System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting and storing information relating to previously placed advertisements; means for analyzing the information relating to the advertisements and identifying highly effective features; means for receiving user input data and combining with the identified features to automatically generate a banner; means for displaying the automatically generated banner to a user and receiving a modification request; means for regenerating a banner based on the modification request; and means for providing the banner after final confirmation by the user.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] Modern advertising sales and advertising clients need to create effective creatives to maximize advertising effectiveness. However, gathering information and creating designs to create effective creatives requires a significant amount of time and effort. For this reason, there is a need to streamline the creative creation process while maximizing advertising effectiveness. There is also a need for a system that allows for the rapid revision and regeneration of effective advertising creatives and easy final confirmation. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. Specifically, it provides a system including means for collecting and saving information about advertisements that have been published in the past, means for analyzing the information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the requested revision, and means for providing the banner after final confirmation by the user. This enables advertising salespeople and clients to efficiently create highly effective banners.

[0006] "Information about advertising" refers to data such as click rates, conversion rates, number of impressions, and creative details of advertisements that have been published in the past.

[0007] "Means for collection and storage" refers to the hardware and software that enable the function of obtaining advertising data using the advertising platform's API and storing it in a database.

[0008] The "means of analysis and identifying highly effective features" refers to an algorithm and its execution environment that analyzes collected advertising data and extracts common features of banners with high click-through rates and conversion rates.

[0009] "Receiving user input data" refers to the act of collecting data on banner requirements (size, color, image, text, etc.) entered by the user through the chat interface.

[0010] The "means for automatically generating a banner in combination with the identified features" refers to software and algorithms that automatically create banners based on the analysis results and user input requirements.

[0011] The "means for displaying an automatically generated banner to a user and receiving requests for revisions" is a function for displaying a preview of the banner to a user and receiving user feedback and requests for revisions via a chat interface.

[0012] The "means for regenerating a banner based on a modification request" refers to software and its algorithm that reflects the modification request received from the user and regenerates the banner.

[0013] The "means for providing a banner after final confirmation by the user" is a function that allows the user to make a final confirmation of the revised banner and provides the approved banner in the form of a download link or the like. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] As an embodiment of the present invention, a system for collecting and analyzing advertising data and automatically generating optimal banners will be described. Below, the program processing of this system will be explained in natural language, and specific examples will be given.

[0036] System configuration

[0037] This system consists of three main components: a server, a terminal, and a user. The server mainly collects data, analyzes it, and generates banners. The terminal provides an interface for users, and users input requirements for creating banners.

[0038] Explanation of program processing

[0039] 1. Advertising Data Collection

[0040] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) for the past year using the advertising platform's API and stores it in a database.

[0041] 2. Data Analysis

[0042] The server analyzes the advertising data stored in the database and identifies the characteristics of banners with high click-through rates and conversion rates. Based on these identified characteristics, it extracts highly effective creative patterns.

[0043] 3. User requirements input

[0044] Through the chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time.

[0045] 4. Automatic banner generation

[0046] The server combines the user's input data with the results of the analysis described above to automatically generate the optimal banner, incorporating highly effective features identified from past data.

[0047] 5. Display banner preview

[0048] The generated banner is displayed as a preview on the user's device in real time, and the user can check the preview and submit correction requests if necessary.

[0049] 6. Modify and Regenerate

[0050] The server receives a modification request from the user, reflects the request, and re-previews the regenerated banner. This process is repeated until the user approves it.

[0051] 7. Final confirmation and banner submission

[0052] Once the user finally approves the banner, the server saves the banner in its final form and generates a download link that can be provided to the user to save the banner on their device.

[0053] Specific examples

[0054] 1. Advertising Data Collection

[0055] The server retrieves advertising data from the past year from the advertising platform's API and stores it in a database. For example, it collects data from a specific platform on "advertising with a click-through rate of 5% or more" or "advertising with a conversion rate of 10% or more."

[0056] 2. Data Analysis

[0057] The server analyzes the collected data using analytical tools such as Python and identifies the characteristics of highly effective banners, such as a blue background and 300x250 pixels.

[0058] 3. User requirements input

[0059] Users use a chat interface to input requests such as "banner size 300x250, background color blue, tagline simple."

[0060] 4. Automatic banner generation

[0061] The server creates an automatically generated banner by combining the user's requests with the characteristics of past highly effective banners.

[0062] 5. Display banner preview

[0063] The device will then display a preview of the generated banner, such as a banner with a product photo, a blue background, and a simple tagline.

[0064] 6. Modify and Regenerate

[0065] The user sends a request for correction to the preview via chat, such as "I want the catchphrase color to be changed to red."

[0066] The server reflects this modification request, regenerates the banner, and updates the preview.

[0067] 7. Final confirmation and banner submission

[0068] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link that is displayed on the device for the user to download the banner.

[0069] In this way, by using this system, it is possible to efficiently create highly effective banners and maximize the effectiveness of advertising.

[0070] The processing flow will be explained below.

[0071] Step 1: Collect advertising data

[0072] The server periodically calls the advertising platform's API to collect data on ads that have been displayed over the past year (e.g., click-through rate, conversion rate, number of impressions, creative details, etc.).

[0073] The server stores the collected data in a database and sets the appropriate fields (e.g., banner size, color, text content, etc.).

[0074] Step 2: Analyze the data

[0075] The server analyzes the advertising data stored in the database and extracts the characteristics of banners with high click-through rates and conversion rates, for example, using a Python analysis library.

[0076] Based on the analysis results, the server identifies common characteristics of effective banners (e.g., specific size, color, placement, etc.).

[0077] Step 3: Enter user requirements

[0078] Users input their banner requirements (size, color, image, text, etc.) through a chat interface.

[0079] The terminal transmits input data from the user to the server in real time.

[0080] Step 4: Auto-generate a banner

[0081] The server combines the user's requirements with the results of the analysis described above and automatically generates an optimal banner, for example, by using a specific template engine to create the banner layout.

[0082] The server converts the generated banner into an image format and saves it.

[0083] Step 5: View the banner preview

[0084] The terminal displays the generated banner preview sent from the server to the user.

[0085] The user checks the displayed preview and sends any correction requests through the chat interface.

[0086] Step 6: Fix and Regenerate

[0087] The server receives the modification request from the user and regenerates a new banner, reflecting the modifications, such as changing the color of the tagline to red or changing the image.

[0088] The server sends the modified banner again to the terminal as a preview.

[0089] Step 7: Final confirmation and banner submission

[0090] The user then finalizes and approves the revised banner preview.

[0091] The server saves the approved banner as the final version and generates a download link.

[0092] The terminal displays the generated download link, allowing the user to download the banner.

[0093] The above steps complete a series of processes, from collecting advertising data to creating and providing optimal banners.

[0094] Example 1

[0095] 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."

[0096] Conventional methods for creating advertising banners often require manual processes, from collecting and analyzing advertising data, generating banners, incorporating user requests for modifications, and finally providing them, resulting in issues of inefficiency and time. Furthermore, there are difficulties in reflecting the specific requirements desired by users, which creates a risk of reducing the effectiveness of the banner. This has led to a demand for a means to quickly and efficiently create optimal banners to maximize advertising effectiveness.

[0097] 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.

[0098] In this invention, the server includes means for collecting and storing information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the requested revision, means for providing the banner after final confirmation by the user, means for automatically generating a banner using a generative AI model, means for collecting user input data through a chat interface, means for displaying a preview of the automatically generated banner on the user's device, means for transmitting user revision requests for the preview to the server in real time, and means for regenerating the banner reflecting the requested revision and displaying the preview again on the user's device. This automates the process from collecting and analyzing advertising data, optimally automatically generating a banner, revising and regenerating it based on user requirements, and finally providing it, enabling efficient and rapid creation of highly effective banners.

[0099] "Information about the advertisement" refers to detailed data about the performance and content of the advertisement, such as click-through rates, conversion rates, impressions, and creative details.

[0100] "Collection means" refers to the means that have the function of obtaining data using the advertising platform's API and storing it in a database.

[0101] "Analysis means" refers to a means that has the function of analyzing collected data using an analysis tool and identifying highly effective features of advertising.

[0102] "Automatic generation means" refers to a means that has the function of generating the optimal banner using a generative AI model based on user input data and analysis results.

[0103] "Display means" refers to a means having a function for previewing an automatically generated banner on a user's terminal.

[0104] The "modification request receiving means" refers to a means having a function of receiving a modification request from a user in real time.

[0105] The "regeneration means" refers to a means having a function of regenerating a banner based on a user's correction request and displaying a preview again.

[0106] The "means for providing after final confirmation" refers to a means having a function of saving the banner that the user finally approves, generating a download link, and providing it to the user.

[0107] A "generative AI model" refers to an artificial intelligence model that generates images based on prompts.

[0108] "Chat Interface" means an interactive interface through which a user can input banner requirements and communicate with a server in real time.

[0109] "Preview" refers to a temporary display of an automatically generated banner that allows a user to review the content and make correction requests.

[0110] A "terminal" is a device that allows a user to access the system and has the function of displaying a preview and receiving user input.

[0111] As an embodiment of the present invention, a system for collecting and analyzing advertising data and automatically generating optimal banners will be described in detail. This system is composed of three main elements: a server, a terminal, and a user.

[0112] System configuration

[0113] This system consists of a server that collects and analyzes advertising data and generates banners, a terminal that provides an interface with users, and a user that inputs requirements for creating banners.

[0114] Advertising data collection

[0115] The server periodically collects advertising data from the past year using the advertising platform's API and stores it in a database. The collected data includes click-through rates, conversion rates, number of impressions, and detailed creative information. For example, data on "advertisements with a click-through rate of 5% or more" and "advertisements with a conversion rate of 10% or more" is collected from a specific platform.

[0116] Data analysis

[0117] The server analyzes the collected data using analytical tools such as Python's Pandas and SciPy to identify the characteristics of banners with high click-through rates and conversion rates. Through this analysis process, highly effective creative patterns such as "blue background" and "300x250 pixels" are extracted.

[0118] User requirements input

[0119] Through a chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time. For example, they can make specific requests such as "banner size 300x250, background color blue, and tagline simple."

[0120] Automatic banner generation

[0121] The server combines the user's input data with the results of the analysis described above to automatically generate the optimal banner. A generative AI model (e.g., DALL-E or GAN model) is used to create a banner that reflects the characteristics of highly effective creative. At this time, prompts are sent to the generative AI model based on the user's requirements. Possible prompts include "banner size 300x250, background color blue, and catchy copy simple."

[0122] View banner preview

[0123] The device displays a preview of the generated banner to the user in real time. The user can check this preview and send correction requests via chat if necessary. For example, if the generated banner has a product photo, a blue background, and a simple tagline, the user can request that the tagline color be changed to red.

[0124] Fix and Regenerate

[0125] The server receives the user's modification request and regenerates the banner to reflect the changes. This process is repeated until the user is satisfied. The regenerated banner is then displayed again as a preview on the user's device.

[0126] Final confirmation and banner provision

[0127] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link that is displayed on the device, allowing the user to download the final version of the banner.

[0128] In this way, by using this system, it is possible to efficiently create highly effective banners and maximize the effectiveness of advertising.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1: Collect advertising data

[0131] The server uses the advertising platform's API to periodically collect advertising data from the past year and store it in a database. The input is data obtained from the advertising platform (click-through rate, conversion rate, number of impressions, and creative details), and the output is a database in which the collected data is stored. Specifically, the server accesses the advertising platform's API at a set time every day, establishes a connection to the database, and stores the data.

[0132] Step 2: Analyze the data

[0133] The server analyzes the advertising data stored in the database using analysis tools such as Python's Pandas and SciPy. The input is the advertising data read from the database, and the output is the analysis results, i.e., the characteristics of highly effective banners (e.g., blue background, 300x250 pixels, etc.). Specifically, the server periodically runs analysis scripts, filters data whose click-through rate or conversion rate exceeds a certain threshold, and performs cluster analysis to extract commonalities among highly effective banners.

[0134] Step 3: Enter user requirements

[0135] The user uses the chat interface to input the requirements for the banner (size, color, image, text, etc.). The input is the banner requirements entered by the user in the chat, and the output is the formatted requirement data sent to the server. Specifically, the user enters instructions to the chatbot on the web browser, such as "banner size 300x250, background color blue, catchy copy simple," and the chatbot confirms the instructions and sends them to the server.

[0136] Step 4: Auto-generate a banner

[0137] The server combines the user's input data with the aforementioned analysis results and automatically generates the optimal banner using a generative AI model. The input is the user's requirement data and the analysis results, and the output is the generated banner image. Specifically, the server sends a prompt to the generative AI model (e.g., DALL-E or GAN model), and the model generates an image based on the specified requirements and saves it in a temporary folder.

[0138] Step 5: View the banner preview

[0139] The terminal displays the generated banner to the user as a preview in real time. The input is the generated banner image, and the output is a preview screen that the user can view. Specifically, the terminal uses technologies such as JavaScript to display the image file, and the preview screen displays an "Approve" button and a "Request correction" button.

[0140] Step 6: Fix and Regenerate

[0141] The user issues a request for correction to the preview. For example, they send a specific instruction via chat, such as "I want the catchphrase color to be changed to red." The input is the user's request for correction, and the output is a new banner image that reflects the correction. Specifically, when the server receives the request for correction, it again sends a prompt to the generation AI model, which generates a new banner. Once the new banner is generated, it is again displayed as a preview on the device.

[0142] Step 7: Final confirmation and banner submission

[0143] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link. The input is the banner image approved by the user, and the output is the download link. Specifically, the server saves the final version of the banner in a database or file system and provides the generated download link through the user's chat interface. The user clicks the provided link to download the final version of the banner.

[0144] (Application example 1)

[0145] 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."

[0146] Conventional banner ad generation systems require a lot of time and effort to create effective banners. In particular, the process of users inputting banner requirements and then reviewing and modifying the generated banner is cumbersome, making it difficult to efficiently create highly effective banners. Furthermore, the lack of a convenient banner generation process that can be used on smartphones makes it difficult for advertisers to easily and quickly generate banners.

[0147] 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.

[0148] In this invention, the server includes means for collecting and saving information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for modification, means for regenerating the banner based on the requested modification, means for providing the banner after final confirmation by the user, means for inputting requirements for banner generation using a smartphone and previewing the generated banner in real time, and means for providing an interactive interface for the user to input modifications to the previewed banner. This enables a user to efficiently and quickly generate highly effective banners using a smartphone and make modifications in real time.

[0149] "Information about previously placed advertisements" refers to data including click-through rates, conversion rates, number of impressions, and creative details of advertisements previously placed on advertising platforms and websites.

[0150] "Analyzing information about advertisements" refers to a process of analyzing collected advertisement data and identifying the characteristics of banners with high click rates and conversion rates.

[0151] "Highly effective features" refer to elements such as color, size, text, and images that are common to banners that have higher click-through rates and conversion rates than other ads.

[0152] "User input data" refers to information including user requirements for creating a banner advertisement (eg, size, color, text content, etc.).

[0153] "Means for automatically generating banners" refers to a system that combines collected and analyzed data with user input data to automatically create banner advertisements using a program.

[0154] The "means for displaying to the user and receiving a request for modification" is an interface for displaying the automatically generated banner advertisement on the user's device and receiving a request for modification from the user.

[0155] The "means for regenerating a banner" is a mechanism for recreating an already generated banner advertisement based on a user's request for modification.

[0156] "Means for providing a banner after final confirmation by the user" refers to a mechanism in which, after the user gives final approval, the banner advertisement is saved as a final version and provided to the user in the form of a download link or the like.

[0157] "Inputting requirements for banner generation using a smartphone" refers to a user inputting requirements such as size, color, and text content of a banner ad using a smartphone.

