement production costs while improving quality, and also uses big data to increase advertising sales. As a result, the video advertisement creation system can solve the problems of corporate clients and service providers. For example, corporate clients can create high-quality video advertisements at low cost, and service providers can increase advertising sales.

[0029] A video advertisement creation system according to an embodiment includes a generation unit, an analysis unit, and a distribution unit. The generation unit generates a video advertisement using a generation AI. For example, the generation AI automatically generates a video advertisement in response to a user's instructions. For example, when a user inputs a prompt such as, "Please create a video introducing a new product," the generation AI creates a video scenario based on the user's instructions and combines video and audio to generate a high-quality video advertisement. The generation AI can also analyze past advertising data and user browsing history to identify what content is most effective and generate a video advertisement based on that. The analysis unit analyzes the effectiveness of the video advertisement generated by the generation unit based on big data. For example, the analysis unit analyzes viewer response data to identify what elements viewers prefer. The analysis unit can also utilize big data to analyze the preferences and behavioral patterns of a target audience and develop an optimal advertising distribution strategy. The distribution unit distributes video advertisements to the target audience based on the results of the analysis by the analysis unit. For example, the distribution unit distributes advertisements to the target audience at the optimal timing to improve viewer ratings. The distribution unit can also maximize the effectiveness of advertisements by monitoring the effectiveness of advertisements in real time and automatically adjusting the advertisement content as needed. As a result, the video advertisement creation system according to the embodiment can reduce the production costs of video advertisements and improve their quality by utilizing generation AI and big data. For example, corporate clients can create high-quality video advertisements at low cost, and service providers can increase their advertising sales.

[0030] The generation unit can learn the user's past advertising production history and automatically generate customized video advertisements tailored to the user's preferences. For example, the generation unit uses a generation AI to analyze the user's past advertising production history and automatically generate customized video advertisements tailored to the user's preferences. For example, the generation unit learns patterns of video and audio used in the past and creates new advertisements based on that. The generation unit also collects data on advertisements the user has previously produced, and the generation AI learns the user's preferences and style based on that data. This makes it possible to automatically generate video advertisements tailored to the advertising tastes desired by the user. The generation unit also uses a generation AI to analyze the user's past advertising production history, extract specific elements (e.g., color use and music selection), and generate customized video advertisements based on those elements. This makes it possible to automatically generate customized video advertisements tailored to the user's preferences.

[0031] The generation unit can learn the visual and auditory preferences of different cultures and regions and generate video advertisements optimized for the target region. For example, the generation AI of the generation unit learns the visual and auditory preferences of different cultures and regions and generates video advertisements optimized for the target region. For example, an advertisement incorporating music and colors popular in a particular region is created. The generation unit also collects data on different cultures and regions, and the generation AI generates video advertisements optimized for the target region based on that data. For example, an advertisement incorporating festivals and events unique to the region can be created. The generation unit also analyzes the visual and auditory preferences of different cultures and regions and generates video advertisements optimized for the target region based on that. For example, an advertisement using the local language or dialect can be created. This allows for the generation of video advertisements optimized for the target region.

[0032] The generation unit can analyze a user's voice instructions in real time and generate a video advertisement based on the voice instructions. For example, the generation unit uses a generation AI to analyze a user's voice instructions in real time and generate a video advertisement based on those instructions. For example, if a user says, "I want you to create a video introducing a new product," the generation unit automatically generates a video based on that instruction. The generation unit also uses voice recognition technology to analyze a user's voice instructions, and the generation AI generates a video advertisement based on those instructions. For example, if a user says, "I want you to create an advertisement with a cheerful atmosphere," a video can be created based on that instruction. The generation unit also builds a system in which the generation AI analyzes a user's voice instructions in real time and generates a video advertisement based on those instructions. For example, if a user says, "I want you to create an advertisement that highlights a specific product," a video can be automatically generated based on that instruction. This makes it possible to generate video advertisements based on the user's voice instructions.

