system

Abstract

The system according to this embodiment aims to automatically generate promotional videos that effectively convey a company's message and product characteristics, and to provide viewers with an interactive experience. [Solution] The system according to the embodiment comprises an information collection unit, a generation unit, an interactive unit, and a personalization unit. The information collection unit collects information such as company messages, product characteristics, and customer testimonials. The generation unit analyzes the information collected by the information collection unit and generates an original promotional video. The interactive unit allows viewers to select and interact with scenarios and characters based on the video generated by the generation unit. The personalization unit personalizes the video generated by the generation unit based on customer profiles and user browsing history.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is room for improvement because a promotional video that effectively conveys a company's message or product characteristics has not been sufficiently automatically generated to provide an interactive experience for viewers.

[0005] The system according to the embodiment aims to automatically generate a promotional video that effectively conveys a company's message or product characteristics and provides an interactive experience for viewers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an information gathering unit, a generation unit, an interactive unit, and a personalization unit. The information gathering unit collects information such as company messages, product characteristics, and customer testimonials. The generation unit analyzes the information collected by the information gathering unit and generates an original promotional video. The interactive unit allows viewers to select and interact with scenarios and characters based on the video generated by the generation unit. The personalization unit personalizes the video generated by the generation unit based on customer profiles and user browsing history. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate promotional videos that effectively convey a company's message and product characteristics, and provide viewers with an interactive experience. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The automated promotional video generation system according to an embodiment of the present invention is a system that collects information such as a company's message, product characteristics, and customer testimonials, and automatically generates original promotional videos using a generation AI. This system generates interactive videos in which viewers can select and manipulate the video's scenario and characters. It also has the function of automatically personalizing videos for individual customers based on customer profiles and user browsing history. For example, it collects information such as a company's message, product characteristics, and customer testimonials. Next, the generation AI analyzes this information and automatically generates an original promotional video. This video is provided in an interactive format in which viewers can select and manipulate the scenario and characters. For example, if a viewer selects a particular character, the generation AI adjusts the video so that that character plays a major role in the video. Furthermore, it personalizes the video content for individual customers based on customer profiles and user browsing history. For example, for a customer who has shown interest in a particular product, a video highlighting information related to that product is generated. This deepens viewer engagement and provides memorable content. This service is extremely useful for companies that want to promote their products and services, especially companies that need high-quality video content in a short amount of time. While video production typically requires professional skills, time, and significant costs, this service automates the entire process, from planning and scriptwriting to filming and editing, enabling the efficient creation of high-quality promotional videos. This automated promotional video generation system collects and analyzes information such as company messages, product characteristics, and customer testimonials to generate original promotional videos, providing interactive videos where viewers can select and control scenarios and characters, and even personalizing videos based on customer profiles and user browsing history.

[0029] The automated promotional video generation system according to the embodiment comprises an information collection unit, a generation unit, an interactive unit, and a personalization unit. The information collection unit collects information such as the company's message, product characteristics, and customer testimonials. For example, the information collection unit collects the company's message, such as its vision, mission statement, and advertising copy. The information collection unit can also collect product characteristics, such as the product's technical specifications, unique features, and usage instructions. Furthermore, the information collection unit can collect customer testimonials, such as video interviews, survey results, and comments from review sites. For example, the information collection unit collects information using methods such as database searches, web scraping, and surveys. The generation unit uses a generation AI to analyze the information collected by the information collection unit and generate an original promotional video. For example, the generation unit analyzes the information using text mining, data analysis, and machine learning algorithms. The generation unit can also use the generation AI to generate a promotional video considering factors such as video length, style, and message consistency. For example, the generation unit inputs "Generate a promotional video based on the company's message" as a prompt to the generation AI, and the generation AI generates the video. The interactive section generates interactive videos in which viewers can select and interact with scenarios and characters. The interactive section provides interactive elements, for example, by considering interface design, the number of choices, and how to operate them. It can also adjust the video so that a specific character plays a major role when the viewer selects one. For example, the interactive section generates the video based on the attributes and role of the character selected by the viewer. The personalization section personalizes the video based on customer profiles and user browsing history. For example, the personalization section customizes the video based on customer profiles such as age, gender, purchase history, and interests. It can also personalize the video based on user browsing history, such as cookies, browser history, and clickstream data.For example, the personalization unit generates a video that highlights information related to a particular product for customers who have shown interest in that product. Thus, the automated promotional video generation system according to this embodiment can collect and analyze information such as the company's message, product characteristics, and customer testimonials to generate an original promotional video, provide an interactive video in which viewers can select and manipulate scenarios and characters, and further personalize the video based on customer profiles and user browsing history.

[0030] The information gathering department collects information such as company messages, product characteristics, and customer testimonials. For example, it collects company messages such as the company's vision, mission statement, and advertising copy. This provides the basic data for generating promotional videos that accurately reflect the company's brand image and values. The information gathering department can also collect product characteristics such as technical specifications, unique features, and usage instructions. This provides detailed information for generating videos that emphasize the product's appeal and convenience. Furthermore, the information gathering department can collect customer testimonials such as video interviews, survey results, and comments from review sites. This enables the generation of reliable videos that reflect the voices of actual users. For example, the information gathering department collects information using methods such as database searches, web scraping, and surveys. Database searches extract necessary information from the company's internal and public databases. Web scraping automatically collects and analyzes publicly available information on the internet. Surveys involve asking questions to customers and employees and collecting their responses. In this way, the information gathering department can collect a wide range of data from diverse sources and comprehensively provide the information necessary for generating promotional videos.

[0031] The generation unit uses a generation AI to analyze information collected by the information collection unit and generate original promotional videos. The generation unit analyzes information using methods such as text mining, data analysis, and machine learning algorithms. Text mining extracts important keywords and phrases from the collected text data and incorporates them into the video content. Data analysis statistically analyzes the collected data to identify trends and patterns. Machine learning algorithms learn from past video generation data to improve the generation of new videos. The generation unit can also use the generation AI to generate promotional videos considering factors such as video length, style, and message consistency. For example, the generation unit might prompt the generation AI with "Generate a promotional video based on the company's message," and the generation AI would then generate the video. Based on the input prompt, the generation AI combines the collected information to generate the optimal video scenario. Furthermore, the generation AI automatically generates visual and audio elements for the video, creating a visually appealing and consistent video. This allows the generation unit to automatically generate efficient and high-quality promotional videos.