[0158] The "means for displaying a preview of the generated banner in real time" refers to a display device or software that allows the user to instantly check the automatically generated banner advertisement.

[0159] The "interactive interface" is an interface that allows a user to input correction requests and send feedback on a generated banner preview using a device such as a smartphone.

[0160] As an embodiment of the present invention, a system consisting of three main elements: a server, a terminal, and a user will be described. Below, the program processing of this system will be explained in natural language, with examples and prompt sentences included.

[0161] System Configuration

[0162] The system mainly includes the following elements:

[0163] 1. Server: This is responsible for collecting, storing, and analyzing data, and creating automatically generated banner ads. Specifically, it consists of a server using Python and Flask, and an SQLite database for storing advertising data.

[0164] 2. Device: Provides the user interface for inputting requirements, previewing banners, and sending correction requests. Smartphones are assumed to be the primary device.

[0165] 3. User: Enters requirements for banner generation, reviews generated banners, requests corrections, and final approval.

[0166] Program processing

[0167] The server uses an API to collect information about past ads and stores it in an SQLite database. This data includes click-through rates, conversion rates, number of impressions, and creative details. A Python analysis tool analyzes the collected data and identifies the characteristics of highly effective banners.

[0168] Users input the requirements for banner generation (size, background color, catchy copy, etc.) using their smartphones, and this data is sent to the server in real time. The server combines the received requirements with the characteristics of highly effective banners and uses an automatic generation algorithm to generate the optimal banner.

[0169] The generated banner is instantly displayed as a preview screen on the device (smartphone), allowing the user to check its contents. The user can then input further correction requests for the preview, which are also sent to the server in real time. The server receives the correction requests, regenerates the banner, and displays the updated preview on the device. This process can be repeated until the user is satisfied.

[0170] Specific examples

[0171] For example, if a user inputs "banner size 300x250, background color blue, catchphrase 'Quick & Easy'", the server will automatically generate a banner based on this and display it on the smartphone. The user can check this preview and input a request to modify the banner, such as "change the color of the catchphrase to red". The server can then generate a new banner that reflects this modification and display it again.

[0172] Prompt Sentence Examples

[0173] Enter the prompt for the generative AI model as follows:

[0174] Generate your banner ad based on the following requirements:

[0175] Size:300x250

[0176] Background color: blue

[0177] Catchphrase: 'Quick & Easy'

[0178] In this way, the present invention allows users to efficiently generate highly effective banners using their smartphones and make real-time modifications.

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1:

[0181] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) from the past year using the API. It receives the advertising data obtained from the API as input, processes it by storing it in an SQLite database, and outputs the stored data.

[0182] Step 2:

[0183] The server analyzes the advertising data stored in the SQLite database. It takes the advertising data stored as input, performs data calculations using Python analysis tools to identify the characteristics of banners with high click-through rates and conversion rates, and outputs the characteristics of highly effective banners.

[0184] Step 3:

[0185] The user inputs the requirements for the banner (size, background color, catchy copy, etc.) using a smartphone. The requirements specified by the user are entered through the smartphone interface, and this requirement data is sent to the server.

[0186] Step 4:

[0187] The server combines user input data with the characteristics of highly effective banners to automatically generate optimal banners. It receives user requirement data and analyzed highly effective banner characteristics as input, performs data calculations to generate banners using an automatic generation algorithm based on this, and outputs the generated banner data.

[0188] Step 5:

[0189] The terminal displays a preview of the generated banner data sent from the server in real time, passes the generated banner data received from the server as input to the display device, and outputs a preview screen that allows the user to visually check the generated banner.

[0190] Step 6:

[0191] The user inputs a correction request for the previewed banner. The correction request is input as specified by the user through the interactive interface of the smartphone, and this correction request data is sent to the server.

[0192] Step 7:

[0193] The server regenerates the banner based on the user's revision request. It receives the user's revision request data and the original banner data as input, performs data calculations to regenerate the banner that reflects the revision request using the automatic generation algorithm, and outputs the new banner data.

[0194] Step 8:

[0195] The terminal again previews the regenerated banner data, passes the new banner data sent from the server as input to the display device, and outputs a preview screen that allows the user to visually confirm the modified banner.

[0196] Step 9:

[0197] The user finally approves the banner, and inputs an operation to confirm the approved banner, which is then sent to the server.

[0198] Step 10:

[0199] The server stores the final banner data approved by the user and generates a download link. It receives the user's approval data as input, processes the data by storing the final banner in a database, and generates a download link, which it then outputs to the device.

[0200] Step 11:

[0201] The terminal displays the generated download link, passes the download link received from the server as input to the display device, and visually displays a link to download the final banner to the user.

[0202] 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.

[0203] As an embodiment of the present invention, we will explain a system that automatically generates banners by collecting and analyzing advertising data and combining it with an emotion engine that recognizes user emotions. Below, we will explain the program processing of this system in natural language, and also provide concrete examples.

[0204] System configuration

[0205] This system consists of three main components: a server, a terminal, and a user. The server mainly collects data, analyzes it, recognizes emotions, and generates banners. The terminal provides an interface with the user, allowing the user to input requirements for banner creation and provide the data necessary for emotion recognition.

[0206] Explanation of program processing

[0207] 1. Advertising Data Collection

[0208] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) from the past year using the advertising platform's API and stores it in a database.

[0209] 2. Data Analysis

[0210] The server analyzes the advertising data stored in the database and identifies the characteristics of highly effective banners. The analysis results include common characteristics of banners with high click-through rates and conversion rates.

[0211] 3. User requirements input

[0212] Through a chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time.

[0213] 4. User Emotion Recognition

[0214] The server uses an emotion engine to analyze the user's emotions, using text and voice data entered by the user in the chat.

[0215] The emotion engine determines the user's emotional state (e.g., joy, surprise, sadness, anger, etc.) and adjusts the banner design and copy accordingly.

[0216] 5. Automatic banner generation

[0217] The server combines the user's requirements, analysis results, and emotion recognition results to automatically generate the optimal banner. For example, if the emotion engine determines that the user's emotional state is "joy," it will use bright colors and a positive catchy copy.

[0218] 6. Display banner preview

[0219] The terminal displays the generated banner as a preview to the user, who then checks the preview and sends a request for correction if necessary.

[0220] 7. Modify and Regenerate

[0221] The server receives a modification request from the user and regenerates a new banner, reflecting the user's request, for example, by changing the color of the tagline.

[0222] The revised banner will be displayed again as a preview on the device.

[0223] 8. Final confirmation and banner submission

[0224] The user finally checks and approves the banner preview.

[0225] The server saves the approved banner in its final form and generates a download link that is displayed on the device, allowing the user to download the banner.

[0226] Specific examples

[0227] 1. Advertising Data Collection

[0228] The server collects advertising data from a specific advertising platform over the past year (e.g., ads with a click-through rate of 5% or more and a conversion rate of 10% or more) via API and stores it in a database.

[0229] 2. Data Analysis

[0230] The server analyzes the stored advertising data using a Python analysis library to identify the characteristics of effective banners, such as a blue background and a size of 300x250 pixels.

[0231] 3. User requirements input

[0232] Through a chat interface, users input information such as "banner size 300x250, background color blue, catchy slogan simple."

[0233] 4. User Emotion Recognition

[0234] The server analyzes the text and voice data entered by the user using an emotion engine and determines that the user is happy.

[0235] The server takes into account your emotional state and suggests banners with positive slogans and upbeat designs.

[0236] 5. Automatic banner generation

[0237] The server runs a banner generation algorithm based on the user's requirements and emotion recognition results to automatically generate the optimal banner.

[0238] 6. Display banner preview

[0239] The device displays a preview of the generated banner, showing the user a banner with a "product photo, a blue background, and a simple, positive tagline."

[0240] 7. Modify and Regenerate

[0241] The user sends a correction request saying, "I want the color of the catchphrase to be changed to red."

[0242] The server reflects this modification request, generates a new banner, and displays the preview again.

[0243] 8. Final confirmation and banner submission

[0244] The user finally checks the preview and clicks the "Approve" button.

[0245] The server saves the final banner and generates a download link that is displayed on the device for the user to download the banner.

[0246] In this way, by using this system, it is possible to efficiently create highly effective banners and provide optimal advertisements that match the user's emotions.

[0247] The processing flow will be explained below.

[0248] Step 1: Collect advertising data

[0249] The server periodically calls the advertising platform's API to collect data on ads that have been displayed over the past year (e.g., click-through rate, conversion rate, number of impressions, creative details, etc.).

[0250] The server stores the collected data in a database and sets the appropriate fields (e.g., banner size, color, text content, etc.).

[0251] Step 2: Analyze the data

[0252] The server analyzes the advertising data stored in the database and uses a Python analysis library to extract the characteristics of banners with high click-through rates and conversion rates.

[0253] Based on the analysis results, the server identifies common characteristics of effective banners (e.g., specific size, color, placement, etc.).

[0254] Step 3: Enter user requirements

[0255] Users input their banner requirements (size, color, image, text, etc.) through a chat interface.

[0256] The terminal transmits the input user data to the server in real time.

[0257] Step 4: Recognizing user emotions

[0258] The server receives text and voice data for analyzing the user's emotions using an emotion engine.

[0259] The emotion engine analyzes input text and voice data to determine the user's emotional state (e.g., joy, surprise, sadness, anger, etc.).

[0260] The server adjusts the banner design and copy taking into account the emotional state identified by the emotion engine.

[0261] Step 5: Auto-generating a banner

[0262] The server combines the requirements entered by the user, the analysis results, and the emotion recognition results to automatically generate the optimal banner.

[0263] For example, if the emotion engine determines that the user's emotion is "joy," it will generate a banner with bright colors and positive copy.

[0264] The server converts the generated banner into an image format and saves it.

[0265] Step 6: View the banner preview

[0266] The terminal displays the generated banner preview to the user.

[0267] The user checks the displayed preview and, if necessary, sends a request for corrections through the chat interface.

[0268] Step 7: Fix and Regenerate

[0269] The server receives a modification request from the user and regenerates a new banner.

[0270] For example, the banner is adjusted in response to a user's request for modification, such as changing the color of the catchphrase to red.

[0271] The server transmits the regenerated banner to the terminal again as a preview.

[0272] Step 8: Final confirmation and banner submission

[0273] The user reviews and approves the final banner preview.

[0274] The server saves the approved banner as the final version and generates a download link.

[0275] The terminal displays the generated download link, allowing the user to download the banner.

[0276] The above steps complete a series of processes, from collecting advertising data to creating and providing optimal banners. By combining this with an emotion recognition engine, it is possible to provide optimal advertising creatives that match the user's emotions.

[0277] Example 2

[0278] 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."

[0279] Conventional banner ad generation systems lack sufficient analysis to maximize advertising effectiveness, making it difficult to provide banner designs that reflect the user's emotional state. As a result, advertising effectiveness is reduced and users are unable to fully respond.

[0280] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and saving information about advertisements that have been posted in the past, means for analyzing information about the advertisements and identifying highly effective features, means for recognizing user input data and emotions, means for automatically generating a banner by combining the identified features with the user's emotional information, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the request for revision, and means for providing the banner after final confirmation by the user. This makes it possible to maximize advertising effectiveness and efficiently generate banners with designs that reflect the user's emotional state.

[0281] "Advertising information" refers to data related to marketing materials that users view and interact with, including click rates, conversion rates, impressions, creative details, and the like.

[0282] "Analysis" is the process of analyzing data and extracting useful information or features from it, and in the case of advertising data in particular, it refers to evaluating performance metrics such as click-through rates and conversion rates.

[0283] "Means for recognizing emotions" refers to technology that analyzes user input data and voice data to determine the user's emotional state (for example, joy, surprise, sadness, anger, etc.).

[0284] A "banner" is an advertising image or animation that appears on a web page and contains a specific marketing message or visual element.

[0285] "Automatic generation means" refers to the process of using programs and algorithms to automatically create advertising banners based on specific input data.

[0286] A "request for modification" is a request for modification made by a user to a generated banner, including partial modifications to the design or text.

[0287] The "regeneration means" is a process for regenerating a banner based on a user's modification request.

[0288] "Final confirmation" is the act of the user checking and approving the final version of the banner.

[0289] The "means of providing" refers to the technology by which the final approved banner is provided to the user and made available for download and use.

[0290] This invention relates to a system that automatically generates banners by collecting advertising data, analyzing it, and combining it with an emotion engine that recognizes user emotions. This system consists of three main elements: a server, a terminal, and a user.

[0291] System configuration

[0292] server

[0293] The server is the heart of the system and performs the following main functions:

[0294] 1. Data collection: The server uses the advertising platform's API to collect advertising data (click-through rate, conversion rate, number of impressions, creative details) from the past year and saves it in a database.

[0295] 2. Data analysis: Analyze the data using Python analysis libraries (e.g., pandas, numpy, scikit-learn) to identify the characteristics of effective banners.

[0296] 3. Emotion recognition: Analyze user input data and voice data using an emotion engine (e.g., Google Cloud Natural Language API, IBM Watson) to determine the user's emotional state.

[0297] 4. Automatic banner generation: By combining user requirements and emotion recognition results, banners are automatically generated using a generative AI model (e.g., GAN, VQ-VAE).

[0298] 5. Banner regeneration: Receives correction requests from users and regenerates banners that reflect the content.

[0299] 6. Banner submission: Save the final approved banner and generate a download link.

[0300] Terminal

[0301] The terminal is responsible for providing the interface with the user:

[0302] 1. Input interface: A chat interface (e.g., a React-based chatbot) for users to input their banner requirements.

[0303] 2. Preview: The generated banner is displayed to the user as a preview and has the function of receiving correction requests.

[0304] 3. Download: Provides a link to download the final version of the banner.

[0305] User

[0306] Users have the following roles:

[0307] 1. Input requirements: Input requirements such as banner size, color, image, text, etc. through the chat interface.

[0308] 2. Providing emotional data: Provide input text and voice data and have the server analyze your emotional state.

[0309] 3. Preview: Check the preview of the generated banner and make any necessary corrections.

[0310] 4. Final Review: Review and approve the final banner.

[0311] Specific examples

[0312] Here's a concrete example of how the system works:

[0313] 1. Collecting advertising data: The server collects advertising data with a click rate of 5% or more and a conversion rate of 10% or more over the past year through the API of a specific advertising platform and stores it in a database.

[0314] 2. Data Analysis: The server analyzes the stored advertising data and identifies the characteristics of effective banners, such as a blue background and a size of 300x250 pixels.

[0315] 3. User requirements input: The user inputs the following through the chat interface: "Banner size 300x250, background color blue, catchy copy simple."

[0316] 4. User emotion recognition: The server analyzes the text and voice data entered by the user using an emotion engine and determines that the user is happy.

[0317] 5. Automatic banner generation: The server runs a banner generation algorithm based on the user requirements and emotion recognition results to automatically generate the optimal banner.

[0318] 6. Display banner preview: The device displays a preview of the generated banner, showing the user a banner with a product photo, a blue background, and a simple, positive tagline.

[0319] 7. Modify and regenerate: The user sends a modification request, such as "I want the tagline color to be changed to red." The server reflects this modification request, generates a new banner, and displays the preview again.

[0320] 8. Final confirmation and banner delivery: The user finally checks the preview and clicks the "Approve" button. The server saves the final version of the banner and generates a download link. This link is displayed on the device, and the user can download the banner.

[0321] Prompt Sentence Examples

[0322] The banner size should be 300x250, with a blue background. The tagline should be simple. Also, please analyze the sentiment of the text entered by the user and suggest a design that matches that sentiment.

[0323] This system makes it possible to maximize advertising effectiveness and efficiently generate optimal banners that reflect the user's emotional state.