[0033] The generation unit can learn advertising trends in different industries and generate video advertisements that incorporate industry-specific elements. For example, the generation AI of the generation unit learns advertising trends in different industries and generates video advertisements that incorporate industry-specific elements. For example, an advertisement that incorporates trends in the fashion industry is created. The generation unit also collects data from different industries, and the generation AI generates video advertisements that incorporate industry-specific elements based on that data. For example, an advertisement that incorporates the latest technology in the technology industry can be created. The generation unit also analyzes advertising trends in different industries and generates video advertisements that incorporate industry-specific elements based on that data. For example, an advertisement that incorporates popular menu items in the food and beverage industry can be created. This makes it possible to generate video advertisements that incorporate industry-specific elements.

[0034] The analysis unit can use big data to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content. For example, the analysis unit can use big data to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content. For example, the analysis unit can create advertisements for related products based on past purchasing history. The analysis unit can also analyze the target audience's social media activity, and the generation AI can identify the most effective advertising content based on that data. For example, advertisements related to topics that users are interested in can be created. The analysis unit can also use big data to analyze the target audience's purchasing history and social media activity, and the generation AI can identify the most effective advertising content based on that data. For example, advertisements can be created based on the user's interests. This makes it possible to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content.

[0035] The analysis unit can use big data to analyze the viewing time and viewing completion rate of advertisements, and generate advertisements that viewers are likely to watch to the end. The analysis unit, for example, uses big data to analyze the viewing time and viewing completion rate of advertisements, and generate advertisements that viewers are likely to watch to the end. For example, advertisements that incorporate elements that have a high viewing completion rate are created. The analysis unit also collects advertisement viewing data, and the generation AI generates advertisements that viewers are likely to watch to the end based on that data. For example, advertisements that incorporate elements that attract the viewer's interest can be created. The analysis unit also uses big data to analyze the viewing time and viewing completion rate of advertisements, and a system can be built in which the generation AI generates advertisements that viewers are likely to watch to the end based on that data. For example, advertisements that incorporate storytelling that attracts the viewer's interest can be created. This makes it possible to generate advertisements that viewers are likely to watch to the end.

[0036] The analysis unit can use big data to analyze the effectiveness of advertisements on different devices (smartphones, tablets, PCs, etc.) and generate advertisements optimized for the devices. For example, the analysis unit can use big data to analyze the effectiveness of advertisements on different devices (smartphones, tablets, PCs, etc.) and generate advertisements optimized for the devices. For example, the analysis unit can create short video advertisements for smartphones. The analysis unit can also collect usage data on different devices, and the generation AI can generate advertisements optimized for the devices based on that data. For example, interactive advertisements can be created for tablets. The analysis unit can also use big data to build a system that analyzes the effectiveness of advertisements on different devices and the generation AI can generate advertisements optimized for the devices based on that data. For example, an advertisement that provides detailed information can be created for PCs. This makes it possible to generate advertisements optimized for the devices.

[0037] The analysis unit can use big data to analyze advertising effectiveness at different times of the day and on different days of the week, and identify the optimal delivery timing. The analysis unit, for example, uses big data to analyze advertising effectiveness at different times of the day and on different days of the week, and identify the optimal delivery timing. For example, an advertisement that is effective during the daytime on a weekday is created. The analysis unit also collects advertising delivery data, and the generation AI identifies the optimal delivery timing based on that data. For example, an advertisement that is effective on a weekend night can be created. The analysis unit also uses big data to build a system that analyzes advertising effectiveness at different times of the day and on different days of the week, and the generation AI identifies the optimal delivery timing based on that data. For example, an advertisement that has a high viewer rate during a specific time of day can be created. This makes it possible to identify the optimal delivery timing.

[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 generation unit can analyze a user's voice instructions in real time and generate a video advertisement based on the voice instructions. For example, the generation AI analyzes a user's voice instructions in real time and generates a video advertisement based on those instructions. If a user says, "I want you to create a video introducing a new product," a video is automatically generated based on that instruction. The generation unit also uses voice recognition technology to analyze the user's voice instructions, and the generation AI generates a video advertisement based on those instructions. If a user says, "I want you to create an advertisement with a cheerful atmosphere," a video can be created based on that instruction. Furthermore, the generation unit builds a system in which the generation AI analyzes a user's voice instructions in real time and generates a video advertisement based on those instructions. If a user says, "I want you to create an advertisement that highlights a specific product," a video can be automatically generated based on that instruction. This makes it possible to generate video advertisements based on the user's voice instructions.