[0032] The interactive unit generates interactive videos that allow viewers to select and manipulate scenarios and characters. The interactive unit provides interactive elements by considering factors such as interface design, the number of choices, and operation methods. By allowing viewers to control the video's progression, it makes the viewing experience more engaging. Furthermore, the interactive unit can adjust the video so that a specific character plays a major role when selected by the viewer. For example, the interactive unit generates a video based on the attributes and role of the character selected by the viewer. It dynamically changes the video content, considering how the selected character influences the video's storyline. This allows viewers to enjoy a more personalized viewing experience, as the video's development changes based on their choices. Additionally, the interactive unit can collect viewer selection data and use it to improve future video generation. This allows it to learn viewer preferences and behavioral patterns, providing more effective interactive videos.

[0033] The personalization unit personalizes videos based on customer profiles and user browsing history. For example, it customizes videos based on customer profiles such as age, gender, purchase history, and interests. This allows for the delivery of videos optimized for each individual viewer. The personalization unit can also personalize videos based on user browsing history, such as cookies, browser history, and clickstream data. For example, for customers who have shown interest in a particular product, it generates a video that highlights information related to that product. This can attract the viewer's attention and increase their purchase intent. Furthermore, the personalization unit can collect viewer feedback and continuously improve the content and structure of videos. For example, if a viewer gives a high rating to a particular part of a video, it generates a video that highlights that part. It also analyzes viewer behavior data to identify what kind of content viewers are interested in and customizes videos based on that information. This allows the personalization unit to deliver videos that meet the viewer's needs and preferences, improving the viewing experience.

[0034] The generation unit can adjust the video so that a specific character plays a major role when the viewer selects one. For example, the generation unit generates the video based on the attributes and role of the character selected by the viewer. The generation unit can also generate a scenario for the character selected by the viewer using a generation AI. For example, the generation unit inputs the actions and lines of the character selected by the viewer into the generation AI, and the generation AI generates the video based on that. This allows the video to be adjusted so that a specific character plays a major role when the viewer selects one. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about the character selected by the viewer into the generation AI, and the generation AI can generate the video.

[0035] The information gathering department can analyze the effectiveness of a company's past promotional videos and select the optimal information gathering method. For example, the information gathering department can analyze the number of views and engagement rates of past promotional videos to select an effective information gathering method. For example, the information gathering department can analyze viewer feedback on past promotional videos and select an information gathering method that reflects areas for improvement. For example, the information gathering department can select an information gathering method using similar methods based on successful past promotional video cases. This enables effective information gathering by analyzing the effectiveness of a company's past promotional videos and selecting the optimal information gathering method. Some or all of the above processing in the information gathering department may be performed using AI, for example, or without AI. For example, the information gathering department can input data from past promotional videos into a generating AI, which can then select the optimal information gathering method.

[0036] The information gathering unit can filter information based on the company's current marketing strategy and target market during the information gathering process. For example, the information gathering unit can prioritize collecting information related to the company's current marketing campaign. The information gathering unit can also filter and collect information related to a specific segment of the target market. For example, the information gathering unit can collect only information that matches the company's brand image. This allows for the collection of highly relevant information by filtering information based on the company's current marketing strategy and target market. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input data on the company's marketing strategy and target market into a generating AI, which can then filter the information.

[0037] The information gathering unit can prioritize the collection of highly relevant information, taking into account the geographical location of the company. For example, the information gathering unit can prioritize the collection of market information in the region related to the company's location. The information gathering unit can also prioritize the collection of information related to the company's geographical target market. The information gathering unit can also prioritize the collection of information about the company's geographical competitors. By prioritizing the collection of highly relevant information, taking into account the company's geographical location, it is possible to collect region-specific information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the company's geographical location into a generating AI, which can then prioritize the collection of highly relevant information.

[0038] The information gathering unit can analyze a company's social media activities and collect relevant information during the information gathering process. For example, the information gathering unit can analyze the engagement rate of a company's social media posts and collect relevant information. For example, the information gathering unit can also analyze feedback from a company's social media followers and collect relevant information. For example, the information gathering unit can analyze the effectiveness of a company's social media campaigns and collect relevant information. This allows for the effective collection of social media-related information by analyzing a company's social media activities and collecting relevant information. Some or all of the above-described processes in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input data on a company's social media activities into a generating AI, which can then collect relevant information.

[0039] The generation unit can adjust the video style based on the company's brand image when generating the video. For example, the generation unit can use the company's brand colors as the main color scheme of the video. The generation unit can also appropriately place the company's logo or slogan within the video. The generation unit can also add narration that emphasizes the company's brand message. By adjusting the video style based on the company's brand image, it is possible to generate a video that matches the brand. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input data on the company's brand image into a generation AI, which can then adjust the video style.

[0040] The generation unit can apply different generation algorithms to the product depending on its characteristics when generating video. For example, for high-end products, the generation unit can apply a generation algorithm that uses sophisticated visual effects. For example, for technology products, the generation unit can apply a generation algorithm that emphasizes technical details. For example, for everyday items, the generation unit can apply a generation algorithm that uses user-friendly visual effects. By applying different generation algorithms depending on the product characteristics, the generation unit can generate videos that are optimal for each product. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input product characteristic data into the generation AI, which can then apply an appropriate generation algorithm.

[0041] The generation unit can prioritize videos based on the timing of a company's marketing campaigns when generating them. For example, the generation unit can prioritize generating relevant videos during the period of a major marketing campaign. The generation unit can also generate appropriate videos to match seasonal campaigns, for example. The generation unit can also adjust video generation to match specific events or promotions, for example. This allows for the generation of videos that are best suited to a campaign by prioritizing them based on the timing of a company's marketing campaigns. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input data on the timing of a company's marketing campaigns into the generation AI, which can then determine the video priorities.

[0042] The generation unit can adjust the video content based on the company's relevant products during video generation. For example, the generation unit can generate a video focusing on the company's new products. The generation unit can also generate a video combining the company's existing and new products. The generation unit can also generate a video showcasing the company's entire product line. This allows for the generation of product-related videos by adjusting the video content based on the company's relevant products. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the company's relevant products into the generation AI, which can then adjust the video content.

[0043] The interactive unit can provide optimal interactive elements by referring to the viewer's past selection history when generating interactive videos. For example, the interactive unit can provide relevant interactive elements based on characters the viewer has previously selected. The interactive unit can also predict and provide preferred scenarios based on the viewer's past selection history. For example, the interactive unit can analyze the viewer's past selection history and provide the most engaging interactive elements. By providing optimal interactive elements by referring to the viewer's past selection history, it is possible to provide more engaging interactive videos for the viewer. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input data on the viewer's past selection history into a generating AI, which can then provide optimal interactive elements.