[0324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0325] Step 1: Collect advertising data

[0326] The server accesses the advertising platform's API and periodically collects advertising data from the past year. This data includes click-through rates, conversion rates, number of impressions, and creative details. The collected data is sent to the server in JSON format and stored in a database. The input is the raw data obtained from the advertising platform's API, and the output is the advertising data stored in the database.

[0327] Step 2: Analyze the data

[0328] The server uses Python analysis libraries (e.g., pandas, numpy, scikit-learn) to analyze the advertising data stored in the database. The analysis script is run periodically to identify the characteristics of highly effective banners. Specifically, it extracts common characteristics (e.g., background color, font style, image placement) of banners with high click-through rates and conversion rates. The input is the advertising data stored in the database, and the output is the identified characteristics of highly effective banners.

[0329] Step 3: Enter user requirements

[0330] Users input their banner requirements through a chat interface on their web browser. For example, they can enter specific requests such as "I want the banner size to be 300x250 pixels, the background color to be blue, and the tagline to be simple." The input information is sent to the server in real time and recorded in a database. The input is the banner requirements provided by the user through the chat interface, and the output is the input data stored in the database.

[0331] Step 4: Recognizing user emotions

[0332] The server sends the text and voice data entered by the user to an emotion recognition engine (e.g., Google Cloud Natural Language API, IBM Watson) to analyze the user's emotions. The emotion recognition engine uses text analysis technology to determine the emotion the user is feeling (e.g., joy, surprise, sadness, anger). The analysis results are stored in a database and reflected in the banner design. The input is the text and voice data entered by the user, and the output is the recognized user's emotional information.

[0333] Step 5: Auto-generating a banner

[0334] The server combines the user's requirements, the analyzed features of effective banners, and emotion recognition results to automatically generate a banner using a generative AI model (e.g., GAN, VQ-VAE). This algorithm creates an optimal banner that matches the input conditions. Specifically, if the user's requirements are "joy," a banner size of 300x250 pixels, and a blue background color, a corresponding bright banner design will be generated. The input is the user's requirements, the features of effective banners, and the emotion recognition results, and the output is the generated banner image.

[0335] Step 6: View the banner preview

[0336] The terminal receives the banner image sent from the server and displays it as a preview to the user. Because it is displayed in real time on the web browser, the user can immediately check the banner. For example, a banner with a "product photo, blue background, and simple, positive tagline" is presented to the user. The input is the generated banner image, and the output is the preview screen that the user checks.

[0337] Step 7: Fix and Regenerate

[0338] The user views the preview and sends a request for revisions, such as "change the color of the catchphrase to red," through the chat interface. The server receives this request and runs the generative AI model again to generate a new banner. The revised banner is then sent back to the device and displayed as a preview to the user. The input is the user's request for revisions, and the output is the revised new banner image.

[0339] Step 8: Final confirmation and banner submission

[0340] The user checks the final preview and clicks the "Approve" button to finalize the banner. The server saves the final banner and generates a download link. This link is displayed on the device, and the user can click it to download the banner. The input is the approval result of the final preview, and the output is the final banner and its download link.

[0341] In this way, by combining advertising data analysis and user emotion recognition, this system can efficiently generate highly effective banners and provide them to users.

[0342] (Application example 2)

[0343] 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."

[0344] While conventional banner generation systems provide a means for generating highly effective banners based on the analysis of advertising data, they lack the ability to adjust banners in response to user emotions and real-time requests. This hinders users' ability to create more effective and emotionally appealing banner ads. Furthermore, the lack of a means for flexibly collecting user requirements via voice or text and generating and adjusting banners based on those requirements limits the user experience.

[0345] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0346] In this invention, the server includes means for collecting and saving information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for analyzing the user's emotions and adjusting the banner design and catchy copy based on the user's emotional state, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the request for revision, and means for providing the banner after final confirmation by the user. This makes it possible to automatically generate, display, modify, and finally provide banners flexibly according to the user's emotional state and specific requirements.

[0347] "Advertising Information" refers to data regarding advertisements that have been previously published, including click-through rates, conversion rates, number of impressions, and creative details.

[0348] "Analysis" refers to the process of analyzing collected data using statistical methods and algorithms to identify highly effective features.

[0349] A "banner" refers to an advertising image or text that appears on a digital medium such as a web page or application.

[0350] "User-input data" refers to various requirements and information provided by the user to the system, including banner size, color, image, text, etc.

[0351] "Emotion analysis" is a technology that recognizes a user's emotional state (joy, surprise, sadness, anger, etc.) based on their voice and text data.

[0352] "Automatic banner generation" is the process by which the system algorithmically generates appropriate banner ads based on collected data and user requirements.

[0353] A "request for modification" refers to a user viewing a banner preview and requesting the system to make changes to the design, text, etc.

[0354] "Regeneration" is the process by which the system regenerates the banner based on the user's modification requests.

[0355] "Final confirmation" refers to the process in which the generated banner is presented to the user for final approval.

[0356] "Storage" refers to recording the generated banners and analysis data in a database or storage so that they can be accessed later.

[0357] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server mainly collects and analyzes advertising data, recognizes emotions, and automatically generates and modifies banners. The terminal provides an interface with the user, allowing the user to input requirements for banner creation and provide the data necessary for emotion recognition.

[0358] The server uses the advertising platform's API to collect and store information about previously displayed ads, including click-through rates, conversion rates, number of impressions, creative details, etc. The collected information is stored in a database and used for analysis.

[0359] The server analyzes the collected advertising information using statistical methods and algorithms to identify the characteristics of effective banners. This analysis reveals which advertising elements are most effective, such as specific colors, sizes, and text styles.

[0360] Users input their banner requirements through the device's operating interface, using voice and text interfaces, such as "banner size 300x250, background color blue, catchy copy simple."

[0361] The server uses an emotion engine to analyze the user's emotions. The emotion analysis uses the user's voice and text data to determine their emotional state (happiness, surprise, sadness, anger, etc.). Based on the results of this emotion analysis, the banner design and catchy copy are adjusted.

[0362] For example, if the emotion engine determines that the user's emotional state is "joy," a banner using bright colors and a positive catchphrase is automatically generated. This banner is displayed as a preview to the user via their device. The user can check the preview and, if necessary, send a request for corrections by voice or text, such as "I want the catchphrase color to be red."

[0363] The server receives the user's request for corrections and regenerates the banner based on the request. This regenerated banner is then displayed to the user as a preview. Finally, when the user checks the preview and approves it, the server saves the final version of the banner and generates a download link. This link is displayed on the device, allowing the user to download the banner.

[0364] A concrete example of this system is its application using smart glasses. The smart glasses can display banner ads in the user's field of vision in real time and adjust the ad content through emotion recognition. When the user verbally commands, "Show me recommended banners," the system performs emotion analysis and displays banners and designs with positive messages in real time. For example, if the user verbally requests, "Change the catchphrase," the banner will be regenerated and displayed in the user's field of vision.

[0365] Example prompt sentence:

[0366] User: "Show me the recommended banner."

[0367] Program: "We're happy to show you a banner with a positive message."

[0368] User: "Change the tagline."

[0369] Program: "Change the tagline and preview again."

[0370] This system allows for the automatic generation of banners that can be flexibly adapted to the user's emotional state and specific requirements, and then displayed, modified, and finally provided.

[0371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0372] Step 1:

[0373] The server uses the advertising platform's API to collect and store information about previously displayed ads (click-through rate, conversion rate, number of impressions, creative details, etc.) The server makes an API request and stores the obtained data in a database, where this information is accumulated and used for later analysis.

[0374] Input: Advertising data obtained from advertising platforms

[0375] Output: Advertisement information stored in the database

[0376] Step 2:

[0377] The server analyzes the advertising information stored in the database using statistical methods and algorithms to identify highly effective features. Specifically, it uses a Python analysis library to extract common features of ads that meet certain criteria, such as a click-through rate of 5% or more and a conversion rate of 10% or more.

[0378] Input: Advertisement information stored in the database

[0379] Output: High-impact banner features (e.g. color, size, text style, etc.)

[0380] Step 3:

[0381] Users can input banner requirements using their device's voice assistant or text interface. For example, if a user requests a banner size of 300x250, a blue background, and a simple tagline, these requirements are sent to the server.

[0382] Input: User-entered data (voice or text)

[0383] Output: User's banner requirements are sent to the server

[0384] Step 4:

[0385] The server uses an emotion engine to analyze the user's emotions. The emotion engine determines the user's emotional state (happiness, surprise, sadness, anger, etc.) based on the user's voice data and text data. For example, if the user enters "I'm feeling happy today," the emotion engine will determine that the emotion is "happiness."

[0386] Input: User voice and text data

[0387] Output: User's emotional state

[0388] Step 5:

[0389] The server automatically generates a banner by combining the user's requirements, the characteristics of effective banners, and the results of sentiment analysis. For example, if the sentiment analysis result is "joy," a banner with bright colors and a positive catchphrase will be generated. The generated banner is temporarily stored on the server.

[0390] Input: User banner requirements, characteristics of effective banners, user emotional state

[0391] Output: Auto-generated banner

[0392] Step 6:

[0393] The device displays the generated banner as a preview to the user. The user checks the preview and sends a request for corrections, such as "I want the catchphrase color to be changed to red," to the device via voice or text. The request data is then sent to the server.

[0394] Input: Auto-generated banner, user request for correction

[0395] Output: The modification request is sent to the server

[0396] Step 7:

[0397] The server regenerates the banner based on the user's request for modification. For example, if the user requests that the color of the catchphrase be changed to red, the server regenerates the banner reflecting the request and temporarily saves it again.

[0398] Input: User's correction request

[0399] Output: Regenerated banner

[0400] Step 8:

[0401] The device will then preview the regenerated banner and display it to the user. If the user confirms the final version and approves it, the server will save the final version of the banner and generate a download link. This link will be displayed on the device, allowing the user to download the banner.

[0402] Input: Regenerated banner, final user confirmation

[0403] Output: A download link will be displayed on the terminal.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Second embodiment]

[0408] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] In the smart glasses 214, the 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.

[0419] 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."

[0420] As an embodiment of the present invention, a system for collecting and analyzing advertising data and automatically generating optimal banners will be described. Below, the program processing of this system will be explained in natural language, and specific examples will be given.

[0421] System configuration

[0422] This system consists of three main components: a server, a terminal, and a user. The server mainly collects data, analyzes it, and generates banners. The terminal provides an interface for users, and users input requirements for creating banners.

[0423] Explanation of program processing

[0424] 1. Advertising Data Collection

[0425] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) for the past year using the advertising platform's API and stores it in a database.

[0426] 2. Data Analysis

[0427] The server analyzes the advertising data stored in the database and identifies the characteristics of banners with high click-through rates and conversion rates. Based on these identified characteristics, it extracts highly effective creative patterns.

[0428] 3. User requirements input

[0429] Through the chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time.

[0430] 4. Automatic banner generation

[0431] The server combines the user's input data with the results of the analysis described above to automatically generate the optimal banner, incorporating highly effective features identified from past data.

[0432] 5. Display banner preview

[0433] The generated banner is displayed as a preview on the user's device in real time, and the user can check the preview and submit correction requests if necessary.

[0434] 6. Modify and Regenerate

[0435] The server receives a modification request from the user, reflects the request, and re-previews the regenerated banner. This process is repeated until the user approves it.

[0436] 7. Final confirmation and banner submission

[0437] Once the user finally approves the banner, the server saves the banner in its final form and generates a download link that can be provided to the user to save the banner on their device.

[0438] Specific examples

[0439] 1. Advertising Data Collection

[0440] The server retrieves advertising data from the past year from the advertising platform's API and stores it in a database. For example, it collects data from a specific platform on "advertising with a click-through rate of 5% or more" or "advertising with a conversion rate of 10% or more."

[0441] 2. Data Analysis

[0442] The server analyzes the collected data using analytical tools such as Python and identifies the characteristics of highly effective banners, such as a blue background and 300x250 pixels.

[0443] 3. User requirements input

[0444] Users use a chat interface to input requests such as "banner size 300x250, background color blue, tagline simple."

[0445] 4. Automatic banner generation

[0446] The server creates an automatically generated banner by combining the user's requests with the characteristics of past highly effective banners.

[0447] 5. Display banner preview

[0448] The device will then display a preview of the generated banner, such as a banner with a product photo, a blue background, and a simple tagline.

[0449] 6. Modify and Regenerate

[0450] The user sends a request for correction to the preview via chat, such as "I want the catchphrase color to be changed to red."

[0451] The server reflects this modification request, regenerates the banner, and updates the preview.

[0452] 7. Final confirmation and banner submission

[0453] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link that is displayed on the device for the user to download the banner.

[0454] In this way, by using this system, it is possible to efficiently create highly effective banners and maximize the effectiveness of advertising.

[0455] The processing flow will be explained below.

[0456] Step 1: Collect advertising data

[0457] The server periodically calls the advertising platform's API to collect data on ads that have been displayed over the past year (e.g., click-through rate, conversion rate, number of impressions, creative details, etc.).

[0458] The server stores the collected data in a database and sets the appropriate fields (e.g., banner size, color, text content, etc.).

[0459] Step 2: Analyze the data

[0460] The server analyzes the advertising data stored in the database and extracts the characteristics of banners with high click-through rates and conversion rates, for example, using a Python analysis library.

[0461] Based on the analysis results, the server identifies common characteristics of effective banners (e.g., specific size, color, placement, etc.).

[0462] Step 3: Enter user requirements

[0463] Users input their banner requirements (size, color, image, text, etc.) through a chat interface.

[0464] The terminal transmits input data from the user to the server in real time.

[0465] Step 4: Auto-generate a banner

[0466] The server combines the user's requirements with the results of the analysis described above and automatically generates an optimal banner, for example, by using a specific template engine to create the banner layout.

[0467] The server converts the generated banner into an image format and saves it.

[0468] Step 5: View the banner preview

[0469] The terminal displays the generated banner preview sent from the server to the user.

[0470] The user checks the displayed preview and sends any correction requests through the chat interface.

[0471] Step 6: Fix and Regenerate

[0472] The server receives the modification request from the user and regenerates a new banner, reflecting the modifications, such as changing the color of the tagline to red or changing the image.

[0473] The server sends the modified banner again to the terminal as a preview.

[0474] Step 7: Final confirmation and banner submission

[0475] The user then finalizes and approves the revised banner preview.

[0476] The server saves the approved banner as the final version and generates a download link.

[0477] The terminal displays the generated download link, allowing the user to download the banner.

[0478] The above steps complete a series of processes, from collecting advertising data to creating and providing optimal banners.

[0479] Example 1

[0480] 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."

[0481] Conventional methods for creating advertising banners often require manual processes, from collecting and analyzing advertising data, generating banners, incorporating user requests for modifications, and finally providing them, resulting in issues of inefficiency and time. Furthermore, there are difficulties in reflecting the specific requirements desired by users, which creates a risk of reducing the effectiveness of the banner. This has led to a demand for a means to quickly and efficiently create optimal banners to maximize advertising effectiveness.

[0482] 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.

[0483] In this invention, the server includes means for collecting and storing information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the requested revision, means for providing the banner after final confirmation by the user, means for automatically generating a banner using a generative AI model, means for collecting user input data through a chat interface, means for displaying a preview of the automatically generated banner on the user's device, means for transmitting user revision requests for the preview to the server in real time, and means for regenerating the banner reflecting the requested revision and displaying the preview again on the user's device. This automates the process from collecting and analyzing advertising data, optimally automatically generating a banner, revising and regenerating it based on user requirements, and finally providing it, enabling efficient and rapid creation of highly effective banners.

[0484] "Information about the advertisement" refers to detailed data about the performance and content of the advertisement, such as click-through rates, conversion rates, impressions, and creative details.

[0485] "Collection means" refers to the means that have the function of obtaining data using the advertising platform's API and storing it in a database.

[0486] "Analysis means" refers to a means that has the function of analyzing collected data using an analysis tool and identifying highly effective features of advertising.

