System

Abstract

A system is provided.SOLUTION: An input unit configured to input simple advertisement video configuration information, a unit configured to receive the configuration information input by the input unit, a generation unit configured to generate an advertisement video based on the received configuration information, a unit configured to acquire click rate data of a past advertisement video, a unit configured to relearn the generation unit based on the acquired click rate data, and a unit configured to optimize the advertisement video using the relearned generation unit; A system comprising: means for generating a final advertising video; means for providing the generated final advertising video to a user; means for confirming and approving the provided advertising video; and means for uploading the approved advertising video to a delivery platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, demand for video advertising on the Internet has skyrocketed, but creating these video advertisements requires a considerable amount of time and resources. Furthermore, analysis to determine which advertisements have high click-through rates is also extremely important, but the effort required to do so is not negligible. This invention addresses these issues by providing a system that efficiently and effectively generates and optimizes advertising videos, thereby significantly reducing the time required to create advertising videos and enabling the rapid implementation of effective advertising campaigns. [Means for solving the problem]

[0005] The present invention uses the following means: providing an input means for a user to input simple advertising video configuration information, and providing a means for receiving the configuration information input by the input means; and including a generation means for generating an advertising video based on the received configuration information; providing a means for acquiring click-through rate data of past advertising videos, and providing a means for retraining the generation means based on the acquired click-through rate data; providing a means for optimizing the advertising video using the retrained generation means and generating a final advertising video; then providing a means for providing the generated final advertising video to a user, and providing a means for reviewing and approving the provided advertising video; and finally constructing a system including a means for uploading the approved advertising video to a distribution platform. This allows advertising videos to be created and optimized efficiently, enabling effective advertising campaigns to be implemented quickly.

[0006] "Advertising video configuration information" is simple information such as product features and target users that is necessary to generate an advertising video.

[0007] The "input means" is an interface for the user to input advertising video configuration information.

[0008] The "receiving means" is a mechanism by which the server receives the advertising video configuration information input by the user via the input means.

[0009] The "generation means" refers to an algorithm or system that generates an advertising video based on the received advertising video configuration information.

[0010] "Click-through rate data" is data indicating the rate at which past advertising videos were clicked by users.

[0011] The "means of acquisition" refers to a mechanism for extracting click-through rate data for past advertising videos from a database, etc.

[0012] The "re-learning means" is a process of re-learning the algorithm of the generation means using the acquired click-through rate data to improve accuracy.

[0013] "Optimization means" refers to a method of improving advertising videos using retrained generation means to make them more effective.

[0014] The "means of providing" is a mechanism for delivering or presenting the optimized advertising video to the user.

[0015] The "means for checking and approving" is an interface that allows the user to check the provided advertising video and approve it if there are no problems.

[0016] The "means of uploading to a distribution platform" refers to a system that automatically uploads the final approved advertising video to a distribution platform on the Internet. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Specific embodiments of this system will be described in detail below.

[0039] System Configuration

[0040] 1. Users create advertising videos

[0041] 1. The user inputs simple information to compose the advertising video on the device. For example, if a user wants to create a promotional video for a new product, they open a browser and use a form to input the product name and features. The information entered is specific sentences such as "A new smartphone with a high-resolution camera and a long-life battery."

[0042] 2. The user submits the entered configuration information. When the user clicks the "Submit" button, the configuration information is sent to the server.

[0043] 2. Generating advertising videos using video generation AI

[0044] 3. The server analyzes the received configuration information. The server analyzes the received text data, breaks down the configuration information, and generates a scenario framework.

[0045] 4. Based on the analyzed configuration information, the server uses the video generation AI to generate an initial version of the advertising video. Based on the analysis results, the video generation AI generates a video that combines images and narration according to the scenario.

[0046] 3. Creating effective advertising videos

[0047] 5. The server retrieves advertising video data with high click rates from the database.

[0048] 6. The server provides the acquired click-through rate data to the video generation AI, which uses this data to retrain and update its model to generate more effective advertising videos.

[0049] 7. The video generation AI uses the retrained model to optimize the initial version of the ad video. The retrained AI model is used to optimize the initial ad video, creating content that will increase click-through rates.

[0050] 4. Distribution of advertising videos

[0051] 8. The server generates an optimized ad video and sends it to the user's device.

[0052] 9. The user checks the provided ad video and approves it if there are no problems. The user plays the ad video on their device, checks that there are no problems with the content, and then clicks the "Approve" button.

[0053] 10. The server uploads the approved ad video to each distribution platform on the Internet. The server automatically uploads the optimized ad video to different distribution platforms such as YouTube and LINE Voom.

[0054] Specific examples

[0055] Case 1: Promoting new products on an online shop

[0056] User Action:

[0057] A user uses a browser to fill out a form on the online shop's administration screen, entering "New product introduction video. New smartphone. With high-resolution camera and long-life battery." Then, the user clicks the "Submit" button to send the information to the server.

[0058] Server behavior:

[0059] The server analyzes the received data and generates a scenario for the ad video. It passes the data to the video generation AI, which generates an initial version of the ad video. It then retrieves data from the database about ad videos with high click rates and supplies it to the video generation AI.

[0060] Video generation AI in action:

[0061] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[0062] User confirmation:

[0063] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to YouTube, LINE Voom, etc.

[0064] This system allows users to quickly generate and distribute effective advertising videos without any hassle.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user inputs the configuration information of the advertising video (e.g., product features and target users) on the device. The user fills in the form with "A new smartphone with a high-resolution camera and a long-life battery."

[0068] Step 2:

[0069] To send the entered configuration information, the user clicks the "Send" button, which sends the configuration information from the terminal to the server.

[0070] Step 3:

[0071] The server analyzes the received configuration information. The server's analysis engine breaks down the received text data into components and organizes them as a scenario framework.

[0072] Step 4:

[0073] The server passes the analyzed configuration information to the video generation AI, which then generates an initial version of the advertising video based on this configuration information.

[0074] Step 5:

[0075] The server retrieves data from the database about advertising videos with high click rates in the past, including metadata such as the number of clicks and viewing time of users.

[0076] Step 6:

[0077] The server provides the acquired click-through rate data to the video generation AI, which then retrains the model to generate new advertising videos based on past effective advertising videos.

[0078] Step 7:

[0079] The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[0080] Step 8:

[0081] The server sends the optimized advertising video to the user's device, where the user can view the video through the interface.

[0082] Step 9:

[0083] The user checks the provided ad video and gives instructions to "approve" or "revise." If there are no problems, the approval is completed by clicking the "Approve" button.

[0084] Step 10:

[0085] The server uploads the approved ad video to various distribution platforms on the Internet (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] Conventional advertising video creation systems require users to manually create a scenario and combine video and narration, resulting in significant time and effort. Furthermore, there was a lack of a way to predict the effectiveness of the created advertising video in advance, so high click-through rates were not necessarily achieved. The present invention aims to solve these problems and provide a system that enables efficient and effective advertising video creation and distribution.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes an input means for inputting simple advertising video configuration information, a means for receiving the configuration information input by the input means, a means for analyzing the received configuration information and generating a video generation scenario framework, a means for generating an initial version of the advertising video using a video generation AI model based on the analysis results, a means for acquiring click-through rate data of past advertising videos, a means for retraining the video generation AI model based on the acquired click-through rate data, a means for optimizing the advertising video using the retrained video generation AI model and generating a final advertising video, a means for providing the generated final advertising video to a user, a means for reviewing and approving the provided advertising video, and a means for automatically uploading the approved advertising video to a distribution platform. This enables highly effective advertising videos to be quickly generated and widely distributed without user effort.

[0091] "Simple advertising video configuration information" is basic information that a user inputs to generate an advertising video, and includes product features and advertising content.

[0092] "Input means" is a function that provides an interface for the user to input simple advertising video configuration information, and includes browser forms, question-and-answer prompts, and the like.

[0093] The "receiving means" is a function by which the server acquires the configuration information sent from the user through the input means.

[0094] The "means of analysis" is a function that generates a scenario framework based on the received configuration information and converts it into a format suitable for the video generation AI model.

[0095] The "video generation scenario framework" is the framework of an advertising video constructed based on the configuration information, and it instructs the distribution of images and narration.

[0096] A "video generation AI model" is an algorithm or program that uses artificial intelligence to automatically generate and optimize advertising videos.

[0097] The "means for generating an initial version of an advertising video" is a function used by the video generation AI model to create an initial version of an advertising video based on the analyzed configuration information.

[0098] "Click-through rate data" is statistically collected data on the number and percentage of users who clicked on a distributed advertising video.

[0099] The "re-learning means" is a function that uses the acquired click-through rate data to further train the video generation AI model and generate more effective advertising videos.

[0100] "Means for optimizing advertising videos" is a function that uses an updated AI model through re-learning to improve the initial version of the advertising video and achieve a high click-through rate.

[0101] The "means for generating the final advertising video" is a function for completing the optimized advertising video as the final version.

[0102] The "means for providing" is a function for sending the generated final advertising video to the user and prompting the user to confirm it.

[0103] The "means for checking and approving" is a function that allows a user to check the provided advertising video and approve it if there is no problem with the content.

[0104] "Means for automatically uploading to distribution platforms" refers to a function for automatically uploading approved advertising videos to various distribution platforms such as YouTube and social media.

[0105] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Specific embodiments of this system will be described in detail below.

[0106] System Configuration

[0107] Users create advertising videos

[0108] First, the user inputs basic information about the advertising video on the device. This input method uses a browser form or similar. For example, if a user wants to create a promotional video for a new product, they open a browser and input "A new smartphone with a high-resolution camera and a long-life battery." This input information is also used as a prompt based on specific content.

[0109] The server receives and analyzes the configuration information

[0110] The server then receives the configuration information entered by the user, analyzes it, extracts keywords and important phrases, and converts them into data for generating a scenario framework suitable for the video generation AI model.

[0111] Generating an early version of an advertising video using a video generation AI model

[0112] Based on the analysis results, the server uses a video generation AI model to generate an initial version of the advertising video, which combines appropriate video footage and narration according to the specified scenario framework.

[0113] The server acquires the click-through rate data and retrains the video generation AI model.

[0114] The server accesses the database to retrieve click-through rate data for past ad videos. This data includes statistical information such as viewing time, number of clicks, and click-through rate. The server then provides this data to the video generation AI model, which then retrains the AI. This retraining improves the AI ​​model's ability to generate more effective ad videos.

[0115] Optimizing advertising videos with a video generation AI model

[0116] The retrained video generation AI model then optimizes the initial version of the ad video, resulting in a video that is expected to generate a high click-through rate from viewers.

[0117] The server provides the optimized ad video to the user.

[0118] The optimized ad video is sent from the server to the user's device. The user checks the video and approves it if there are no problems. Specifically, the user plays the video in the browser and clicks the "Approve" button.

[0119] The server uploads the ad video to the distribution platform

[0120] Finally, approved ad videos are automatically uploaded from the server to various distribution platforms on the Internet, with the option to upload to YouTube or other social media platforms.

[0121] Specific examples

[0122] Case 1: Promoting new products in an online shop

[0123] 1. User Action:

[0124] The user opens a browser and enters the following information into a form on the online shop's management screen: "New product introduction video. New smartphone. With high-resolution camera and long-life battery."

[0125] Once you have completed entering the information, click the "Submit" button.

[0126] 2. Server operation:

[0127] The server receives the input data and analyzes it using a text analysis engine. The analysis results are generated as a scenario framework and passed to the video generation AI model.

[0128] The server retrieves past click-through rate data from the database and provides it to the video generation AI model, which then retrains and generates optimized ad videos.

[0129] 3. User Verification and Authorization:

[0130] The user checks the generated ad video and clicks the "Approve" button if there are no problems. An example of a confirmation prompt sentence here is "Check out the video introducing the new smartphone. It has a high-resolution camera and a long-life battery. Are there any problems with the content?"

[0131] 4. Server uploading:

[0132] Approved ad videos are automatically uploaded from the server to YouTube and other distribution platforms.

[0133] The above process makes it possible to generate and distribute advertising videos that are expected to be highly effective without much effort.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] The user inputs the advertising video configuration information.

[0137] Specifically, the user opens a browser and enters information about the ad video into a form, such as "A new smartphone with a high-resolution camera and a long-life battery." The input method is a web form, and the input data is also used as a prompt. This input data is then sent to the server.

[0138] Step 2:

[0139] The server receives the input configuration information.

[0140] The server receives the advertising video composition information sent from the user's device. The received data is in text format and is saved in the system. The server then passes this data to the analysis engine.

[0141] Step 3:

[0142] The server analyzes the configuration information and generates a scenario framework.

[0143] The received text data is analyzed by an analysis engine to extract keywords and important phrases. Based on the extracted content, a scenario framework is generated and converted into a data format suitable for the video generation AI model. For example, phrases such as "new smartphone," "high-resolution camera," and "long-life battery" are extracted as part of the scenario.

[0144] Step 4:

[0145] The video generation AI model generates an initial version of the ad video.

[0146] The server then provides the scenario framework obtained from the analysis results to the video generation AI model. Based on the provided data, the AI ​​model combines the footage and narration to generate an initial version of the advertising video. This video is generated taking into account current trends and optimization of viewing time.

[0147] Step 5:

[0148] The server retrieves historical click-through rate data.

[0149] The server accesses the database to retrieve click-through rate data for past advertising videos, including viewing time, number of clicks, and click-through rate. The retrieved data is used as input data for the video generation AI model.

[0150] Step 6:

[0151] The video generation AI model retrains and optimizes the advertising video.

[0152] The server provides the click-through rate data to the AI ​​model for retraining. The retrained AI model then builds a new algorithm to generate more effective ad videos. The generated initial version of the ad video is then re-edited to output an optimized version.

[0153] Step 7:

[0154] The server provides the optimized advertising video to the user.

[0155] The optimized ad video is sent from the server to the user's device. The user reviews the video and approves it if there are no problems with the ad content. Specifically, the user plays the video and clicks the "Approve" button.

[0156] Step 8:

[0157] The server uploads the advertising video to the distribution platform.

[0158] Approved advertising videos are automatically uploaded from the server to YouTube and other social media platforms, and the uploaded videos are optimized for the characteristics of each platform.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] Conventional advertising video creation systems require users to spend a lot of time and effort creating videos, and a great deal of specialized knowledge is required for effective distribution. For this reason, there is a demand for more efficient advertising creation and maximizing distribution effectiveness. There is also a need for a system that is easy for users to operate and that can produce effective results.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes an input means for inputting simple advertising video configuration information, a means for receiving the configuration information input by the input means, and a generation means for generating an advertising video based on the received configuration information. This enables users to easily create and effectively distribute advertising videos.

[0164] "Simple advertising video configuration information" is simple information that a user inputs to create an advertising video, and includes basic data such as product names and features.

[0165] "Input means" refers to a means by which a user inputs information constituting an advertising video, and refers to an interface such as a form on a smartphone.

[0166] The "receiving means" is a means by which the server receives the configuration information input by the input means.

[0167] The "generation means" is a means for generating an advertising video based on the received configuration information.

[0168] "Click-through rate data" refers to data relating to the percentage of users clicking on past advertising videos.

[0169] The "means for obtaining" refers to a means for obtaining click-through rate data of past advertising videos from a database.

[0170] The "means for relearning" refers to a means for relearning the generation means (video generation AI) based on the acquired click rate data.

[0171] The "optimizing means" is a means for optimizing the advertising video using the retrained generating means and generating the final advertising video.

[0172] The "means for providing" refers to a means for providing the final generated advertising video to the user.

[0173] The "means for checking and approving" is a means for a user to check the generated advertising video and approve it if there is no problem.

[0174] "Means for uploading" refers to the means for uploading the approved advertising video to a distribution platform on the Internet.

[0175] "Smartphone application" refers to an application that allows users to create, review, approve, and distribute advertising videos on their smartphones.

[0176] "Scenario generation" is a process of automatically generating a scenario for an advertising video based on input product information.

[0177] "Video generation" is the process of creating an advertising video according to a generated scenario.

[0178] MODE FOR CARRYING OUT THE INVENTION

[0179] The present invention relates to a system that allows users to easily create advertising videos and maximize the effectiveness of those videos. Specific embodiments of this system will be described in detail below.

[0180] System Configuration

[0181] This system consists of the following elements:

[0182] 1. Input method:

[0183] The smartphone application allows users to input simple information about the composition of the advertising video, such as the product name and features, in text format.

[0184] 2. Receiving means:

[0185] The application sends the input information to the server, which receives the information.

[0186] 3. Generation means:

[0187] The server analyzes the received configuration information and generates scenarios and videos. It automatically generates the initial version of the advertising video using a video generation AI model (e.g., TensorFlow / Keras).

[0188] 4. Acquisition method:

[0189] The server retrieves click-through rate data for past advertising videos from a database (e.g., MySQL or PostgreSQL).

[0190] 5. Retraining methods:

[0191] The server retrains the video generation AI model based on the acquired click-through rate data, updating the model to maximize the effectiveness of advertising videos.

[0192] 6. Optimization measures:

[0193] The retrained model is used to optimize the initial version of the ad video to generate the final ad video.

[0194] 7. Means of providing:

[0195] The server then sends the final ad video to the user's smartphone, where the user can review and approve it.

[0196] 8. Verification and Approval Methods:

[0197] The user checks the provided advertising video and clicks the "Approve" button if there are no problems.

[0198] 9. Uploading Method:

[0199] The server automatically uploads approved ad videos to distribution platforms (such as YouTube and social media), allowing users to distribute ad videos quickly and effectively.

[0200] Specific examples of processing

[0201] Let's take the example of a user creating an advertising video for a new wearable device. They enter "A new wearable device with a high-precision heart rate monitor and a long-life battery" into the application form and click the "Submit" button. The server analyzes the received information and starts generating a scenario and video.

[0202] The server retrains the video generation AI model based on past data with high click rates, generates an optimized ad video, and then sends the video to the user. Once the user reviews and approves the sent video, the server automatically uploads the ad video to the distribution platform.

[0203] Prompt Sentence Examples

[0204] "The latest smartphone is finally here! With a high-resolution camera and long-lasting battery, it's perfect for videography. Order now!"

[0205] In this way, users can generate and distribute effective advertising videos with minimal operations.

[0206] Hardware / Software Used

[0207] Hardware: Smartphone

[0208] software:

[0209] Server side: Python (Flask framework)

[0210] Video generation AI: TensorFlow / Keras

[0211] Database: MySQL or PostgreSQL

[0212] Frontend: React Native

[0213] This allows users to create and distribute advertising videos with simple operations, maximizing the effectiveness of their advertising.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Step 1:

[0216] The user enters the configuration information for the advertising video into a form on the smartphone application. This configuration information includes the product name and features. For example, the user might enter "A new wearable device with a high-precision heart rate monitor and a long-life battery." When the user clicks the "Submit" button, this information is sent to the server.

[0217] Input: Ad video configuration information

[0218] Output: Configuration information sent to the server

[0219] Step 2:

[0220] The server analyzes the received configuration information. Specifically, it parses the text data to extract attributes such as product names and features. For example, it extracts the product name "wearable device" and the features "high-precision heart rate monitor and long-life battery."

[0221] Input: Configuration information submitted by the user

[0222] Output: Attributes of extracted configuration information

[0223] Step 3:

[0224] The server generates a scenario based on the extracted attribute information, and uses a video generation AI model (TensorFlow / Keras) to generate prompts for creating an initial version of the advertising video, which are then used to generate the video.

[0225] Input: Attributes of extracted configuration information

[0226] Output: Initial version of the ad video

[0227] Step 4:

[0228] The server retrieves click-through rate data for past ad videos from a database (MySQL or PostgreSQL). This data includes data related to ad videos with high click-through rates, and is used to measure the effectiveness of the videos.

[0229] Input: Database query

[0230] Output: Click-through rate data for past ad videos

[0231] Step 5:

[0232] The server retrains the video generation AI model based on the acquired click-through rate data, using TensorFlow / Keras to update previous learning results and create a model for generating more effective advertising videos.

[0233] Input: Click-through rate data

[0234] Output: Retrained video generation AI model

[0235] Step 6:

[0236] The server then uses the retrained AI model to optimize the initial version of the ad video, generating a final ad video that may have a higher click-through rate.

[0237] Input: Retrained AI model and initial version of ad video

[0238] Output: Optimized ad video

[0239] Step 7:

[0240] The server then sends the final ad video to the user's smartphone. The user reviews the video and clicks the "Approve" button if there are no problems with the content.