[0040] The generation unit can learn advertising trends in different industries and generate video ads that incorporate industry-specific elements. For example, the generation AI can learn advertising trends in different industries and generate video ads that incorporate industry-specific elements. An advertisement can be created that incorporates trends in the fashion industry. The generation unit can also collect data from different industries, and the generation AI can use that data to generate video ads that incorporate industry-specific elements. An advertisement can be created that incorporates the latest technology in the technology industry. Furthermore, the generation unit can analyze advertising trends in different industries and use that data to generate video ads that incorporate industry-specific elements. An advertisement can be created that incorporates popular menu items in the food and beverage industry. This makes it possible to generate video ads that incorporate industry-specific elements.

[0041] The analysis unit can use big data to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content. For example, big data can be used to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content. Advertisements for related products are created based on past purchasing history. The analysis unit also analyzes the target audience's social media activity, and the generation AI identifies the most effective advertising content based on that data. Advertisements related to topics that users are interested in can be created. Furthermore, the analysis unit uses big data to analyze the target audience's purchasing history and social media activity, building a system in which the generation AI identifies the most effective advertising content based on that data. Advertisements can be created based on the user's interests. This makes it possible to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content.

[0042] The analysis unit uses big data to analyze the viewing time and viewing completion rate of advertisements, and can generate advertisements that viewers are likely to watch to the end. For example, big data can be used to analyze the viewing time and viewing completion rate of advertisements, and generate advertisements that viewers are likely to watch to the end. Advertisements are created that incorporate elements that have a high viewing completion rate. The analysis unit also collects advertisement viewing data, and the generation AI uses that data to generate advertisements that viewers are likely to watch to the end. Advertisements that incorporate elements that attract viewers' interest can be created. Furthermore, the analysis unit uses big data to analyze the viewing time and viewing completion rate of advertisements, and a system can be built in which the generation AI uses that data to generate advertisements that viewers are likely to watch to the end. Advertisements can be created that incorporate storytelling that attracts viewers' interest. This makes it possible to generate advertisements that viewers are likely to watch to the end.

[0043] The analysis unit can use big data to analyze the effectiveness of advertisements on different devices (smartphones, tablets, PCs, etc.) and generate advertisements optimized for each device. For example, big data can be used to analyze the effectiveness of advertisements on different devices (smartphones, tablets, PCs, etc.) and generate advertisements optimized for each device. Short video advertisements can be created for smartphones. The analysis unit also collects usage data on different devices, and the generation AI generates advertisements optimized for each device based on that data. Interactive advertisements can be created for tablets. Furthermore, the analysis unit can use big data to build a system that analyzes the effectiveness of advertisements on different devices and the generation AI generates advertisements optimized for each device based on that data. Advertisements that provide detailed information can be created for PCs. This makes it possible to generate advertisements optimized for each device.

[0044] The generation unit can learn the user's past advertising production history and automatically generate customized video ads tailored to the user's preferences. For example, the generation AI analyzes the user's past advertising production history and automatically generates customized video ads tailored to the user's preferences. It learns the video and audio patterns used in the past and creates new ads based on them. The generation unit also collects data on advertisements the user has previously produced, and the generation AI learns the user's preferences and style based on that data. This makes it possible to automatically generate video ads tailored to the advertising taste desired by the user. Furthermore, the generation AI analyzes the user's past advertising production history, extracts specific elements (e.g., color use and music selection), and generates customized video ads based on those elements. This makes it possible to automatically generate customized video ads tailored to the user's preferences.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The generation unit uses generation AI to generate video ads. For example, the generation AI automatically generates video ads based on instructions from the user. When a user inputs a prompt such as "Please create a video introducing a new product," the generation AI creates a video scenario based on the user's instructions and combines video and audio to generate a high-quality video ad. The generation AI can also analyze past advertising data and user browsing history to identify what type of content is most effective and generate video ads based on that. Step 2: The analysis unit uses big data to analyze the effectiveness of the video ads generated by the generation unit. For example, it analyzes viewer response data to identify what elements viewers like. It can also use big data to analyze the preferences and behavioral patterns of the target audience and develop optimal ad distribution strategies. Step 3: The distribution unit distributes video ads to the target audience based on the results of the analysis by the analysis unit. For example, it can distribute ads to the target audience at the optimal time to improve viewer ratings. It can also maximize the effectiveness of ads by monitoring the effectiveness of ads in real time and automatically adjusting the ad content as needed.