[0487] "Automatic generation means" refers to a means that has the function of generating the optimal banner using a generative AI model based on user input data and analysis results.

[0488] "Display means" refers to a means having a function for previewing an automatically generated banner on a user's terminal.

[0489] The "modification request receiving means" refers to a means having a function of receiving a modification request from a user in real time.

[0490] The "regeneration means" refers to a means having a function of regenerating a banner based on a user's correction request and displaying a preview again.

[0491] The "means for providing after final confirmation" refers to a means having a function of saving the banner that the user finally approves, generating a download link, and providing it to the user.

[0492] A "generative AI model" refers to an artificial intelligence model that generates images based on prompts.

[0493] "Chat Interface" means an interactive interface through which a user can input banner requirements and communicate with a server in real time.

[0494] "Preview" refers to a temporary display of an automatically generated banner that allows a user to review the content and make correction requests.

[0495] A "terminal" is a device that allows a user to access the system and has the function of displaying a preview and receiving user input.

[0496] As an embodiment of the present invention, a system for collecting and analyzing advertising data and automatically generating optimal banners will be described in detail. This system is composed of three main elements: a server, a terminal, and a user.

[0497] System configuration

[0498] This system consists of a server that collects and analyzes advertising data and generates banners, a terminal that provides an interface with users, and a user that inputs requirements for creating banners.

[0499] Advertising data collection

[0500] The server periodically collects advertising data from the past year using the advertising platform's API and stores it in a database. The collected data includes click-through rates, conversion rates, number of impressions, and detailed creative information. For example, data on "advertisements with a click-through rate of 5% or more" and "advertisements with a conversion rate of 10% or more" is collected from a specific platform.

[0501] Data analysis

[0502] The server analyzes the collected data using analytical tools such as Python's Pandas and SciPy to identify the characteristics of banners with high click-through rates and conversion rates. Through this analysis process, highly effective creative patterns such as "blue background" and "300x250 pixels" are extracted.

[0503] User requirements input

[0504] Through a chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time. For example, they can make specific requests such as "banner size 300x250, background color blue, and tagline simple."

[0505] Automatic banner generation

[0506] The server combines the user's input data with the results of the analysis described above to automatically generate the optimal banner. A generative AI model (e.g., DALL-E or GAN model) is used to create a banner that reflects the characteristics of highly effective creative. At this time, prompts are sent to the generative AI model based on the user's requirements. Possible prompts include "banner size 300x250, background color blue, and catchy copy simple."

[0507] View banner preview

[0508] The device displays a preview of the generated banner to the user in real time. The user can check this preview and send correction requests via chat if necessary. For example, if the generated banner has a product photo, a blue background, and a simple tagline, the user can request that the tagline color be changed to red.

[0509] Fix and Regenerate

[0510] The server receives the user's modification request and regenerates the banner to reflect the changes. This process is repeated until the user is satisfied. The regenerated banner is then displayed again as a preview on the user's device.

[0511] Final confirmation and banner provision

[0512] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link that is displayed on the device, allowing the user to download the final version of the banner.

[0513] In this way, by using this system, it is possible to efficiently create highly effective banners and maximize the effectiveness of advertising.

[0514] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0515] Step 1: Collect advertising data

[0516] The server uses the advertising platform's API to periodically collect advertising data from the past year and store it in a database. The input is data obtained from the advertising platform (click-through rate, conversion rate, number of impressions, and creative details), and the output is a database in which the collected data is stored. Specifically, the server accesses the advertising platform's API at a set time every day, establishes a connection to the database, and stores the data.

[0517] Step 2: Analyze the data

[0518] The server analyzes the advertising data stored in the database using analysis tools such as Python's Pandas and SciPy. The input is the advertising data read from the database, and the output is the analysis results, i.e., the characteristics of highly effective banners (e.g., blue background, 300x250 pixels, etc.). Specifically, the server periodically runs analysis scripts, filters data whose click-through rate or conversion rate exceeds a certain threshold, and performs cluster analysis to extract commonalities among highly effective banners.

[0519] Step 3: Enter user requirements

[0520] The user uses the chat interface to input the requirements for the banner (size, color, image, text, etc.). The input is the banner requirements entered by the user in the chat, and the output is the formatted requirement data sent to the server. Specifically, the user enters instructions to the chatbot on the web browser, such as "banner size 300x250, background color blue, catchy copy simple," and the chatbot confirms the instructions and sends them to the server.

[0521] Step 4: Auto-generate a banner

[0522] The server combines the user's input data with the aforementioned analysis results and automatically generates the optimal banner using a generative AI model. The input is the user's requirement data and the analysis results, and the output is the generated banner image. Specifically, the server sends a prompt to the generative AI model (e.g., DALL-E or GAN model), and the model generates an image based on the specified requirements and saves it in a temporary folder.

[0523] Step 5: View the banner preview

[0524] The terminal displays the generated banner to the user as a preview in real time. The input is the generated banner image, and the output is a preview screen that the user can view. Specifically, the terminal uses technologies such as JavaScript to display the image file, and the preview screen displays an "Approve" button and a "Request correction" button.

[0525] Step 6: Fix and Regenerate

[0526] The user issues a request for correction to the preview. For example, they send a specific instruction via chat, such as "I want the catchphrase color to be changed to red." The input is the user's request for correction, and the output is a new banner image that reflects the correction. Specifically, when the server receives the request for correction, it again sends a prompt to the generation AI model, which generates a new banner. Once the new banner is generated, it is again displayed as a preview on the device.

[0527] Step 7: Final confirmation and banner submission

[0528] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link. The input is the banner image approved by the user, and the output is the download link. Specifically, the server saves the final version of the banner in a database or file system and provides the generated download link through the user's chat interface. The user clicks the provided link to download the final version of the banner.

[0529] (Application example 1)

[0530] 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."

[0531] Conventional banner ad generation systems require a lot of time and effort to create effective banners. In particular, the process of users inputting banner requirements and then reviewing and modifying the generated banner is cumbersome, making it difficult to efficiently create highly effective banners. Furthermore, the lack of a convenient banner generation process that can be used on smartphones makes it difficult for advertisers to easily and quickly generate banners.

[0532] 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.

[0533] In this invention, the server includes means for collecting and saving information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for modification, means for regenerating the banner based on the requested modification, means for providing the banner after final confirmation by the user, means for inputting requirements for banner generation using a smartphone and previewing the generated banner in real time, and means for providing an interactive interface for the user to input modifications to the previewed banner. This enables a user to efficiently and quickly generate highly effective banners using a smartphone and make modifications in real time.

[0534] "Information about previously placed advertisements" refers to data including click-through rates, conversion rates, number of impressions, and creative details of advertisements previously placed on advertising platforms and websites.

[0535] "Analyzing information about advertisements" refers to a process of analyzing collected advertisement data and identifying the characteristics of banners with high click rates and conversion rates.

[0536] "Highly effective features" refer to elements such as color, size, text, and images that are common to banners that have higher click-through rates and conversion rates than other ads.

[0537] "User input data" refers to information including user requirements for creating a banner advertisement (eg, size, color, text content, etc.).

[0538] "Means for automatically generating banners" refers to a system that combines collected and analyzed data with user input data to automatically create banner advertisements using a program.

[0539] The "means for displaying to the user and receiving a request for modification" is an interface for displaying the automatically generated banner advertisement on the user's device and receiving a request for modification from the user.

[0540] The "means for regenerating a banner" is a mechanism for recreating an already generated banner advertisement based on a user's request for modification.

[0541] "Means for providing a banner after final confirmation by the user" refers to a mechanism in which, after the user gives final approval, the banner advertisement is saved as a final version and provided to the user in the form of a download link or the like.

[0542] "Inputting requirements for banner generation using a smartphone" refers to a user inputting requirements such as size, color, and text content of a banner ad using a smartphone.

[0543] The "means for displaying a preview of the generated banner in real time" refers to a display device or software that allows the user to instantly check the automatically generated banner advertisement.

[0544] The "interactive interface" is an interface that allows a user to input correction requests and send feedback on a generated banner preview using a device such as a smartphone.

[0545] As an embodiment of the present invention, a system consisting of three main elements: a server, a terminal, and a user will be described. Below, the program processing of this system will be explained in natural language, with examples and prompt sentences included.

[0546] System Configuration

[0547] The system mainly includes the following elements:

[0548] 1. Server: This is responsible for collecting, storing, and analyzing data, and creating automatically generated banner ads. Specifically, it consists of a server using Python and Flask, and an SQLite database for storing advertising data.

[0549] 2. Device: Provides the user interface for inputting requirements, previewing banners, and sending correction requests. Smartphones are assumed to be the primary device.

[0550] 3. User: Enters requirements for banner generation, reviews generated banners, requests corrections, and final approval.

[0551] Program processing

[0552] The server uses an API to collect information about past ads and stores it in an SQLite database. This data includes click-through rates, conversion rates, number of impressions, and creative details. A Python analysis tool analyzes the collected data and identifies the characteristics of highly effective banners.

[0553] Users input the requirements for banner generation (size, background color, catchy copy, etc.) using their smartphones, and this data is sent to the server in real time. The server combines the received requirements with the characteristics of highly effective banners and uses an automatic generation algorithm to generate the optimal banner.

[0554] The generated banner is instantly displayed as a preview screen on the device (smartphone), allowing the user to check its contents. The user can then input further correction requests for the preview, which are also sent to the server in real time. The server receives the correction requests, regenerates the banner, and displays the updated preview on the device. This process can be repeated until the user is satisfied.

[0555] Specific examples

[0556] For example, if a user inputs "banner size 300x250, background color blue, catchphrase 'Quick & Easy'", the server will automatically generate a banner based on this and display it on the smartphone. The user can check this preview and input a request to modify the banner, such as "change the color of the catchphrase to red". The server can then generate a new banner that reflects this modification and display it again.

[0557] Prompt Sentence Examples

[0558] Enter the prompt for the generative AI model as follows:

[0559] Generate your banner ad based on the following requirements:

[0560] Size:300x250

[0561] Background color: blue

[0562] Catchphrase: 'Quick & Easy'

[0563] In this way, the present invention allows users to efficiently generate highly effective banners using their smartphones and make real-time modifications.

[0564] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0565] Step 1:

[0566] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) from the past year using the API. It receives the advertising data obtained from the API as input, processes it by storing it in an SQLite database, and outputs the stored data.

[0567] Step 2:

[0568] The server analyzes the advertising data stored in the SQLite database. It takes the advertising data stored as input, performs data calculations using Python analysis tools to identify the characteristics of banners with high click-through rates and conversion rates, and outputs the characteristics of highly effective banners.

[0569] Step 3:

[0570] The user inputs the requirements for the banner (size, background color, catchy copy, etc.) using a smartphone. The requirements specified by the user are entered through the smartphone interface, and this requirement data is sent to the server.

[0571] Step 4:

[0572] The server combines user input data with the characteristics of highly effective banners to automatically generate optimal banners. It receives user requirement data and analyzed highly effective banner characteristics as input, performs data calculations to generate banners using an automatic generation algorithm based on this, and outputs the generated banner data.

[0573] Step 5:

[0574] The terminal displays a preview of the generated banner data sent from the server in real time, passes the generated banner data received from the server as input to the display device, and outputs a preview screen that allows the user to visually check the generated banner.

[0575] Step 6:

[0576] The user inputs a correction request for the previewed banner. The correction request is input as specified by the user through the interactive interface of the smartphone, and this correction request data is sent to the server.

[0577] Step 7:

[0578] The server regenerates the banner based on the user's revision request. It receives the user's revision request data and the original banner data as input, performs data calculations to regenerate the banner that reflects the revision request using the automatic generation algorithm, and outputs the new banner data.

[0579] Step 8:

[0580] The terminal again previews the regenerated banner data, passes the new banner data sent from the server as input to the display device, and outputs a preview screen that allows the user to visually confirm the modified banner.

[0581] Step 9:

[0582] The user finally approves the banner, and inputs an operation to confirm the approved banner, which is then sent to the server.

[0583] Step 10:

[0584] The server stores the final banner data approved by the user and generates a download link. It receives the user's approval data as input, processes the data by storing the final banner in a database, and generates a download link, which it then outputs to the device.

[0585] Step 11:

[0586] The terminal displays the generated download link, passes the download link received from the server as input to the display device, and visually displays a link to download the final banner to the user.

[0587] 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.

[0588] As an embodiment of the present invention, we will explain a system that automatically generates banners by collecting and analyzing advertising data and combining it with an emotion engine that recognizes user emotions. Below, we will explain the program processing of this system in natural language, and also provide concrete examples.

[0589] System configuration

[0590] This system consists of three main components: a server, a terminal, and a user. The server mainly collects data, analyzes it, recognizes emotions, and generates banners. The terminal provides an interface with the user, allowing the user to input requirements for banner creation and provide the data necessary for emotion recognition.

[0591] Explanation of program processing

[0592] 1. Advertising Data Collection

[0593] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) from the past year using the advertising platform's API and stores it in a database.

[0594] 2. Data Analysis

[0595] The server analyzes the advertising data stored in the database and identifies the characteristics of highly effective banners. The analysis results include common characteristics of banners with high click-through rates and conversion rates.

[0596] 3. User requirements input

[0597] Through a chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time.

[0598] 4. User Emotion Recognition

[0599] The server uses an emotion engine to analyze the user's emotions, using text and voice data entered by the user in the chat.

[0600] The emotion engine determines the user's emotional state (e.g., joy, surprise, sadness, anger, etc.) and adjusts the banner design and copy accordingly.

[0601] 5. Automatic banner generation

[0602] The server combines the user's requirements, analysis results, and emotion recognition results to automatically generate the optimal banner. For example, if the emotion engine determines that the user's emotional state is "joy," it will use bright colors and a positive catchy copy.

[0603] 6. Display banner preview

[0604] The terminal displays the generated banner as a preview to the user, who then checks the preview and sends a request for correction if necessary.

[0605] 7. Modify and Regenerate

[0606] The server receives a modification request from the user and regenerates a new banner, reflecting the user's request, for example, by changing the color of the tagline.

[0607] The revised banner will be displayed again as a preview on the device.

[0608] 8. Final confirmation and banner submission

[0609] The user finally checks and approves the banner preview.

[0610] The server saves the approved banner in its final form and generates a download link that is displayed on the device, allowing the user to download the banner.

[0611] Specific examples

[0612] 1. Advertising Data Collection

[0613] The server collects advertising data from a specific advertising platform over the past year (e.g., ads with a click-through rate of 5% or more and a conversion rate of 10% or more) via API and stores it in a database.

[0614] 2. Data Analysis

[0615] The server analyzes the stored advertising data using a Python analysis library to identify the characteristics of effective banners, such as a blue background and a size of 300x250 pixels.

[0616] 3. User requirements input

[0617] Through a chat interface, users input information such as "banner size 300x250, background color blue, catchy slogan simple."

[0618] 4. User Emotion Recognition

[0619] The server analyzes the text and voice data entered by the user using an emotion engine and determines that the user is happy.

[0620] The server takes into account your emotional state and suggests banners with positive slogans and upbeat designs.

[0621] 5. Automatic banner generation

[0622] The server runs a banner generation algorithm based on the user's requirements and emotion recognition results to automatically generate the optimal banner.

[0623] 6. Display banner preview

[0624] The device displays a preview of the generated banner, showing the user a banner with a "product photo, a blue background, and a simple, positive tagline."

[0625] 7. Modify and Regenerate

[0626] The user sends a correction request saying, "I want the color of the catchphrase to be changed to red."

[0627] The server reflects this modification request, generates a new banner, and displays the preview again.

[0628] 8. Final confirmation and banner submission

[0629] The user finally checks the preview and clicks the "Approve" button.

[0630] The server saves the final banner and generates a download link that is displayed on the device for the user to download the banner.