[0241] Input: Optimized ad video

[0242] Output: Ad video viewable on user device

[0243] Step 8:

[0244] The user checks the provided advertisement video and, if there are no problems, clicks the "Approve" button, which triggers the server to upload the advertisement video to the distribution platform.

[0245] Input: User review and approval

[0246] Output: Approval trigger

[0247] Step 9:

[0248] The server automatically uploads approved ad videos to distribution platforms such as YouTube and social media, allowing them to be widely published and reach their target audience.

[0249] Inputs: Approval trigger and ad video

[0250] Output: Ad video published on distribution platform

[0251] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0252] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. In particular, by combining an emotion engine that recognizes user emotions, it is possible to generate more personalized advertising videos. Specific embodiments of this system are described in detail below.

[0253] System Configuration

[0254] 1. Users create advertising videos

[0255] 1. The user inputs simple advertising video configuration information on the device. For example, if a user wants to create a promotional video for a new product, they open a browser and use a form to input the product name and features. The information entered is specific sentences such as "A new smartphone with a high-resolution camera and a long-life battery."

[0256] 2. The user submits the configuration information they entered. When the user clicks the "Submit" button, the configuration information is sent to the server. At this stage, the camera and microphone are automatically activated so that the emotion engine can analyze the user's facial expressions and tone of voice.

[0257] 2. Analysis by emotion engine

[0258] 3. The server receives the configuration information and analysis data from the emotion engine. The emotion engine analyzes the user's facial expressions and tone of voice to evaluate the user's emotional state (e.g., joy, surprise, anticipation, etc.).

[0259] 4. The analysis results are sent to the server and reflected in the composition of the ad video. For example, if the user is talking in a happy manner, the scenario will be adjusted to generate an ad video with a bright and positive tone.

[0260] 3. Generating advertising videos using video generation AI

[0261] 5. The server analyzes the received configuration information and emotion data and generates a scenario framework.

[0262] 6. Based on the analyzed information, the server uses the video generation AI to generate an initial version of the advertising video. The video generation AI generates a video that combines images and narration according to the scenario.

[0263] 4. Creating effective advertising videos

[0264] 7. The server retrieves data from the database about advertising videos with high click-through rates in the past.

[0265] 8. The server provides the acquired click-through rate data to the video generation AI, which uses this data to retrain and update its model to generate more effective advertising videos.

[0266] 9. The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[0267] 5. Distribution of advertising videos

[0268] 10. The server sends the optimized ad video to the user's device, where the user can view the video through the interface.

[0269] 11. The user checks the provided ad video and gives instructions to "approve" or "modify." If there are no problems, the user clicks the "Approve" button to complete the approval.

[0270] 12. The server uploads the approved ad video to various online distribution platforms (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[0271] Specific examples

[0272] Case 1: Promoting new products on an online shop

[0273] User Action:

[0274] The user uses a browser to enter the following information into a form on the online shop's administration screen: "New product introduction video. New smartphone. With high-resolution camera and long-life battery." Then, they click the "Submit" button to send the information to the server. During this time, the camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice.

[0275] Server behavior:

[0276] The server analyzes the received data and generates a scenario for the ad video. Based on the analysis results of the emotion engine, the video generation AI generates an initial version of the ad video. After that, data on ad videos with high click rates in the past is retrieved from the database and provided to the video generation AI.

[0277] Video generation AI in action:

[0278] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[0279] User confirmation:

[0280] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to YouTube, LINE Voom, etc.

[0281] This system allows users to quickly generate and distribute effective advertising videos without much effort. In addition, the introduction of an emotion engine makes it possible to generate personalized advertising videos according to the user's emotional state.

[0282] The processing flow will be explained below.

[0283] Step 1:

[0284] The user inputs the configuration information of the advertising video (e.g., product features and target users) on the device. The user enters "A new smartphone with a high-resolution camera and a long-life battery" into the form.

[0285] Step 2:

[0286] To send the entered configuration information, the user clicks the "Send" button, which sends the configuration information from the terminal to the server.

[0287] Step 3:

[0288] The device's camera and microphone will automatically activate and record the user's facial expressions and tone of voice, and this data will be sent to the emotion engine in real time.

[0289] Step 4:

[0290] The server analyzes the received configuration information. The server's analysis engine breaks down the received text data into components and organizes them as a scenario framework.

[0291] Step 5:

[0292] The emotion engine analyzes the user's facial expressions and tone of voice received in real time to assess the user's emotional state (e.g., joy, surprise, anticipation, etc.).

[0293] Step 6:

[0294] The server receives the analysis results from the emotion engine and reflects them in the scenario of the advertising video. For example, if the user is talking in a happy manner, the scenario will be adjusted to generate an advertising video with a bright and positive tone.

[0295] Step 7:

[0296] Based on the analyzed configuration information and emotional data, the server's video generation AI generates an initial version of the advertising video.

[0297] Step 8:

[0298] The server retrieves data from a database about advertising videos with high click rates in the past, including metadata such as the number of clicks and viewing time of users.

[0299] Step 9:

[0300] The server provides the acquired click-through rate data to the video generation AI, which then retrains the model to generate new advertising videos based on past effective advertising videos.

[0301] Step 10:

[0302] The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[0303] Step 11:

[0304] The server sends the optimized advertising video to the user's device, where the user can view the video through the interface.

[0305] Step 12:

[0306] The user checks the provided advertising video and gives instructions to "approve" or "modify." If there are no problems, the user clicks the "Approve" button.

[0307] Step 13:

[0308] The server uploads the approved ad video to various distribution platforms on the Internet (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[0309] Step 14:

[0310] The server collects click-through rate data for published ad videos in real time and uses it to generate future ad videos. This feedback loop is expected to continuously improve the accuracy and effectiveness of the entire system.

[0311] Example 2

[0312] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0313] Creating advertising videos distributed over the Internet is time-consuming and labor-intensive, and requires advanced expertise to generate effective videos. It is also difficult to generate personalized advertising videos that reflect user emotions, and optimizing them to improve click-through rates is not easy. To address these challenges, a system is needed that allows users to easily create highly effective advertising videos and optimizes those videos to attract the interest of many users.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0315] In this invention, the server includes an input means for inputting simple configuration information, a means for receiving the configuration information input by the input means, a means for integrating and analyzing the configuration information and user emotion data, a generation means for generating an advertising video based on the analyzed information, a means for acquiring click-through rate data of past advertising videos, a means for relearning the generation means based on the acquired click-through rate data, a means for optimizing the advertising video using the re-trained generation means to generate a final advertising video, a means for providing the generated final advertising video to a user, a means for reviewing and approving the provided advertising video, and a means for uploading the approved advertising video to a distribution platform. This enables users to easily generate effective advertising videos, create personalized videos using emotion data, and provide videos optimized to further improve click-through rates.

[0316] "Simple configuration information" is basic information such as product name and features that is input by the user in order to generate an advertising video.

[0317] An "input means" is a device or interface that a user uses to input configuration information, such as a form on a browser.

[0318] The "receiving means" is a mechanism by which the server acquires the configuration information input by the user.

[0319] "Emotion data" is information about the user's emotional state obtained by analyzing the user's facial expression and tone of voice.

[0320] The "analyzing means" is a mechanism for integrating the received configuration information and emotion data to generate a scenario for the advertising video.

[0321] "Generation means" refers to technologies such as video generation AI that create advertising videos based on analyzed information.

[0322] "Click-through rate data" is numerical data that indicates user responses to past advertising videos. It is an index used to evaluate the effectiveness of advertising.

[0323] The "re-learning means" is a mechanism for updating the video generation AI model based on the acquired click-through rate data, thereby improving the accuracy of generating advertising videos.

[0324] The "optimization means" is a mechanism for more effectively adjusting and editing advertising videos using the retrained generation means.

[0325] The "means for providing" refers to a mechanism for displaying the generated advertising video to a user, such as a system for transmitting a video file to a user's terminal.

[0326] The "means for checking and approving" refers to an interface or mechanism that allows a user to check the generated advertising video and approve it if there are no problems.

[0327] "Means for uploading to a distribution platform" refers to a mechanism for uploading approved advertising videos to online video sharing services, etc.

[0328] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. In particular, by combining an emotion engine that recognizes user emotions, it is possible to generate more personalized advertising videos. Specific embodiments of this system are described in detail below.

[0329] System Configuration

[0330] This system mainly consists of a server, a terminal, an emotion engine, and a video generation AI.

[0331] Users create advertising videos

[0332] The user opens a browser on their device and enters configuration information such as the product name and features into a form for creating an advertising video. For example, they might enter "A new smartphone with a high-resolution camera and a long-life battery." This input information is treated as simple configuration information.

[0333] Start sending data and analyzing sentiment

[0334] When the user clicks the "Send" button, the entered configuration information is sent to the server. At the same time, the device's camera and microphone are automatically activated, and the emotion engine begins analyzing the user's facial expressions and tone of voice. This analyzed data is sent to the server as emotion data.

[0335] Integrating emotion data and compositional information

[0336] The server receives the received configuration information and the analysis data from the emotion engine, integrates and analyzes them. The emotion data reflects the user's emotional state (e.g., joy, surprise, anticipation, etc.), and generates a scenario for the advertising video based on this.

[0337] Generate initial ad video

[0338] The server then uses the integrated information to have the video generation AI generate an initial version of the advertising video, which then combines footage and narration according to the advertising scenario to create the initial advertising video.

[0339] Obtaining and relearning click-through rate data

[0340] The server retrieves click-through rate data for past ad videos from the database and supplies it to the video generation AI. The video generation AI re-learns based on this click-through rate data and updates its ad video generation model to generate even more effective ad videos.

[0341] Ad video optimization

[0342] The retrained generator is used to optimize the initial version of the ad video, which is expected to improve click-through rates.

[0343] Video review and approval

[0344] The server sends the optimized ad video to the user's device. The user checks the provided ad video and, if there are no problems, clicks the "Approve" button to complete the approval.

[0345] Upload to a distribution platform

[0346] The server then uploads the approved ad video to various distribution platforms on the Internet (e.g., video sharing services and social media), which automatically publishes the ad video and reaches a large audience.

[0347] Specific examples

[0348] Case 1: Promoting new products on an online shop

[0349] User Action:

[0350] The user uses a browser on the online shop's management screen to enter "New product introduction video. New smartphone. Equipped with a high-resolution camera and long-life battery," and clicks the "Send" button. During this time, the camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice.

[0351] Server behavior:

[0352] The server analyzes the received data and generates a scenario for the ad video based on the analysis results of the emotion engine. The video generation AI generates an initial version of the ad video and then retrieves data from a database of ad videos with high click rates in the past.

[0353] Video generation AI in action:

[0354] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[0355] User confirmation:

[0356] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to video sharing services and social media.

[0357] This system allows users to quickly generate and distribute highly effective advertising videos easily and efficiently. The introduction of an emotion engine makes it possible to generate personalized advertising videos according to the user's emotional state.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1:

[0360] The user provides input for creating an advertising video.

[0361] Input: The user opens a browser on their device and fills in a form with configuration information, such as the product name and features. For example, "New smartphone. With a high-resolution camera and long-life battery."

[0362] Data processing: The information entered in the form is formatted into a certain format.

[0363] Output: Formatted configuration information.

[0364] Step 2:

[0365] The user sends the configuration information to the server and sentiment analysis begins.

[0366] Input: The user clicks the "Submit" button.

[0367] Data calculation: The device's camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice in real time.

[0368] Output: The configuration information is sent to the server and the analyzed emotion data is obtained.

[0369] Step 3:

[0370] The server receives and integrates the configuration information and emotion data.

[0371] Input: The server receives configuration information from the user and emotion data from the emotion engine.

[0372] Data calculation: Integrates received data and performs analysis to generate scenarios based on the user's emotions.

[0373] Output: An advertising video scenario based on the analysis results is obtained.

[0374] Step 4:

[0375] The server generates the initial advertising video.

[0376] Input: Parsed configuration information and scenario

[0377] Data calculation: Video generation AI combines video clips and narration based on a scenario.

[0378] Output: The initial version of the ad video is generated.

[0379] Step 5:

[0380] The server retrieves the click-through rate data and performs re-learning.

[0381] Input: Click-through rate data for past ad videos

[0382] Data calculation: The video generation AI re-learns based on click-rate data and updates the generation model.

[0383] Output: An updated ad video generation model is obtained.

[0384] Step 6:

[0385] Video generation AI generates optimized advertising videos.

[0386] Input: Updated generative model and initial version of ad video

[0387] Data calculation: Adjust the timing of video transitions and the tone of the narration according to the optimization procedure.

[0388] Output: The optimized ad video is generated.

[0389] Step 7:

[0390] The server sends the optimized advertising video to the user.

[0391] Input: Optimized ad video

[0392] Data calculation: Performs the procedure for sending the optimized advertising video to the user's device.

[0393] Output: The ad video is sent to the user.

[0394] Step 8:

[0395] The user reviews the ad video and approves or modifies it.

[0396] Input: Ad video sent to user

[0397] Data Calculation: The user checks the video and clicks the "Approve" or "Modify" button.

[0398] Output: If the user approves, the video is marked as the final version, and if there are any corrections required, the cycle starts again.

[0399] Step 9:

[0400] The server uploads the approved advertising video to the distribution platform.

[0401] Input: Approved ad video

[0402] Data Computing: Processing the video for uploading to the appropriate distribution platform.

[0403] Output: The advertising video is published on the Internet and reaches a large audience.

[0404] (Application example 2)

[0405] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0406] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Conventional advertising video generation systems generally generate advertising videos based solely on user input information, limiting innovations in personalization. While generating advertising videos that take user emotions into consideration would enable more effective personalization, building such a system has been challenging. Furthermore, there has been a lack of means for relearning and optimizing the generation method using past click-through rate data. This has made it challenging to realize a system that maximizes advertising effectiveness.

[0407] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: an input means for inputting simple advertising video configuration information; a means for receiving the configuration information input by the input means; an emotion analysis means for analyzing a user's facial expression and tone of voice and evaluating their emotional state; a generation means for generating an advertising video based on the received configuration information and analysis data from the emotion analysis means; a means for acquiring click-through rate data of past advertising videos; a means for relearning the generation means based on the acquired click-through rate data; a means for optimizing the advertising video using the relearned generation means to generate a final advertising video; a means for providing the generated final advertising video to a user; a means for reviewing and approving the provided advertising video; and a means for uploading the approved advertising video to a distribution platform. This enables the generation of personalized advertising videos that reflect the user's emotional state, and further enables effective optimization of advertising videos through relearning using past click-through rate data.

[0408] The "input means" is a means for a user to input information about the configuration of an advertising video.

[0409] The "receiving means" is a means for receiving configuration information input by the input means.

[0410] The "emotion analysis means" is a means for analyzing the user's facial expression and tone of voice to evaluate the user's emotional state.

[0411] The "generation means" is a means for generating an advertising video based on the received configuration information and the analysis data from the emotion analysis means.

[0412] The "means of acquisition" refers to a means for acquiring click-through rate data for past advertising videos.

[0413] The "means for relearning" is a means for relearning the generation means based on the acquired click rate data.

[0414] The "optimizing means" is a means for optimizing the advertising video using the retrained generating means and generating the final advertising video.

[0415] The "means for providing" refers to a means for providing the final generated advertising video to the user.

[0416] The "means for checking and approving" is a means for a user to check and approve the provided advertising video.

[0417] "Means for uploading" refers to the means for uploading the approved advertising video to the distribution platform.

[0418] The present invention is a system for realizing efficient and effective creation and distribution of advertising videos on the Internet. A specific embodiment of the present invention will be described in detail based on the above description.

[0419] Program processing

[0420] The system includes multiple components, including a user terminal, a server, a sentiment analysis engine, and a generative AI model.

[0421] User terminal processing

[0422] The user terminal is used through a web browser or a dedicated application, and performs the following processes.

[0423] A form is displayed for the user to input configuration information for the advertising video.

[0424] It accepts user input and uses a camera and microphone to capture facial expressions and voice.

[0425] The configuration information and the captured data are sent to a server.

[0426] Server Processing

[0427] The server performs the following main processes:

[0428] The configuration information and capture data transmitted from the user terminal are received.

[0429] A sentiment analysis engine is used to analyze the user's emotional state.

[0430] The composition information and sentiment analysis data are sent to a generative AI model to generate advertising videos.

[0431] Advertising videos are optimized by obtaining past click-through rate data from a database and supplying it to a generative AI model.

[0432] The optimized advertising video is sent to the user terminal for review and approval.

[0433] Upload the approved ad video to a distribution platform.

[0434] Sentiment Analysis Engine

[0435] The emotion analysis engine analyzes the user's facial expressions and tone of voice to assess their emotional state, leveraging known emotion recognition algorithms such as OpenFace and DeepVoice.

[0436] Generative AI Models

[0437] The generative AI model generates videos based on the received composition information and emotional data, and then re-trains using past click-through rate data to optimize the ad video.

[0438] Hardware and software used

[0439] Hardware: Smartphone (camera, microphone), server

[0440] Software: Browser or dedicated app, emotion analysis algorithm (OpenFace, DeepVoice), video generation AI (e.g., OpenAI's DALL-E)

[0441] Adding specific examples

[0442] Example of new product introduction using a smartphone app

[0443] The flow for creating an advertising video for a new product using a smartphone app used by a user is as follows.

[0444] User operations

[0445] 1. Launch the smartphone app and go to the screen for creating advertising videos.

[0446] 2. Enter the name and features of the new product in text format as configuration information.

[0447] 3. The camera and microphone will automatically activate and capture your facial expressions and voice.

[0448] 4. Press the "Submit" button to send the input information and captured data to the server.

[0449] Server Operation

[0450] 1. The server analyzes the received configuration information and capture data.

[0451] 2. The sentiment analysis engine evaluates the user's emotional state.

[0452] 3. The generative AI model generates the initial advertising video based on the received data.

[0453] 4. Retrain the generative AI model based on past click-through rate data to optimize the ad video.

[0454] 5. Send the optimized ad video to the user's device for review and approval.

[0455] Prompt Sentence Examples

[0456] "Create a video to showcase your new smartphone, highlighting its high-resolution camera and long-lasting battery life. Use an upbeat tone to reflect customer expectations."

[0457] Thus, the present invention provides a user-friendly interface while utilizing sentiment analysis and database information to generate and optimize personalized advertising videos.

[0458] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0459] Step 1:

[0460] The user launches the smartphone app and inputs the information that makes up the advertising video. Specifically, the name and features of the new product are entered in text format. This input data is the basic scenario information for the advertising video. A specific example of input includes information such as "A new smartphone with a high-resolution camera and a long-life battery."

[0461] Step 2:

[0462] The device receives the configuration information and automatically activates the camera and microphone, capturing the user's facial expressions and voice. The captured video and audio data is stored unprocessed.

[0463] Step 3:

[0464] The device sends the saved configuration information and captured data, including text configuration information and video and audio files, to the server for further processing.

[0465] Step 4:

[0466] The server analyzes the received configuration information and captured data. First, it uses an emotion analysis engine to analyze the user's facial expressions and tone of voice to evaluate the user's emotional state. The emotion analysis engine uses algorithms such as OpenFace and DeepVoice. The analysis results in emotional data such as "expectation."

[0467] Step 5:

[0468] The server queries the generative AI model based on the analysis results and configuration information. The generative AI model uses the obtained data to generate an initial ad video. In this case, the generative AI model uses advanced algorithms such as OpenAI's DALL-E. The generated result is an initial version of the ad video file.

[0469] Step 6:

[0470] The server retrieves click-through rate data for past ad videos from the database. This data includes the click-through rate and characteristics of each ad video. The retrieved click-through rate data is fed to the generative AI model for retraining. The retrained generative AI model generates optimized ad videos based on the past data.

[0471] Step 7:

[0472] The server then sends the optimized ad video file to the user's device. The optimized video is adjusted to expect a higher click-through rate. The ad video is then sent from the server so that the user can view it.

[0473] Step 8:

[0474] The user terminal displays the received advertising video on the user interface and provides a function for the user to review and approve it. The user reviews the video and clicks the "Approve" button if there are no problems. If corrections are required, the user issues a "correction instruction."

[0475] Step 9:

[0476] The server receives the approval data from the user and uploads the approved ad video to a distribution platform, such as YouTube or LINE Voom. This completes the release of the ad video.

[0477] In this way, each processing step works in conjunction to realize a system that generates and optimizes personalized advertising videos based on user emotions.