[0047] (Example 2) A video advertisement creation system according to an embodiment of the present invention is a system that creates video advertisements using generation AI and big data. This system uses generation AI to reduce video advertisement production costs while improving quality, and also uses big data to increase advertising sales. As a result, the video advertisement creation system can solve the problems of corporate clients and service providers. For example, corporate clients can create high-quality video advertisements at low cost, and service providers can increase advertising sales.

[0048] A video advertisement creation system according to an embodiment includes a generation unit, an analysis unit, and a distribution unit. The generation unit generates a video advertisement using a generation AI. For example, the generation AI automatically generates a video advertisement in response to a user's instructions. For example, when a user inputs a prompt such as, "Please create a video introducing a new product," the generation AI creates a video scenario based on the user's instructions and combines video and audio to generate a high-quality video advertisement. The generation AI can also analyze past advertising data and user browsing history to identify what content is most effective and generate a video advertisement based on that. The analysis unit analyzes the effectiveness of the video advertisement generated by the generation unit based on big data. For example, the analysis unit analyzes viewer response data to identify what elements viewers prefer. The analysis unit can also utilize big data to analyze the preferences and behavioral patterns of a target audience and develop an optimal advertising distribution strategy. The distribution unit distributes video advertisements to the target audience based on the results of the analysis by the analysis unit. For example, the distribution unit distributes advertisements to the target audience at the optimal timing to improve viewer ratings. The distribution unit can also maximize the effectiveness of advertisements by monitoring the effectiveness of advertisements in real time and automatically adjusting the advertisement content as needed. As a result, the video advertisement creation system according to the embodiment can reduce the production costs of video advertisements and improve their quality by utilizing generation AI and big data. For example, corporate clients can create high-quality video advertisements at low cost, and service providers can increase their advertising sales.

[0049] The generation unit can learn the user's past advertising production history and automatically generate customized video advertisements tailored to the user's preferences. For example, the generation unit uses a generation AI to analyze the user's past advertising production history and automatically generate customized video advertisements tailored to the user's preferences. For example, the generation unit learns patterns of video and audio used in the past and creates new advertisements based on that. The generation unit also collects data on advertisements the user has previously produced, and the generation AI learns the user's preferences and style based on that data. This makes it possible to automatically generate video advertisements tailored to the advertising tastes desired by the user. The generation unit also uses a generation AI to analyze the user's past advertising production history, extract specific elements (e.g., color use and music selection), and generate customized video advertisements based on those elements. This makes it possible to automatically generate customized video advertisements tailored to the user's preferences.

[0050] The generation unit can learn the visual and auditory preferences of different cultures and regions and generate video advertisements optimized for the target region. For example, the generation AI of the generation unit learns the visual and auditory preferences of different cultures and regions and generates video advertisements optimized for the target region. For example, an advertisement incorporating music and colors popular in a particular region is created. The generation unit also collects data on different cultures and regions, and the generation AI generates video advertisements optimized for the target region based on that data. For example, an advertisement incorporating festivals and events unique to the region can be created. The generation unit also analyzes the visual and auditory preferences of different cultures and regions and generates video advertisements optimized for the target region based on that. For example, an advertisement using the local language or dialect can be created. This allows for the generation of video advertisements optimized for the target region.

[0051] The generation unit can use the emotion estimation function to analyze the user's emotional state in real time and generate a video advertisement that will evoke the most positive emotions in the user. The generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and generate a video advertisement that will evoke the most positive emotions in the user. For example, the generation unit analyzes the user's facial expressions and tone of voice and incorporates elements that elicit positive emotions. The generation unit also monitors the user's emotional state in real time and the generation AI generates a video advertisement that elicits positive emotions based on that data. For example, it can select images and music that will make the user smile. The generation unit also uses the emotion estimation function to analyze the user's emotional state and the generation AI generates a video advertisement that elicits positive emotions based on that data. For example, it can incorporate images and music that will relax the user. This makes it possible to generate a video advertisement that will evoke the most positive emotions in the user.