[0044] The interactive unit can customize interactive elements based on the viewer's age and gender when generating interactive videos. For example, the interactive unit can provide appropriate interactive elements according to the viewer's age. The interactive unit can also provide preferred interactive elements based on the viewer's gender. The interactive unit can also provide visually appealing interactive elements based on the viewer's age and gender. By customizing interactive elements based on the viewer's age and gender, it is possible to provide more engaging interactive videos for viewers. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input viewer age and gender data into a generating AI, which can then customize the interactive elements.

[0045] The interactive unit can provide optimal interactive elements when generating interactive videos, taking into account the viewer's geographical location information. For example, the interactive unit can provide interactive elements related to the viewer's location. The interactive unit can also customize interactive elements based on the viewer's geographical culture and customs. The interactive unit can also provide interactive elements based on the viewer's geographical interests. By providing optimal interactive elements while considering the viewer's geographical location information, it is possible to provide more engaging interactive videos for the viewer. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the viewer's geographical location data into a generating AI, which can then provide optimal interactive elements.

[0046] The interactive unit can analyze the viewer's social media activity and suggest interactive elements when generating interactive videos. For example, the interactive unit can suggest interactive elements based on the viewer's interests and preferences on social media. The interactive unit can also analyze the viewer's social media activity history and suggest relevant interactive elements. For example, the interactive unit can suggest optimal interactive elements based on the viewer's social media engagement. This allows for the provision of more engaging interactive videos for viewers by analyzing their social media activity and suggesting interactive elements. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input data on the viewer's social media activity into a generating AI, which can then suggest interactive elements.

[0047] The personalization unit can analyze the viewer's past viewing history to select the optimal personalization method during the personalization process. For example, the personalization unit can select a relevant personalization method based on the content of videos the viewer has previously viewed. The personalization unit can also predict and provide the viewer's preferred video style based on their past viewing history. For example, the personalization unit can analyze the viewer's past viewing history and select the personalization method that will generate the most engagement. By analyzing the viewer's past viewing history and selecting the optimal personalization method, it is possible to provide viewers with more engaging videos. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input data on the viewer's past viewing history into a generating AI, which can then select the optimal personalization method.

[0048] The personalization unit can customize video content based on the viewer's interests during the personalization process. For example, if a viewer shows interest in a particular product, the personalization unit can generate a video that highlights information related to that product. The personalization unit can also provide relevant video content based on the viewer's interests. The personalization unit can also customize the video scenario and characters based on the viewer's interests. This allows for the provision of more engaging videos by customizing video content based on the viewer's interests. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input viewer interest data into a generating AI, which can then customize the video content.

[0049] The personalization unit can select the optimal personalization method when personalizing content, taking into account the viewer's geographical location. For example, the personalization unit may prioritize providing information related to the viewer's location. The personalization unit can also customize video content based on the viewer's geographical culture and customs. The personalization unit can also provide video content based on the viewer's geographical interests. By selecting the optimal personalization method while considering the viewer's geographical location, it is possible to provide viewers with more engaging videos. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit may input the viewer's geographical location data into a generating AI, which can then select the optimal personalization method.

[0050] The personalization unit can analyze the viewer's social media activity and suggest personalized content during the personalization process. For example, the personalization unit can suggest personalized content based on the viewer's interests and preferences on social media. The personalization unit can also analyze the viewer's social media activity history and suggest relevant personalized content. For example, the personalization unit can suggest optimal personalized content based on the viewer's social media engagement. This allows for the provision of more engaging videos to viewers by analyzing their social media activity and suggesting personalized content. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input data on the viewer's social media activity into a generating AI, which can then suggest personalized content.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The information gathering department can analyze the promotional videos of a company's competitors, extract effective elements, and incorporate them into its information gathering process. For example, the information gathering department can analyze the number of views and engagement rates of competitors' videos to identify effective elements. The information gathering department can also analyze viewer feedback on competitors' videos and select information gathering methods that reflect areas for improvement. For example, the information gathering department can select information gathering methods using similar methods based on the success stories of competitors. This enables effective information gathering by analyzing the promotional videos of a company's competitors, extracting effective elements, and incorporating them into the information gathering process. Some or all of the above processes in the information gathering department may be performed using AI, for example, or without AI. For example, the information gathering department can input data from competitors' videos into a generating AI, which can then select the optimal information gathering method.

[0053] The information gathering department can collect opinions and feedback from company employees and reflect them in the content of promotional videos. For example, the information gathering department can collect opinions through employee interviews and surveys. For example, the information gathering department can adjust the video content based on employee feedback. For example, the information gathering department can select information gathering methods that incorporate employee suggestions. This allows for the creation of more realistic and relatable videos by collecting opinions and feedback from company employees and reflecting them in the content of promotional videos. Some or all of the above processes in the information gathering department may be performed using AI, for example, or not. For example, the information gathering department can input employee opinion and feedback data into a generating AI, which can then adjust the video content.

[0054] The information gathering unit can collect information on a company's supply chain and reflect it in the content of the promotional video. For example, the information gathering unit can collect information on the transparency and sustainability of the supply chain. For example, the information gathering unit can collect information on the environmental impact at each stage of the supply chain and reflect it in the video content. For example, the information gathering unit can collect information on the efficiency and cost reduction of the supply chain. In this way, by collecting information on a company's supply chain and reflecting it in the content of the promotional video, it is possible to generate a video that emphasizes the company's transparency and sustainability. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or not using AI. For example, the information gathering unit can input supply chain information data into a generating AI, and the generating AI can adjust the content of the video.

[0055] The information gathering department can collect information on a company's CSR (Corporate Social Responsibility) activities and reflect it in the content of the promotional video. For example, the information gathering department can collect information on a company's environmental protection activities and social contribution activities. For example, the information gathering department can collect information on a company's employee volunteer activities and reflect it in the video content. For example, the information gathering department can collect information on a company's efforts toward the Sustainable Development Goals (SDGs). By collecting information on a company's CSR activities and reflecting it in the content of the promotional video, it is possible to generate a video that emphasizes the company's social responsibility. Some or all of the above processing in the information gathering department may be performed using AI, for example, or not using AI. For example, the information gathering department can input CSR activity data into a generating AI, and the generating AI can adjust the content of the video.