[0478] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0479] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0480] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0481] [Second embodiment]

[0482] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0483] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0484] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0485] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0486] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0487] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0488] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0489] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0490] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0491] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0492] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0493] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0494] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Specific embodiments of this system will be described in detail below.

[0495] System Configuration

[0496] 1. Users create advertising videos

[0497] 1. The user inputs simple information to compose the advertising video on the device. For example, if a user wants to create a promotional video for a new product, they open a browser and use a form to input the product name and features. The information entered is specific sentences such as "A new smartphone with a high-resolution camera and a long-life battery."

[0498] 2. The user submits the entered configuration information. When the user clicks the "Submit" button, the configuration information is sent to the server.

[0499] 2. Generating advertising videos using video generation AI

[0500] 3. The server analyzes the received configuration information. The server analyzes the received text data, breaks down the configuration information, and generates a scenario framework.

[0501] 4. Based on the analyzed configuration information, the server uses the video generation AI to generate an initial version of the advertising video. Based on the analysis results, the video generation AI generates a video that combines images and narration according to the scenario.

[0502] 3. Creating effective advertising videos

[0503] 5. The server retrieves advertising video data with high click rates from the database.

[0504] 6. The server provides the acquired click-through rate data to the video generation AI, which uses this data to retrain and update its model to generate more effective advertising videos.

[0505] 7. The video generation AI uses the retrained model to optimize the initial version of the ad video. The retrained AI model is used to optimize the initial ad video, creating content that will increase click-through rates.

[0506] 4. Distribution of advertising videos

[0507] 8. The server generates an optimized ad video and sends it to the user's device.

[0508] 9. The user checks the provided ad video and approves it if there are no problems. The user plays the ad video on their device, checks that there are no problems with the content, and then clicks the "Approve" button.

[0509] 10. The server uploads the approved ad video to each distribution platform on the Internet. The server automatically uploads the optimized ad video to different distribution platforms such as YouTube and LINE Voom.

[0510] Specific examples

[0511] Case 1: Promoting new products on an online shop

[0512] User Action:

[0513] A user uses a browser to fill out a form on the online shop's administration screen, entering "New product introduction video. New smartphone. With high-resolution camera and long-life battery." Then, the user clicks the "Submit" button to send the information to the server.

[0514] Server behavior:

[0515] The server analyzes the received data and generates a scenario for the ad video. It passes the data to the video generation AI, which generates an initial version of the ad video. It then retrieves data from the database about ad videos with high click rates and supplies it to the video generation AI.

[0516] Video generation AI in action:

[0517] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[0518] User confirmation:

[0519] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to YouTube, LINE Voom, etc.

[0520] This system allows users to quickly generate and distribute effective advertising videos without any hassle.

[0521] The processing flow will be explained below.

[0522] Step 1:

[0523] The user inputs the configuration information of the advertising video (e.g., product features and target users) on the device. The user fills in the form with "A new smartphone with a high-resolution camera and a long-life battery."

[0524] Step 2:

[0525] To send the entered configuration information, the user clicks the "Send" button, which sends the configuration information from the terminal to the server.

[0526] Step 3:

[0527] The server analyzes the received configuration information. The server's analysis engine breaks down the received text data into components and organizes them as a scenario framework.

[0528] Step 4:

[0529] The server passes the analyzed configuration information to the video generation AI, which then generates an initial version of the advertising video based on this configuration information.

[0530] Step 5:

[0531] The server retrieves data from the database about advertising videos with high click rates in the past, including metadata such as the number of clicks and viewing time of users.

[0532] Step 6:

[0533] The server provides the acquired click-through rate data to the video generation AI, which then retrains the model to generate new advertising videos based on past effective advertising videos.

[0534] Step 7:

[0535] The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[0536] Step 8:

[0537] The server sends the optimized advertising video to the user's device, where the user can view the video through the interface.

[0538] Step 9:

[0539] The user checks the provided ad video and gives instructions to "approve" or "revise." If there are no problems, the approval is completed by clicking the "Approve" button.

[0540] Step 10:

[0541] The server uploads the approved ad video to various distribution platforms on the Internet (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[0542] Example 1

[0543] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0544] Conventional advertising video creation systems require users to manually create a scenario and combine video and narration, resulting in significant time and effort. Furthermore, there was a lack of a way to predict the effectiveness of the created advertising video in advance, so high click-through rates were not necessarily achieved. The present invention aims to solve these problems and provide a system that enables efficient and effective advertising video creation and distribution.

[0545] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0546] In this invention, the server includes an input means for inputting simple advertising video configuration information, a means for receiving the configuration information input by the input means, a means for analyzing the received configuration information and generating a video generation scenario framework, a means for generating an initial version of the advertising video using a video generation AI model based on the analysis results, a means for acquiring click-through rate data of past advertising videos, a means for retraining the video generation AI model based on the acquired click-through rate data, a means for optimizing the advertising video using the retrained video generation AI model and generating a final advertising video, a means for providing the generated final advertising video to a user, a means for reviewing and approving the provided advertising video, and a means for automatically uploading the approved advertising video to a distribution platform. This enables highly effective advertising videos to be quickly generated and widely distributed without user effort.

[0547] "Simple advertising video configuration information" is basic information that a user inputs to generate an advertising video, and includes product features and advertising content.

[0548] "Input means" is a function that provides an interface for the user to input simple advertising video configuration information, and includes browser forms, question-and-answer prompts, and the like.

[0549] The "receiving means" is a function by which the server acquires the configuration information sent from the user through the input means.

[0550] The "means of analysis" is a function that generates a scenario framework based on the received configuration information and converts it into a format suitable for the video generation AI model.

[0551] The "video generation scenario framework" is the framework of an advertising video constructed based on the configuration information, and it instructs the distribution of images and narration.

[0552] A "video generation AI model" is an algorithm or program that uses artificial intelligence to automatically generate and optimize advertising videos.

[0553] The "means for generating an initial version of an advertising video" is a function used by the video generation AI model to create an initial version of an advertising video based on the analyzed configuration information.

[0554] "Click-through rate data" is statistically collected data on the number and percentage of users who clicked on a distributed advertising video.

[0555] The "re-learning means" is a function that uses the acquired click-through rate data to further train the video generation AI model and generate more effective advertising videos.

[0556] "Means for optimizing advertising videos" is a function that uses an updated AI model through re-learning to improve the initial version of the advertising video and achieve a high click-through rate.

[0557] The "means for generating the final advertising video" is a function for completing the optimized advertising video as the final version.

[0558] The "means for providing" is a function for sending the generated final advertising video to the user and prompting the user to confirm it.

[0559] The "means for checking and approving" is a function that allows a user to check the provided advertising video and approve it if there is no problem with the content.

[0560] "Means for automatically uploading to distribution platforms" refers to a function for automatically uploading approved advertising videos to various distribution platforms such as YouTube and social media.

[0561] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Specific embodiments of this system will be described in detail below.

[0562] System Configuration

[0563] Users create advertising videos

[0564] First, the user inputs basic information about the advertising video on the device. This input method uses a browser form or similar. For example, if a user wants to create a promotional video for a new product, they open a browser and input "A new smartphone with a high-resolution camera and a long-life battery." This input information is also used as a prompt based on specific content.

[0565] The server receives and analyzes the configuration information

[0566] The server then receives the configuration information entered by the user, analyzes it, extracts keywords and important phrases, and converts them into data for generating a scenario framework suitable for the video generation AI model.

[0567] Generating an early version of an advertising video using a video generation AI model

[0568] Based on the analysis results, the server uses a video generation AI model to generate an initial version of the advertising video, which combines appropriate video footage and narration according to the specified scenario framework.

[0569] The server acquires the click-through rate data and retrains the video generation AI model.

[0570] The server accesses the database to retrieve click-through rate data for past ad videos. This data includes statistical information such as viewing time, number of clicks, and click-through rate. The server then provides this data to the video generation AI model, which then retrains the AI. This retraining improves the AI ​​model's ability to generate more effective ad videos.

[0571] Optimizing advertising videos with a video generation AI model

[0572] The retrained video generation AI model then optimizes the initial version of the ad video, resulting in a video that is expected to generate a high click-through rate from viewers.

[0573] The server provides the optimized ad video to the user.

[0574] The optimized ad video is sent from the server to the user's device. The user checks the video and approves it if there are no problems. Specifically, the user plays the video in the browser and clicks the "Approve" button.

[0575] The server uploads the ad video to the distribution platform

[0576] Finally, approved ad videos are automatically uploaded from the server to various distribution platforms on the Internet, with the option to upload to YouTube or other social media platforms.

[0577] Specific examples

[0578] Case 1: Promoting new products in an online shop

[0579] 1. User Action:

[0580] The user opens a browser and enters the following information into a form on the online shop's management screen: "New product introduction video. New smartphone. With high-resolution camera and long-life battery."

[0581] Once you have completed entering the information, click the "Submit" button.

[0582] 2. Server operation:

[0583] The server receives the input data and analyzes it using a text analysis engine. The analysis results are generated as a scenario framework and passed to the video generation AI model.

[0584] The server retrieves past click-through rate data from the database and provides it to the video generation AI model, which then retrains and generates optimized ad videos.

[0585] 3. User Verification and Authorization:

[0586] The user checks the generated ad video and clicks the "Approve" button if there are no problems. An example of a confirmation prompt sentence here is "Check out the video introducing the new smartphone. It has a high-resolution camera and a long-life battery. Are there any problems with the content?"

[0587] 4. Server uploading:

[0588] Approved ad videos are automatically uploaded from the server to YouTube and other distribution platforms.

[0589] The above process makes it possible to generate and distribute advertising videos that are expected to be highly effective without much effort.

[0590] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0591] Step 1:

[0592] The user inputs the advertising video configuration information.

[0593] Specifically, the user opens a browser and enters information about the ad video into a form, such as "A new smartphone with a high-resolution camera and a long-life battery." The input method is a web form, and the input data is also used as a prompt. This input data is then sent to the server.

[0594] Step 2:

[0595] The server receives the input configuration information.

[0596] The server receives the advertising video composition information sent from the user's device. The received data is in text format and is saved in the system. The server then passes this data to the analysis engine.

[0597] Step 3:

[0598] The server analyzes the configuration information and generates a scenario framework.

[0599] The received text data is analyzed by an analysis engine to extract keywords and important phrases. Based on the extracted content, a scenario framework is generated and converted into a data format suitable for the video generation AI model. For example, phrases such as "new smartphone," "high-resolution camera," and "long-life battery" are extracted as part of the scenario.

[0600] Step 4:

[0601] The video generation AI model generates an initial version of the ad video.

[0602] The server then provides the scenario framework obtained from the analysis results to the video generation AI model. Based on the provided data, the AI ​​model combines the footage and narration to generate an initial version of the advertising video. This video is generated taking into account current trends and optimization of viewing time.

[0603] Step 5:

[0604] The server retrieves historical click-through rate data.

[0605] The server accesses the database to retrieve click-through rate data for past advertising videos, including viewing time, number of clicks, and click-through rate. The retrieved data is used as input data for the video generation AI model.

[0606] Step 6:

[0607] The video generation AI model retrains and optimizes the advertising video.

[0608] The server provides the click-through rate data to the AI ​​model for retraining. The retrained AI model then builds a new algorithm to generate more effective ad videos. The generated initial version of the ad video is then re-edited to output an optimized version.

[0609] Step 7:

[0610] The server provides the optimized advertising video to the user.

[0611] The optimized ad video is sent from the server to the user's device. The user reviews the video and approves it if there are no problems with the ad content. Specifically, the user plays the video and clicks the "Approve" button.

[0612] Step 8:

[0613] The server uploads the advertising video to the distribution platform.

[0614] Approved advertising videos are automatically uploaded from the server to YouTube and other social media platforms, and the uploaded videos are optimized for the characteristics of each platform.

[0615] (Application example 1)

[0616] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0617] Conventional advertising video creation systems require users to spend a lot of time and effort creating videos, and a great deal of specialized knowledge is required for effective distribution. For this reason, there is a demand for more efficient advertising creation and maximizing distribution effectiveness. There is also a need for a system that is easy for users to operate and that can produce effective results.

[0618] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0619] In this invention, the server includes an input means for inputting simple advertising video configuration information, a means for receiving the configuration information input by the input means, and a generation means for generating an advertising video based on the received configuration information. This enables users to easily create and effectively distribute advertising videos.

[0620] "Simple advertising video configuration information" is simple information that a user inputs to create an advertising video, and includes basic data such as product names and features.

[0621] "Input means" refers to a means by which a user inputs information constituting an advertising video, and refers to an interface such as a form on a smartphone.

[0622] The "receiving means" is a means by which the server receives the configuration information input by the input means.

[0623] The "generation means" is a means for generating an advertising video based on the received configuration information.

[0624] "Click-through rate data" refers to data relating to the percentage of users clicking on past advertising videos.

[0625] The "means for obtaining" refers to a means for obtaining click-through rate data of past advertising videos from a database.

[0626] The "means for relearning" refers to a means for relearning the generation means (video generation AI) based on the acquired click rate data.

[0627] The "optimizing means" is a means for optimizing the advertising video using the retrained generating means and generating the final advertising video.

[0628] The "means for providing" refers to a means for providing the final generated advertising video to the user.

[0629] The "means for checking and approving" is a means for a user to check the generated advertising video and approve it if there is no problem.

[0630] "Means for uploading" refers to the means for uploading the approved advertising video to a distribution platform on the Internet.

[0631] "Smartphone application" refers to an application that allows users to create, review, approve, and distribute advertising videos on their smartphones.

[0632] "Scenario generation" is a process of automatically generating a scenario for an advertising video based on input product information.

[0633] "Video generation" is the process of creating an advertising video according to a generated scenario.

[0634] MODE FOR CARRYING OUT THE INVENTION

[0635] The present invention relates to a system that allows users to easily create advertising videos and maximize the effectiveness of those videos. Specific embodiments of this system will be described in detail below.

[0636] System Configuration

[0637] This system consists of the following elements:

[0638] 1. Input method:

[0639] The smartphone application allows users to input simple information about the composition of the advertising video, such as the product name and features, in text format.

[0640] 2. Receiving means:

[0641] The application sends the input information to the server, which receives the information.

[0642] 3. Generation means:

[0643] The server analyzes the received configuration information and generates scenarios and videos. It automatically generates the initial version of the advertising video using a video generation AI model (e.g., TensorFlow / Keras).

[0644] 4. Acquisition method:

[0645] The server retrieves click-through rate data for past advertising videos from a database (e.g., MySQL or PostgreSQL).

[0646] 5. Retraining methods:

[0647] The server retrains the video generation AI model based on the acquired click-through rate data, updating the model to maximize the effectiveness of advertising videos.

[0648] 6. Optimization measures:

[0649] The retrained model is used to optimize the initial version of the ad video to generate the final ad video.

[0650] 7. Means of providing:

[0651] The server then sends the final ad video to the user's smartphone, where the user can review and approve it.

[0652] 8. Verification and Approval Methods:

[0653] The user checks the provided advertising video and clicks the "Approve" button if there are no problems.

[0654] 9. Uploading Method:

[0655] The server automatically uploads approved ad videos to distribution platforms (such as YouTube and social media), allowing users to distribute ad videos quickly and effectively.

[0656] Specific examples of processing

[0657] Let's take the example of a user creating an advertising video for a new wearable device. They enter "A new wearable device with a high-precision heart rate monitor and a long-life battery" into the application form and click the "Submit" button. The server analyzes the received information and starts generating a scenario and video.

[0658] The server retrains the video generation AI model based on past data with high click rates, generates an optimized ad video, and then sends the video to the user. Once the user reviews and approves the sent video, the server automatically uploads the ad video to the distribution platform.

[0659] Prompt Sentence Examples

[0660] "The latest smartphone is finally here! With a high-resolution camera and long-lasting battery, it's perfect for videography. Order now!"

[0661] In this way, users can generate and distribute effective advertising videos with minimal operations.

[0662] Hardware / Software Used

[0663] Hardware: Smartphone

[0664] software:

[0665] Server side: Python (Flask framework)

[0666] Video generation AI: TensorFlow / Keras

[0667] Database: MySQL or PostgreSQL

[0668] Frontend: React Native

[0669] This allows users to create and distribute advertising videos with simple operations, maximizing the effectiveness of their advertising.

[0670] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0671] Step 1:

[0672] The user enters the configuration information for the advertising video into a form on the smartphone application. This configuration information includes the product name and features. For example, the user might enter "A new wearable device with a high-precision heart rate monitor and a long-life battery." When the user clicks the "Submit" button, this information is sent to the server.

[0673] Input: Ad video configuration information

[0674] Output: Configuration information sent to the server

[0675] Step 2:

[0676] The server analyzes the received configuration information. Specifically, it parses the text data to extract attributes such as product names and features. For example, it extracts the product name "wearable device" and the features "high-precision heart rate monitor and long-life battery."

[0677] Input: Configuration information submitted by the user

[0678] Output: Attributes of extracted configuration information

[0679] Step 3:

[0680] The server generates a scenario based on the extracted attribute information, and uses a video generation AI model (TensorFlow / Keras) to generate prompts for creating an initial version of the advertising video, which are then used to generate the video.

[0681] Input: Attributes of extracted configuration information

[0682] Output: Initial version of the ad video

[0683] Step 4:

[0684] The server retrieves click-through rate data for past ad videos from a database (MySQL or PostgreSQL). This data includes data related to ad videos with high click-through rates, and is used to measure the effectiveness of the videos.

[0685] Input: Database query

[0686] Output: Click-through rate data for past ad videos

[0687] Step 5:

[0688] The server retrains the video generation AI model based on the acquired click-through rate data, using TensorFlow / Keras to update previous learning results and create a model for generating more effective advertising videos.

[0689] Input: Click-through rate data

[0690] Output: Retrained video generation AI model

[0691] Step 6:

[0692] The server then uses the retrained AI model to optimize the initial version of the ad video, generating a final ad video that may have a higher click-through rate.

[0693] Input: Retrained AI model and initial version of ad video

[0694] Output: Optimized ad video

[0695] Step 7:

[0696] The server then sends the final ad video to the user's smartphone. The user reviews the video and clicks the "Approve" button if there are no problems with the content.

[0697] Input: Optimized ad video

[0698] Output: Ad video viewable on user device

[0699] Step 8:

[0700] The user checks the provided advertisement video and, if there are no problems, clicks the "Approve" button, which triggers the server to upload the advertisement video to the distribution platform.

[0701] Input: User review and approval

[0702] Output: Approval trigger

[0703] Step 9:

[0704] The server automatically uploads approved ad videos to distribution platforms such as YouTube and social media, allowing them to be widely published and reach their target audience.

[0705] Inputs: Approval trigger and ad video

[0706] Output: Ad video published on distribution platform

[0707] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0708] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. In particular, by combining an emotion engine that recognizes user emotions, it is possible to generate more personalized advertising videos. Specific embodiments of this system are described in detail below.

[0709] System Configuration

[0710] 1. Users create advertising videos

[0711] 1. The user inputs simple advertising video configuration information on the device. For example, if a user wants to create a promotional video for a new product, they open a browser and use a form to input the product name and features. The information entered is specific sentences such as "A new smartphone with a high-resolution camera and a long-life battery."

[0712] 2. The user submits the configuration information they entered. When the user clicks the "Submit" button, the configuration information is sent to the server. At this stage, the camera and microphone are automatically activated so that the emotion engine can analyze the user's facial expressions and tone of voice.

[0713] 2. Analysis by emotion engine

[0714] 3. The server receives the configuration information and analysis data from the emotion engine. The emotion engine analyzes the user's facial expressions and tone of voice to evaluate the user's emotional state (e.g., joy, surprise, anticipation, etc.).

[0715] 4. The analysis results are sent to the server and reflected in the composition of the ad video. For example, if the user is talking in a happy manner, the scenario will be adjusted to generate an ad video with a bright and positive tone.

[0716] 3. Generating advertising videos using video generation AI

[0717] 5. The server analyzes the received configuration information and emotion data and generates a scenario framework.

[0718] 6. Based on the analyzed information, the server uses the video generation AI to generate an initial version of the advertising video. The video generation AI generates a video that combines images and narration according to the scenario.

[0719] 4. Creating effective advertising videos

[0720] 7. The server retrieves data from the database about advertising videos with high click-through rates in the past.

[0721] 8. The server provides the acquired click-through rate data to the video generation AI, which uses this data to retrain and update its model to generate more effective advertising videos.