[0052] The generation unit can analyze a user's voice instructions in real time and generate a video advertisement based on the voice instructions. For example, the generation unit uses a generation AI to analyze a user's voice instructions in real time and generate a video advertisement based on those instructions. For example, if a user says, "I want you to create a video introducing a new product," the generation unit automatically generates a video based on that instruction. The generation unit also uses voice recognition technology to analyze a user's voice instructions, and the generation AI generates a video advertisement based on those instructions. For example, if a user says, "I want you to create an advertisement with a cheerful atmosphere," a video can be created based on that instruction. The generation unit also builds a system in which the generation AI analyzes a user's voice instructions in real time and generates a video advertisement based on those instructions. For example, if a user says, "I want you to create an advertisement that highlights a specific product," a video can be automatically generated based on that instruction. This makes it possible to generate video advertisements based on the user's voice instructions.

[0053] The generation unit can learn advertising trends in different industries and generate video advertisements that incorporate industry-specific elements. For example, the generation AI of the generation unit learns advertising trends in different industries and generates video advertisements that incorporate industry-specific elements. For example, an advertisement that incorporates trends in the fashion industry is created. The generation unit also collects data from different industries, and the generation AI generates video advertisements that incorporate industry-specific elements based on that data. For example, an advertisement that incorporates the latest technology in the technology industry can be created. The generation unit also analyzes advertising trends in different industries and generates video advertisements that incorporate industry-specific elements based on that data. For example, an advertisement that incorporates popular menu items in the food and beverage industry can be created. This makes it possible to generate video advertisements that incorporate industry-specific elements.

[0054] The generation unit can use the emotion estimation function to automatically insert video and audio with a relaxing effect to reduce the stress a user feels while creating an advertisement. For example, the generation unit uses the emotion estimation function to automatically insert video and audio with a relaxing effect to reduce the stress a user feels while creating an advertisement. For example, natural scenery or relaxing music can be incorporated. The generation unit also monitors the user's emotional state in real time, and the generation AI automatically inserts video and audio with a relaxing effect based on that data. For example, it can display video that helps the user relax when the user feels stressed. The generation unit also uses the emotion estimation function to build a system that automatically inserts video and audio with a relaxing effect to reduce the stress a user feels while creating an advertisement. For example, it can automatically play music that helps the user relax. This makes it possible to automatically insert video and audio with a relaxing effect to reduce the stress a user feels while creating an advertisement.

[0055] The analysis unit can use big data to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content. For example, the analysis unit can use big data to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content. For example, the analysis unit can create advertisements for related products based on past purchasing history. The analysis unit can also analyze the target audience's social media activity, and the generation AI can identify the most effective advertising content based on that data. For example, advertisements related to topics that users are interested in can be created. The analysis unit can also use big data to analyze the target audience's purchasing history and social media activity, and the generation AI can identify the most effective advertising content based on that data. For example, advertisements can be created based on the user's interests. This makes it possible to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content.

[0056] The analysis unit can use big data to analyze the viewing time and viewing completion rate of advertisements, and generate advertisements that viewers are likely to watch to the end. The analysis unit, for example, uses big data to analyze the viewing time and viewing completion rate of advertisements, and generate advertisements that viewers are likely to watch to the end. For example, advertisements that incorporate elements that have a high viewing completion rate are created. The analysis unit also collects advertisement viewing data, and the generation AI generates advertisements that viewers are likely to watch to the end based on that data. For example, advertisements that incorporate elements that attract the viewer's interest can be created. The analysis unit also uses big data to analyze the viewing time and viewing completion rate of advertisements, and a system can be built in which the generation AI generates advertisements that viewers are likely to watch to the end based on that data. For example, advertisements that incorporate storytelling that attracts the viewer's interest can be created. This makes it possible to generate advertisements that viewers are likely to watch to the end.