[0631] In this way, by using this system, it is possible to efficiently create highly effective banners and provide optimal advertisements that match the user's emotions.

[0632] The processing flow will be explained below.

[0633] Step 1: Collect advertising data

[0634] The server periodically calls the advertising platform's API to collect data on ads that have been displayed over the past year (e.g., click-through rate, conversion rate, number of impressions, creative details, etc.).

[0635] The server stores the collected data in a database and sets the appropriate fields (e.g., banner size, color, text content, etc.).

[0636] Step 2: Analyze the data

[0637] The server analyzes the advertising data stored in the database and uses a Python analysis library to extract the characteristics of banners with high click-through rates and conversion rates.

[0638] Based on the analysis results, the server identifies common characteristics of effective banners (e.g., specific size, color, placement, etc.).

[0639] Step 3: Enter user requirements

[0640] Users input their banner requirements (size, color, image, text, etc.) through a chat interface.

[0641] The terminal transmits the input user data to the server in real time.

[0642] Step 4: Recognizing user emotions

[0643] The server receives text and voice data for analyzing the user's emotions using an emotion engine.

[0644] The emotion engine analyzes input text and voice data to determine the user's emotional state (e.g., joy, surprise, sadness, anger, etc.).

[0645] The server adjusts the banner design and copy taking into account the emotional state identified by the emotion engine.

[0646] Step 5: Auto-generating a banner

[0647] The server combines the requirements entered by the user, the analysis results, and the emotion recognition results to automatically generate the optimal banner.

[0648] For example, if the emotion engine determines that the user's emotion is "joy," it will generate a banner with bright colors and positive copy.

[0649] The server converts the generated banner into an image format and saves it.

[0650] Step 6: View the banner preview

[0651] The terminal displays the generated banner preview to the user.

[0652] The user checks the displayed preview and, if necessary, sends a request for corrections through the chat interface.

[0653] Step 7: Fix and Regenerate

[0654] The server receives a modification request from the user and regenerates a new banner.

[0655] For example, the banner is adjusted in response to a user's request for modification, such as changing the color of the catchphrase to red.

[0656] The server transmits the regenerated banner to the terminal again as a preview.

[0657] Step 8: Final confirmation and banner submission

[0658] The user reviews and approves the final banner preview.

[0659] The server saves the approved banner as the final version and generates a download link.

[0660] The terminal displays the generated download link, allowing the user to download the banner.

[0661] The above steps complete a series of processes, from collecting advertising data to creating and providing optimal banners. By combining this with an emotion recognition engine, it is possible to provide optimal advertising creatives that match the user's emotions.

[0662] Example 2

[0663] 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."

[0664] Conventional banner ad generation systems lack sufficient analysis to maximize advertising effectiveness, making it difficult to provide banner designs that reflect the user's emotional state. As a result, advertising effectiveness is reduced and users are unable to fully respond.

[0665] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and saving information about advertisements that have been posted in the past, means for analyzing information about the advertisements and identifying highly effective features, means for recognizing user input data and emotions, means for automatically generating a banner by combining the identified features with the user's emotional information, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the request for revision, and means for providing the banner after final confirmation by the user. This makes it possible to maximize advertising effectiveness and efficiently generate banners with designs that reflect the user's emotional state.

[0666] "Advertising information" refers to data related to marketing materials that users view and interact with, including click rates, conversion rates, impressions, creative details, and the like.

[0667] "Analysis" is the process of analyzing data and extracting useful information or features from it, and in the case of advertising data in particular, it refers to evaluating performance metrics such as click-through rates and conversion rates.

[0668] "Means for recognizing emotions" refers to technology that analyzes user input data and voice data to determine the user's emotional state (for example, joy, surprise, sadness, anger, etc.).

[0669] A "banner" is an advertising image or animation that appears on a web page and contains a specific marketing message or visual element.

[0670] "Automatic generation means" refers to the process of using programs and algorithms to automatically create advertising banners based on specific input data.

[0671] A "request for modification" is a request for modification made by a user to a generated banner, including partial modifications to the design or text.

[0672] The "regeneration means" is a process for regenerating a banner based on a user's modification request.

[0673] "Final confirmation" is the act of the user checking and approving the final version of the banner.

[0674] The "means of providing" refers to the technology by which the final approved banner is provided to the user and made available for download and use.

[0675] This invention relates to a system that automatically generates banners by collecting advertising data, analyzing it, and combining it with an emotion engine that recognizes user emotions. This system consists of three main elements: a server, a terminal, and a user.

[0676] System configuration

[0677] server

[0678] The server is the heart of the system and performs the following main functions:

[0679] 1. Data collection: The server uses the advertising platform's API to collect advertising data (click-through rate, conversion rate, number of impressions, creative details) from the past year and saves it in a database.

[0680] 2. Data analysis: Analyze the data using Python analysis libraries (e.g., pandas, numpy, scikit-learn) to identify the characteristics of effective banners.

[0681] 3. Emotion recognition: Analyze user input data and voice data using an emotion engine (e.g., Google Cloud Natural Language API, IBM Watson) to determine the user's emotional state.

[0682] 4. Automatic banner generation: By combining user requirements and emotion recognition results, banners are automatically generated using a generative AI model (e.g., GAN, VQ-VAE).

[0683] 5. Banner regeneration: Receives correction requests from users and regenerates banners that reflect the content.

[0684] 6. Banner submission: Save the final approved banner and generate a download link.

[0685] Terminal

[0686] The terminal is responsible for providing the interface with the user:

[0687] 1. Input interface: A chat interface (e.g., a React-based chatbot) for users to input their banner requirements.

[0688] 2. Preview: The generated banner is displayed to the user as a preview and has the function of receiving correction requests.

[0689] 3. Download: Provides a link to download the final version of the banner.

[0690] User

[0691] Users have the following roles:

[0692] 1. Input requirements: Input requirements such as banner size, color, image, text, etc. through the chat interface.

[0693] 2. Providing emotional data: Provide input text and voice data and have the server analyze your emotional state.

[0694] 3. Preview: Check the preview of the generated banner and make any necessary corrections.

[0695] 4. Final Review: Review and approve the final banner.

[0696] Specific examples

[0697] Here's a concrete example of how the system works:

[0698] 1. Collecting advertising data: The server collects advertising data with a click rate of 5% or more and a conversion rate of 10% or more over the past year through the API of a specific advertising platform and stores it in a database.

[0699] 2. Data Analysis: The server analyzes the stored advertising data and identifies the characteristics of effective banners, such as a blue background and a size of 300x250 pixels.

[0700] 3. User requirements input: The user inputs the following through the chat interface: "Banner size 300x250, background color blue, catchy copy simple."

[0701] 4. User emotion recognition: The server analyzes the text and voice data entered by the user using an emotion engine and determines that the user is happy.

[0702] 5. Automatic banner generation: The server runs a banner generation algorithm based on the user requirements and emotion recognition results to automatically generate the optimal banner.

[0703] 6. Display banner preview: The device displays a preview of the generated banner, showing the user a banner with a product photo, a blue background, and a simple, positive tagline.

[0704] 7. Modify and regenerate: The user sends a modification request, such as "I want the tagline color to be changed to red." The server reflects this modification request, generates a new banner, and displays the preview again.

[0705] 8. Final confirmation and banner delivery: The user finally checks the preview and clicks the "Approve" button. The server saves the final version of the banner and generates a download link. This link is displayed on the device, and the user can download the banner.

[0706] Prompt Sentence Examples

[0707] The banner size should be 300x250, with a blue background. The tagline should be simple. Also, please analyze the sentiment of the text entered by the user and suggest a design that matches that sentiment.

[0708] This system makes it possible to maximize advertising effectiveness and efficiently generate optimal banners that reflect the user's emotional state.

[0709] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0710] Step 1: Collect advertising data

[0711] The server accesses the advertising platform's API and periodically collects advertising data from the past year. This data includes click-through rates, conversion rates, number of impressions, and creative details. The collected data is sent to the server in JSON format and stored in a database. The input is the raw data obtained from the advertising platform's API, and the output is the advertising data stored in the database.

[0712] Step 2: Analyze the data

[0713] The server uses Python analysis libraries (e.g., pandas, numpy, scikit-learn) to analyze the advertising data stored in the database. The analysis script is run periodically to identify the characteristics of highly effective banners. Specifically, it extracts common characteristics (e.g., background color, font style, image placement) of banners with high click-through rates and conversion rates. The input is the advertising data stored in the database, and the output is the identified characteristics of highly effective banners.

[0714] Step 3: Enter user requirements

[0715] Users input their banner requirements through a chat interface on their web browser. For example, they can enter specific requests such as "I want the banner size to be 300x250 pixels, the background color to be blue, and the tagline to be simple." The input information is sent to the server in real time and recorded in a database. The input is the banner requirements provided by the user through the chat interface, and the output is the input data stored in the database.

[0716] Step 4: Recognizing user emotions

[0717] The server sends the text and voice data entered by the user to an emotion recognition engine (e.g., Google Cloud Natural Language API, IBM Watson) to analyze the user's emotions. The emotion recognition engine uses text analysis technology to determine the emotion the user is feeling (e.g., joy, surprise, sadness, anger). The analysis results are stored in a database and reflected in the banner design. The input is the text and voice data entered by the user, and the output is the recognized user's emotional information.

[0718] Step 5: Auto-generating a banner

[0719] The server combines the user's requirements, the analyzed features of effective banners, and emotion recognition results to automatically generate a banner using a generative AI model (e.g., GAN, VQ-VAE). This algorithm creates an optimal banner that matches the input conditions. Specifically, if the user's requirements are "joy," a banner size of 300x250 pixels, and a blue background color, a corresponding bright banner design will be generated. The input is the user's requirements, the features of effective banners, and the emotion recognition results, and the output is the generated banner image.

[0720] Step 6: View the banner preview

[0721] The terminal receives the banner image sent from the server and displays it as a preview to the user. Because it is displayed in real time on the web browser, the user can immediately check the banner. For example, a banner with a "product photo, blue background, and simple, positive tagline" is presented to the user. The input is the generated banner image, and the output is the preview screen that the user checks.

[0722] Step 7: Fix and Regenerate

[0723] The user views the preview and sends a request for revisions, such as "change the color of the catchphrase to red," through the chat interface. The server receives this request and runs the generative AI model again to generate a new banner. The revised banner is then sent back to the device and displayed as a preview to the user. The input is the user's request for revisions, and the output is the revised new banner image.

[0724] Step 8: Final confirmation and banner submission

[0725] The user checks the final preview and clicks the "Approve" button to finalize the banner. The server saves the final banner and generates a download link. This link is displayed on the device, and the user can click it to download the banner. The input is the approval result of the final preview, and the output is the final banner and its download link.

[0726] In this way, by combining advertising data analysis and user emotion recognition, this system can efficiently generate highly effective banners and provide them to users.

[0727] (Application example 2)

[0728] 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."

[0729] While conventional banner generation systems provide a means for generating highly effective banners based on the analysis of advertising data, they lack the ability to adjust banners in response to user emotions and real-time requests. This hinders users' ability to create more effective and emotionally appealing banner ads. Furthermore, the lack of a means for flexibly collecting user requirements via voice or text and generating and adjusting banners based on those requirements limits the user experience.

[0730] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0731] In this invention, the server includes means for collecting and saving information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for analyzing the user's emotions and adjusting the banner design and catchy copy based on the user's emotional state, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the request for revision, and means for providing the banner after final confirmation by the user. This makes it possible to automatically generate, display, modify, and finally provide banners flexibly according to the user's emotional state and specific requirements.

[0732] "Advertising Information" refers to data regarding advertisements that have been previously published, including click-through rates, conversion rates, number of impressions, and creative details.

[0733] "Analysis" refers to the process of analyzing collected data using statistical methods and algorithms to identify highly effective features.

[0734] A "banner" refers to an advertising image or text that appears on a digital medium such as a web page or application.

[0735] "User-input data" refers to various requirements and information provided by the user to the system, including banner size, color, image, text, etc.

[0736] "Emotion analysis" is a technology that recognizes a user's emotional state (joy, surprise, sadness, anger, etc.) based on their voice and text data.

[0737] "Automatic banner generation" is the process by which the system algorithmically generates appropriate banner ads based on collected data and user requirements.

[0738] A "request for modification" refers to a user viewing a banner preview and requesting the system to make changes to the design, text, etc.

[0739] "Regeneration" is the process by which the system regenerates the banner based on the user's modification requests.

[0740] "Final confirmation" refers to the process in which the generated banner is presented to the user for final approval.

[0741] "Storage" refers to recording the generated banners and analysis data in a database or storage so that they can be accessed later.

[0742] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server mainly collects and analyzes advertising data, recognizes emotions, and automatically generates and modifies banners. The terminal provides an interface with the user, allowing the user to input requirements for banner creation and provide the data necessary for emotion recognition.

[0743] The server uses the advertising platform's API to collect and store information about previously displayed ads, including click-through rates, conversion rates, number of impressions, creative details, etc. The collected information is stored in a database and used for analysis.

[0744] The server analyzes the collected advertising information using statistical methods and algorithms to identify the characteristics of effective banners. This analysis reveals which advertising elements are most effective, such as specific colors, sizes, and text styles.

[0745] Users input their banner requirements through the device's operating interface, using voice and text interfaces, such as "banner size 300x250, background color blue, catchy copy simple."

[0746] The server uses an emotion engine to analyze the user's emotions. The emotion analysis uses the user's voice and text data to determine their emotional state (happiness, surprise, sadness, anger, etc.). Based on the results of this emotion analysis, the banner design and catchy copy are adjusted.

[0747] For example, if the emotion engine determines that the user's emotional state is "joy," a banner using bright colors and a positive catchphrase is automatically generated. This banner is displayed as a preview to the user via their device. The user can check the preview and, if necessary, send a request for corrections by voice or text, such as "I want the catchphrase color to be red."

[0748] The server receives the user's request for corrections and regenerates the banner based on the request. This regenerated banner is then displayed to the user as a preview. Finally, when the user checks the preview and approves it, the server saves the final version of the banner and generates a download link. This link is displayed on the device, allowing the user to download the banner.

[0749] A concrete example of this system is its application using smart glasses. The smart glasses can display banner ads in the user's field of vision in real time and adjust the ad content through emotion recognition. When the user verbally commands, "Show me recommended banners," the system performs emotion analysis and displays banners and designs with positive messages in real time. For example, if the user verbally requests, "Change the catchphrase," the banner will be regenerated and displayed in the user's field of vision.

[0750] Example prompt sentence:

[0751] User: "Show me the recommended banner."

[0752] Program: "We're happy to show you a banner with a positive message."

[0753] User: "Change the tagline."

[0754] Program: "Change the tagline and preview again."

[0755] This system allows for the automatic generation of banners that can be flexibly adapted to the user's emotional state and specific requirements, and then displayed, modified, and finally provided.

[0756] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0757] Step 1:

[0758] The server uses the advertising platform's API to collect and store information about previously displayed ads (click-through rate, conversion rate, number of impressions, creative details, etc.) The server makes an API request and stores the obtained data in a database, where this information is accumulated and used for later analysis.

[0759] Input: Advertising data obtained from advertising platforms

[0760] Output: Advertisement information stored in the database

[0761] Step 2:

[0762] The server analyzes the advertising information stored in the database using statistical methods and algorithms to identify highly effective features. Specifically, it uses a Python analysis library to extract common features of ads that meet certain criteria, such as a click-through rate of 5% or more and a conversion rate of 10% or more.

[0763] Input: Advertisement information stored in the database

[0764] Output: High-impact banner features (e.g. color, size, text style, etc.)

[0765] Step 3:

[0766] Users can input banner requirements using their device's voice assistant or text interface. For example, if a user requests a banner size of 300x250, a blue background, and a simple tagline, these requirements are sent to the server.