[0722] 9. The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[0723] 5. Distribution of advertising videos

[0724] 10. The server sends the optimized ad video to the user's device, where the user can view the video through the interface.

[0725] 11. The user checks the provided ad video and gives instructions to "approve" or "modify." If there are no problems, the user clicks the "Approve" button to complete the approval.

[0726] 12. The server uploads the approved ad video to various online distribution platforms (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[0727] Specific examples

[0728] Case 1: Promoting new products on an online shop

[0729] User Action:

[0730] The user uses a browser to enter the following information into a form on the online shop's administration screen: "New product introduction video. New smartphone. With high-resolution camera and long-life battery." Then, they click the "Submit" button to send the information to the server. During this time, the camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice.

[0731] Server behavior:

[0732] The server analyzes the received data and generates a scenario for the ad video. Based on the analysis results of the emotion engine, the video generation AI generates an initial version of the ad video. After that, data on ad videos with high click rates in the past is retrieved from the database and provided to the video generation AI.

[0733] Video generation AI in action:

[0734] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[0735] User confirmation:

[0736] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to YouTube, LINE Voom, etc.

[0737] This system allows users to quickly generate and distribute effective advertising videos without much effort. In addition, the introduction of an emotion engine makes it possible to generate personalized advertising videos according to the user's emotional state.

[0738] The processing flow will be explained below.

[0739] Step 1:

[0740] The user inputs the configuration information of the advertising video (e.g., product features and target users) on the device. The user enters "A new smartphone with a high-resolution camera and a long-life battery" into the form.

[0741] Step 2:

[0742] To send the entered configuration information, the user clicks the "Send" button, which sends the configuration information from the terminal to the server.

[0743] Step 3:

[0744] The device's camera and microphone will automatically activate and record the user's facial expressions and tone of voice, and this data will be sent to the emotion engine in real time.

[0745] Step 4:

[0746] The server analyzes the received configuration information. The server's analysis engine breaks down the received text data into components and organizes them as a scenario framework.

[0747] Step 5:

[0748] The emotion engine analyzes the user's facial expressions and tone of voice received in real time to assess the user's emotional state (e.g., joy, surprise, anticipation, etc.).

[0749] Step 6:

[0750] The server receives the analysis results from the emotion engine and reflects them in the scenario of the advertising video. For example, if the user is talking in a happy manner, the scenario will be adjusted to generate an advertising video with a bright and positive tone.

[0751] Step 7:

[0752] Based on the analyzed configuration information and emotional data, the server's video generation AI generates an initial version of the advertising video.

[0753] Step 8:

[0754] The server retrieves data from a database about advertising videos with high click rates in the past, including metadata such as the number of clicks and viewing time of users.

[0755] Step 9:

[0756] The server provides the acquired click-through rate data to the video generation AI, which then retrains the model to generate new advertising videos based on past effective advertising videos.

[0757] Step 10:

[0758] The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[0759] Step 11:

[0760] The server sends the optimized advertising video to the user's device, where the user can view the video through the interface.

[0761] Step 12:

[0762] The user checks the provided advertising video and gives instructions to "approve" or "modify." If there are no problems, the user clicks the "Approve" button.

[0763] Step 13:

[0764] The server uploads the approved ad video to various distribution platforms on the Internet (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[0765] Step 14:

[0766] The server collects click-through rate data for published ad videos in real time and uses it to generate future ad videos. This feedback loop is expected to continuously improve the accuracy and effectiveness of the entire system.

[0767] Example 2

[0768] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0769] Creating advertising videos distributed over the Internet is time-consuming and labor-intensive, and requires advanced expertise to generate effective videos. It is also difficult to generate personalized advertising videos that reflect user emotions, and optimizing them to improve click-through rates is not easy. To address these challenges, a system is needed that allows users to easily create highly effective advertising videos and optimizes those videos to attract the interest of many users.

[0770] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0771] In this invention, the server includes an input means for inputting simple configuration information, a means for receiving the configuration information input by the input means, a means for integrating and analyzing the configuration information and user emotion data, a generation means for generating an advertising video based on the analyzed information, a means for acquiring click-through rate data of past advertising videos, a means for relearning the generation means based on the acquired click-through rate data, a means for optimizing the advertising video using the re-trained generation means to generate a final advertising video, a means for providing the generated final advertising video to a user, a means for reviewing and approving the provided advertising video, and a means for uploading the approved advertising video to a distribution platform. This enables users to easily generate effective advertising videos, create personalized videos using emotion data, and provide videos optimized to further improve click-through rates.

[0772] "Simple configuration information" is basic information such as product name and features that is input by the user in order to generate an advertising video.

[0773] An "input means" is a device or interface that a user uses to input configuration information, such as a form on a browser.

[0774] The "receiving means" is a mechanism by which the server acquires the configuration information input by the user.

[0775] "Emotion data" is information about the user's emotional state obtained by analyzing the user's facial expression and tone of voice.

[0776] The "analyzing means" is a mechanism for integrating the received configuration information and emotion data to generate a scenario for the advertising video.

[0777] "Generation means" refers to technologies such as video generation AI that create advertising videos based on analyzed information.

[0778] "Click-through rate data" is numerical data that indicates user responses to past advertising videos. It is an index used to evaluate the effectiveness of advertising.

[0779] The "re-learning means" is a mechanism for updating the video generation AI model based on the acquired click-through rate data, thereby improving the accuracy of generating advertising videos.

[0780] The "optimization means" is a mechanism for more effectively adjusting and editing advertising videos using the retrained generation means.

[0781] The "means for providing" refers to a mechanism for displaying the generated advertising video to a user, such as a system for transmitting a video file to a user's terminal.

[0782] The "means for checking and approving" refers to an interface or mechanism that allows a user to check the generated advertising video and approve it if there are no problems.

[0783] "Means for uploading to a distribution platform" refers to a mechanism for uploading approved advertising videos to online video sharing services, etc.

[0784] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. In particular, by combining an emotion engine that recognizes user emotions, it is possible to generate more personalized advertising videos. Specific embodiments of this system are described in detail below.

[0785] System Configuration

[0786] This system mainly consists of a server, a terminal, an emotion engine, and a video generation AI.

[0787] Users create advertising videos

[0788] The user opens a browser on their device and enters configuration information such as the product name and features into a form for creating an advertising video. For example, they might enter "A new smartphone with a high-resolution camera and a long-life battery." This input information is treated as simple configuration information.

[0789] Start sending data and analyzing sentiment

[0790] When the user clicks the "Send" button, the entered configuration information is sent to the server. At the same time, the device's camera and microphone are automatically activated, and the emotion engine begins analyzing the user's facial expressions and tone of voice. This analyzed data is sent to the server as emotion data.

[0791] Integrating emotion data and compositional information

[0792] The server receives the received configuration information and the analysis data from the emotion engine, integrates and analyzes them. The emotion data reflects the user's emotional state (e.g., joy, surprise, anticipation, etc.), and generates a scenario for the advertising video based on this.

[0793] Generate initial ad video

[0794] The server then uses the integrated information to have the video generation AI generate an initial version of the advertising video, which then combines footage and narration according to the advertising scenario to create the initial advertising video.

[0795] Obtaining and relearning click-through rate data

[0796] The server retrieves click-through rate data for past ad videos from the database and supplies it to the video generation AI. The video generation AI re-learns based on this click-through rate data and updates its ad video generation model to generate even more effective ad videos.

[0797] Ad video optimization

[0798] The retrained generator is used to optimize the initial version of the ad video, which is expected to improve click-through rates.

[0799] Video review and approval

[0800] The server sends the optimized ad video to the user's device. The user checks the provided ad video and, if there are no problems, clicks the "Approve" button to complete the approval.

[0801] Upload to a distribution platform

[0802] The server then uploads the approved ad video to various distribution platforms on the Internet (e.g., video sharing services and social media), which automatically publishes the ad video and reaches a large audience.

[0803] Specific examples

[0804] Case 1: Promoting new products on an online shop

[0805] User Action:

[0806] The user uses a browser on the online shop's management screen to enter "New product introduction video. New smartphone. Equipped with a high-resolution camera and long-life battery," and clicks the "Send" button. During this time, the camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice.

[0807] Server behavior:

[0808] The server analyzes the received data and generates a scenario for the ad video based on the analysis results of the emotion engine. The video generation AI generates an initial version of the ad video and then retrieves data from a database of ad videos with high click rates in the past.

[0809] Video generation AI in action:

[0810] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[0811] User confirmation:

[0812] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to video sharing services and social media.

[0813] This system allows users to quickly generate and distribute highly effective advertising videos easily and efficiently. The introduction of an emotion engine makes it possible to generate personalized advertising videos according to the user's emotional state.

[0814] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0815] Step 1:

[0816] The user provides input for creating an advertising video.

[0817] Input: The user opens a browser on their device and fills in a form with configuration information, such as the product name and features. For example, "New smartphone. With a high-resolution camera and long-life battery."

[0818] Data processing: The information entered in the form is formatted into a certain format.

[0819] Output: Formatted configuration information.

[0820] Step 2:

[0821] The user sends the configuration information to the server and sentiment analysis begins.

[0822] Input: The user clicks the "Submit" button.

[0823] Data calculation: The device's camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice in real time.

[0824] Output: The configuration information is sent to the server and the analyzed emotion data is obtained.

[0825] Step 3:

[0826] The server receives and integrates the configuration information and emotion data.

[0827] Input: The server receives configuration information from the user and emotion data from the emotion engine.

[0828] Data calculation: Integrates received data and performs analysis to generate scenarios based on the user's emotions.

[0829] Output: An advertising video scenario based on the analysis results is obtained.

[0830] Step 4:

[0831] The server generates the initial advertising video.

[0832] Input: Parsed configuration information and scenario

[0833] Data calculation: Video generation AI combines video clips and narration based on a scenario.

[0834] Output: The initial version of the ad video is generated.

[0835] Step 5:

[0836] The server retrieves the click-through rate data and performs re-learning.

[0837] Input: Click-through rate data for past ad videos

[0838] Data calculation: The video generation AI re-learns based on click-rate data and updates the generation model.

[0839] Output: An updated ad video generation model is obtained.

[0840] Step 6:

[0841] Video generation AI generates optimized advertising videos.

[0842] Input: Updated generative model and initial version of ad video

[0843] Data calculation: Adjust the timing of video transitions and the tone of the narration according to the optimization procedure.

[0844] Output: The optimized ad video is generated.

[0845] Step 7:

[0846] The server sends the optimized advertising video to the user.

[0847] Input: Optimized ad video

[0848] Data calculation: Performs the procedure for sending the optimized advertising video to the user's device.

[0849] Output: The ad video is sent to the user.

[0850] Step 8:

[0851] The user reviews the ad video and approves or modifies it.

[0852] Input: Ad video sent to user

[0853] Data Calculation: The user checks the video and clicks the "Approve" or "Modify" button.

[0854] Output: If the user approves, the video is marked as the final version, and if there are any corrections required, the cycle starts again.

[0855] Step 9:

[0856] The server uploads the approved advertising video to the distribution platform.

[0857] Input: Approved ad video

[0858] Data Computing: Processing the video for uploading to the appropriate distribution platform.

[0859] Output: The advertising video is published on the Internet and reaches a large audience.

[0860] (Application example 2)

[0861] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0862] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Conventional advertising video generation systems generally generate advertising videos based solely on user input information, limiting innovations in personalization. While generating advertising videos that take user emotions into consideration would enable more effective personalization, building such a system has been challenging. Furthermore, there has been a lack of means for relearning and optimizing the generation method using past click-through rate data. This has made it challenging to realize a system that maximizes advertising effectiveness.

[0863] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: an input means for inputting simple advertising video configuration information; a means for receiving the configuration information input by the input means; an emotion analysis means for analyzing a user's facial expression and tone of voice and evaluating their emotional state; a generation means for generating an advertising video based on the received configuration information and analysis data from the emotion analysis means; a means for acquiring click-through rate data of past advertising videos; a means for relearning the generation means based on the acquired click-through rate data; a means for optimizing the advertising video using the relearned generation means to generate a final advertising video; a means for providing the generated final advertising video to a user; a means for reviewing and approving the provided advertising video; and a means for uploading the approved advertising video to a distribution platform. This enables the generation of personalized advertising videos that reflect the user's emotional state, and further enables effective optimization of advertising videos through relearning using past click-through rate data.

[0864] The "input means" is a means for a user to input information about the configuration of an advertising video.

[0865] The "receiving means" is a means for receiving configuration information input by the input means.

[0866] The "emotion analysis means" is a means for analyzing the user's facial expression and tone of voice to evaluate the user's emotional state.

[0867] The "generation means" is a means for generating an advertising video based on the received configuration information and the analysis data from the emotion analysis means.

[0868] The "means of acquisition" refers to a means for acquiring click-through rate data for past advertising videos.

[0869] The "means for relearning" is a means for relearning the generation means based on the acquired click rate data.

[0870] The "optimizing means" is a means for optimizing the advertising video using the retrained generating means and generating the final advertising video.

[0871] The "means for providing" refers to a means for providing the final generated advertising video to the user.

[0872] The "means for checking and approving" is a means for a user to check and approve the provided advertising video.

[0873] "Means for uploading" refers to the means for uploading the approved advertising video to the distribution platform.

[0874] The present invention is a system for realizing efficient and effective creation and distribution of advertising videos on the Internet. A specific embodiment of the present invention will be described in detail based on the above description.

[0875] Program processing

[0876] The system includes multiple components, including a user terminal, a server, a sentiment analysis engine, and a generative AI model.

[0877] User terminal processing

[0878] The user terminal is used through a web browser or a dedicated application, and performs the following processes.

[0879] A form is displayed for the user to input configuration information for the advertising video.

[0880] It accepts user input and uses a camera and microphone to capture facial expressions and voice.

[0881] The configuration information and the captured data are sent to a server.

[0882] Server Processing

[0883] The server performs the following main processes:

[0884] The configuration information and capture data transmitted from the user terminal are received.

[0885] A sentiment analysis engine is used to analyze the user's emotional state.

[0886] The composition information and sentiment analysis data are sent to a generative AI model to generate advertising videos.

[0887] Advertising videos are optimized by obtaining past click-through rate data from a database and supplying it to a generative AI model.

[0888] The optimized advertising video is sent to the user terminal for review and approval.

[0889] Upload the approved ad video to a distribution platform.

[0890] Sentiment Analysis Engine

[0891] The emotion analysis engine analyzes the user's facial expressions and tone of voice to assess their emotional state, leveraging known emotion recognition algorithms such as OpenFace and DeepVoice.

[0892] Generative AI Models

[0893] The generative AI model generates videos based on the received composition information and emotional data, and then re-trains using past click-through rate data to optimize the ad video.

[0894] Hardware and software used

[0895] Hardware: Smartphone (camera, microphone), server

[0896] Software: Browser or dedicated app, emotion analysis algorithm (OpenFace, DeepVoice), video generation AI (e.g., OpenAI's DALL-E)

[0897] Adding specific examples

[0898] Example of new product introduction using a smartphone app

[0899] The flow for creating an advertising video for a new product using a smartphone app used by a user is as follows.

[0900] User operations

[0901] 1. Launch the smartphone app and go to the screen for creating advertising videos.

[0902] 2. Enter the name and features of the new product in text format as configuration information.

[0903] 3. The camera and microphone will automatically activate and capture your facial expressions and voice.

[0904] 4. Press the "Submit" button to send the input information and captured data to the server.

[0905] Server Operation

[0906] 1. The server analyzes the received configuration information and capture data.

[0907] 2. The sentiment analysis engine evaluates the user's emotional state.

[0908] 3. The generative AI model generates the initial advertising video based on the received data.

[0909] 4. Retrain the generative AI model based on past click-through rate data to optimize the ad video.

[0910] 5. Send the optimized ad video to the user's device for review and approval.

[0911] Prompt Sentence Examples

[0912] "Create a video to showcase your new smartphone, highlighting its high-resolution camera and long-lasting battery life. Use an upbeat tone to reflect customer expectations."

[0913] Thus, the present invention provides a user-friendly interface while utilizing sentiment analysis and database information to generate and optimize personalized advertising videos.

[0914] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0915] Step 1:

[0916] The user launches the smartphone app and inputs the information that makes up the advertising video. Specifically, the name and features of the new product are entered in text format. This input data is the basic scenario information for the advertising video. A specific example of input includes information such as "A new smartphone with a high-resolution camera and a long-life battery."

[0917] Step 2:

[0918] The device receives the configuration information and automatically activates the camera and microphone, capturing the user's facial expressions and voice. The captured video and audio data is stored unprocessed.

[0919] Step 3:

[0920] The device sends the saved configuration information and captured data, including text configuration information and video and audio files, to the server for further processing.

[0921] Step 4:

[0922] The server analyzes the received configuration information and captured data. First, it uses an emotion analysis engine to analyze the user's facial expressions and tone of voice to evaluate the user's emotional state. The emotion analysis engine uses algorithms such as OpenFace and DeepVoice. The analysis results in emotional data such as "expectation."

[0923] Step 5:

[0924] The server queries the generative AI model based on the analysis results and configuration information. The generative AI model uses the obtained data to generate an initial ad video. In this case, the generative AI model uses advanced algorithms such as OpenAI's DALL-E. The generated result is an initial version of the ad video file.

[0925] Step 6:

[0926] The server retrieves click-through rate data for past ad videos from the database. This data includes the click-through rate and characteristics of each ad video. The retrieved click-through rate data is fed to the generative AI model for retraining. The retrained generative AI model generates optimized ad videos based on the past data.

[0927] Step 7:

[0928] The server then sends the optimized ad video file to the user's device. The optimized video is adjusted to expect a higher click-through rate. The ad video is then sent from the server so that the user can view it.

[0929] Step 8:

[0930] The user terminal displays the received advertising video on the user interface and provides a function for the user to review and approve it. The user reviews the video and clicks the "Approve" button if there are no problems. If corrections are required, the user issues a "correction instruction."

[0931] Step 9:

[0932] The server receives the approval data from the user and uploads the approved ad video to a distribution platform, such as YouTube or LINE Voom. This completes the release of the ad video.

[0933] In this way, each processing step works in conjunction to realize a system that generates and optimizes personalized advertising videos based on user emotions.

[0934] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0935] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0936] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0937] [Third embodiment]

[0938] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0939] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0940] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0941] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0942] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0943] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0944] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0945] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0946] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0947] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0948] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0949] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0950] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Specific embodiments of this system will be described in detail below.

[0951] System Configuration

[0952] 1. Users create advertising videos

[0953] 1. The user inputs simple information to compose the advertising video on the device. For example, if a user wants to create a promotional video for a new product, they open a browser and use a form to input the product name and features. The information entered is specific sentences such as "A new smartphone with a high-resolution camera and a long-life battery."

[0954] 2. The user submits the entered configuration information. When the user clicks the "Submit" button, the configuration information is sent to the server.

[0955] 2. Generating advertising videos using video generation AI

[0956] 3. The server analyzes the received configuration information. The server analyzes the received text data, breaks down the configuration information, and generates a scenario framework.

[0957] 4. Based on the analyzed configuration information, the server uses the video generation AI to generate an initial version of the advertising video. Based on the analysis results, the video generation AI generates a video that combines images and narration according to the scenario.

[0958] 3. Creating effective advertising videos

[0959] 5. The server retrieves advertising video data with high click rates from the database.

[0960] 6. The server provides the acquired click-through rate data to the video generation AI, which uses this data to retrain and update its model to generate more effective advertising videos.

[0961] 7. The video generation AI uses the retrained model to optimize the initial version of the ad video. The retrained AI model is used to optimize the initial ad video, creating content that will increase click-through rates.

[0962] 4. Distribution of advertising videos

[0963] 8. The server generates an optimized ad video and sends it to the user's device.

[0964] 9. The user checks the provided ad video and approves it if there are no problems. The user plays the ad video on their device, checks that there are no problems with the content, and then clicks the "Approve" button.

[0965] 10. The server uploads the approved ad video to each distribution platform on the Internet. The server automatically uploads the optimized ad video to different distribution platforms such as YouTube and LINE Voom.

[0966] Specific examples

[0967] Case 1: Promoting new products on an online shop

[0968] User Action:

[0969] A user uses a browser to fill out a form on the online shop's administration screen, entering "New product introduction video. New smartphone. With high-resolution camera and long-life battery." Then, the user clicks the "Submit" button to send the information to the server.