[0057] The analysis unit can use the emotion estimation function to analyze the viewer's emotional response in real time and identify advertising content that will evoke the most positive emotions in the viewer. The analysis unit, for example, uses the emotion estimation function to analyze the viewer's emotional response in real time and identify advertising content that will evoke the most positive emotions in the viewer. For example, it can analyze the viewer's facial expressions and tone of voice and incorporate elements that elicit positive emotions. The analysis unit also monitors the viewer's emotional response in real time and the generation AI can use that data to identify advertising content that will evoke positive emotions. For example, it can select images and music that will make the viewer smile. The analysis unit can also use the emotion estimation function to analyze the viewer's emotional response and build a system in which the generation AI can use that data to identify advertising content that will evoke positive emotions. For example, it can incorporate images and music that will relax the viewer. This makes it possible to identify advertising content that will evoke the most positive emotions in the viewer.

[0058] The analysis unit can use big data to analyze the effectiveness of advertisements on different devices (smartphones, tablets, PCs, etc.) and generate advertisements optimized for the devices. For example, the analysis unit can use big data to analyze the effectiveness of advertisements on different devices (smartphones, tablets, PCs, etc.) and generate advertisements optimized for the devices. For example, the analysis unit can create short video advertisements for smartphones. The analysis unit can also collect usage data on different devices, and the generation AI can generate advertisements optimized for the devices based on that data. For example, interactive advertisements can be created for tablets. The analysis unit can also use big data to build a system that analyzes the effectiveness of advertisements on different devices and the generation AI can generate advertisements optimized for the devices based on that data. For example, an advertisement that provides detailed information can be created for PCs. This makes it possible to generate advertisements optimized for the devices.

[0059] The analysis unit can use big data to analyze advertising effectiveness at different times of the day and on different days of the week, and identify the optimal delivery timing. The analysis unit, for example, uses big data to analyze advertising effectiveness at different times of the day and on different days of the week, and identify the optimal delivery timing. For example, an advertisement that is effective during the daytime on a weekday is created. The analysis unit also collects advertising delivery data, and the generation AI identifies the optimal delivery timing based on that data. For example, an advertisement that is effective on a weekend night can be created. The analysis unit also uses big data to build a system that analyzes advertising effectiveness at different times of the day and on different days of the week, and the generation AI identifies the optimal delivery timing based on that data. For example, an advertisement that has a high viewer rate during a specific time of day can be created. This makes it possible to identify the optimal delivery timing.

[0060] The analysis unit can use the emotion estimation function to analyze the environment (e.g., quiet or noisy) when the viewer views the advertisement and generate an advertisement optimized for the environment. The analysis unit, for example, uses the emotion estimation function to analyze the environment when the viewer views the advertisement and generate an advertisement optimized for the environment. For example, if the viewer views the advertisement in a quiet place, relaxing music is used. The analysis unit also collects environmental data about the viewer, and the generation AI generates an advertisement optimized for the environment based on that data. For example, if the viewer views the advertisement in a noisy place, video with a strong visual impact can be used. The analysis unit also uses the emotion estimation function to analyze the environment when the viewer views the advertisement in real time, and a generation AI can generate an advertisement optimized for the environment based on that data. For example, a short advertisement can be created when the viewer is viewing the advertisement while on the move. This makes it possible to generate an advertisement optimized for the environment when the viewer views the advertisement.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The generation unit can analyze a user's voice instructions in real time and generate a video advertisement based on the voice instructions. For example, the generation AI analyzes a user's voice instructions in real time and generates a video advertisement based on those instructions. If a user says, "I want you to create a video introducing a new product," a video is automatically generated based on that instruction. The generation unit also uses voice recognition technology to analyze the user's voice instructions, and the generation AI generates a video advertisement based on those instructions. If a user says, "I want you to create an advertisement with a cheerful atmosphere," a video can be created based on that instruction. Furthermore, the generation unit builds a system in which the generation AI analyzes a user's voice instructions in real time and generates a video advertisement based on those instructions. If a user says, "I want you to create an advertisement that highlights a specific product," a video can be automatically generated based on that instruction. This makes it possible to generate video advertisements based on the user's voice instructions.