[0767] Input: User-entered data (voice or text)

[0768] Output: User's banner requirements are sent to the server

[0769] Step 4:

[0770] The server uses an emotion engine to analyze the user's emotions. The emotion engine determines the user's emotional state (happiness, surprise, sadness, anger, etc.) based on the user's voice data and text data. For example, if the user enters "I'm feeling happy today," the emotion engine will determine that the emotion is "happiness."

[0771] Input: User voice and text data

[0772] Output: User's emotional state

[0773] Step 5:

[0774] The server automatically generates a banner by combining the user's requirements, the characteristics of effective banners, and the results of sentiment analysis. For example, if the sentiment analysis result is "joy," a banner with bright colors and a positive catchphrase will be generated. The generated banner is temporarily stored on the server.

[0775] Input: User banner requirements, characteristics of effective banners, user emotional state

[0776] Output: Auto-generated banner

[0777] Step 6:

[0778] The device displays the generated banner as a preview to the user. The user checks the preview and sends a request for corrections, such as "I want the catchphrase color to be changed to red," to the device via voice or text. The request data is then sent to the server.

[0779] Input: Auto-generated banner, user request for correction

[0780] Output: The modification request is sent to the server

[0781] Step 7:

[0782] The server regenerates the banner based on the user's request for modification. For example, if the user requests that the color of the catchphrase be changed to red, the server regenerates the banner reflecting the request and temporarily saves it again.

[0783] Input: User's correction request

[0784] Output: Regenerated banner

[0785] Step 8:

[0786] The device will then preview the regenerated banner and display it to the user. If the user confirms the final version and approves it, the server will save the final version of the banner and generate a download link. This link will be displayed on the device, allowing the user to download the banner.

[0787] Input: Regenerated banner, final user confirmation

[0788] Output: A download link will be displayed on the terminal.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] [Third embodiment]

[0793] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0794] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0795] 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).

[0796] 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.

[0797] 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.

[0798] 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).

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] As an embodiment of the present invention, a system for collecting and analyzing advertising data and automatically generating optimal banners will be described. Below, the program processing of this system will be explained in natural language, and specific examples will be given.

[0806] System configuration

[0807] This system consists of three main components: a server, a terminal, and a user. The server mainly collects data, analyzes it, and generates banners. The terminal provides an interface for users, and users input requirements for creating banners.

[0808] Explanation of program processing

[0809] 1. Advertising Data Collection

[0810] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) for the past year using the advertising platform's API and stores it in a database.

[0811] 2. Data Analysis

[0812] The server analyzes the advertising data stored in the database and identifies the characteristics of banners with high click-through rates and conversion rates. Based on these identified characteristics, it extracts highly effective creative patterns.

[0813] 3. User requirements input

[0814] Through the chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time.

[0815] 4. Automatic banner generation

[0816] The server combines the user's input data with the results of the analysis described above to automatically generate the optimal banner, incorporating highly effective features identified from past data.

[0817] 5. Display banner preview

[0818] The generated banner is displayed as a preview on the user's device in real time, and the user can check the preview and submit correction requests if necessary.

[0819] 6. Modify and Regenerate

[0820] The server receives a modification request from the user, reflects the request, and re-previews the regenerated banner. This process is repeated until the user approves it.

[0821] 7. Final confirmation and banner submission

[0822] Once the user finally approves the banner, the server saves the banner in its final form and generates a download link that can be provided to the user to save the banner on their device.

[0823] Specific examples

[0824] 1. Advertising Data Collection

[0825] The server retrieves advertising data from the past year from the advertising platform's API and stores it in a database. For example, it collects data from a specific platform on "advertising with a click-through rate of 5% or more" or "advertising with a conversion rate of 10% or more."

[0826] 2. Data Analysis

[0827] The server analyzes the collected data using analytical tools such as Python and identifies the characteristics of highly effective banners, such as a blue background and 300x250 pixels.

[0828] 3. User requirements input

[0829] Users use a chat interface to input requests such as "banner size 300x250, background color blue, tagline simple."

[0830] 4. Automatic banner generation

[0831] The server creates an automatically generated banner by combining the user's requests with the characteristics of past highly effective banners.

[0832] 5. Display banner preview

[0833] The device will then display a preview of the generated banner, such as a banner with a product photo, a blue background, and a simple tagline.

[0834] 6. Modify and Regenerate

[0835] The user sends a request for correction to the preview via chat, such as "I want the catchphrase color to be changed to red."

[0836] The server reflects this modification request, regenerates the banner, and updates the preview.

[0837] 7. Final confirmation and banner submission

[0838] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link that is displayed on the device for the user to download the banner.

[0839] In this way, by using this system, it is possible to efficiently create highly effective banners and maximize the effectiveness of advertising.

[0840] The processing flow will be explained below.

[0841] Step 1: Collect advertising data

[0842] The server periodically calls the advertising platform's API to collect data on ads that have been displayed over the past year (e.g., click-through rate, conversion rate, number of impressions, creative details, etc.).

[0843] The server stores the collected data in a database and sets the appropriate fields (e.g., banner size, color, text content, etc.).

[0844] Step 2: Analyze the data

[0845] The server analyzes the advertising data stored in the database and extracts the characteristics of banners with high click-through rates and conversion rates, for example, using a Python analysis library.

[0846] Based on the analysis results, the server identifies common characteristics of effective banners (e.g., specific size, color, placement, etc.).

[0847] Step 3: Enter user requirements

[0848] Users input their banner requirements (size, color, image, text, etc.) through a chat interface.

[0849] The terminal transmits input data from the user to the server in real time.

[0850] Step 4: Auto-generate a banner

[0851] The server combines the user's requirements with the results of the analysis described above and automatically generates an optimal banner, for example, by using a specific template engine to create the banner layout.

[0852] The server converts the generated banner into an image format and saves it.

[0853] Step 5: View the banner preview

[0854] The terminal displays the generated banner preview sent from the server to the user.

[0855] The user checks the displayed preview and sends any correction requests through the chat interface.

[0856] Step 6: Fix and Regenerate

[0857] The server receives the modification request from the user and regenerates a new banner, reflecting the modifications, such as changing the color of the tagline to red or changing the image.

[0858] The server sends the modified banner again to the terminal as a preview.

[0859] Step 7: Final confirmation and banner submission

[0860] The user then finalizes and approves the revised banner preview.

[0861] The server saves the approved banner as the final version and generates a download link.

[0862] The terminal displays the generated download link, allowing the user to download the banner.

[0863] The above steps complete a series of processes, from collecting advertising data to creating and providing optimal banners.

[0864] Example 1

[0865] 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."

[0866] Conventional methods for creating advertising banners often require manual processes, from collecting and analyzing advertising data, generating banners, incorporating user requests for modifications, and finally providing them, resulting in issues of inefficiency and time. Furthermore, there are difficulties in reflecting the specific requirements desired by users, which creates a risk of reducing the effectiveness of the banner. This has led to a demand for a means to quickly and efficiently create optimal banners to maximize advertising effectiveness.

[0867] 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.

[0868] In this invention, the server includes means for collecting and storing information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the requested revision, means for providing the banner after final confirmation by the user, means for automatically generating a banner using a generative AI model, means for collecting user input data through a chat interface, means for displaying a preview of the automatically generated banner on the user's device, means for transmitting user revision requests for the preview to the server in real time, and means for regenerating the banner reflecting the requested revision and displaying the preview again on the user's device. This automates the process from collecting and analyzing advertising data, optimally automatically generating a banner, revising and regenerating it based on user requirements, and finally providing it, enabling efficient and rapid creation of highly effective banners.

[0869] "Information about the advertisement" refers to detailed data about the performance and content of the advertisement, such as click-through rates, conversion rates, impressions, and creative details.

[0870] "Collection means" refers to the means that have the function of obtaining data using the advertising platform's API and storing it in a database.

[0871] "Analysis means" refers to a means that has the function of analyzing collected data using an analysis tool and identifying highly effective features of advertising.

[0872] "Automatic generation means" refers to a means that has the function of generating the optimal banner using a generative AI model based on user input data and analysis results.

[0873] "Display means" refers to a means having a function for previewing an automatically generated banner on a user's terminal.

[0874] The "modification request receiving means" refers to a means having a function of receiving a modification request from a user in real time.

[0875] The "regeneration means" refers to a means having a function of regenerating a banner based on a user's correction request and displaying a preview again.

[0876] The "means for providing after final confirmation" refers to a means having a function of saving the banner that the user finally approves, generating a download link, and providing it to the user.

[0877] A "generative AI model" refers to an artificial intelligence model that generates images based on prompts.

[0878] "Chat Interface" means an interactive interface through which a user can input banner requirements and communicate with a server in real time.

[0879] "Preview" refers to a temporary display of an automatically generated banner that allows a user to review the content and make correction requests.

[0880] A "terminal" is a device that allows a user to access the system and has the function of displaying a preview and receiving user input.

[0881] As an embodiment of the present invention, a system for collecting and analyzing advertising data and automatically generating optimal banners will be described in detail. This system is composed of three main elements: a server, a terminal, and a user.

[0882] System configuration

[0883] This system consists of a server that collects and analyzes advertising data and generates banners, a terminal that provides an interface with users, and a user that inputs requirements for creating banners.

[0884] Advertising data collection

[0885] The server periodically collects advertising data from the past year using the advertising platform's API and stores it in a database. The collected data includes click-through rates, conversion rates, number of impressions, and detailed creative information. For example, data on "advertisements with a click-through rate of 5% or more" and "advertisements with a conversion rate of 10% or more" is collected from a specific platform.

[0886] Data analysis

[0887] The server analyzes the collected data using analytical tools such as Python's Pandas and SciPy to identify the characteristics of banners with high click-through rates and conversion rates. Through this analysis process, highly effective creative patterns such as "blue background" and "300x250 pixels" are extracted.

[0888] User requirements input

[0889] Through a chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time. For example, they can make specific requests such as "banner size 300x250, background color blue, and tagline simple."

[0890] Automatic banner generation

[0891] The server combines the user's input data with the results of the analysis described above to automatically generate the optimal banner. A generative AI model (e.g., DALL-E or GAN model) is used to create a banner that reflects the characteristics of highly effective creative. At this time, prompts are sent to the generative AI model based on the user's requirements. Possible prompts include "banner size 300x250, background color blue, and catchy copy simple."

[0892] View banner preview

[0893] The device displays a preview of the generated banner to the user in real time. The user can check this preview and send correction requests via chat if necessary. For example, if the generated banner has a product photo, a blue background, and a simple tagline, the user can request that the tagline color be changed to red.

[0894] Fix and Regenerate

[0895] The server receives the user's modification request and regenerates the banner to reflect the changes. This process is repeated until the user is satisfied. The regenerated banner is then displayed again as a preview on the user's device.

[0896] Final confirmation and banner provision

[0897] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link that is displayed on the device, allowing the user to download the final version of the banner.

[0898] In this way, by using this system, it is possible to efficiently create highly effective banners and maximize the effectiveness of advertising.

[0899] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0900] Step 1: Collect advertising data

[0901] The server uses the advertising platform's API to periodically collect advertising data from the past year and store it in a database. The input is data obtained from the advertising platform (click-through rate, conversion rate, number of impressions, and creative details), and the output is a database in which the collected data is stored. Specifically, the server accesses the advertising platform's API at a set time every day, establishes a connection to the database, and stores the data.

[0902] Step 2: Analyze the data

[0903] The server analyzes the advertising data stored in the database using analysis tools such as Python's Pandas and SciPy. The input is the advertising data read from the database, and the output is the analysis results, i.e., the characteristics of highly effective banners (e.g., blue background, 300x250 pixels, etc.). Specifically, the server periodically runs analysis scripts, filters data whose click-through rate or conversion rate exceeds a certain threshold, and performs cluster analysis to extract commonalities among highly effective banners.

[0904] Step 3: Enter user requirements

[0905] The user uses the chat interface to input the requirements for the banner (size, color, image, text, etc.). The input is the banner requirements entered by the user in the chat, and the output is the formatted requirement data sent to the server. Specifically, the user enters instructions to the chatbot on the web browser, such as "banner size 300x250, background color blue, catchy copy simple," and the chatbot confirms the instructions and sends them to the server.

[0906] Step 4: Auto-generate a banner

[0907] The server combines the user's input data with the aforementioned analysis results and automatically generates the optimal banner using a generative AI model. The input is the user's requirement data and the analysis results, and the output is the generated banner image. Specifically, the server sends a prompt to the generative AI model (e.g., DALL-E or GAN model), and the model generates an image based on the specified requirements and saves it in a temporary folder.

[0908] Step 5: View the banner preview

[0909] The terminal displays the generated banner to the user as a preview in real time. The input is the generated banner image, and the output is a preview screen that the user can view. Specifically, the terminal uses technologies such as JavaScript to display the image file, and the preview screen displays an "Approve" button and a "Request correction" button.

[0910] Step 6: Fix and Regenerate

[0911] The user issues a request for correction to the preview. For example, they send a specific instruction via chat, such as "I want the catchphrase color to be changed to red." The input is the user's request for correction, and the output is a new banner image that reflects the correction. Specifically, when the server receives the request for correction, it again sends a prompt to the generation AI model, which generates a new banner. Once the new banner is generated, it is again displayed as a preview on the device.

[0912] Step 7: Final confirmation and banner submission

[0913] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link. The input is the banner image approved by the user, and the output is the download link. Specifically, the server saves the final version of the banner in a database or file system and provides the generated download link through the user's chat interface. The user clicks the provided link to download the final version of the banner.

[0914] (Application example 1)

[0915] 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."

[0916] Conventional banner ad generation systems require a lot of time and effort to create effective banners. In particular, the process of users inputting banner requirements and then reviewing and modifying the generated banner is cumbersome, making it difficult to efficiently create highly effective banners. Furthermore, the lack of a convenient banner generation process that can be used on smartphones makes it difficult for advertisers to easily and quickly generate banners.

[0917] 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.

[0918] In this invention, the server includes means for collecting and saving information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for modification, means for regenerating the banner based on the requested modification, means for providing the banner after final confirmation by the user, means for inputting requirements for banner generation using a smartphone and previewing the generated banner in real time, and means for providing an interactive interface for the user to input modifications to the previewed banner. This enables a user to efficiently and quickly generate highly effective banners using a smartphone and make modifications in real time.

[0919] "Information about previously placed advertisements" refers to data including click-through rates, conversion rates, number of impressions, and creative details of advertisements previously placed on advertising platforms and websites.

[0920] "Analyzing information about advertisements" refers to a process of analyzing collected advertisement data and identifying the characteristics of banners with high click rates and conversion rates.

[0921] "Highly effective features" refer to elements such as color, size, text, and images that are common to banners that have higher click-through rates and conversion rates than other ads.

[0922] "User input data" refers to information including user requirements for creating a banner advertisement (eg, size, color, text content, etc.).

[0923] "Means for automatically generating banners" refers to a system that combines collected and analyzed data with user input data to automatically create banner advertisements using a program.

[0924] The "means for displaying to the user and receiving a request for modification" is an interface for displaying the automatically generated banner advertisement on the user's device and receiving a request for modification from the user.

[0925] The "means for regenerating a banner" is a mechanism for recreating an already generated banner advertisement based on a user's request for modification.

[0926] "Means for providing a banner after final confirmation by the user" refers to a mechanism in which, after the user gives final approval, the banner advertisement is saved as a final version and provided to the user in the form of a download link or the like.

[0927] "Inputting requirements for banner generation using a smartphone" refers to a user inputting requirements such as size, color, and text content of a banner ad using a smartphone.

[0928] The "means for displaying a preview of the generated banner in real time" refers to a display device or software that allows the user to instantly check the automatically generated banner advertisement.