[0970] Server behavior:

[0971] The server analyzes the received data and generates a scenario for the ad video. It passes the data to the video generation AI, which generates an initial version of the ad video. It then retrieves data from the database about ad videos with high click rates and supplies it to the video generation AI.

[0972] Video generation AI in action:

[0973] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[0974] User confirmation:

[0975] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to YouTube, LINE Voom, etc.

[0976] This system allows users to quickly generate and distribute effective advertising videos without any hassle.

[0977] The processing flow will be explained below.

[0978] Step 1:

[0979] The user inputs the configuration information of the advertising video (e.g., product features and target users) on the device. The user fills in the form with "A new smartphone with a high-resolution camera and a long-life battery."

[0980] Step 2:

[0981] To send the entered configuration information, the user clicks the "Send" button, which sends the configuration information from the terminal to the server.

[0982] Step 3:

[0983] The server analyzes the received configuration information. The server's analysis engine breaks down the received text data into components and organizes them as a scenario framework.

[0984] Step 4:

[0985] The server passes the analyzed configuration information to the video generation AI, which then generates an initial version of the advertising video based on this configuration information.

[0986] Step 5:

[0987] The server retrieves data from the database about advertising videos with high click rates in the past, including metadata such as the number of clicks and viewing time of users.

[0988] Step 6:

[0989] The server provides the acquired click-through rate data to the video generation AI, which then retrains the model to generate new advertising videos based on past effective advertising videos.

[0990] Step 7:

[0991] The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[0992] Step 8:

[0993] The server sends the optimized advertising video to the user's device, where the user can view the video through the interface.

[0994] Step 9:

[0995] The user checks the provided ad video and gives instructions to "approve" or "revise." If there are no problems, the approval is completed by clicking the "Approve" button.

[0996] Step 10:

[0997] The server uploads the approved ad video to various distribution platforms on the Internet (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[0998] Example 1

[0999] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1000] Conventional advertising video creation systems require users to manually create a scenario and combine video and narration, resulting in significant time and effort. Furthermore, there was a lack of a way to predict the effectiveness of the created advertising video in advance, so high click-through rates were not necessarily achieved. The present invention aims to solve these problems and provide a system that enables efficient and effective advertising video creation and distribution.

[1001] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1002] In this invention, the server includes an input means for inputting simple advertising video configuration information, a means for receiving the configuration information input by the input means, a means for analyzing the received configuration information and generating a video generation scenario framework, a means for generating an initial version of the advertising video using a video generation AI model based on the analysis results, a means for acquiring click-through rate data of past advertising videos, a means for retraining the video generation AI model based on the acquired click-through rate data, a means for optimizing the advertising video using the retrained video generation AI model and generating a final advertising video, a means for providing the generated final advertising video to a user, a means for reviewing and approving the provided advertising video, and a means for automatically uploading the approved advertising video to a distribution platform. This enables highly effective advertising videos to be quickly generated and widely distributed without user effort.

[1003] "Simple advertising video configuration information" is basic information that a user inputs to generate an advertising video, and includes product features and advertising content.

[1004] "Input means" is a function that provides an interface for the user to input simple advertising video configuration information, and includes browser forms, question-and-answer prompts, and the like.

[1005] The "receiving means" is a function by which the server acquires the configuration information sent from the user through the input means.

[1006] The "means of analysis" is a function that generates a scenario framework based on the received configuration information and converts it into a format suitable for the video generation AI model.

[1007] The "video generation scenario framework" is the framework of an advertising video constructed based on the configuration information, and it instructs the distribution of images and narration.

[1008] A "video generation AI model" is an algorithm or program that uses artificial intelligence to automatically generate and optimize advertising videos.

[1009] The "means for generating an initial version of an advertising video" is a function used by the video generation AI model to create an initial version of an advertising video based on the analyzed configuration information.

[1010] "Click-through rate data" is statistically collected data on the number and percentage of users who clicked on a distributed advertising video.

[1011] The "re-learning means" is a function that uses the acquired click-through rate data to further train the video generation AI model and generate more effective advertising videos.

[1012] "Means for optimizing advertising videos" is a function that uses an updated AI model through re-learning to improve the initial version of the advertising video and achieve a high click-through rate.

[1013] The "means for generating the final advertising video" is a function for completing the optimized advertising video as the final version.

[1014] The "means for providing" is a function for sending the generated final advertising video to the user and prompting the user to confirm it.

[1015] The "means for checking and approving" is a function that allows a user to check the provided advertising video and approve it if there is no problem with the content.

[1016] "Means for automatically uploading to distribution platforms" refers to a function for automatically uploading approved advertising videos to various distribution platforms such as YouTube and social media.

[1017] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Specific embodiments of this system will be described in detail below.

[1018] System Configuration

[1019] Users create advertising videos

[1020] First, the user inputs basic information about the advertising video on the device. This input method uses a browser form or similar. For example, if a user wants to create a promotional video for a new product, they open a browser and input "A new smartphone with a high-resolution camera and a long-life battery." This input information is also used as a prompt based on specific content.

[1021] The server receives and analyzes the configuration information

[1022] The server then receives the configuration information entered by the user, analyzes it, extracts keywords and important phrases, and converts them into data for generating a scenario framework suitable for the video generation AI model.

[1023] Generating an early version of an advertising video using a video generation AI model

[1024] Based on the analysis results, the server uses a video generation AI model to generate an initial version of the advertising video, which combines appropriate video footage and narration according to the specified scenario framework.

[1025] The server acquires the click-through rate data and retrains the video generation AI model.

[1026] The server accesses the database to retrieve click-through rate data for past ad videos. This data includes statistical information such as viewing time, number of clicks, and click-through rate. The server then provides this data to the video generation AI model, which then retrains the AI. This retraining improves the AI ​​model's ability to generate more effective ad videos.

[1027] Optimizing advertising videos with a video generation AI model

[1028] The retrained video generation AI model then optimizes the initial version of the ad video, resulting in a video that is expected to generate a high click-through rate from viewers.

[1029] The server provides the optimized ad video to the user.

[1030] The optimized ad video is sent from the server to the user's device. The user checks the video and approves it if there are no problems. Specifically, the user plays the video in the browser and clicks the "Approve" button.

[1031] The server uploads the ad video to the distribution platform

[1032] Finally, approved ad videos are automatically uploaded from the server to various distribution platforms on the Internet, with the option to upload to YouTube or other social media platforms.

[1033] Specific examples

[1034] Case 1: Promoting new products in an online shop

[1035] 1. User Action:

[1036] The user opens a browser and enters the following information into a form on the online shop's management screen: "New product introduction video. New smartphone. With high-resolution camera and long-life battery."

[1037] Once you have completed entering the information, click the "Submit" button.

[1038] 2. Server operation:

[1039] The server receives the input data and analyzes it using a text analysis engine. The analysis results are generated as a scenario framework and passed to the video generation AI model.

[1040] The server retrieves past click-through rate data from the database and provides it to the video generation AI model, which then retrains and generates optimized ad videos.

[1041] 3. User Verification and Authorization:

[1042] The user checks the generated ad video and clicks the "Approve" button if there are no problems. An example of a confirmation prompt sentence here is "Check out the video introducing the new smartphone. It has a high-resolution camera and a long-life battery. Are there any problems with the content?"

[1043] 4. Server uploading:

[1044] Approved ad videos are automatically uploaded from the server to YouTube and other distribution platforms.

[1045] The above process makes it possible to generate and distribute advertising videos that are expected to be highly effective without much effort.

[1046] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1047] Step 1:

[1048] The user inputs the advertising video configuration information.

[1049] Specifically, the user opens a browser and enters information about the ad video into a form, such as "A new smartphone with a high-resolution camera and a long-life battery." The input method is a web form, and the input data is also used as a prompt. This input data is then sent to the server.

[1050] Step 2:

[1051] The server receives the input configuration information.

[1052] The server receives the advertising video composition information sent from the user's device. The received data is in text format and is saved in the system. The server then passes this data to the analysis engine.

[1053] Step 3:

[1054] The server analyzes the configuration information and generates a scenario framework.

[1055] The received text data is analyzed by an analysis engine to extract keywords and important phrases. Based on the extracted content, a scenario framework is generated and converted into a data format suitable for the video generation AI model. For example, phrases such as "new smartphone," "high-resolution camera," and "long-life battery" are extracted as part of the scenario.

[1056] Step 4:

[1057] The video generation AI model generates an initial version of the ad video.

[1058] The server then provides the scenario framework obtained from the analysis results to the video generation AI model. Based on the provided data, the AI ​​model combines the footage and narration to generate an initial version of the advertising video. This video is generated taking into account current trends and optimization of viewing time.

[1059] Step 5:

[1060] The server retrieves historical click-through rate data.

[1061] The server accesses the database to retrieve click-through rate data for past advertising videos, including viewing time, number of clicks, and click-through rate. The retrieved data is used as input data for the video generation AI model.

[1062] Step 6:

[1063] The video generation AI model retrains and optimizes the advertising video.

[1064] The server provides the click-through rate data to the AI ​​model for retraining. The retrained AI model then builds a new algorithm to generate more effective ad videos. The generated initial version of the ad video is then re-edited to output an optimized version.

[1065] Step 7:

[1066] The server provides the optimized advertising video to the user.

[1067] The optimized ad video is sent from the server to the user's device. The user reviews the video and approves it if there are no problems with the ad content. Specifically, the user plays the video and clicks the "Approve" button.

[1068] Step 8:

[1069] The server uploads the advertising video to the distribution platform.

[1070] Approved advertising videos are automatically uploaded from the server to YouTube and other social media platforms, and the uploaded videos are optimized for the characteristics of each platform.

[1071] (Application example 1)

[1072] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1073] Conventional advertising video creation systems require users to spend a lot of time and effort creating videos, and a great deal of specialized knowledge is required for effective distribution. For this reason, there is a demand for more efficient advertising creation and maximizing distribution effectiveness. There is also a need for a system that is easy for users to operate and that can produce effective results.

[1074] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1075] In this invention, the server includes an input means for inputting simple advertising video configuration information, a means for receiving the configuration information input by the input means, and a generation means for generating an advertising video based on the received configuration information. This enables users to easily create and effectively distribute advertising videos.

[1076] "Simple advertising video configuration information" is simple information that a user inputs to create an advertising video, and includes basic data such as product names and features.

[1077] "Input means" refers to a means by which a user inputs information constituting an advertising video, and refers to an interface such as a form on a smartphone.

[1078] The "receiving means" is a means by which the server receives the configuration information input by the input means.

[1079] The "generation means" is a means for generating an advertising video based on the received configuration information.

[1080] "Click-through rate data" refers to data relating to the percentage of users clicking on past advertising videos.

[1081] The "means for obtaining" refers to a means for obtaining click-through rate data of past advertising videos from a database.

[1082] The "means for relearning" refers to a means for relearning the generation means (video generation AI) based on the acquired click rate data.

[1083] The "optimizing means" is a means for optimizing the advertising video using the retrained generating means and generating the final advertising video.

[1084] The "means for providing" refers to a means for providing the final generated advertising video to the user.

[1085] The "means for checking and approving" is a means for a user to check the generated advertising video and approve it if there is no problem.

[1086] "Means for uploading" refers to the means for uploading the approved advertising video to a distribution platform on the Internet.

[1087] "Smartphone application" refers to an application that allows users to create, review, approve, and distribute advertising videos on their smartphones.

[1088] "Scenario generation" is a process of automatically generating a scenario for an advertising video based on input product information.

[1089] "Video generation" is the process of creating an advertising video according to a generated scenario.

[1090] MODE FOR CARRYING OUT THE INVENTION

[1091] The present invention relates to a system that allows users to easily create advertising videos and maximize the effectiveness of those videos. Specific embodiments of this system will be described in detail below.

[1092] System Configuration

[1093] This system consists of the following elements:

[1094] 1. Input method:

[1095] The smartphone application allows users to input simple information about the composition of the advertising video, such as the product name and features, in text format.

[1096] 2. Receiving means:

[1097] The application sends the input information to the server, which receives the information.

[1098] 3. Generation means:

[1099] The server analyzes the received configuration information and generates scenarios and videos. It automatically generates the initial version of the advertising video using a video generation AI model (e.g., TensorFlow / Keras).

[1100] 4. Acquisition method:

[1101] The server retrieves click-through rate data for past advertising videos from a database (e.g., MySQL or PostgreSQL).

[1102] 5. Retraining methods:

[1103] The server retrains the video generation AI model based on the acquired click-through rate data, updating the model to maximize the effectiveness of advertising videos.

[1104] 6. Optimization measures:

[1105] The retrained model is used to optimize the initial version of the ad video to generate the final ad video.

[1106] 7. Means of providing:

[1107] The server then sends the final ad video to the user's smartphone, where the user can review and approve it.

[1108] 8. Verification and Approval Methods:

[1109] The user checks the provided advertising video and clicks the "Approve" button if there are no problems.

[1110] 9. Uploading Method:

[1111] The server automatically uploads approved ad videos to distribution platforms (such as YouTube and social media), allowing users to distribute ad videos quickly and effectively.

[1112] Specific examples of processing

[1113] Let's take the example of a user creating an advertising video for a new wearable device. They enter "A new wearable device with a high-precision heart rate monitor and a long-life battery" into the application form and click the "Submit" button. The server analyzes the received information and starts generating a scenario and video.

[1114] The server retrains the video generation AI model based on past data with high click rates, generates an optimized ad video, and then sends the video to the user. Once the user reviews and approves the sent video, the server automatically uploads the ad video to the distribution platform.

[1115] Prompt Sentence Examples

[1116] "The latest smartphone is finally here! With a high-resolution camera and long-lasting battery, it's perfect for videography. Order now!"

[1117] In this way, users can generate and distribute effective advertising videos with minimal operations.

[1118] Hardware / Software Used

[1119] Hardware: Smartphone

[1120] software:

[1121] Server side: Python (Flask framework)

[1122] Video generation AI: TensorFlow / Keras

[1123] Database: MySQL or PostgreSQL

[1124] Frontend: React Native

[1125] This allows users to create and distribute advertising videos with simple operations, maximizing the effectiveness of their advertising.

[1126] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1127] Step 1:

[1128] The user enters the configuration information for the advertising video into a form on the smartphone application. This configuration information includes the product name and features. For example, the user might enter "A new wearable device with a high-precision heart rate monitor and a long-life battery." When the user clicks the "Submit" button, this information is sent to the server.

[1129] Input: Ad video configuration information

[1130] Output: Configuration information sent to the server

[1131] Step 2:

[1132] The server analyzes the received configuration information. Specifically, it parses the text data to extract attributes such as product names and features. For example, it extracts the product name "wearable device" and the features "high-precision heart rate monitor and long-life battery."

[1133] Input: Configuration information submitted by the user

[1134] Output: Attributes of extracted configuration information

[1135] Step 3:

[1136] The server generates a scenario based on the extracted attribute information, and uses a video generation AI model (TensorFlow / Keras) to generate prompts for creating an initial version of the advertising video, which are then used to generate the video.

[1137] Input: Attributes of extracted configuration information

[1138] Output: Initial version of the ad video

[1139] Step 4:

[1140] The server retrieves click-through rate data for past ad videos from a database (MySQL or PostgreSQL). This data includes data related to ad videos with high click-through rates, and is used to measure the effectiveness of the videos.

[1141] Input: Database query

[1142] Output: Click-through rate data for past ad videos

[1143] Step 5:

[1144] The server retrains the video generation AI model based on the acquired click-through rate data, using TensorFlow / Keras to update previous learning results and create a model for generating more effective advertising videos.

[1145] Input: Click-through rate data

[1146] Output: Retrained video generation AI model

[1147] Step 6:

[1148] The server then uses the retrained AI model to optimize the initial version of the ad video, generating a final ad video that may have a higher click-through rate.

[1149] Input: Retrained AI model and initial version of ad video

[1150] Output: Optimized ad video

[1151] Step 7:

[1152] The server then sends the final ad video to the user's smartphone. The user reviews the video and clicks the "Approve" button if there are no problems with the content.

[1153] Input: Optimized ad video

[1154] Output: Ad video viewable on user device

[1155] Step 8:

[1156] The user checks the provided advertisement video and, if there are no problems, clicks the "Approve" button, which triggers the server to upload the advertisement video to the distribution platform.

[1157] Input: User review and approval

[1158] Output: Approval trigger

[1159] Step 9:

[1160] The server automatically uploads approved ad videos to distribution platforms such as YouTube and social media, allowing them to be widely published and reach their target audience.

[1161] Inputs: Approval trigger and ad video

[1162] Output: Ad video published on distribution platform

[1163] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1164] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. In particular, by combining an emotion engine that recognizes user emotions, it is possible to generate more personalized advertising videos. Specific embodiments of this system are described in detail below.

[1165] System Configuration

[1166] 1. Users create advertising videos

[1167] 1. The user inputs simple advertising video configuration information on the device. For example, if a user wants to create a promotional video for a new product, they open a browser and use a form to input the product name and features. The information entered is specific sentences such as "A new smartphone with a high-resolution camera and a long-life battery."

[1168] 2. The user submits the configuration information they entered. When the user clicks the "Submit" button, the configuration information is sent to the server. At this stage, the camera and microphone are automatically activated so that the emotion engine can analyze the user's facial expressions and tone of voice.

[1169] 2. Analysis by emotion engine

[1170] 3. The server receives the configuration information and analysis data from the emotion engine. The emotion engine analyzes the user's facial expressions and tone of voice to evaluate the user's emotional state (e.g., joy, surprise, anticipation, etc.).

[1171] 4. The analysis results are sent to the server and reflected in the composition of the ad video. For example, if the user is talking in a happy manner, the scenario will be adjusted to generate an ad video with a bright and positive tone.

[1172] 3. Generating advertising videos using video generation AI

[1173] 5. The server analyzes the received configuration information and emotion data and generates a scenario framework.

[1174] 6. Based on the analyzed information, the server uses the video generation AI to generate an initial version of the advertising video. The video generation AI generates a video that combines images and narration according to the scenario.

[1175] 4. Creating effective advertising videos

[1176] 7. The server retrieves data from the database about advertising videos with high click-through rates in the past.

[1177] 8. The server provides the acquired click-through rate data to the video generation AI, which uses this data to retrain and update its model to generate more effective advertising videos.

[1178] 9. The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[1179] 5. Distribution of advertising videos

[1180] 10. The server sends the optimized ad video to the user's device, where the user can view the video through the interface.

[1181] 11. The user checks the provided ad video and gives instructions to "approve" or "modify." If there are no problems, the user clicks the "Approve" button to complete the approval.

[1182] 12. The server uploads the approved ad video to various online distribution platforms (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[1183] Specific examples

[1184] Case 1: Promoting new products on an online shop

[1185] User Action:

[1186] The user uses a browser to enter the following information into a form on the online shop's administration screen: "New product introduction video. New smartphone. With high-resolution camera and long-life battery." Then, they click the "Submit" button to send the information to the server. During this time, the camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice.

[1187] Server behavior:

[1188] The server analyzes the received data and generates a scenario for the ad video. Based on the analysis results of the emotion engine, the video generation AI generates an initial version of the ad video. After that, data on ad videos with high click rates in the past is retrieved from the database and provided to the video generation AI.

[1189] Video generation AI in action:

[1190] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[1191] User confirmation:

[1192] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to YouTube, LINE Voom, etc.

[1193] This system allows users to quickly generate and distribute effective advertising videos without much effort. In addition, the introduction of an emotion engine makes it possible to generate personalized advertising videos according to the user's emotional state.

[1194] The processing flow will be explained below.

[1195] Step 1:

[1196] The user inputs the configuration information of the advertising video (e.g., product features and target users) on the device. The user enters "A new smartphone with a high-resolution camera and a long-life battery" into the form.

[1197] Step 2:

[1198] To send the entered configuration information, the user clicks the "Send" button, which sends the configuration information from the terminal to the server.

[1199] Step 3:

[1200] The device's camera and microphone will automatically activate and record the user's facial expressions and tone of voice, and this data will be sent to the emotion engine in real time.

[1201] Step 4:

[1202] The server analyzes the received configuration information. The server's analysis engine breaks down the received text data into components and organizes them as a scenario framework.

[1203] Step 5:

[1204] The emotion engine analyzes the user's facial expressions and tone of voice received in real time to assess the user's emotional state (e.g., joy, surprise, anticipation, etc.).