[0056] The information gathering unit can collect information about a company's customer support and reflect it in the content of the promotional video. For example, the information gathering unit can collect information on successful customer support cases and customer satisfaction. For example, the information gathering unit can adjust the video content based on customer support feedback. For example, the information gathering unit can select information gathering methods that reflect areas for improvement in customer support. This allows for the creation of videos that emphasize customer satisfaction by collecting information about a company's customer support and reflecting it in the content of the promotional video. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or not using AI. For example, the information gathering unit can input customer support data into a generating AI, which can then adjust the video content.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The Information Gathering Department collects information such as the company's message, product characteristics, and customer testimonials. For example, they collect the company's message, such as its vision, mission statement, and advertising copy, and product characteristics such as the product's technical specifications, unique features, and usage instructions. They also collect customer testimonials such as video interviews, survey results, and comments from review sites. The Information Gathering Department collects information using methods such as database searches, web scraping, and surveys. Step 2: The generation unit uses a generation AI to analyze the information collected by the information gathering unit and generate an original promotional video. For example, it analyzes the information using text mining, data analysis, and machine learning algorithms, and generates a promotional video considering the video's length, style, and message consistency. The generation unit prompts the generation AI with "Generate a promotional video based on the company's message," and the generation AI generates the video. Step 3: The interactive section generates an interactive video that allows viewers to select and interact with scenarios and characters. For example, it provides interactive elements by considering the interface design, the number of choices, and the method of operation, and when a viewer selects a specific character, it adjusts the video so that that character plays a major role in the video. The video is generated based on the attributes and role of the character selected by the viewer. Step 4: The personalization section personalizes videos based on customer profiles and user browsing history. For example, it customizes videos based on customer profiles such as age, gender, purchase history, and interests, and personalizes videos based on user browsing history such as cookies, browser history, and clickstream data. For customers who have shown interest in a particular product, it generates a video that highlights information related to that product.

[0059] (Example of form 2) The automated promotional video generation system according to an embodiment of the present invention is a system that collects information such as a company's message, product characteristics, and customer testimonials, and automatically generates original promotional videos using a generation AI. This system generates interactive videos in which viewers can select and manipulate the video's scenario and characters. It also has the function of automatically personalizing videos for individual customers based on customer profiles and user browsing history. For example, it collects information such as a company's message, product characteristics, and customer testimonials. Next, the generation AI analyzes this information and automatically generates an original promotional video. This video is provided in an interactive format in which viewers can select and manipulate the scenario and characters. For example, if a viewer selects a particular character, the generation AI adjusts the video so that that character plays a major role in the video. Furthermore, it personalizes the video content for individual customers based on customer profiles and user browsing history. For example, for a customer who has shown interest in a particular product, a video highlighting information related to that product is generated. This deepens viewer engagement and provides memorable content. This service is extremely useful for companies that want to promote their products and services, especially companies that need high-quality video content in a short amount of time. While video production typically requires professional skills, time, and significant costs, this service automates the entire process, from planning and scriptwriting to filming and editing, enabling the efficient creation of high-quality promotional videos. This automated promotional video generation system collects and analyzes information such as company messages, product characteristics, and customer testimonials to generate original promotional videos, providing interactive videos where viewers can select and control scenarios and characters, and even personalizing videos based on customer profiles and user browsing history.

[0060] The automated promotional video generation system according to the embodiment comprises an information collection unit, a generation unit, an interactive unit, and a personalization unit. The information collection unit collects information such as the company's message, product characteristics, and customer testimonials. For example, the information collection unit collects the company's message, such as its vision, mission statement, and advertising copy. The information collection unit can also collect product characteristics, such as the product's technical specifications, unique features, and usage instructions. Furthermore, the information collection unit can collect customer testimonials, such as video interviews, survey results, and comments from review sites. For example, the information collection unit collects information using methods such as database searches, web scraping, and surveys. The generation unit uses a generation AI to analyze the information collected by the information collection unit and generate an original promotional video. For example, the generation unit analyzes the information using text mining, data analysis, and machine learning algorithms. The generation unit can also use the generation AI to generate a promotional video considering factors such as video length, style, and message consistency. For example, the generation unit inputs "Generate a promotional video based on the company's message" as a prompt to the generation AI, and the generation AI generates the video. The interactive section generates interactive videos in which viewers can select and interact with scenarios and characters. The interactive section provides interactive elements, for example, by considering interface design, the number of choices, and how to operate them. It can also adjust the video so that a specific character plays a major role when the viewer selects one. For example, the interactive section generates the video based on the attributes and role of the character selected by the viewer. The personalization section personalizes the video based on customer profiles and user browsing history. For example, the personalization section customizes the video based on customer profiles such as age, gender, purchase history, and interests. It can also personalize the video based on user browsing history, such as cookies, browser history, and clickstream data.For example, the personalization unit generates a video that highlights information related to a particular product for customers who have shown interest in that product. Thus, the automated promotional video generation system according to this embodiment can collect and analyze information such as the company's message, product characteristics, and customer testimonials to generate an original promotional video, provide an interactive video in which viewers can select and manipulate scenarios and characters, and further personalize the video based on customer profiles and user browsing history.

[0061] The information gathering department collects information such as company messages, product characteristics, and customer testimonials. For example, it collects company messages such as the company's vision, mission statement, and advertising copy. This provides the basic data for generating promotional videos that accurately reflect the company's brand image and values. The information gathering department can also collect product characteristics such as technical specifications, unique features, and usage instructions. This provides detailed information for generating videos that emphasize the product's appeal and convenience. Furthermore, the information gathering department can collect customer testimonials such as video interviews, survey results, and comments from review sites. This enables the generation of reliable videos that reflect the voices of actual users. For example, the information gathering department collects information using methods such as database searches, web scraping, and surveys. Database searches extract necessary information from the company's internal and public databases. Web scraping automatically collects and analyzes publicly available information on the internet. Surveys involve asking questions to customers and employees and collecting their responses. In this way, the information gathering department can collect a wide range of data from diverse sources and comprehensively provide the information necessary for generating promotional videos.

[0062] The generation unit uses a generation AI to analyze information collected by the information collection unit and generate original promotional videos. The generation unit analyzes information using methods such as text mining, data analysis, and machine learning algorithms. Text mining extracts important keywords and phrases from the collected text data and incorporates them into the video content. Data analysis statistically analyzes the collected data to identify trends and patterns. Machine learning algorithms learn from past video generation data to improve the generation of new videos. The generation unit can also use the generation AI to generate promotional videos considering factors such as video length, style, and message consistency. For example, the generation unit might prompt the generation AI with "Generate a promotional video based on the company's message," and the generation AI would then generate the video. Based on the input prompt, the generation AI combines the collected information to generate the optimal video scenario. Furthermore, the generation AI automatically generates visual and audio elements for the video, creating a visually appealing and consistent video. This allows the generation unit to automatically generate efficient and high-quality promotional videos.