[0929] The "interactive interface" is an interface that allows a user to input correction requests and send feedback on a generated banner preview using a device such as a smartphone.

[0930] As an embodiment of the present invention, a system consisting of three main elements: a server, a terminal, and a user will be described. Below, the program processing of this system will be explained in natural language, with examples and prompt sentences included.

[0931] System Configuration

[0932] The system mainly includes the following elements:

[0933] 1. Server: This is responsible for collecting, storing, and analyzing data, and creating automatically generated banner ads. Specifically, it consists of a server using Python and Flask, and an SQLite database for storing advertising data.

[0934] 2. Device: Provides the user interface for inputting requirements, previewing banners, and sending correction requests. Smartphones are assumed to be the primary device.

[0935] 3. User: Enters requirements for banner generation, reviews generated banners, requests corrections, and final approval.

[0936] Program processing

[0937] The server uses an API to collect information about past ads and stores it in an SQLite database. This data includes click-through rates, conversion rates, number of impressions, and creative details. A Python analysis tool analyzes the collected data and identifies the characteristics of highly effective banners.

[0938] Users input the requirements for banner generation (size, background color, catchy copy, etc.) using their smartphones, and this data is sent to the server in real time. The server combines the received requirements with the characteristics of highly effective banners and uses an automatic generation algorithm to generate the optimal banner.

[0939] The generated banner is instantly displayed as a preview screen on the device (smartphone), allowing the user to check its contents. The user can then input further correction requests for the preview, which are also sent to the server in real time. The server receives the correction requests, regenerates the banner, and displays the updated preview on the device. This process can be repeated until the user is satisfied.

[0940] Specific examples

[0941] For example, if a user inputs "banner size 300x250, background color blue, catchphrase 'Quick & Easy'", the server will automatically generate a banner based on this and display it on the smartphone. The user can check this preview and input a request to modify the banner, such as "change the color of the catchphrase to red". The server can then generate a new banner that reflects this modification and display it again.

[0942] Prompt Sentence Examples

[0943] Enter the prompt for the generative AI model as follows:

[0944] Generate your banner ad based on the following requirements:

[0945] Size:300x250

[0946] Background color: blue

[0947] Catchphrase: 'Quick & Easy'

[0948] In this way, the present invention allows users to efficiently generate highly effective banners using their smartphones and make real-time modifications.

[0949] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0950] Step 1:

[0951] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) from the past year using the API. It receives the advertising data obtained from the API as input, processes it by storing it in an SQLite database, and outputs the stored data.

[0952] Step 2:

[0953] The server analyzes the advertising data stored in the SQLite database. It takes the advertising data stored as input, performs data calculations using Python analysis tools to identify the characteristics of banners with high click-through rates and conversion rates, and outputs the characteristics of highly effective banners.

[0954] Step 3:

[0955] The user inputs the requirements for the banner (size, background color, catchy copy, etc.) using a smartphone. The requirements specified by the user are entered through the smartphone interface, and this requirement data is sent to the server.

[0956] Step 4:

[0957] The server combines user input data with the characteristics of highly effective banners to automatically generate optimal banners. It receives user requirement data and analyzed highly effective banner characteristics as input, performs data calculations to generate banners using an automatic generation algorithm based on this, and outputs the generated banner data.

[0958] Step 5:

[0959] The terminal displays a preview of the generated banner data sent from the server in real time, passes the generated banner data received from the server as input to the display device, and outputs a preview screen that allows the user to visually check the generated banner.

[0960] Step 6:

[0961] The user inputs a correction request for the previewed banner. The correction request is input as specified by the user through the interactive interface of the smartphone, and this correction request data is sent to the server.

[0962] Step 7:

[0963] The server regenerates the banner based on the user's revision request. It receives the user's revision request data and the original banner data as input, performs data calculations to regenerate the banner that reflects the revision request using the automatic generation algorithm, and outputs the new banner data.

[0964] Step 8:

[0965] The terminal again previews the regenerated banner data, passes the new banner data sent from the server as input to the display device, and outputs a preview screen that allows the user to visually confirm the modified banner.

[0966] Step 9:

[0967] The user finally approves the banner, and inputs an operation to confirm the approved banner, which is then sent to the server.

[0968] Step 10:

[0969] The server stores the final banner data approved by the user and generates a download link. It receives the user's approval data as input, processes the data by storing the final banner in a database, and generates a download link, which it then outputs to the device.

[0970] Step 11:

[0971] The terminal displays the generated download link, passes the download link received from the server as input to the display device, and visually displays a link to download the final banner to the user.

[0972] 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.

[0973] As an embodiment of the present invention, we will explain a system that automatically generates banners by collecting and analyzing advertising data and combining it with an emotion engine that recognizes user emotions. Below, we will explain the program processing of this system in natural language, and also provide concrete examples.

[0974] System configuration

[0975] This system consists of three main components: a server, a terminal, and a user. The server mainly collects data, analyzes it, recognizes emotions, and generates banners. The terminal provides an interface with the user, allowing the user to input requirements for banner creation and provide the data necessary for emotion recognition.

[0976] Explanation of program processing

[0977] 1. Advertising Data Collection

[0978] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) from the past year using the advertising platform's API and stores it in a database.

[0979] 2. Data Analysis

[0980] The server analyzes the advertising data stored in the database and identifies the characteristics of highly effective banners. The analysis results include common characteristics of banners with high click-through rates and conversion rates.

[0981] 3. User requirements input

[0982] Through a chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time.

[0983] 4. User Emotion Recognition

[0984] The server uses an emotion engine to analyze the user's emotions, using text and voice data entered by the user in the chat.

[0985] The emotion engine determines the user's emotional state (e.g., joy, surprise, sadness, anger, etc.) and adjusts the banner design and copy accordingly.

[0986] 5. Automatic banner generation

[0987] The server combines the user's requirements, analysis results, and emotion recognition results to automatically generate the optimal banner. For example, if the emotion engine determines that the user's emotional state is "joy," it will use bright colors and a positive catchy copy.

[0988] 6. Display banner preview

[0989] The terminal displays the generated banner as a preview to the user, who then checks the preview and sends a request for correction if necessary.

[0990] 7. Modify and Regenerate

[0991] The server receives a modification request from the user and regenerates a new banner, reflecting the user's request, for example, by changing the color of the tagline.

[0992] The revised banner will be displayed again as a preview on the device.

[0993] 8. Final confirmation and banner submission

[0994] The user finally checks and approves the banner preview.

[0995] The server saves the approved banner in its final form and generates a download link that is displayed on the device, allowing the user to download the banner.

[0996] Specific examples

[0997] 1. Advertising Data Collection

[0998] The server collects advertising data from a specific advertising platform over the past year (e.g., ads with a click-through rate of 5% or more and a conversion rate of 10% or more) via API and stores it in a database.

[0999] 2. Data Analysis

[1000] The server analyzes the stored advertising data using a Python analysis library to identify the characteristics of effective banners, such as a blue background and a size of 300x250 pixels.

[1001] 3. User requirements input

[1002] Through a chat interface, users input information such as "banner size 300x250, background color blue, catchy slogan simple."

[1003] 4. User Emotion Recognition

[1004] The server analyzes the text and voice data entered by the user using an emotion engine and determines that the user is happy.

[1005] The server takes into account your emotional state and suggests banners with positive slogans and upbeat designs.

[1006] 5. Automatic banner generation

[1007] The server runs a banner generation algorithm based on the user's requirements and emotion recognition results to automatically generate the optimal banner.

[1008] 6. Display banner preview

[1009] The device displays a preview of the generated banner, showing the user a banner with a "product photo, a blue background, and a simple, positive tagline."

[1010] 7. Modify and Regenerate

[1011] The user sends a correction request saying, "I want the color of the catchphrase to be changed to red."

[1012] The server reflects this modification request, generates a new banner, and displays the preview again.

[1013] 8. Final confirmation and banner submission

[1014] The user finally checks the preview and clicks the "Approve" button.

[1015] The server saves the final banner and generates a download link that is displayed on the device for the user to download the banner.

[1016] In this way, by using this system, it is possible to efficiently create highly effective banners and provide optimal advertisements that match the user's emotions.

[1017] The processing flow will be explained below.

[1018] Step 1: Collect advertising data

[1019] The server periodically calls the advertising platform's API to collect data on ads that have been displayed over the past year (e.g., click-through rate, conversion rate, number of impressions, creative details, etc.).

[1020] The server stores the collected data in a database and sets the appropriate fields (e.g., banner size, color, text content, etc.).

[1021] Step 2: Analyze the data

[1022] The server analyzes the advertising data stored in the database and uses a Python analysis library to extract the characteristics of banners with high click-through rates and conversion rates.

[1023] Based on the analysis results, the server identifies common characteristics of effective banners (e.g., specific size, color, placement, etc.).

[1024] Step 3: Enter user requirements

[1025] Users input their banner requirements (size, color, image, text, etc.) through a chat interface.

[1026] The terminal transmits the input user data to the server in real time.

[1027] Step 4: Recognizing user emotions

[1028] The server receives text and voice data for analyzing the user's emotions using an emotion engine.

[1029] The emotion engine analyzes input text and voice data to determine the user's emotional state (e.g., joy, surprise, sadness, anger, etc.).

[1030] The server adjusts the banner design and copy taking into account the emotional state identified by the emotion engine.

[1031] Step 5: Auto-generating a banner

[1032] The server combines the requirements entered by the user, the analysis results, and the emotion recognition results to automatically generate the optimal banner.

[1033] For example, if the emotion engine determines that the user's emotion is "joy," it will generate a banner with bright colors and positive copy.

[1034] The server converts the generated banner into an image format and saves it.

[1035] Step 6: View the banner preview

[1036] The terminal displays the generated banner preview to the user.

[1037] The user checks the displayed preview and, if necessary, sends a request for corrections through the chat interface.

[1038] Step 7: Fix and Regenerate

[1039] The server receives a modification request from the user and regenerates a new banner.

[1040] For example, the banner is adjusted in response to a user's request for modification, such as changing the color of the catchphrase to red.

[1041] The server transmits the regenerated banner to the terminal again as a preview.

[1042] Step 8: Final confirmation and banner submission

[1043] The user reviews and approves the final banner preview.

[1044] The server saves the approved banner as the final version and generates a download link.

[1045] The terminal displays the generated download link, allowing the user to download the banner.

[1046] The above steps complete a series of processes, from collecting advertising data to creating and providing optimal banners. By combining this with an emotion recognition engine, it is possible to provide optimal advertising creatives that match the user's emotions.

[1047] Example 2

[1048] 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."

[1049] Conventional banner ad generation systems lack sufficient analysis to maximize advertising effectiveness, making it difficult to provide banner designs that reflect the user's emotional state. As a result, advertising effectiveness is reduced and users are unable to fully respond.

[1050] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and saving information about advertisements that have been posted in the past, means for analyzing information about the advertisements and identifying highly effective features, means for recognizing user input data and emotions, means for automatically generating a banner by combining the identified features with the user's emotional information, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the request for revision, and means for providing the banner after final confirmation by the user. This makes it possible to maximize advertising effectiveness and efficiently generate banners with designs that reflect the user's emotional state.

[1051] "Advertising information" refers to data related to marketing materials that users view and interact with, including click rates, conversion rates, impressions, creative details, and the like.

[1052] "Analysis" is the process of analyzing data and extracting useful information or features from it, and in the case of advertising data in particular, it refers to evaluating performance metrics such as click-through rates and conversion rates.

[1053] "Means for recognizing emotions" refers to technology that analyzes user input data and voice data to determine the user's emotional state (for example, joy, surprise, sadness, anger, etc.).

[1054] A "banner" is an advertising image or animation that appears on a web page and contains a specific marketing message or visual element.

[1055] "Automatic generation means" refers to the process of using programs and algorithms to automatically create advertising banners based on specific input data.

[1056] A "request for modification" is a request for modification made by a user to a generated banner, including partial modifications to the design or text.

[1057] The "regeneration means" is a process for regenerating a banner based on a user's modification request.

[1058] "Final confirmation" is the act of the user checking and approving the final version of the banner.

[1059] The "means of providing" refers to the technology by which the final approved banner is provided to the user and made available for download and use.

[1060] This invention relates to a system that automatically generates banners by collecting advertising data, analyzing it, and combining it with an emotion engine that recognizes user emotions. This system consists of three main elements: a server, a terminal, and a user.

[1061] System configuration

[1062] server

[1063] The server is the heart of the system and performs the following main functions:

[1064] 1. Data collection: The server uses the advertising platform's API to collect advertising data (click-through rate, conversion rate, number of impressions, creative details) from the past year and saves it in a database.

[1065] 2. Data analysis: Analyze the data using Python analysis libraries (e.g., pandas, numpy, scikit-learn) to identify the characteristics of effective banners.

[1066] 3. Emotion recognition: Analyze user input data and voice data using an emotion engine (e.g., Google Cloud Natural Language API, IBM Watson) to determine the user's emotional state.

[1067] 4. Automatic banner generation: By combining user requirements and emotion recognition results, banners are automatically generated using a generative AI model (e.g., GAN, VQ-VAE).

[1068] 5. Banner regeneration: Receives correction requests from users and regenerates banners that reflect the content.

[1069] 6. Banner submission: Save the final approved banner and generate a download link.

[1070] Terminal

[1071] The terminal is responsible for providing the interface with the user:

[1072] 1. Input interface: A chat interface (e.g., a React-based chatbot) for users to input their banner requirements.

[1073] 2. Preview: The generated banner is displayed to the user as a preview and has the function of receiving correction requests.

[1074] 3. Download: Provides a link to download the final version of the banner.

[1075] User

[1076] Users have the following roles:

[1077] 1. Input requirements: Input requirements such as banner size, color, image, text, etc. through the chat interface.

[1078] 2. Providing emotional data: Provide input text and voice data and have the server analyze your emotional state.

[1079] 3. Preview: Check the preview of the generated banner and make any necessary corrections.

[1080] 4. Final Review: Review and approve the final banner.

[1081] Specific examples

[1082] Here's a concrete example of how the system works:

[1083] 1. Collecting advertising data: The server collects advertising data with a click rate of 5% or more and a conversion rate of 10% or more over the past year through the API of a specific advertising platform and stores it in a database.

[1084] 2. Data Analysis: The server analyzes the stored advertising data and identifies the characteristics of effective banners, such as a blue background and a size of 300x250 pixels.

[1085] 3. User requirements input: The user inputs the following through the chat interface: "Banner size 300x250, background color blue, catchy copy simple."

[1086] 4. User emotion recognition: The server analyzes the text and voice data entered by the user using an emotion engine and determines that the user is happy.

[1087] 5. Automatic banner generation: The server runs a banner generation algorithm based on the user requirements and emotion recognition results to automatically generate the optimal banner.

[1088] 6. Display banner preview: The device displays a preview of the generated banner, showing the user a banner with a product photo, a blue background, and a simple, positive tagline.

[1089] 7. Modify and regenerate: The user sends a modification request, such as "I want the tagline color to be changed to red." The server reflects this modification request, generates a new banner, and displays the preview again.

[1090] 8. Final confirmation and banner delivery: The user finally checks the preview and clicks the "Approve" button. The server saves the final version of the banner and generates a download link. This link is displayed on the device, and the user can download the banner.

[1091] Prompt Sentence Examples

[1092] The banner size should be 300x250, with a blue background. The tagline should be simple. Also, please analyze the sentiment of the text entered by the user and suggest a design that matches that sentiment.

[1093] This system makes it possible to maximize advertising effectiveness and efficiently generate optimal banners that reflect the user's emotional state.

[1094] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1095] Step 1: Collect advertising data

[1096] The server accesses the advertising platform's API and periodically collects advertising data from the past year. This data includes click-through rates, conversion rates, number of impressions, and creative details. The collected data is sent to the server in JSON format and stored in a database. The input is the raw data obtained from the advertising platform's API, and the output is the advertising data stored in the database.