[1205] Step 6:

[1206] The server receives the analysis results from the emotion engine and reflects them in the scenario of the advertising video. For example, if the user is talking in a happy manner, the scenario will be adjusted to generate an advertising video with a bright and positive tone.

[1207] Step 7:

[1208] Based on the analyzed configuration information and emotional data, the server's video generation AI generates an initial version of the advertising video.

[1209] Step 8:

[1210] The server retrieves data from a database about advertising videos with high click rates in the past, including metadata such as the number of clicks and viewing time of users.

[1211] Step 9:

[1212] The server provides the acquired click-through rate data to the video generation AI, which then retrains the model to generate new advertising videos based on past effective advertising videos.

[1213] Step 10:

[1214] The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[1215] Step 11:

[1216] The server sends the optimized advertising video to the user's device, where the user can view the video through the interface.

[1217] Step 12:

[1218] The user checks the provided advertising video and gives instructions to "approve" or "modify." If there are no problems, the user clicks the "Approve" button.

[1219] Step 13:

[1220] The server uploads the approved ad video to various distribution platforms on the Internet (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[1221] Step 14:

[1222] The server collects click-through rate data for published ad videos in real time and uses it to generate future ad videos. This feedback loop is expected to continuously improve the accuracy and effectiveness of the entire system.

[1223] Example 2

[1224] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1225] Creating advertising videos distributed over the Internet is time-consuming and labor-intensive, and requires advanced expertise to generate effective videos. It is also difficult to generate personalized advertising videos that reflect user emotions, and optimizing them to improve click-through rates is not easy. To address these challenges, a system is needed that allows users to easily create highly effective advertising videos and optimizes those videos to attract the interest of many users.

[1226] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1227] In this invention, the server includes an input means for inputting simple configuration information, a means for receiving the configuration information input by the input means, a means for integrating and analyzing the configuration information and user emotion data, a generation means for generating an advertising video based on the analyzed information, a means for acquiring click-through rate data of past advertising videos, a means for relearning the generation means based on the acquired click-through rate data, a means for optimizing the advertising video using the re-trained generation means to generate a final advertising video, a means for providing the generated final advertising video to a user, a means for reviewing and approving the provided advertising video, and a means for uploading the approved advertising video to a distribution platform. This enables users to easily generate effective advertising videos, create personalized videos using emotion data, and provide videos optimized to further improve click-through rates.

[1228] "Simple configuration information" is basic information such as product name and features that is input by the user in order to generate an advertising video.

[1229] An "input means" is a device or interface that a user uses to input configuration information, such as a form on a browser.

[1230] The "receiving means" is a mechanism by which the server acquires the configuration information input by the user.

[1231] "Emotion data" is information about the user's emotional state obtained by analyzing the user's facial expression and tone of voice.

[1232] The "analyzing means" is a mechanism for integrating the received configuration information and emotion data to generate a scenario for the advertising video.

[1233] "Generation means" refers to technologies such as video generation AI that create advertising videos based on analyzed information.

[1234] "Click-through rate data" is numerical data that indicates user responses to past advertising videos. It is an index used to evaluate the effectiveness of advertising.

[1235] The "re-learning means" is a mechanism for updating the video generation AI model based on the acquired click-through rate data, thereby improving the accuracy of generating advertising videos.

[1236] The "optimization means" is a mechanism for more effectively adjusting and editing advertising videos using the retrained generation means.

[1237] The "means for providing" refers to a mechanism for displaying the generated advertising video to a user, such as a system for transmitting a video file to a user's terminal.

[1238] The "means for checking and approving" refers to an interface or mechanism that allows a user to check the generated advertising video and approve it if there are no problems.

[1239] "Means for uploading to a distribution platform" refers to a mechanism for uploading approved advertising videos to online video sharing services, etc.

[1240] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. In particular, by combining an emotion engine that recognizes user emotions, it is possible to generate more personalized advertising videos. Specific embodiments of this system are described in detail below.

[1241] System Configuration

[1242] This system mainly consists of a server, a terminal, an emotion engine, and a video generation AI.

[1243] Users create advertising videos

[1244] The user opens a browser on their device and enters configuration information such as the product name and features into a form for creating an advertising video. For example, they might enter "A new smartphone with a high-resolution camera and a long-life battery." This input information is treated as simple configuration information.

[1245] Start sending data and analyzing sentiment

[1246] When the user clicks the "Send" button, the entered configuration information is sent to the server. At the same time, the device's camera and microphone are automatically activated, and the emotion engine begins analyzing the user's facial expressions and tone of voice. This analyzed data is sent to the server as emotion data.

[1247] Integrating emotion data and compositional information

[1248] The server receives the received configuration information and the analysis data from the emotion engine, integrates and analyzes them. The emotion data reflects the user's emotional state (e.g., joy, surprise, anticipation, etc.), and generates a scenario for the advertising video based on this.

[1249] Generate initial ad video

[1250] The server then uses the integrated information to have the video generation AI generate an initial version of the advertising video, which then combines footage and narration according to the advertising scenario to create the initial advertising video.

[1251] Obtaining and relearning click-through rate data

[1252] The server retrieves click-through rate data for past ad videos from the database and supplies it to the video generation AI. The video generation AI re-learns based on this click-through rate data and updates its ad video generation model to generate even more effective ad videos.

[1253] Ad video optimization

[1254] The retrained generator is used to optimize the initial version of the ad video, which is expected to improve click-through rates.

[1255] Video review and approval

[1256] The server sends the optimized ad video to the user's device. The user checks the provided ad video and, if there are no problems, clicks the "Approve" button to complete the approval.

[1257] Upload to a distribution platform

[1258] The server then uploads the approved ad video to various distribution platforms on the Internet (e.g., video sharing services and social media), which automatically publishes the ad video and reaches a large audience.

[1259] Specific examples

[1260] Case 1: Promoting new products on an online shop

[1261] User Action:

[1262] The user uses a browser on the online shop's management screen to enter "New product introduction video. New smartphone. Equipped with a high-resolution camera and long-life battery," and clicks the "Send" button. During this time, the camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice.

[1263] Server behavior:

[1264] The server analyzes the received data and generates a scenario for the ad video based on the analysis results of the emotion engine. The video generation AI generates an initial version of the ad video and then retrieves data from a database of ad videos with high click rates in the past.

[1265] Video generation AI in action:

[1266] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[1267] User confirmation:

[1268] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to video sharing services and social media.

[1269] This system allows users to quickly generate and distribute highly effective advertising videos easily and efficiently. The introduction of an emotion engine makes it possible to generate personalized advertising videos according to the user's emotional state.

[1270] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1271] Step 1:

[1272] The user provides input for creating an advertising video.

[1273] Input: The user opens a browser on their device and fills in a form with configuration information, such as the product name and features. For example, "New smartphone. With a high-resolution camera and long-life battery."

[1274] Data processing: The information entered in the form is formatted into a certain format.

[1275] Output: Formatted configuration information.

[1276] Step 2:

[1277] The user sends the configuration information to the server and sentiment analysis begins.

[1278] Input: The user clicks the "Submit" button.

[1279] Data calculation: The device's camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice in real time.

[1280] Output: The configuration information is sent to the server and the analyzed emotion data is obtained.

[1281] Step 3:

[1282] The server receives and integrates the configuration information and emotion data.

[1283] Input: The server receives configuration information from the user and emotion data from the emotion engine.

[1284] Data calculation: Integrates received data and performs analysis to generate scenarios based on the user's emotions.

[1285] Output: An advertising video scenario based on the analysis results is obtained.

[1286] Step 4:

[1287] The server generates the initial advertising video.

[1288] Input: Parsed configuration information and scenario

[1289] Data calculation: Video generation AI combines video clips and narration based on a scenario.

[1290] Output: The initial version of the ad video is generated.

[1291] Step 5:

[1292] The server retrieves the click-through rate data and performs re-learning.

[1293] Input: Click-through rate data for past ad videos

[1294] Data calculation: The video generation AI re-learns based on click-rate data and updates the generation model.

[1295] Output: An updated ad video generation model is obtained.

[1296] Step 6:

[1297] Video generation AI generates optimized advertising videos.

[1298] Input: Updated generative model and initial version of ad video

[1299] Data calculation: Adjust the timing of video transitions and the tone of the narration according to the optimization procedure.

[1300] Output: The optimized ad video is generated.

[1301] Step 7:

[1302] The server sends the optimized advertising video to the user.

[1303] Input: Optimized ad video

[1304] Data calculation: Performs the procedure for sending the optimized advertising video to the user's device.

[1305] Output: The ad video is sent to the user.

[1306] Step 8:

[1307] The user reviews the ad video and approves or modifies it.

[1308] Input: Ad video sent to user

[1309] Data Calculation: The user checks the video and clicks the "Approve" or "Modify" button.

[1310] Output: If the user approves, the video is marked as the final version, and if there are any corrections required, the cycle starts again.

[1311] Step 9:

[1312] The server uploads the approved advertising video to the distribution platform.

[1313] Input: Approved ad video

[1314] Data Computing: Processing the video for uploading to the appropriate distribution platform.

[1315] Output: The advertising video is published on the Internet and reaches a large audience.

[1316] (Application example 2)

[1317] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1318] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Conventional advertising video generation systems generally generate advertising videos based solely on user input information, limiting innovations in personalization. While generating advertising videos that take user emotions into consideration would enable more effective personalization, building such a system has been challenging. Furthermore, there has been a lack of means for relearning and optimizing the generation method using past click-through rate data. This has made it challenging to realize a system that maximizes advertising effectiveness.

[1319] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: an input means for inputting simple advertising video configuration information; a means for receiving the configuration information input by the input means; an emotion analysis means for analyzing a user's facial expression and tone of voice and evaluating their emotional state; a generation means for generating an advertising video based on the received configuration information and analysis data from the emotion analysis means; a means for acquiring click-through rate data of past advertising videos; a means for relearning the generation means based on the acquired click-through rate data; a means for optimizing the advertising video using the relearned generation means to generate a final advertising video; a means for providing the generated final advertising video to a user; a means for reviewing and approving the provided advertising video; and a means for uploading the approved advertising video to a distribution platform. This enables the generation of personalized advertising videos that reflect the user's emotional state, and further enables effective optimization of advertising videos through relearning using past click-through rate data.

[1320] The "input means" is a means for a user to input information about the configuration of an advertising video.

[1321] The "receiving means" is a means for receiving configuration information input by the input means.

[1322] The "emotion analysis means" is a means for analyzing the user's facial expression and tone of voice to evaluate the user's emotional state.

[1323] The "generation means" is a means for generating an advertising video based on the received configuration information and the analysis data from the emotion analysis means.

[1324] The "means of acquisition" refers to a means for acquiring click-through rate data for past advertising videos.

[1325] The "means for relearning" is a means for relearning the generation means based on the acquired click rate data.

[1326] The "optimizing means" is a means for optimizing the advertising video using the retrained generating means and generating the final advertising video.

[1327] The "means for providing" refers to a means for providing the final generated advertising video to the user.

[1328] The "means for checking and approving" is a means for a user to check and approve the provided advertising video.

[1329] "Means for uploading" refers to the means for uploading the approved advertising video to the distribution platform.

[1330] The present invention is a system for realizing efficient and effective creation and distribution of advertising videos on the Internet. A specific embodiment of the present invention will be described in detail based on the above description.

[1331] Program processing

[1332] The system includes multiple components, including a user terminal, a server, a sentiment analysis engine, and a generative AI model.

[1333] User terminal processing

[1334] The user terminal is used through a web browser or a dedicated application, and performs the following processes.

[1335] A form is displayed for the user to input configuration information for the advertising video.

[1336] It accepts user input and uses a camera and microphone to capture facial expressions and voice.

[1337] The configuration information and the captured data are sent to a server.

[1338] Server Processing

[1339] The server performs the following main processes:

[1340] The configuration information and capture data transmitted from the user terminal are received.

[1341] A sentiment analysis engine is used to analyze the user's emotional state.

[1342] The composition information and sentiment analysis data are sent to a generative AI model to generate advertising videos.

[1343] Advertising videos are optimized by obtaining past click-through rate data from a database and supplying it to a generative AI model.

[1344] The optimized advertising video is sent to the user terminal for review and approval.

[1345] Upload the approved ad video to a distribution platform.

[1346] Sentiment Analysis Engine

[1347] The emotion analysis engine analyzes the user's facial expressions and tone of voice to assess their emotional state, leveraging known emotion recognition algorithms such as OpenFace and DeepVoice.

[1348] Generative AI Models

[1349] The generative AI model generates videos based on the received composition information and emotional data, and then re-trains using past click-through rate data to optimize the ad video.

[1350] Hardware and software used

[1351] Hardware: Smartphone (camera, microphone), server

[1352] Software: Browser or dedicated app, emotion analysis algorithm (OpenFace, DeepVoice), video generation AI (e.g., OpenAI's DALL-E)

[1353] Adding specific examples

[1354] Example of new product introduction using a smartphone app

[1355] The flow for creating an advertising video for a new product using a smartphone app used by a user is as follows.

[1356] User operations

[1357] 1. Launch the smartphone app and go to the screen for creating advertising videos.

[1358] 2. Enter the name and features of the new product in text format as configuration information.

[1359] 3. The camera and microphone will automatically activate and capture your facial expressions and voice.

[1360] 4. Press the "Submit" button to send the input information and captured data to the server.

[1361] Server Operation

[1362] 1. The server analyzes the received configuration information and capture data.

[1363] 2. The sentiment analysis engine evaluates the user's emotional state.

[1364] 3. The generative AI model generates the initial advertising video based on the received data.

[1365] 4. Retrain the generative AI model based on past click-through rate data to optimize the ad video.

[1366] 5. Send the optimized ad video to the user's device for review and approval.

[1367] Prompt Sentence Examples

[1368] "Create a video to showcase your new smartphone, highlighting its high-resolution camera and long-lasting battery life. Use an upbeat tone to reflect customer expectations."

[1369] Thus, the present invention provides a user-friendly interface while utilizing sentiment analysis and database information to generate and optimize personalized advertising videos.

[1370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1371] Step 1:

[1372] The user launches the smartphone app and inputs the information that makes up the advertising video. Specifically, the name and features of the new product are entered in text format. This input data is the basic scenario information for the advertising video. A specific example of input includes information such as "A new smartphone with a high-resolution camera and a long-life battery."

[1373] Step 2:

[1374] The device receives the configuration information and automatically activates the camera and microphone, capturing the user's facial expressions and voice. The captured video and audio data is stored unprocessed.

[1375] Step 3:

[1376] The device sends the saved configuration information and captured data, including text configuration information and video and audio files, to the server for further processing.

[1377] Step 4:

[1378] The server analyzes the received configuration information and captured data. First, it uses an emotion analysis engine to analyze the user's facial expressions and tone of voice to evaluate the user's emotional state. The emotion analysis engine uses algorithms such as OpenFace and DeepVoice. The analysis results in emotional data such as "expectation."

[1379] Step 5:

[1380] The server queries the generative AI model based on the analysis results and configuration information. The generative AI model uses the obtained data to generate an initial ad video. In this case, the generative AI model uses advanced algorithms such as OpenAI's DALL-E. The generated result is an initial version of the ad video file.

[1381] Step 6:

[1382] The server retrieves click-through rate data for past ad videos from the database. This data includes the click-through rate and characteristics of each ad video. The retrieved click-through rate data is fed to the generative AI model for retraining. The retrained generative AI model generates optimized ad videos based on the past data.

[1383] Step 7:

[1384] The server then sends the optimized ad video file to the user's device. The optimized video is adjusted to expect a higher click-through rate. The ad video is then sent from the server so that the user can view it.

[1385] Step 8:

[1386] The user terminal displays the received advertising video on the user interface and provides a function for the user to review and approve it. The user reviews the video and clicks the "Approve" button if there are no problems. If corrections are required, the user issues a "correction instruction."

[1387] Step 9:

[1388] The server receives the approval data from the user and uploads the approved ad video to a distribution platform, such as YouTube or LINE Voom. This completes the release of the ad video.

[1389] In this way, each processing step works in conjunction to realize a system that generates and optimizes personalized advertising videos based on user emotions.

[1390] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1391] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1392] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1393] [Fourth embodiment]

[1394] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1395] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1396] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1397] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1398] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1399] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1400] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1401] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1402] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1403] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1404] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1405] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1406] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1407] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Specific embodiments of this system will be described in detail below.

[1408] System Configuration

[1409] 1. Users create advertising videos

[1410] 1. The user inputs simple information to compose the advertising video on the device. For example, if a user wants to create a promotional video for a new product, they open a browser and use a form to input the product name and features. The information entered is specific sentences such as "A new smartphone with a high-resolution camera and a long-life battery."

[1411] 2. The user submits the entered configuration information. When the user clicks the "Submit" button, the configuration information is sent to the server.

[1412] 2. Generating advertising videos using video generation AI

[1413] 3. The server analyzes the received configuration information. The server analyzes the received text data, breaks down the configuration information, and generates a scenario framework.

[1414] 4. Based on the analyzed configuration information, the server uses the video generation AI to generate an initial version of the advertising video. Based on the analysis results, the video generation AI generates a video that combines images and narration according to the scenario.

[1415] 3. Creating effective advertising videos

[1416] 5. The server retrieves advertising video data with high click rates from the database.

[1417] 6. The server provides the acquired click-through rate data to the video generation AI, which uses this data to retrain and update its model to generate more effective advertising videos.

[1418] 7. The video generation AI uses the retrained model to optimize the initial version of the ad video. The retrained AI model is used to optimize the initial ad video, creating content that will increase click-through rates.

[1419] 4. Distribution of advertising videos

[1420] 8. The server generates an optimized ad video and sends it to the user's device.

[1421] 9. The user checks the provided ad video and approves it if there are no problems. The user plays the ad video on their device, checks that there are no problems with the content, and then clicks the "Approve" button.

[1422] 10. The server uploads the approved ad video to each distribution platform on the Internet. The server automatically uploads the optimized ad video to different distribution platforms such as YouTube and LINE Voom.

[1423] Specific examples

[1424] Case 1: Promoting new products on an online shop

[1425] User Action:

[1426] A user uses a browser to fill out a form on the online shop's administration screen, entering "New product introduction video. New smartphone. With high-resolution camera and long-life battery." Then, the user clicks the "Submit" button to send the information to the server.

[1427] Server behavior:

[1428] The server analyzes the received data and generates a scenario for the ad video. It passes the data to the video generation AI, which generates an initial version of the ad video. It then retrieves data from the database about ad videos with high click rates and supplies it to the video generation AI.

[1429] Video generation AI in action:

[1430] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[1431] User confirmation:

[1432] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to YouTube, LINE Voom, etc.

[1433] This system allows users to quickly generate and distribute effective advertising videos without any hassle.

[1434] The processing flow will be explained below.

[1435] Step 1:

[1436] The user inputs the configuration information of the advertising video (e.g., product features and target users) on the device. The user fills in the form with "A new smartphone with a high-resolution camera and a long-life battery."

[1437] Step 2:

[1438] To send the entered configuration information, the user clicks the "Send" button, which sends the configuration information from the terminal to the server.

[1439] Step 3:

[1440] The server analyzes the received configuration information. The server's analysis engine breaks down the received text data into components and organizes them as a scenario framework.

[1441] Step 4:

[1442] The server passes the analyzed configuration information to the video generation AI, which then generates an initial version of the advertising video based on this configuration information.

[1443] Step 5:

[1444] The server retrieves data from the database about advertising videos with high click rates in the past, including metadata such as the number of clicks and viewing time of users.

[1445] Step 6:

[1446] The server provides the acquired click-through rate data to the video generation AI, which then retrains the model to generate new advertising videos based on past effective advertising videos.

[1447] Step 7:

[1448] The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[1449] Step 8:

[1450] The server sends the optimized advertising video to the user's device, where the user can view the video through the interface.

[1451] Step 9:

[1452] The user checks the provided ad video and gives instructions to "approve" or "revise." If there are no problems, the approval is completed by clicking the "Approve" button.

[1453] Step 10:

[1454] The server uploads the approved ad video to various distribution platforms on the Internet (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[1455] Example 1

[1456] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1457] Conventional advertising video creation systems require users to manually create a scenario and combine video and narration, resulting in significant time and effort. Furthermore, there was a lack of a way to predict the effectiveness of the created advertising video in advance, so high click-through rates were not necessarily achieved. The present invention aims to solve these problems and provide a system that enables efficient and effective advertising video creation and distribution.