[0063] The interactive unit generates interactive videos that allow viewers to select and manipulate scenarios and characters. The interactive unit provides interactive elements by considering factors such as interface design, the number of choices, and operation methods. By allowing viewers to control the video's progression, it makes the viewing experience more engaging. Furthermore, the interactive unit can adjust the video so that a specific character plays a major role when selected by the viewer. For example, the interactive unit generates a video based on the attributes and role of the character selected by the viewer. It dynamically changes the video content, considering how the selected character influences the video's storyline. This allows viewers to enjoy a more personalized viewing experience, as the video's development changes based on their choices. Additionally, the interactive unit can collect viewer selection data and use it to improve future video generation. This allows it to learn viewer preferences and behavioral patterns, providing more effective interactive videos.

[0064] The personalization unit personalizes videos based on customer profiles and user browsing history. For example, it customizes videos based on customer profiles such as age, gender, purchase history, and interests. This allows for the delivery of videos optimized for each individual viewer. The personalization unit can also personalize videos based on user browsing history, such as cookies, browser history, and clickstream data. For example, for customers who have shown interest in a particular product, it generates a video that highlights information related to that product. This can attract the viewer's attention and increase their purchase intent. Furthermore, the personalization unit can collect viewer feedback and continuously improve the content and structure of videos. For example, if a viewer gives a high rating to a particular part of a video, it generates a video that highlights that part. It also analyzes viewer behavior data to identify what kind of content viewers are interested in and customizes videos based on that information. This allows the personalization unit to deliver videos that meet the viewer's needs and preferences, improving the viewing experience.

[0065] The generation unit can adjust the video so that a specific character plays a major role when the viewer selects one. For example, the generation unit generates the video based on the attributes and role of the character selected by the viewer. The generation unit can also generate a scenario for the character selected by the viewer using a generation AI. For example, the generation unit inputs the actions and lines of the character selected by the viewer into the generation AI, and the generation AI generates the video based on that. This allows the video to be adjusted so that a specific character plays a major role when the viewer selects one. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about the character selected by the viewer into the generation AI, and the generation AI can generate the video.

[0066] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. For example, if the user is excited, the information gathering unit can increase the frequency of information gathering and collect the latest information in real time. For example, if the user is relaxed, the information gathering unit can also decrease the frequency of information gathering and collect only the necessary information. For example, if the user is stressed, the information gathering unit can adjust the timing of information gathering to times when the user is less active. This allows information to be collected at a more appropriate time by adjusting the timing of information gathering based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input user emotion data into a generative AI, which can then adjust the timing of information gathering.

[0067] The information gathering department can analyze the effectiveness of a company's past promotional videos and select the optimal information gathering method. For example, the information gathering department can analyze the number of views and engagement rates of past promotional videos to select an effective information gathering method. For example, the information gathering department can analyze viewer feedback on past promotional videos and select an information gathering method that reflects areas for improvement. For example, the information gathering department can select an information gathering method using similar methods based on successful past promotional video cases. This enables effective information gathering by analyzing the effectiveness of a company's past promotional videos and selecting the optimal information gathering method. Some or all of the above processing in the information gathering department may be performed using AI, for example, or without AI. For example, the information gathering department can input data from past promotional videos into a generating AI, which can then select the optimal information gathering method.

[0068] The information gathering unit can filter information based on the company's current marketing strategy and target market during the information gathering process. For example, the information gathering unit can prioritize collecting information related to the company's current marketing campaign. The information gathering unit can also filter and collect information related to a specific segment of the target market. For example, the information gathering unit can collect only information that matches the company's brand image. This allows for the collection of highly relevant information by filtering information based on the company's current marketing strategy and target market. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input data on the company's marketing strategy and target market into a generating AI, which can then filter the information.

[0069] The information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the information gathering unit may prioritize collecting the latest trend information. For example, if the user is relaxed, the information gathering unit may prioritize collecting detailed product information. For example, if the user is stressed, the information gathering unit may prioritize collecting concise and to-the-point information. This allows for the collection of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input user emotion data into a generative AI and determine the priority of information to be collected by the generative AI.

[0070] The information gathering unit can prioritize the collection of highly relevant information, taking into account the geographical location of the company. For example, the information gathering unit can prioritize the collection of market information in the region related to the company's location. The information gathering unit can also prioritize the collection of information related to the company's geographical target market. The information gathering unit can also prioritize the collection of information about the company's geographical competitors. By prioritizing the collection of highly relevant information, taking into account the company's geographical location, it is possible to collect region-specific information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the company's geographical location into a generating AI, which can then prioritize the collection of highly relevant information.

[0071] The information gathering unit can analyze a company's social media activities and collect relevant information during the information gathering process. For example, the information gathering unit can analyze the engagement rate of a company's social media posts and collect relevant information. For example, the information gathering unit can also analyze feedback from a company's social media followers and collect relevant information. For example, the information gathering unit can analyze the effectiveness of a company's social media campaigns and collect relevant information. This allows for the effective collection of social media-related information by analyzing a company's social media activities and collecting relevant information. Some or all of the above-described processes in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input data on a company's social media activities into a generating AI, which can then collect relevant information.

[0072] The generation unit can estimate the user's emotions and adjust the video's presentation based on those emotions. For example, if the user is relaxed, the generation unit can generate a video with calm music and subdued colors. If the user is excited, the generation unit can also generate a video with lively music and vibrant colors. If the user is stressed, the generation unit can also generate a simple and visually less stressful video. By adjusting the video's presentation based on the user's emotions, it is possible to generate a more engaging video for the viewer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the video's presentation.

[0073] The generation unit can adjust the video style based on the company's brand image when generating the video. For example, the generation unit can use the company's brand colors as the main color scheme of the video. The generation unit can also appropriately place the company's logo or slogan within the video. The generation unit can also add narration that emphasizes the company's brand message. By adjusting the video style based on the company's brand image, it is possible to generate a video that matches the brand. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input data on the company's brand image into a generation AI, which can then adjust the video style.