[0063] The generation unit can learn advertising trends in different industries and generate video ads that incorporate industry-specific elements. For example, the generation AI can learn advertising trends in different industries and generate video ads that incorporate industry-specific elements. An advertisement can be created that incorporates trends in the fashion industry. The generation unit can also collect data from different industries, and the generation AI can use that data to generate video ads that incorporate industry-specific elements. An advertisement can be created that incorporates the latest technology in the technology industry. Furthermore, the generation unit can analyze advertising trends in different industries and use that data to generate video ads that incorporate industry-specific elements. An advertisement can be created that incorporates popular menu items in the food and beverage industry. This makes it possible to generate video ads that incorporate industry-specific elements.

[0064] The analysis unit can use big data to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content. For example, big data can be used to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content. Advertisements for related products are created based on past purchasing history. The analysis unit also analyzes the target audience's social media activity, and the generation AI identifies the most effective advertising content based on that data. Advertisements related to topics that users are interested in can be created. Furthermore, the analysis unit uses big data to analyze the target audience's purchasing history and social media activity, building a system in which the generation AI identifies the most effective advertising content based on that data. Advertisements can be created based on the user's interests. This makes it possible to analyze the target audience's purchasing history and social media activity to identify the most effective advertising content.

[0065] The analysis unit uses big data to analyze the viewing time and viewing completion rate of advertisements, and can generate advertisements that viewers are likely to watch to the end. For example, big data can be used to analyze the viewing time and viewing completion rate of advertisements, and generate advertisements that viewers are likely to watch to the end. Advertisements are created that incorporate elements that have a high viewing completion rate. The analysis unit also collects advertisement viewing data, and the generation AI uses that data to generate advertisements that viewers are likely to watch to the end. Advertisements that incorporate elements that attract viewers' interest can be created. Furthermore, the analysis unit uses big data to analyze the viewing time and viewing completion rate of advertisements, and a system can be built in which the generation AI uses that data to generate advertisements that viewers are likely to watch to the end. Advertisements can be created that incorporate storytelling that attracts viewers' interest. This makes it possible to generate advertisements that viewers are likely to watch to the end.

[0066] The analysis unit can use big data to analyze the effectiveness of advertisements on different devices (smartphones, tablets, PCs, etc.) and generate advertisements optimized for each device. For example, big data can be used to analyze the effectiveness of advertisements on different devices (smartphones, tablets, PCs, etc.) and generate advertisements optimized for each device. Short video advertisements can be created for smartphones. The analysis unit also collects usage data on different devices, and the generation AI generates advertisements optimized for each device based on that data. Interactive advertisements can be created for tablets. Furthermore, the analysis unit can use big data to build a system that analyzes the effectiveness of advertisements on different devices and the generation AI generates advertisements optimized for each device based on that data. Advertisements that provide detailed information can be created for PCs. This makes it possible to generate advertisements optimized for each device.

[0067] The generation unit can use the emotion estimation function to analyze the user's emotional state in real time and generate a video advertisement that will evoke the most positive emotions in the user. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and generate a video advertisement that will evoke the most positive emotions in the user. The user's facial expressions and tone of voice can be analyzed and elements that elicit positive emotions can be incorporated. The generation unit can also monitor the user's emotional state in real time and generate a video advertisement that elicits positive emotions based on that data using the generation AI. Images and music that will make the user smile can be selected. The generation unit can also use the emotion estimation function to analyze the user's emotional state and generate a video advertisement that elicits positive emotions based on that data using the generation AI. Images and music that will relax the user can be incorporated. This makes it possible to generate a video advertisement that will evoke the most positive emotions in the user.

[0068] The generation unit can use the emotion estimation function to automatically insert video and audio with a relaxing effect to reduce the stress the user feels while creating the advertisement. For example, the emotion estimation function can be used to automatically insert video and audio with a relaxing effect to reduce the stress the user feels while creating the advertisement. Natural scenery and relaxing music can be incorporated. The generation unit also monitors the user's emotional state in real time, and the generation AI automatically inserts video and audio with a relaxing effect based on that data. Video that helps the user relax can be displayed when the user feels stressed. Furthermore, the generation unit uses the emotion estimation function to build a system that automatically inserts video and audio with a relaxing effect to reduce the stress the user feels while creating the advertisement. Music that helps the user relax can be automatically played. This makes it possible to automatically insert video and audio with a relaxing effect to reduce the stress the user feels while creating the advertisement.