[1458] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1459] In this invention, the server includes an input means for inputting simple advertising video configuration information, a means for receiving the configuration information input by the input means, a means for analyzing the received configuration information and generating a video generation scenario framework, a means for generating an initial version of the advertising video using a video generation AI model based on the analysis results, a means for acquiring click-through rate data of past advertising videos, a means for retraining the video generation AI model based on the acquired click-through rate data, a means for optimizing the advertising video using the retrained video generation AI model and generating a final advertising video, a means for providing the generated final advertising video to a user, a means for reviewing and approving the provided advertising video, and a means for automatically uploading the approved advertising video to a distribution platform. This enables highly effective advertising videos to be quickly generated and widely distributed without user effort.

[1460] "Simple advertising video configuration information" is basic information that a user inputs to generate an advertising video, and includes product features and advertising content.

[1461] "Input means" is a function that provides an interface for the user to input simple advertising video configuration information, and includes browser forms, question-and-answer prompts, and the like.

[1462] The "receiving means" is a function by which the server acquires the configuration information sent from the user through the input means.

[1463] The "means of analysis" is a function that generates a scenario framework based on the received configuration information and converts it into a format suitable for the video generation AI model.

[1464] The "video generation scenario framework" is the framework of an advertising video constructed based on the configuration information, and it instructs the distribution of images and narration.

[1465] A "video generation AI model" is an algorithm or program that uses artificial intelligence to automatically generate and optimize advertising videos.

[1466] The "means for generating an initial version of an advertising video" is a function used by the video generation AI model to create an initial version of an advertising video based on the analyzed configuration information.

[1467] "Click-through rate data" is statistically collected data on the number and percentage of users who clicked on a distributed advertising video.

[1468] The "re-learning means" is a function that uses the acquired click-through rate data to further train the video generation AI model and generate more effective advertising videos.

[1469] "Means for optimizing advertising videos" is a function that uses an updated AI model through re-learning to improve the initial version of the advertising video and achieve a high click-through rate.

[1470] The "means for generating the final advertising video" is a function for completing the optimized advertising video as the final version.

[1471] The "means for providing" is a function for sending the generated final advertising video to the user and prompting the user to confirm it.

[1472] The "means for checking and approving" is a function that allows a user to check the provided advertising video and approve it if there is no problem with the content.

[1473] "Means for automatically uploading to distribution platforms" refers to a function for automatically uploading approved advertising videos to various distribution platforms such as YouTube and social media.

[1474] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Specific embodiments of this system will be described in detail below.

[1475] System Configuration

[1476] Users create advertising videos

[1477] First, the user inputs basic information about the advertising video on the device. This input method uses a browser form or similar. For example, if a user wants to create a promotional video for a new product, they open a browser and input "A new smartphone with a high-resolution camera and a long-life battery." This input information is also used as a prompt based on specific content.

[1478] The server receives and analyzes the configuration information

[1479] The server then receives the configuration information entered by the user, analyzes it, extracts keywords and important phrases, and converts them into data for generating a scenario framework suitable for the video generation AI model.

[1480] Generating an early version of an advertising video using a video generation AI model

[1481] Based on the analysis results, the server uses a video generation AI model to generate an initial version of the advertising video, which combines appropriate video footage and narration according to the specified scenario framework.

[1482] The server acquires the click-through rate data and retrains the video generation AI model.

[1483] The server accesses the database to retrieve click-through rate data for past ad videos. This data includes statistical information such as viewing time, number of clicks, and click-through rate. The server then provides this data to the video generation AI model, which then retrains the AI. This retraining improves the AI ​​model's ability to generate more effective ad videos.

[1484] Optimizing advertising videos with a video generation AI model

[1485] The retrained video generation AI model then optimizes the initial version of the ad video, resulting in a video that is expected to generate a high click-through rate from viewers.

[1486] The server provides the optimized ad video to the user.

[1487] The optimized ad video is sent from the server to the user's device. The user checks the video and approves it if there are no problems. Specifically, the user plays the video in the browser and clicks the "Approve" button.

[1488] The server uploads the ad video to the distribution platform

[1489] Finally, approved ad videos are automatically uploaded from the server to various distribution platforms on the Internet, with the option to upload to YouTube or other social media platforms.

[1490] Specific examples

[1491] Case 1: Promoting new products in an online shop

[1492] 1. User Action:

[1493] The user opens a browser and enters the following information into a form on the online shop's management screen: "New product introduction video. New smartphone. With high-resolution camera and long-life battery."

[1494] Once you have completed entering the information, click the "Submit" button.

[1495] 2. Server operation:

[1496] The server receives the input data and analyzes it using a text analysis engine. The analysis results are generated as a scenario framework and passed to the video generation AI model.

[1497] The server retrieves past click-through rate data from the database and provides it to the video generation AI model, which then retrains and generates optimized ad videos.

[1498] 3. User Verification and Authorization:

[1499] The user checks the generated ad video and clicks the "Approve" button if there are no problems. An example of a confirmation prompt sentence here is "Check out the video introducing the new smartphone. It has a high-resolution camera and a long-life battery. Are there any problems with the content?"

[1500] 4. Server uploading:

[1501] Approved ad videos are automatically uploaded from the server to YouTube and other distribution platforms.

[1502] The above process makes it possible to generate and distribute advertising videos that are expected to be highly effective without much effort.

[1503] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1504] Step 1:

[1505] The user inputs the advertising video configuration information.

[1506] Specifically, the user opens a browser and enters information about the ad video into a form, such as "A new smartphone with a high-resolution camera and a long-life battery." The input method is a web form, and the input data is also used as a prompt. This input data is then sent to the server.

[1507] Step 2:

[1508] The server receives the input configuration information.

[1509] The server receives the advertising video composition information sent from the user's device. The received data is in text format and is saved in the system. The server then passes this data to the analysis engine.

[1510] Step 3:

[1511] The server analyzes the configuration information and generates a scenario framework.

[1512] The received text data is analyzed by an analysis engine to extract keywords and important phrases. Based on the extracted content, a scenario framework is generated and converted into a data format suitable for the video generation AI model. For example, phrases such as "new smartphone," "high-resolution camera," and "long-life battery" are extracted as part of the scenario.

[1513] Step 4:

[1514] The video generation AI model generates an initial version of the ad video.

[1515] The server then provides the scenario framework obtained from the analysis results to the video generation AI model. Based on the provided data, the AI ​​model combines the footage and narration to generate an initial version of the advertising video. This video is generated taking into account current trends and optimization of viewing time.

[1516] Step 5:

[1517] The server retrieves historical click-through rate data.

[1518] The server accesses the database to retrieve click-through rate data for past advertising videos, including viewing time, number of clicks, and click-through rate. The retrieved data is used as input data for the video generation AI model.

[1519] Step 6:

[1520] The video generation AI model retrains and optimizes the advertising video.

[1521] The server provides the click-through rate data to the AI ​​model for retraining. The retrained AI model then builds a new algorithm to generate more effective ad videos. The generated initial version of the ad video is then re-edited to output an optimized version.

[1522] Step 7:

[1523] The server provides the optimized advertising video to the user.

[1524] The optimized ad video is sent from the server to the user's device. The user reviews the video and approves it if there are no problems with the ad content. Specifically, the user plays the video and clicks the "Approve" button.

[1525] Step 8:

[1526] The server uploads the advertising video to the distribution platform.

[1527] Approved advertising videos are automatically uploaded from the server to YouTube and other social media platforms, and the uploaded videos are optimized for the characteristics of each platform.

[1528] (Application example 1)

[1529] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1530] Conventional advertising video creation systems require users to spend a lot of time and effort creating videos, and a great deal of specialized knowledge is required for effective distribution. For this reason, there is a demand for more efficient advertising creation and maximizing distribution effectiveness. There is also a need for a system that is easy for users to operate and that can produce effective results.

[1531] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1532] In this invention, the server includes an input means for inputting simple advertising video configuration information, a means for receiving the configuration information input by the input means, and a generation means for generating an advertising video based on the received configuration information. This enables users to easily create and effectively distribute advertising videos.

[1533] "Simple advertising video configuration information" is simple information that a user inputs to create an advertising video, and includes basic data such as product names and features.

[1534] "Input means" refers to a means by which a user inputs information constituting an advertising video, and refers to an interface such as a form on a smartphone.

[1535] The "receiving means" is a means by which the server receives the configuration information input by the input means.

[1536] The "generation means" is a means for generating an advertising video based on the received configuration information.

[1537] "Click-through rate data" refers to data relating to the percentage of users clicking on past advertising videos.

[1538] The "means for obtaining" refers to a means for obtaining click-through rate data of past advertising videos from a database.

[1539] The "means for relearning" refers to a means for relearning the generation means (video generation AI) based on the acquired click rate data.

[1540] The "optimizing means" is a means for optimizing the advertising video using the retrained generating means and generating the final advertising video.

[1541] The "means for providing" refers to a means for providing the final generated advertising video to the user.

[1542] The "means for checking and approving" is a means for a user to check the generated advertising video and approve it if there is no problem.

[1543] "Means for uploading" refers to the means for uploading the approved advertising video to a distribution platform on the Internet.

[1544] "Smartphone application" refers to an application that allows users to create, review, approve, and distribute advertising videos on their smartphones.

[1545] "Scenario generation" is a process of automatically generating a scenario for an advertising video based on input product information.

[1546] "Video generation" is the process of creating an advertising video according to a generated scenario.

[1547] MODE FOR CARRYING OUT THE INVENTION

[1548] The present invention relates to a system that allows users to easily create advertising videos and maximize the effectiveness of those videos. Specific embodiments of this system will be described in detail below.

[1549] System Configuration

[1550] This system consists of the following elements:

[1551] 1. Input method:

[1552] The smartphone application allows users to input simple information about the composition of the advertising video, such as the product name and features, in text format.

[1553] 2. Receiving means:

[1554] The application sends the input information to the server, which receives the information.

[1555] 3. Generation means:

[1556] The server analyzes the received configuration information and generates scenarios and videos. It automatically generates the initial version of the advertising video using a video generation AI model (e.g., TensorFlow / Keras).

[1557] 4. Acquisition method:

[1558] The server retrieves click-through rate data for past advertising videos from a database (e.g., MySQL or PostgreSQL).

[1559] 5. Retraining methods:

[1560] The server retrains the video generation AI model based on the acquired click-through rate data, updating the model to maximize the effectiveness of advertising videos.

[1561] 6. Optimization measures:

[1562] The retrained model is used to optimize the initial version of the ad video to generate the final ad video.

[1563] 7. Means of providing:

[1564] The server then sends the final ad video to the user's smartphone, where the user can review and approve it.

[1565] 8. Verification and Approval Methods:

[1566] The user checks the provided advertising video and clicks the "Approve" button if there are no problems.

[1567] 9. Uploading Method:

[1568] The server automatically uploads approved ad videos to distribution platforms (such as YouTube and social media), allowing users to distribute ad videos quickly and effectively.

[1569] Specific examples of processing

[1570] Let's take the example of a user creating an advertising video for a new wearable device. They enter "A new wearable device with a high-precision heart rate monitor and a long-life battery" into the application form and click the "Submit" button. The server analyzes the received information and starts generating a scenario and video.

[1571] The server retrains the video generation AI model based on past data with high click rates, generates an optimized ad video, and then sends the video to the user. Once the user reviews and approves the sent video, the server automatically uploads the ad video to the distribution platform.

[1572] Prompt Sentence Examples

[1573] "The latest smartphone is finally here! With a high-resolution camera and long-lasting battery, it's perfect for videography. Order now!"

[1574] In this way, users can generate and distribute effective advertising videos with minimal operations.

[1575] Hardware / Software Used

[1576] Hardware: Smartphone

[1577] software:

[1578] Server side: Python (Flask framework)

[1579] Video generation AI: TensorFlow / Keras

[1580] Database: MySQL or PostgreSQL

[1581] Frontend: React Native

[1582] This allows users to create and distribute advertising videos with simple operations, maximizing the effectiveness of their advertising.

[1583] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1584] Step 1:

[1585] The user enters the configuration information for the advertising video into a form on the smartphone application. This configuration information includes the product name and features. For example, the user might enter "A new wearable device with a high-precision heart rate monitor and a long-life battery." When the user clicks the "Submit" button, this information is sent to the server.

[1586] Input: Ad video configuration information

[1587] Output: Configuration information sent to the server

[1588] Step 2:

[1589] The server analyzes the received configuration information. Specifically, it parses the text data to extract attributes such as product names and features. For example, it extracts the product name "wearable device" and the features "high-precision heart rate monitor and long-life battery."

[1590] Input: Configuration information submitted by the user

[1591] Output: Attributes of extracted configuration information

[1592] Step 3:

[1593] The server generates a scenario based on the extracted attribute information, and uses a video generation AI model (TensorFlow / Keras) to generate prompts for creating an initial version of the advertising video, which are then used to generate the video.

[1594] Input: Attributes of extracted configuration information

[1595] Output: Initial version of the ad video

[1596] Step 4:

[1597] The server retrieves click-through rate data for past ad videos from a database (MySQL or PostgreSQL). This data includes data related to ad videos with high click-through rates, and is used to measure the effectiveness of the videos.

[1598] Input: Database query

[1599] Output: Click-through rate data for past ad videos

[1600] Step 5:

[1601] The server retrains the video generation AI model based on the acquired click-through rate data, using TensorFlow / Keras to update previous learning results and create a model for generating more effective advertising videos.

[1602] Input: Click-through rate data

[1603] Output: Retrained video generation AI model

[1604] Step 6:

[1605] The server then uses the retrained AI model to optimize the initial version of the ad video, generating a final ad video that may have a higher click-through rate.

[1606] Input: Retrained AI model and initial version of ad video

[1607] Output: Optimized ad video

[1608] Step 7:

[1609] The server then sends the final ad video to the user's smartphone. The user reviews the video and clicks the "Approve" button if there are no problems with the content.

[1610] Input: Optimized ad video

[1611] Output: Ad video viewable on user device

[1612] Step 8:

[1613] The user checks the provided advertisement video and, if there are no problems, clicks the "Approve" button, which triggers the server to upload the advertisement video to the distribution platform.

[1614] Input: User review and approval

[1615] Output: Approval trigger

[1616] Step 9:

[1617] The server automatically uploads approved ad videos to distribution platforms such as YouTube and social media, allowing them to be widely published and reach their target audience.

[1618] Inputs: Approval trigger and ad video

[1619] Output: Ad video published on distribution platform

[1620] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1621] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. In particular, by combining an emotion engine that recognizes user emotions, it is possible to generate more personalized advertising videos. Specific embodiments of this system are described in detail below.

[1622] System Configuration

[1623] 1. Users create advertising videos

[1624] 1. The user inputs simple advertising video configuration information on the device. For example, if a user wants to create a promotional video for a new product, they open a browser and use a form to input the product name and features. The information entered is specific sentences such as "A new smartphone with a high-resolution camera and a long-life battery."

[1625] 2. The user submits the configuration information they entered. When the user clicks the "Submit" button, the configuration information is sent to the server. At this stage, the camera and microphone are automatically activated so that the emotion engine can analyze the user's facial expressions and tone of voice.

[1626] 2. Analysis by emotion engine

[1627] 3. The server receives the configuration information and analysis data from the emotion engine. The emotion engine analyzes the user's facial expressions and tone of voice to evaluate the user's emotional state (e.g., joy, surprise, anticipation, etc.).

[1628] 4. The analysis results are sent to the server and reflected in the composition of the ad video. For example, if the user is talking in a happy manner, the scenario will be adjusted to generate an ad video with a bright and positive tone.

[1629] 3. Generating advertising videos using video generation AI

[1630] 5. The server analyzes the received configuration information and emotion data and generates a scenario framework.

[1631] 6. Based on the analyzed information, the server uses the video generation AI to generate an initial version of the advertising video. The video generation AI generates a video that combines images and narration according to the scenario.

[1632] 4. Creating effective advertising videos

[1633] 7. The server retrieves data from the database about advertising videos with high click-through rates in the past.

[1634] 8. The server provides the acquired click-through rate data to the video generation AI, which uses this data to retrain and update its model to generate more effective advertising videos.

[1635] 9. The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[1636] 5. Distribution of advertising videos

[1637] 10. The server sends the optimized ad video to the user's device, where the user can view the video through the interface.

[1638] 11. The user checks the provided ad video and gives instructions to "approve" or "modify." If there are no problems, the user clicks the "Approve" button to complete the approval.

[1639] 12. The server uploads the approved ad video to various online distribution platforms (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[1640] Specific examples

[1641] Case 1: Promoting new products on an online shop

[1642] User Action:

[1643] The user uses a browser to enter the following information into a form on the online shop's administration screen: "New product introduction video. New smartphone. With high-resolution camera and long-life battery." Then, they click the "Submit" button to send the information to the server. During this time, the camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice.

[1644] Server behavior:

[1645] The server analyzes the received data and generates a scenario for the ad video. Based on the analysis results of the emotion engine, the video generation AI generates an initial version of the ad video. After that, data on ad videos with high click rates in the past is retrieved from the database and provided to the video generation AI.

[1646] Video generation AI in action:

[1647] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[1648] User confirmation:

[1649] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to YouTube, LINE Voom, etc.

[1650] This system allows users to quickly generate and distribute effective advertising videos without much effort. In addition, the introduction of an emotion engine makes it possible to generate personalized advertising videos according to the user's emotional state.

[1651] The processing flow will be explained below.

[1652] Step 1:

[1653] The user inputs the configuration information of the advertising video (e.g., product features and target users) on the device. The user enters "A new smartphone with a high-resolution camera and a long-life battery" into the form.

[1654] Step 2:

[1655] To send the entered configuration information, the user clicks the "Send" button, which sends the configuration information from the terminal to the server.

[1656] Step 3:

[1657] The device's camera and microphone will automatically activate and record the user's facial expressions and tone of voice, and this data will be sent to the emotion engine in real time.

[1658] Step 4:

[1659] The server analyzes the received configuration information. The server's analysis engine breaks down the received text data into components and organizes them as a scenario framework.

[1660] Step 5:

[1661] The emotion engine analyzes the user's facial expressions and tone of voice received in real time to assess the user's emotional state (e.g., joy, surprise, anticipation, etc.).

[1662] Step 6:

[1663] The server receives the analysis results from the emotion engine and reflects them in the scenario of the advertising video. For example, if the user is talking in a happy manner, the scenario will be adjusted to generate an advertising video with a bright and positive tone.

[1664] Step 7:

[1665] Based on the analyzed configuration information and emotional data, the server's video generation AI generates an initial version of the advertising video.

[1666] Step 8:

[1667] The server retrieves data from a database about advertising videos with high click rates in the past, including metadata such as the number of clicks and viewing time of users.

[1668] Step 9:

[1669] The server provides the acquired click-through rate data to the video generation AI, which then retrains the model to generate new advertising videos based on past effective advertising videos.

[1670] Step 10:

[1671] The video generation AI uses the retrained model to optimize the initial version of the ad video, which is expected to increase click-through rates.

[1672] Step 11:

[1673] The server sends the optimized advertising video to the user's device, where the user can view the video through the interface.

[1674] Step 12:

[1675] The user checks the provided advertising video and gives instructions to "approve" or "modify." If there are no problems, the user clicks the "Approve" button.

[1676] Step 13:

[1677] The server uploads the approved ad video to various distribution platforms on the Internet (e.g. YouTube, LINE Voom), which automatically makes the ad video public.

[1678] Step 14:

[1679] The server collects click-through rate data for published ad videos in real time and uses it to generate future ad videos. This feedback loop is expected to continuously improve the accuracy and effectiveness of the entire system.

[1680] Example 2

[1681] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1682] Creating advertising videos distributed over the Internet is time-consuming and labor-intensive, and requires advanced expertise to generate effective videos. It is also difficult to generate personalized advertising videos that reflect user emotions, and optimizing them to improve click-through rates is not easy. To address these challenges, a system is needed that allows users to easily create highly effective advertising videos and optimizes those videos to attract the interest of many users.

[1683] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1684] In this invention, the server includes an input means for inputting simple configuration information, a means for receiving the configuration information input by the input means, a means for integrating and analyzing the configuration information and user emotion data, a generation means for generating an advertising video based on the analyzed information, a means for acquiring click-through rate data of past advertising videos, a means for relearning the generation means based on the acquired click-through rate data, a means for optimizing the advertising video using the re-trained generation means to generate a final advertising video, a means for providing the generated final advertising video to a user, a means for reviewing and approving the provided advertising video, and a means for uploading the approved advertising video to a distribution platform. This enables users to easily generate effective advertising videos, create personalized videos using emotion data, and provide videos optimized to further improve click-through rates.