[0074] The generation unit can apply different generation algorithms to the product depending on its characteristics when generating video. For example, for high-end products, the generation unit can apply a generation algorithm that uses sophisticated visual effects. For example, for technology products, the generation unit can apply a generation algorithm that emphasizes technical details. For example, for everyday items, the generation unit can apply a generation algorithm that uses user-friendly visual effects. By applying different generation algorithms depending on the product characteristics, the generation unit can generate videos that are optimal for each product. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input product characteristic data into the generation AI, which can then apply an appropriate generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the video length based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise video. If the user is relaxed, the generation unit can also generate a longer video with detailed explanations. If the user is excited, the generation unit can also generate a video with visually stimulating effects. By adjusting the video length based on the user's emotions, it is possible to generate a video of the optimal length for the viewer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the video length.

[0076] The generation unit can prioritize videos based on the timing of a company's marketing campaigns when generating them. For example, the generation unit can prioritize generating relevant videos during the period of a major marketing campaign. The generation unit can also generate appropriate videos to match seasonal campaigns, for example. The generation unit can also adjust video generation to match specific events or promotions, for example. This allows for the generation of videos that are best suited to a campaign by prioritizing them based on the timing of a company's marketing campaigns. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input data on the timing of a company's marketing campaigns into the generation AI, which can then determine the video priorities.

[0077] The generation unit can adjust the video content based on the company's relevant products during video generation. For example, the generation unit can generate a video focusing on the company's new products. The generation unit can also generate a video combining the company's existing and new products. The generation unit can also generate a video showcasing the company's entire product line. This allows for the generation of product-related videos by adjusting the video content based on the company's relevant products. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the company's relevant products into the generation AI, which can then adjust the video content.

[0078] The interactive section can estimate the user's emotions and adjust how interactive elements are displayed based on the estimated emotions. For example, if the user is relaxed, the interactive section may display detailed interactive elements. If the user is in a hurry, the interactive section may display concise and to-the-point interactive elements. If the user is excited, the interactive section may display visually stimulating interactive elements. By adjusting how interactive elements are displayed based on the user's emotions, a more engaging interactive video can be provided to the viewer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interactive section may be performed using AI or not using AI. For example, the interactive section can input user emotion data into a generative AI, which can then adjust how interactive elements are displayed.

[0079] The interactive unit can provide optimal interactive elements by referring to the viewer's past selection history when generating interactive videos. For example, the interactive unit can provide relevant interactive elements based on characters the viewer has previously selected. The interactive unit can also predict and provide preferred scenarios based on the viewer's past selection history. For example, the interactive unit can analyze the viewer's past selection history and provide the most engaging interactive elements. By providing optimal interactive elements by referring to the viewer's past selection history, it is possible to provide more engaging interactive videos for the viewer. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input data on the viewer's past selection history into a generating AI, which can then provide optimal interactive elements.

[0080] The interactive unit can customize interactive elements based on the viewer's age and gender when generating interactive videos. For example, the interactive unit can provide appropriate interactive elements according to the viewer's age. The interactive unit can also provide preferred interactive elements based on the viewer's gender. The interactive unit can also provide visually appealing interactive elements based on the viewer's age and gender. By customizing interactive elements based on the viewer's age and gender, it is possible to provide more engaging interactive videos for viewers. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input viewer age and gender data into a generating AI, which can then customize the interactive elements.

[0081] The interactive section can estimate the user's emotions and prioritize interactive elements based on those emotions. For example, if the user is relaxed, the interactive section may prioritize displaying detailed interactive elements. If the user is in a hurry, the interactive section may also prioritize displaying concise and to-the-point interactive elements. If the user is excited, the interactive section may also prioritize displaying visually stimulating interactive elements. By prioritizing interactive elements based on the user's emotions, a more engaging interactive video can be provided to the viewer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the interactive section may be performed using AI or not. For example, the interactive section can input user emotion data into a generative AI, which can then determine the priority of interactive elements.

[0082] The interactive unit can provide optimal interactive elements when generating interactive videos, taking into account the viewer's geographical location information. For example, the interactive unit can provide interactive elements related to the viewer's location. The interactive unit can also customize interactive elements based on the viewer's geographical culture and customs. The interactive unit can also provide interactive elements based on the viewer's geographical interests. By providing optimal interactive elements while considering the viewer's geographical location information, it is possible to provide more engaging interactive videos for the viewer. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the viewer's geographical location data into a generating AI, which can then provide optimal interactive elements.

[0083] The interactive unit can analyze the viewer's social media activity and suggest interactive elements when generating interactive videos. For example, the interactive unit can suggest interactive elements based on the viewer's interests and preferences on social media. The interactive unit can also analyze the viewer's social media activity history and suggest relevant interactive elements. For example, the interactive unit can suggest optimal interactive elements based on the viewer's social media engagement. This allows for the provision of more engaging interactive videos for viewers by analyzing their social media activity and suggesting interactive elements. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input data on the viewer's social media activity into a generating AI, which can then suggest interactive elements.

[0084] The personalization unit can estimate the user's emotions and adjust the personalization method based on the estimated emotions. For example, if the user is relaxed, the personalization unit can provide detailed personalization options. For example, if the user is in a hurry, the personalization unit can provide concise and to-the-point personalization options. For example, if the user is excited, the personalization unit can provide visually stimulating personalization options. This allows for more engaging videos for viewers by adjusting the personalization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the personalization unit may be performed using AI or not. For example, the personalization unit can input user emotion data into a generative AI, which can then adjust the personalization method.

[0085] The personalization unit can analyze the viewer's past viewing history to select the optimal personalization method during the personalization process. For example, the personalization unit can select a relevant personalization method based on the content of videos the viewer has previously viewed. The personalization unit can also predict and provide the viewer's preferred video style based on their past viewing history. For example, the personalization unit can analyze the viewer's past viewing history and select the personalization method that will generate the most engagement. By analyzing the viewer's past viewing history and selecting the optimal personalization method, it is possible to provide viewers with more engaging videos. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input data on the viewer's past viewing history into a generating AI, which can then select the optimal personalization method.

[0086] The personalization unit can customize video content based on the viewer's interests during the personalization process. For example, if a viewer shows interest in a particular product, the personalization unit can generate a video that highlights information related to that product. The personalization unit can also provide relevant video content based on the viewer's interests. The personalization unit can also customize the video scenario and characters based on the viewer's interests. This allows for the provision of more engaging videos by customizing video content based on the viewer's interests. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input viewer interest data into a generating AI, which can then customize the video content.