[0069] The analysis unit can use the emotion estimation function to analyze the viewer's emotional response in real time and identify advertising content that will evoke the most positive emotions in the viewer. For example, the emotion estimation function can be used to analyze the viewer's emotional response in real time and identify advertising content that will evoke the most positive emotions in the viewer. The viewer's facial expressions and tone of voice can be analyzed and elements that elicit positive emotions can be incorporated. The analysis unit can also monitor the viewer's emotional response in real time and use the generation AI to identify advertising content that will evoke positive emotions based on that data. Images and music that will make the viewer smile can be selected. Furthermore, the analysis unit can use the emotion estimation function to analyze the viewer's emotional response and use the generation AI to identify advertising content that will evoke positive emotions based on that data. Images and music that will relax the viewer can be incorporated. This makes it possible to identify advertising content that will evoke the most positive emotions in the viewer.

[0070] The analysis unit can use the emotion estimation function to analyze the environment (e.g., quiet or noisy) when the viewer views the advertisement and generate an advertisement optimized for the environment. For example, the emotion estimation function can be used to analyze the environment when the viewer views the advertisement and generate an advertisement optimized for the environment. If the advertisement is viewed in a quiet place, relaxing music can be used. The analysis unit also collects data on the viewer's environment, and the generation AI generates an advertisement optimized for the environment based on that data. If the advertisement is viewed in a noisy place, images with a strong visual impact can be used. Furthermore, the analysis unit uses the emotion estimation function to analyze the environment when the viewer views the advertisement in real time, and a system can be built in which the generation AI generates an advertisement optimized for the environment based on that data. If the viewer is viewing the advertisement while on the move, a short advertisement can be created. This makes it possible to generate an advertisement optimized for the environment when the viewer views the advertisement.

[0071] The generation unit can learn the user's past advertising production history and automatically generate customized video ads tailored to the user's preferences. For example, the generation AI analyzes the user's past advertising production history and automatically generates customized video ads tailored to the user's preferences. It learns the video and audio patterns used in the past and creates new ads based on them. The generation unit also collects data on advertisements the user has previously produced, and the generation AI learns the user's preferences and style based on that data. This makes it possible to automatically generate video ads tailored to the advertising taste desired by the user. Furthermore, the generation AI analyzes the user's past advertising production history, extracts specific elements (e.g., color use and music selection), and generates customized video ads based on those elements. This makes it possible to automatically generate customized video ads tailored to the user's preferences.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The generation unit uses generation AI to generate video ads. For example, the generation AI automatically generates video ads based on instructions from the user. When a user inputs a prompt such as "Please create a video introducing a new product," the generation AI creates a video scenario based on the user's instructions and combines video and audio to generate a high-quality video ad. The generation AI can also analyze past advertising data and user browsing history to identify what type of content is most effective and generate video ads based on that. Step 2: The analysis unit uses big data to analyze the effectiveness of the video ads generated by the generation unit. For example, it analyzes viewer response data to identify what elements viewers like. It can also use big data to analyze the preferences and behavioral patterns of the target audience and develop optimal ad distribution strategies. Step 3: The distribution unit distributes video ads to the target audience based on the results of the analysis by the analysis unit. For example, it can distribute ads to the target audience at the optimal time to improve viewer ratings. It can also maximize the effectiveness of ads by monitoring the effectiveness of ads in real time and automatically adjusting the ad content as needed.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 generation unit that generates a video advertisement using a generation AI; an analysis unit that analyzes the effectiveness of the video advertisement generated by the generation unit based on big data; a distribution unit that distributes the video advertisement to a target audience based on the results of the analysis by the analysis unit. A system characterized by:

2. The generation unit The system learns the user's past advertising production history and automatically generates the video advertisement customized to the user's preferences.

2. The system of claim 1.

3. The generation unit Learn the visual and auditory preferences of different cultures and regions to generate video ads optimized for the target region 2. The system of claim 1.

4. The generation unit Analyzing the user's emotional state in real time and generating the video advertisement that will evoke the most positive emotions in the user 2. The system of claim 1.

5. The generation unit Analyzing a user's voice instructions in real time and generating the video advertisement based on the voice instructions 2. The system of claim 1.

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