[1685] "Simple configuration information" is basic information such as product name and features that is input by the user in order to generate an advertising video.

[1686] An "input means" is a device or interface that a user uses to input configuration information, such as a form on a browser.

[1687] The "receiving means" is a mechanism by which the server acquires the configuration information input by the user.

[1688] "Emotion data" is information about the user's emotional state obtained by analyzing the user's facial expression and tone of voice.

[1689] The "analyzing means" is a mechanism for integrating the received configuration information and emotion data to generate a scenario for the advertising video.

[1690] "Generation means" refers to technologies such as video generation AI that create advertising videos based on analyzed information.

[1691] "Click-through rate data" is numerical data that indicates user responses to past advertising videos. It is an index used to evaluate the effectiveness of advertising.

[1692] The "re-learning means" is a mechanism for updating the video generation AI model based on the acquired click-through rate data, thereby improving the accuracy of generating advertising videos.

[1693] The "optimization means" is a mechanism for more effectively adjusting and editing advertising videos using the retrained generation means.

[1694] The "means for providing" refers to a mechanism for displaying the generated advertising video to a user, such as a system for transmitting a video file to a user's terminal.

[1695] The "means for checking and approving" refers to an interface or mechanism that allows a user to check the generated advertising video and approve it if there are no problems.

[1696] "Means for uploading to a distribution platform" refers to a mechanism for uploading approved advertising videos to online video sharing services, etc.

[1697] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. In particular, by combining an emotion engine that recognizes user emotions, it is possible to generate more personalized advertising videos. Specific embodiments of this system are described in detail below.

[1698] System Configuration

[1699] This system mainly consists of a server, a terminal, an emotion engine, and a video generation AI.

[1700] Users create advertising videos

[1701] The user opens a browser on their device and enters configuration information such as the product name and features into a form for creating an advertising video. For example, they might enter "A new smartphone with a high-resolution camera and a long-life battery." This input information is treated as simple configuration information.

[1702] Start sending data and analyzing sentiment

[1703] When the user clicks the "Send" button, the entered configuration information is sent to the server. At the same time, the device's camera and microphone are automatically activated, and the emotion engine begins analyzing the user's facial expressions and tone of voice. This analyzed data is sent to the server as emotion data.

[1704] Integrating emotion data and compositional information

[1705] The server receives the received configuration information and the analysis data from the emotion engine, integrates and analyzes them. The emotion data reflects the user's emotional state (e.g., joy, surprise, anticipation, etc.), and generates a scenario for the advertising video based on this.

[1706] Generate initial ad video

[1707] The server then uses the integrated information to have the video generation AI generate an initial version of the advertising video, which then combines footage and narration according to the advertising scenario to create the initial advertising video.

[1708] Obtaining and relearning click-through rate data

[1709] The server retrieves click-through rate data for past ad videos from the database and supplies it to the video generation AI. The video generation AI re-learns based on this click-through rate data and updates its ad video generation model to generate even more effective ad videos.

[1710] Ad video optimization

[1711] The retrained generator is used to optimize the initial version of the ad video, which is expected to improve click-through rates.

[1712] Video review and approval

[1713] The server sends the optimized ad video to the user's device. The user checks the provided ad video and, if there are no problems, clicks the "Approve" button to complete the approval.

[1714] Upload to a distribution platform

[1715] The server then uploads the approved ad video to various distribution platforms on the Internet (e.g., video sharing services and social media), which automatically publishes the ad video and reaches a large audience.

[1716] Specific examples

[1717] Case 1: Promoting new products on an online shop

[1718] User Action:

[1719] The user uses a browser on the online shop's management screen to enter "New product introduction video. New smartphone. Equipped with a high-resolution camera and long-life battery," and clicks the "Send" button. During this time, the camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice.

[1720] Server behavior:

[1721] The server analyzes the received data and generates a scenario for the ad video based on the analysis results of the emotion engine. The video generation AI generates an initial version of the ad video and then retrieves data from a database of ad videos with high click rates in the past.

[1722] Video generation AI in action:

[1723] The video generation AI retrains based on the acquired click-through rate data and optimizes the initially generated ad video. The server then sends this optimized ad video to the user's device.

[1724] User confirmation:

[1725] The user checks the ad video sent to them and, if there are no problems, clicks the "Approve" button. The server then uploads the ad video to video sharing services or social media.

[1726] This system allows users to quickly generate and distribute highly effective advertising videos easily and efficiently. The introduction of an emotion engine makes it possible to generate personalized advertising videos according to the user's emotional state.

[1727] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1728] Step 1:

[1729] The user provides input for creating an advertising video.

[1730] Input: The user opens a browser on their device and fills in a form with configuration information, such as the product name and features. For example, "New smartphone. With a high-resolution camera and long-life battery."

[1731] Data processing: The information entered in the form is formatted into a certain format.

[1732] Output: Formatted configuration information.

[1733] Step 2:

[1734] The user sends the configuration information to the server and sentiment analysis begins.

[1735] Input: The user clicks the "Submit" button.

[1736] Data calculation: The device's camera and microphone are automatically activated, and the emotion engine analyzes the user's facial expressions and tone of voice in real time.

[1737] Output: The configuration information is sent to the server and the analyzed emotion data is obtained.

[1738] Step 3:

[1739] The server receives and integrates the configuration information and emotion data.

[1740] Input: The server receives configuration information from the user and emotion data from the emotion engine.

[1741] Data calculation: Integrates received data and performs analysis to generate scenarios based on the user's emotions.

[1742] Output: An advertising video scenario based on the analysis results is obtained.

[1743] Step 4:

[1744] The server generates the initial advertising video.

[1745] Input: Parsed configuration information and scenario

[1746] Data calculation: Video generation AI combines video clips and narration based on a scenario.

[1747] Output: The initial version of the ad video is generated.

[1748] Step 5:

[1749] The server retrieves the click-through rate data and performs re-learning.

[1750] Input: Click-through rate data for past ad videos

[1751] Data calculation: The video generation AI re-learns based on click-rate data and updates the generation model.

[1752] Output: An updated ad video generation model is obtained.

[1753] Step 6:

[1754] Video generation AI generates optimized advertising videos.

[1755] Input: Updated generative model and initial version of ad video

[1756] Data calculation: Adjust the timing of video transitions and the tone of the narration according to the optimization procedure.

[1757] Output: The optimized ad video is generated.

[1758] Step 7:

[1759] The server sends the optimized advertising video to the user.

[1760] Input: Optimized ad video

[1761] Data calculation: Performs the procedure for sending the optimized advertising video to the user's device.

[1762] Output: The ad video is sent to the user.

[1763] Step 8:

[1764] The user reviews the ad video and approves or modifies it.

[1765] Input: Ad video sent to user

[1766] Data Calculation: The user checks the video and clicks the "Approve" or "Modify" button.

[1767] Output: If the user approves, the video is marked as the final version, and if there are any corrections required, the cycle starts again.

[1768] Step 9:

[1769] The server uploads the approved advertising video to the distribution platform.

[1770] Input: Approved ad video

[1771] Data Computing: Processing the video for uploading to the appropriate distribution platform.

[1772] Output: The advertising video is published on the Internet and reaches a large audience.

[1773] (Application example 2)

[1774] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1775] The present invention relates to a system for efficiently and effectively creating advertising videos to be distributed over the Internet. Conventional advertising video generation systems generally generate advertising videos based solely on user input information, limiting innovations in personalization. While generating advertising videos that take user emotions into consideration would enable more effective personalization, building such a system has been challenging. Furthermore, there has been a lack of means for relearning and optimizing the generation method using past click-through rate data. This has made it challenging to realize a system that maximizes advertising effectiveness.

[1776] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: an input means for inputting simple advertising video configuration information; a means for receiving the configuration information input by the input means; an emotion analysis means for analyzing a user's facial expression and tone of voice and evaluating their emotional state; a generation means for generating an advertising video based on the received configuration information and analysis data from the emotion analysis means; a means for acquiring click-through rate data of past advertising videos; a means for relearning the generation means based on the acquired click-through rate data; a means for optimizing the advertising video using the relearned generation means to generate a final advertising video; a means for providing the generated final advertising video to a user; a means for reviewing and approving the provided advertising video; and a means for uploading the approved advertising video to a distribution platform. This enables the generation of personalized advertising videos that reflect the user's emotional state, and further enables effective optimization of advertising videos through relearning using past click-through rate data.

[1777] The "input means" is a means for a user to input information about the configuration of an advertising video.

[1778] The "receiving means" is a means for receiving configuration information input by the input means.

[1779] The "emotion analysis means" is a means for analyzing the user's facial expression and tone of voice to evaluate the user's emotional state.

[1780] The "generation means" is a means for generating an advertising video based on the received configuration information and the analysis data from the emotion analysis means.

[1781] The "means of acquisition" refers to a means for acquiring click-through rate data for past advertising videos.

[1782] The "means for relearning" is a means for relearning the generation means based on the acquired click rate data.

[1783] The "optimizing means" is a means for optimizing the advertising video using the retrained generating means and generating the final advertising video.

[1784] The "means for providing" refers to a means for providing the final generated advertising video to the user.

[1785] The "means for checking and approving" is a means for a user to check and approve the provided advertising video.

[1786] "Means for uploading" refers to the means for uploading the approved advertising video to the distribution platform.

[1787] The present invention is a system for realizing efficient and effective creation and distribution of advertising videos on the Internet. A specific embodiment of the present invention will be described in detail based on the above description.

[1788] Program processing

[1789] The system includes multiple components, including a user terminal, a server, a sentiment analysis engine, and a generative AI model.

[1790] User terminal processing

[1791] The user terminal is used through a web browser or a dedicated application, and performs the following processes.

[1792] A form is displayed for the user to input configuration information for the advertising video.

[1793] It accepts user input and uses a camera and microphone to capture facial expressions and voice.

[1794] The configuration information and the captured data are sent to a server.

[1795] Server Processing

[1796] The server performs the following main processes:

[1797] The configuration information and capture data transmitted from the user terminal are received.

[1798] A sentiment analysis engine is used to analyze the user's emotional state.

[1799] The composition information and sentiment analysis data are sent to a generative AI model to generate advertising videos.

[1800] Advertising videos are optimized by obtaining past click-through rate data from a database and supplying it to a generative AI model.

[1801] The optimized advertising video is sent to the user terminal for review and approval.

[1802] Upload the approved ad video to a distribution platform.

[1803] Sentiment Analysis Engine

[1804] The emotion analysis engine analyzes the user's facial expressions and tone of voice to assess their emotional state, leveraging known emotion recognition algorithms such as OpenFace and DeepVoice.

[1805] Generative AI Models

[1806] The generative AI model generates videos based on the received composition information and emotional data, and then re-trains using past click-through rate data to optimize the ad video.

[1807] Hardware and software used

[1808] Hardware: Smartphone (camera, microphone), server

[1809] Software: Browser or dedicated app, emotion analysis algorithm (OpenFace, DeepVoice), video generation AI (e.g., OpenAI's DALL-E)

[1810] Adding specific examples

[1811] Example of new product introduction using a smartphone app

[1812] The flow for creating an advertising video for a new product using a smartphone app used by a user is as follows.

[1813] User operations

[1814] 1. Launch the smartphone app and go to the screen for creating advertising videos.

[1815] 2. Enter the name and features of the new product in text format as configuration information.

[1816] 3. The camera and microphone will automatically activate and capture your facial expressions and voice.

[1817] 4. Press the "Submit" button to send the input information and captured data to the server.

[1818] Server Operation

[1819] 1. The server analyzes the received configuration information and capture data.

[1820] 2. The sentiment analysis engine evaluates the user's emotional state.

[1821] 3. The generative AI model generates the initial advertising video based on the received data.

[1822] 4. Retrain the generative AI model based on past click-through rate data to optimize the ad video.

[1823] 5. Send the optimized ad video to the user's device for review and approval.

[1824] Prompt Sentence Examples

[1825] "Create a video to showcase your new smartphone, highlighting its high-resolution camera and long-lasting battery life. Use an upbeat tone to reflect customer expectations."

[1826] Thus, the present invention provides a user-friendly interface while utilizing sentiment analysis and database information to generate and optimize personalized advertising videos.

[1827] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1828] Step 1:

[1829] The user launches the smartphone app and inputs the information that makes up the advertising video. Specifically, the name and features of the new product are entered in text format. This input data is the basic scenario information for the advertising video. A specific example of input includes information such as "A new smartphone with a high-resolution camera and a long-life battery."

[1830] Step 2:

[1831] The device receives the configuration information and automatically activates the camera and microphone, capturing the user's facial expressions and voice. The captured video and audio data is stored unprocessed.

[1832] Step 3:

[1833] The device sends the saved configuration information and captured data, including text configuration information and video and audio files, to the server for further processing.

[1834] Step 4:

[1835] The server analyzes the received configuration information and captured data. First, it uses an emotion analysis engine to analyze the user's facial expressions and tone of voice to evaluate the user's emotional state. The emotion analysis engine uses algorithms such as OpenFace and DeepVoice. The analysis results in emotional data such as "expectation."

[1836] Step 5:

[1837] The server queries the generative AI model based on the analysis results and configuration information. The generative AI model uses the obtained data to generate an initial ad video. In this case, the generative AI model uses advanced algorithms such as OpenAI's DALL-E. The generated result is an initial version of the ad video file.

[1838] Step 6:

[1839] The server retrieves click-through rate data for past ad videos from the database. This data includes the click-through rate and characteristics of each ad video. The retrieved click-through rate data is fed to the generative AI model for retraining. The retrained generative AI model generates optimized ad videos based on the past data.

[1840] Step 7:

[1841] The server then sends the optimized ad video file to the user's device. The optimized video is adjusted to expect a higher click-through rate. The ad video is then sent from the server so that the user can view it.

[1842] Step 8:

[1843] The user terminal displays the received advertising video on the user interface and provides a function for the user to review and approve it. The user reviews the video and clicks the "Approve" button if there are no problems. If corrections are required, the user issues a "correction instruction."

[1844] Step 9:

[1845] The server receives the approval data from the user and uploads the approved ad video to a distribution platform, such as YouTube or LINE Voom. This completes the release of the ad video.

[1846] In this way, each processing step works in conjunction to realize a system that generates and optimizes personalized advertising videos based on user emotions.

[1847] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1848] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1849] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1850] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1851] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1852] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1853] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1854] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1855] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1856] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1857] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1858] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1859] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1860] 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.

[1861] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1862] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1863] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1864] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1865] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1866] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1867] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1868] The following is further disclosed regarding the above embodiment.

[1869] (Claim 1)

[1870] [Input means for inputting simple advertising video configuration information];

[1871] [Means for receiving configuration information input by the input means];

[1872] [a generating means for generating an advertising video based on the received configuration information];

[1873] [Method to obtain click-through rate data for past advertising videos]

[1874] [Means for relearning the generating means based on the acquired click rate data];

[1875] [Means for optimizing the ad video using the retrained generation means and generating the final ad video];

[1876] [Means for providing the generated final advertising video to a user];

[1877] [Means for verifying and approving the provided advertising video];

[1878] A system including: [means for uploading the approved advertising video to a distribution platform].

[1879] (Claim 2)

[1880] The system according to claim 1, further comprising: a means for inputting the simple advertising video configuration information in a sentence format.

[1881] (Claim 3)

[1882] The system according to claim 1, further comprising: a means for transmitting the generated final advertising video to a user terminal.

[1883] "Example 1"

[1884] (Claim 1)

[1885] [Input means for inputting simple advertising video configuration information];

[1886] [Means for receiving configuration information input by the input means];

[1887] [Means for analyzing the received configuration information and generating a framework for a video generation scenario];

[1888] [Means for generating an initial version of an advertising video using a video generation AI model based on the analysis results];

[1889] [Method to obtain click-through rate data for past advertising videos]

[1890] [Means for re-training the video generation AI model based on the acquired click rate data];

[1891] [Means of optimizing advertising videos using the retrained video generation AI model and generating the final advertising videos]

[1892] [Means for providing the generated final advertising video to a user];

[1893] [Means for verifying and approving the provided advertising video];

[1894] [Means for automatically uploading the approved advertising video to a distribution platform]

[1895] A system including:

[1896] (Claim 2)

[1897] The system according to claim 1, further comprising: [an input means for inputting the simple advertising video configuration information in a sentence format].

[1898] (Claim 3)

[1899] The system according to claim 1, further comprising: a means for transmitting the generated final advertising video to a user terminal.

[1900] "Application Example 1"

[1901] (Claim 1)

[1902] [Input means for inputting simple advertising video configuration information];

[1903] [Means for receiving configuration information input by the input means];

[1904] [a generating means for generating an advertising video based on the received configuration information];

[1905] [Method to obtain click-through rate data for past advertising videos]

[1906] [Means for relearning the generating means based on the acquired click rate data];

[1907] [Means for optimizing the ad video using the retrained generation means and generating the final ad video];

[1908] [Means for providing the generated final advertising video to a user];

[1909] [Means for verifying and approving the provided advertising video];

[1910] [Means for uploading the approved advertising video to a distribution platform];

[1911] [Means for running smartphone applications to create and effectively distribute advertising videos],

[1912] A system including a means for inputting product information into a smartphone form in the application and automatically generating a scenario and video.

[1913] (Claim 2)

[1914] The system according to claim 1, further comprising: a means for inputting the simple advertising video configuration information in a sentence format.

[1915] (Claim 3)

[1916] The system according to claim 1, further comprising: a means for transmitting the generated final advertising video to a user terminal.

[1917] "Example 2: Combining Emotion Engines"

[1918] (Claim 1)

[1919] [input means for inputting simple configuration information] means;

[1920] [Means for receiving configuration information input by the input means];

[1921] [Means for integrating and analyzing the configuration information and user emotion data] means;

[1922] [a generating means for generating an advertising video based on the analyzed information] means;

[1923] [Means for obtaining click-through rate data for past advertising videos]Means,

[1924] [Means for relearning the generating means based on the acquired click rate data] means;

[1925] [Means for optimizing the advertising video using the retrained generation means and generating a final advertising video];

[1926] [Means for providing the generated final advertising video to a user] means;

[1927] [Means for verifying and approving the provided advertising video] means;

[1928] A system including a means for uploading the approved advertising video to a distribution platform.

[1929] (Claim 2)

[1930] [Means for inputting the simple configuration information in a sentence format] The system according to claim 1.

[1931] (Claim 3)

[1932] The system according to claim 1, wherein the means for transmitting the generated final advertising video to a user terminal.

[1933] "Application example 2 when combining emotion engines"

[1934] (Claim 1)

[1935] [Input means for inputting simple advertising video configuration information];

[1936] [Means for receiving configuration information input by the input means];

[1937] [An emotion analysis method that analyzes the user's facial expressions and tone of voice to evaluate their emotional state],

[1938] [a generating means for generating an advertising video based on the received configuration information and analysis data from the emotion analysis means];

[1939] [Method to obtain click-through rate data for past advertising videos]

[1940] [Means for relearning the generating means based on the acquired click rate data];

[1941] [Means for optimizing the ad video using the retrained generation means and generating the final ad video];

[1942] [Means for providing the generated final advertising video to a user];

[1943] [Means for verifying and approving the provided advertising video];

[1944] A system including: [means for uploading the approved advertising video to a distribution platform].

[1945] (Claim 2)

[1946] The system according to claim 1, further comprising: a means for inputting the simple advertising video configuration information in a sentence format.

[1947] (Claim 3)

[1948] The system according to claim 1, further comprising: a means for transmitting the generated final advertising video to a user terminal. [Explanation of symbols]

[1949] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting simple advertising video configuration information; a means for receiving the configuration information input by the input means; a generating means for generating an advertising video based on the received configuration information; A means to obtain click-through rate data for past advertising videos, means for causing the generation means to re-learn based on the acquired click rate data; A means for optimizing the advertising video using the retrained generation means and generating a final advertising video; means for providing the generated final advertising video to a user; means for reviewing and approving the provided advertising video; The system includes a means for uploading the approved advertising video to a distribution platform.

2. 2. The system according to claim 1, further comprising means for inputting said simple advertising video composition information in a sentence format.

3. The system of claim 1 , further comprising: means for transmitting the generated final advertising video to a user terminal.

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