[0087] The personalization unit can estimate the user's emotions and determine personalization priorities based on those emotions. For example, if the user is relaxed, the personalization unit may prioritize detailed personalization options. If the user is in a hurry, the personalization unit may also prioritize concise and to-the-point personalization options. If the user is excited, the personalization unit may also prioritize visually stimulating personalization options. This allows for the delivery of more engaging videos to viewers by prioritizing personalization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the personalization unit may be performed using AI or not. For example, the personalization unit can input user emotion data into a generative AI, which can then determine personalization priorities.

[0088] The personalization unit can select the optimal personalization method when personalizing content, taking into account the viewer's geographical location. For example, the personalization unit may prioritize providing information related to the viewer's location. The personalization unit can also customize video content based on the viewer's geographical culture and customs. The personalization unit can also provide video content based on the viewer's geographical interests. By selecting the optimal personalization method while considering the viewer's geographical location, it is possible to provide viewers with more engaging videos. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit may input the viewer's geographical location data into a generating AI, which can then select the optimal personalization method.

[0089] The personalization unit can analyze the viewer's social media activity and suggest personalized content during the personalization process. For example, the personalization unit can suggest personalized content based on the viewer's interests and preferences on social media. The personalization unit can also analyze the viewer's social media activity history and suggest relevant personalized content. For example, the personalization unit can suggest optimal personalized content based on the viewer's social media engagement. This allows for the provision of more engaging videos to viewers by analyzing their social media activity and suggesting personalized content. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input data on the viewer's social media activity into a generating AI, which can then suggest personalized content.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The generation unit can estimate the user's emotions and adjust the video's audio narration based on the estimated emotions. For example, if the user is relaxed, the generation unit may use a calm tone of narration. If the user is excited, the generation unit may also use an energetic tone of narration. If the user is stressed, the generation unit may also use a calm tone of narration. This allows for the creation of more engaging videos for viewers by adjusting the video's audio narration based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generative AI, which can then adjust the video's audio narration.

[0092] The information gathering department can analyze the promotional videos of a company's competitors, extract effective elements, and incorporate them into its information gathering process. For example, the information gathering department can analyze the number of views and engagement rates of competitors' videos to identify effective elements. The information gathering department can also analyze viewer feedback on competitors' videos and select information gathering methods that reflect areas for improvement. For example, the information gathering department can select information gathering methods using similar methods based on the success stories of competitors. This enables effective information gathering by analyzing the promotional videos of a company's competitors, extracting effective elements, and incorporating them into the information gathering process. Some or all of the above processes in the information gathering department may be performed using AI, for example, or without AI. For example, the information gathering department can input data from competitors' videos into a generating AI, which can then select the optimal information gathering method.

[0093] The generation unit can estimate the user's emotions and adjust the video's tempo based on those emotions. For example, if the user is relaxed, the generation unit can generate a video with a relaxed tempo. If the user is excited, the generation unit can also generate a video with a fast tempo. If the user is stressed, the generation unit can also generate a video with a steady tempo. By adjusting the video's tempo based on the user's emotions, it is possible to generate a more engaging video for the viewer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the video's tempo.

[0094] The information gathering department can collect opinions and feedback from company employees and reflect them in the content of promotional videos. For example, the information gathering department can collect opinions through employee interviews and surveys. For example, the information gathering department can adjust the video content based on employee feedback. For example, the information gathering department can select information gathering methods that incorporate employee suggestions. This allows for the creation of more realistic and relatable videos by collecting opinions and feedback from company employees and reflecting them in the content of promotional videos. Some or all of the above processes in the information gathering department may be performed using AI, for example, or not. For example, the information gathering department can input employee opinion and feedback data into a generating AI, which can then adjust the video content.

[0095] The generation unit can estimate the user's emotions and adjust the video effects based on the estimated emotions. For example, if the user is relaxed, the generation unit may use gentle effects. If the user is excited, the generation unit may use dynamic effects. If the user is stressed, the generation unit may use simple, visually less stressful effects. By adjusting the video effects based on the user's emotions, it is possible to generate videos that are more engaging for viewers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the video effects.

[0096] The information gathering unit can collect information on a company's supply chain and reflect it in the content of the promotional video. For example, the information gathering unit can collect information on the transparency and sustainability of the supply chain. For example, the information gathering unit can collect information on the environmental impact at each stage of the supply chain and reflect it in the video content. For example, the information gathering unit can collect information on the efficiency and cost reduction of the supply chain. In this way, by collecting information on a company's supply chain and reflecting it in the content of the promotional video, it is possible to generate a video that emphasizes the company's transparency and sustainability. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or not using AI. For example, the information gathering unit can input supply chain information data into a generating AI, and the generating AI can adjust the content of the video.

[0097] The generation unit can estimate the user's emotions and adjust the video's camera angle based on the estimated emotions. For example, if the user is relaxed, the generation unit may use a calm camera angle. If the user is excited, the generation unit may also use a dynamic camera angle. If the user is stressed, the generation unit may also use a stable camera angle. By adjusting the video's camera angle based on the user's emotions, it is possible to generate videos that are more engaging for viewers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the video's camera angle.

[0098] The information gathering department can collect information on a company's CSR (Corporate Social Responsibility) activities and reflect it in the content of the promotional video. For example, the information gathering department can collect information on a company's environmental protection activities and social contribution activities. For example, the information gathering department can collect information on a company's employee volunteer activities and reflect it in the video content. For example, the information gathering department can collect information on a company's efforts toward the Sustainable Development Goals (SDGs). By collecting information on a company's CSR activities and reflecting it in the content of the promotional video, it is possible to generate a video that emphasizes the company's social responsibility. Some or all of the above processing in the information gathering department may be performed using AI, for example, or not using AI. For example, the information gathering department can input CSR activity data into a generating AI, and the generating AI can adjust the content of the video.

[0099] The generation unit can estimate the user's emotions and adjust the frequency of scene transitions in the video based on the estimated emotions. For example, if the user is relaxed, the generation unit may decrease the frequency of scene transitions. For example, if the user is excited, the generation unit may increase the frequency of scene transitions. For example, if the user is stressed, the generation unit may use a steady transition. By adjusting the frequency of scene transitions in the video based on the user's emotions, it is possible to generate videos that are more engaging for the viewer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can adjust the frequency of scene transitions in the video.

[0100] The information gathering unit can collect information about a company's customer support and reflect it in the content of the promotional video. For example, the information gathering unit can collect information on successful customer support cases and customer satisfaction. For example, the information gathering unit can adjust the video content based on customer support feedback. For example, the information gathering unit can select information gathering methods that reflect areas for improvement in customer support. This allows for the creation of videos that emphasize customer satisfaction by collecting information about a company's customer support and reflecting it in the content of the promotional video. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or not using AI. For example, the information gathering unit can input customer support data into a generating AI, which can then adjust the video content.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The Information Gathering Department collects information such as the company's message, product characteristics, and customer testimonials. For example, they collect the company's message, such as its vision, mission statement, and advertising copy, and product characteristics such as the product's technical specifications, unique features, and usage instructions. They also collect customer testimonials such as video interviews, survey results, and comments from review sites. The Information Gathering Department collects information using methods such as database searches, web scraping, and surveys. Step 2: The generation unit uses a generation AI to analyze the information collected by the information gathering unit and generate an original promotional video. For example, it analyzes the information using text mining, data analysis, and machine learning algorithms, and generates a promotional video considering the video's length, style, and message consistency. The generation unit prompts the generation AI with "Generate a promotional video based on the company's message," and the generation AI generates the video. Step 3: The interactive section generates an interactive video that allows viewers to select and interact with scenarios and characters. For example, it provides interactive elements by considering the interface design, the number of choices, and the method of operation, and when a viewer selects a specific character, it adjusts the video so that that character plays a major role in the video. The video is generated based on the attributes and role of the character selected by the viewer. Step 4: The personalization section personalizes videos based on customer profiles and user browsing history. For example, it customizes videos based on customer profiles such as age, gender, purchase history, and interests, and personalizes videos based on user browsing history such as cookies, browser history, and clickstream data. For customers who have shown interest in a particular product, it generates a video that highlights information related to that product.

[0103] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0106] Each of the multiple elements described above, including the information gathering unit, generation unit, interactive unit, and personalization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the smart device 14 and collects information such as corporate messages, product characteristics, and customer testimonials. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an original promotional video using a generation AI. The interactive unit is implemented by the control unit 46A of the smart device 14 and provides an interactive video in which viewers can select and manipulate scenarios and characters. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12 and personalizes the video based on customer profiles and user browsing history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the information gathering unit, generation unit, interactive unit, and personalization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the smart glasses 214 and collects information such as company messages, product characteristics, and customer testimonials. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an original promotional video using generation AI. The interactive unit is implemented by the control unit 46A of the smart glasses 214 and provides an interactive video in which viewers can select and manipulate scenarios and characters. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12 and personalizes the video based on customer profiles and user browsing history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the information gathering unit, generation unit, interactive unit, and personalization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the headset terminal 314 and collects information such as corporate messages, product characteristics, and customer testimonials. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an original promotional video using a generation AI. The interactive unit is implemented by the control unit 46A of the headset terminal 314 and provides an interactive video in which viewers can select and manipulate scenarios and characters. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12 and personalizes the video based on customer profiles and user browsing history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the information gathering unit, generation unit, interactive unit, and personalization unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the robot 414 and collects information such as corporate messages, product characteristics, and customer testimonials. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an original promotional video using a generation AI. The interactive unit is implemented by the control unit 46A of the robot 414 and provides an interactive video in which viewers can select and manipulate scenarios and characters. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12 and personalizes the video based on customer profiles and user browsing history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0156] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) The Information Gathering Department collects information such as the company's message, product characteristics, and customer testimonials. A generation unit analyzes the information collected by the aforementioned information collection unit and generates an original promotional video, An interactive unit that allows viewers to select and manipulate scenarios and characters based on the video generated by the generation unit, The system includes a personalization unit that personalizes the video generated by the generation unit based on the customer profile and the user's browsing history. A system characterized by the following features. (Note 2) The generating unit is When a viewer selects a specific character, the video adjusts to ensure that character plays a major role within the video. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information gathering unit, Analyze the effectiveness of a company's past promotional videos and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information gathering unit, When gathering information, filter it based on the company's current marketing strategy and target market. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information gathering unit, When gathering information, prioritize collecting highly relevant information by considering the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned information gathering unit, When gathering information, we analyze the company's social media activities and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is It estimates the user's emotions and adjusts the video's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating a video, adjust the video style based on the company's brand image. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating videos, different generation algorithms are applied depending on the characteristics of the product. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the video length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating videos, prioritize them based on the timing of the company's marketing campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating videos, adjust the video content based on the company's relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned interactive unit is It estimates the user's emotions and adjusts how interactive elements are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned interactive unit is When generating interactive videos, refer to the viewer's past selection history to provide the most suitable interactive elements. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned interactive unit is When generating interactive videos, customize interactive elements based on the viewer's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned interactive unit is It estimates the user's emotions and prioritizes interactive elements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned interactive unit is When generating interactive videos, consider the viewer's geographical location to provide optimal interactive elements. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned interactive unit is When generating interactive videos, we analyze viewers' social media activity and suggest interactive elements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The personalization unit described above is It estimates the user's emotions and adjusts the personalization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The personalization unit described above is During personalization, the system analyzes the viewer's past browsing history to select the most suitable personalization method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The personalization unit described above is When personalizing, the video content is customized based on the viewer's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 24) The personalization unit described above is It estimates the user's emotions and determines personalization priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The personalization unit described above is When personalizing, the optimal personalization method is selected by considering the viewer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The personalization unit described above is During personalization, the system analyzes the viewer's social media activity to suggest personalized content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Information Gathering Department collects information such as the company's message, product characteristics, and customer testimonials. A generation unit analyzes the information collected by the aforementioned information collection unit and generates an original promotional video, An interactive unit that allows viewers to select and manipulate scenarios and characters based on the video generated by the generation unit, The system includes a personalization unit that personalizes the video generated by the generation unit based on the customer profile and the user's browsing history. A system characterized by the following features.

2. The generating unit is When a viewer selects a specific character, the video adjusts to ensure that character plays a major role within the video. The system according to feature 1.

3. The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

4. The aforementioned information gathering unit, Analyze the effectiveness of a company's past promotional videos and select the most suitable information gathering method. The system according to feature 1.

5. The aforementioned information gathering unit, When gathering information, filter it based on the company's current marketing strategy and target market. The system according to feature 1.

6. The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned information gathering unit, When gathering information, prioritize collecting highly relevant information by considering the geographical location of companies. The system according to feature 1.

8. The aforementioned information gathering unit, When gathering information, we analyze the company's social media activities and collect relevant information. The system according to feature 1.

9. The generating unit is It estimates the user's emotions and adjusts the video's presentation based on those estimated emotions. The system according to feature 1.

10. The generating unit is When generating a video, adjust the video style based on the company's brand image. The system according to feature 1.

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