[1097] Step 2: Analyze the data

[1098] The server uses Python analysis libraries (e.g., pandas, numpy, scikit-learn) to analyze the advertising data stored in the database. The analysis script is run periodically to identify the characteristics of highly effective banners. Specifically, it extracts common characteristics (e.g., background color, font style, image placement) of banners with high click-through rates and conversion rates. The input is the advertising data stored in the database, and the output is the identified characteristics of highly effective banners.

[1099] Step 3: Enter user requirements

[1100] Users input their banner requirements through a chat interface on their web browser. For example, they can enter specific requests such as "I want the banner size to be 300x250 pixels, the background color to be blue, and the tagline to be simple." The input information is sent to the server in real time and recorded in a database. The input is the banner requirements provided by the user through the chat interface, and the output is the input data stored in the database.

[1101] Step 4: Recognizing user emotions

[1102] The server sends the text and voice data entered by the user to an emotion recognition engine (e.g., Google Cloud Natural Language API, IBM Watson) to analyze the user's emotions. The emotion recognition engine uses text analysis technology to determine the emotion the user is feeling (e.g., joy, surprise, sadness, anger). The analysis results are stored in a database and reflected in the banner design. The input is the text and voice data entered by the user, and the output is the recognized user's emotional information.

[1103] Step 5: Auto-generating a banner

[1104] The server combines the user's requirements, the analyzed features of effective banners, and emotion recognition results to automatically generate a banner using a generative AI model (e.g., GAN, VQ-VAE). This algorithm creates an optimal banner that matches the input conditions. Specifically, if the user's requirements are "joy," a banner size of 300x250 pixels, and a blue background color, a corresponding bright banner design will be generated. The input is the user's requirements, the features of effective banners, and the emotion recognition results, and the output is the generated banner image.

[1105] Step 6: View the banner preview

[1106] The terminal receives the banner image sent from the server and displays it as a preview to the user. Because it is displayed in real time on the web browser, the user can immediately check the banner. For example, a banner with a "product photo, blue background, and simple, positive tagline" is presented to the user. The input is the generated banner image, and the output is the preview screen that the user checks.

[1107] Step 7: Fix and Regenerate

[1108] The user views the preview and sends a request for revisions, such as "change the color of the catchphrase to red," through the chat interface. The server receives this request and runs the generative AI model again to generate a new banner. The revised banner is then sent back to the device and displayed as a preview to the user. The input is the user's request for revisions, and the output is the revised new banner image.

[1109] Step 8: Final confirmation and banner submission

[1110] The user checks the final preview and clicks the "Approve" button to finalize the banner. The server saves the final banner and generates a download link. This link is displayed on the device, and the user can click it to download the banner. The input is the approval result of the final preview, and the output is the final banner and its download link.

[1111] In this way, by combining advertising data analysis and user emotion recognition, this system can efficiently generate highly effective banners and provide them to users.

[1112] (Application example 2)

[1113] 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."

[1114] While conventional banner generation systems provide a means for generating highly effective banners based on the analysis of advertising data, they lack the ability to adjust banners in response to user emotions and real-time requests. This hinders users' ability to create more effective and emotionally appealing banner ads. Furthermore, the lack of a means for flexibly collecting user requirements via voice or text and generating and adjusting banners based on those requirements limits the user experience.

[1115] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1116] In this invention, the server includes means for collecting and saving information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for analyzing the user's emotions and adjusting the banner design and catchy copy based on the user's emotional state, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the request for revision, and means for providing the banner after final confirmation by the user. This makes it possible to automatically generate, display, modify, and finally provide banners flexibly according to the user's emotional state and specific requirements.

[1117] "Advertising Information" refers to data regarding advertisements that have been previously published, including click-through rates, conversion rates, number of impressions, and creative details.

[1118] "Analysis" refers to the process of analyzing collected data using statistical methods and algorithms to identify highly effective features.

[1119] A "banner" refers to an advertising image or text that appears on a digital medium such as a web page or application.

[1120] "User-input data" refers to various requirements and information provided by the user to the system, including banner size, color, image, text, etc.

[1121] "Emotion analysis" is a technology that recognizes a user's emotional state (joy, surprise, sadness, anger, etc.) based on their voice and text data.

[1122] "Automatic banner generation" is the process by which the system algorithmically generates appropriate banner ads based on collected data and user requirements.

[1123] A "request for modification" refers to a user viewing a banner preview and requesting the system to make changes to the design, text, etc.

[1124] "Regeneration" is the process by which the system regenerates the banner based on the user's modification requests.

[1125] "Final confirmation" refers to the process in which the generated banner is presented to the user for final approval.

[1126] "Storage" refers to recording the generated banners and analysis data in a database or storage so that they can be accessed later.

[1127] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server mainly collects and analyzes advertising data, recognizes emotions, and automatically generates and modifies banners. The terminal provides an interface with the user, allowing the user to input requirements for banner creation and provide the data necessary for emotion recognition.

[1128] The server uses the advertising platform's API to collect and store information about previously displayed ads, including click-through rates, conversion rates, number of impressions, creative details, etc. The collected information is stored in a database and used for analysis.

[1129] The server analyzes the collected advertising information using statistical methods and algorithms to identify the characteristics of effective banners. This analysis reveals which advertising elements are most effective, such as specific colors, sizes, and text styles.

[1130] Users input their banner requirements through the device's operating interface, using voice and text interfaces, such as "banner size 300x250, background color blue, catchy copy simple."

[1131] The server uses an emotion engine to analyze the user's emotions. The emotion analysis uses the user's voice and text data to determine their emotional state (happiness, surprise, sadness, anger, etc.). Based on the results of this emotion analysis, the banner design and catchy copy are adjusted.

[1132] For example, if the emotion engine determines that the user's emotional state is "joy," a banner using bright colors and a positive catchphrase is automatically generated. This banner is displayed as a preview to the user via their device. The user can check the preview and, if necessary, send a request for corrections by voice or text, such as "I want the catchphrase color to be red."

[1133] The server receives the user's request for corrections and regenerates the banner based on the request. This regenerated banner is then displayed to the user as a preview. Finally, when the user checks the preview and approves it, the server saves the final version of the banner and generates a download link. This link is displayed on the device, allowing the user to download the banner.

[1134] A concrete example of this system is its application using smart glasses. The smart glasses can display banner ads in the user's field of vision in real time and adjust the ad content through emotion recognition. When the user verbally commands, "Show me recommended banners," the system performs emotion analysis and displays banners and designs with positive messages in real time. For example, if the user verbally requests, "Change the catchphrase," the banner will be regenerated and displayed in the user's field of vision.

[1135] Example prompt sentence:

[1136] User: "Show me the recommended banner."

[1137] Program: "We're happy to show you a banner with a positive message."

[1138] User: "Change the tagline."

[1139] Program: "Change the tagline and preview again."

[1140] This system allows for the automatic generation of banners that can be flexibly adapted to the user's emotional state and specific requirements, and then displayed, modified, and finally provided.

[1141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1142] Step 1:

[1143] The server uses the advertising platform's API to collect and store information about previously displayed ads (click-through rate, conversion rate, number of impressions, creative details, etc.) The server makes an API request and stores the obtained data in a database, where this information is accumulated and used for later analysis.

[1144] Input: Advertising data obtained from advertising platforms

[1145] Output: Advertisement information stored in the database

[1146] Step 2:

[1147] The server analyzes the advertising information stored in the database using statistical methods and algorithms to identify highly effective features. Specifically, it uses a Python analysis library to extract common features of ads that meet certain criteria, such as a click-through rate of 5% or more and a conversion rate of 10% or more.

[1148] Input: Advertisement information stored in the database

[1149] Output: High-impact banner features (e.g. color, size, text style, etc.)

[1150] Step 3:

[1151] Users can input banner requirements using their device's voice assistant or text interface. For example, if a user requests a banner size of 300x250, a blue background, and a simple tagline, these requirements are sent to the server.

[1152] Input: User-entered data (voice or text)

[1153] Output: User's banner requirements are sent to the server

[1154] Step 4:

[1155] The server uses an emotion engine to analyze the user's emotions. The emotion engine determines the user's emotional state (happiness, surprise, sadness, anger, etc.) based on the user's voice data and text data. For example, if the user enters "I'm feeling happy today," the emotion engine will determine that the emotion is "happiness."

[1156] Input: User voice and text data

[1157] Output: User's emotional state

[1158] Step 5:

[1159] The server automatically generates a banner by combining the user's requirements, the characteristics of effective banners, and the results of sentiment analysis. For example, if the sentiment analysis result is "joy," a banner with bright colors and a positive catchphrase will be generated. The generated banner is temporarily stored on the server.

[1160] Input: User banner requirements, characteristics of effective banners, user emotional state

[1161] Output: Auto-generated banner

[1162] Step 6:

[1163] The device displays the generated banner as a preview to the user. The user checks the preview and sends a request for corrections, such as "I want the catchphrase color to be changed to red," to the device via voice or text. The request data is then sent to the server.

[1164] Input: Auto-generated banner, user request for correction

[1165] Output: The modification request is sent to the server

[1166] Step 7:

[1167] The server regenerates the banner based on the user's request for modification. For example, if the user requests that the color of the catchphrase be changed to red, the server regenerates the banner reflecting the request and temporarily saves it again.

[1168] Input: User's correction request

[1169] Output: Regenerated banner

[1170] Step 8:

[1171] The device will then preview the regenerated banner and display it to the user. If the user confirms the final version and approves it, the server will save the final version of the banner and generate a download link. This link will be displayed on the device, allowing the user to download the banner.

[1172] Input: Regenerated banner, final user confirmation

[1173] Output: A download link will be displayed on the terminal.

[1174] 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.

[1175] 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.

[1176] 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.

[1177] [Fourth embodiment]

[1178] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1179] 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.

[1180] 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).

[1181] 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.

[1182] 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.

[1183] 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).

[1184] 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.

[1185] 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.

[1186] 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.

[1187] 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.

[1188] 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.

[1189] 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.

[1190] 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."

[1191] As an embodiment of the present invention, a system for collecting and analyzing advertising data and automatically generating optimal banners will be described. Below, the program processing of this system will be explained in natural language, and specific examples will be given.

[1192] System configuration

[1193] This system consists of three main components: a server, a terminal, and a user. The server mainly collects data, analyzes it, and generates banners. The terminal provides an interface for users, and users input requirements for creating banners.

[1194] Explanation of program processing

[1195] 1. Advertising Data Collection

[1196] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) for the past year using the advertising platform's API and stores it in a database.

[1197] 2. Data Analysis

[1198] The server analyzes the advertising data stored in the database and identifies the characteristics of banners with high click-through rates and conversion rates. Based on these identified characteristics, it extracts highly effective creative patterns.

[1199] 3. User requirements input

[1200] Through the chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time.

[1201] 4. Automatic banner generation

[1202] The server combines the user's input data with the results of the analysis described above to automatically generate the optimal banner, incorporating highly effective features identified from past data.

[1203] 5. Display banner preview

[1204] The generated banner is displayed as a preview on the user's device in real time, and the user can check the preview and submit correction requests if necessary.

[1205] 6. Modify and Regenerate

[1206] The server receives a modification request from the user, reflects the request, and re-previews the regenerated banner. This process is repeated until the user approves it.

[1207] 7. Final confirmation and banner submission

[1208] Once the user finally approves the banner, the server saves the banner in its final form and generates a download link that can be provided to the user to save the banner on their device.

[1209] Specific examples

[1210] 1. Advertising Data Collection

[1211] The server retrieves advertising data from the past year from the advertising platform's API and stores it in a database. For example, it collects data from a specific platform on "advertising with a click-through rate of 5% or more" or "advertising with a conversion rate of 10% or more."

[1212] 2. Data Analysis

[1213] The server analyzes the collected data using analytical tools such as Python and identifies the characteristics of highly effective banners, such as a blue background and 300x250 pixels.

[1214] 3. User requirements input

[1215] Users use a chat interface to input requests such as "banner size 300x250, background color blue, tagline simple."

[1216] 4. Automatic banner generation

[1217] The server creates an automatically generated banner by combining the user's requests with the characteristics of past highly effective banners.

[1218] 5. Display banner preview

[1219] The device will then display a preview of the generated banner, such as a banner with a product photo, a blue background, and a simple tagline.

[1220] 6. Modify and Regenerate

[1221] The user sends a request for correction to the preview via chat, such as "I want the catchphrase color to be changed to red."

[1222] The server reflects this modification request, regenerates the banner, and updates the preview.

[1223] 7. Final confirmation and banner submission

[1224] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link that is displayed on the device for the user to download the banner.

[1225] In this way, by using this system, it is possible to efficiently create highly effective banners and maximize the effectiveness of advertising.

[1226] The processing flow will be explained below.

[1227] Step 1: Collect advertising data

[1228] The server periodically calls the advertising platform's API to collect data on ads that have been displayed over the past year (e.g., click-through rate, conversion rate, number of impressions, creative details, etc.).

[1229] The server stores the collected data in a database and sets the appropriate fields (e.g., banner size, color, text content, etc.).

[1230] Step 2: Analyze the data

[1231] The server analyzes the advertising data stored in the database and extracts the characteristics of banners with high click-through rates and conversion rates, for example, using a Python analysis library.

[1232] Based on the analysis results, the server identifies common characteristics of effective banners (e.g., specific size, color, placement, etc.).

[1233] Step 3: Enter user requirements

[1234] Users input their banner requirements (size, color, image, text, etc.) through a chat interface.

[1235] The terminal transmits input data from the user to the server in real time.

[1236] Step 4: Auto-generate a banner

[1237] The server combines the user's requirements with the results of the analysis described above and automatically generates an optimal banner, for example, by using a specific template engine to create the banner layout.

[1238] The server converts the generated banner into an image format and saves it.

[1239] Step 5: View the banner preview

[1240] The terminal displays the generated banner preview sent from the server to the user.

[1241] The user checks the displayed preview and sends any correction requests through the chat interface.

[1242] Step 6: Fix and Regenerate

[1243] The server receives the modification request from the user and regenerates a new banner, reflecting the modifications, such as changing the color of the tagline to red or changing the image.

[1244] The server sends the modified banner again to the terminal as a preview.

[1245] Step 7: Final confirmation and banner submission

[1246] The user then finalizes and approves the revised banner preview.

[1247] The server saves the approved banner as the final version and generates a download link.

[1248] The terminal displays the generated download link, allowing the user to download the banner.

[1249] The above steps complete a series of processes, from collecting advertising data to creating and providing optimal banners.

[1250] Example 1

[1251] 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."

[1252] Conventional methods for creating advertising banners often require manual processes, from collecting and analyzing advertising data, generating banners, incorporating user requests for modifications, and finally providing them, resulting in issues of inefficiency and time. Furthermore, there are difficulties in reflecting the specific requirements desired by users, which creates a risk of reducing the effectiveness of the banner. This has led to a demand for a means to quickly and efficiently create optimal banners to maximize advertising effectiveness.

[1253] 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.

[1254] In this invention, the server includes means for collecting and storing information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the requested revision, means for providing the banner after final confirmation by the user, means for automatically generating a banner using a generative AI model, means for collecting user input data through a chat interface, means for displaying a preview of the automatically generated banner on the user's device, means for transmitting user revision requests for the preview to the server in real time, and means for regenerating the banner reflecting the requested revision and displaying the preview again on the user's device. This automates the process from collecting and analyzing advertising data, optimally automatically generating a banner, revising and regenerating it based on user requirements, and finally providing it, enabling efficient and rapid creation of highly effective banners.

[1255] "Information about the advertisement" refers to detailed data about the performance and content of the advertisement, such as click-through rates, conversion rates, impressions, and creative details.

[1256] "Collection means" refers to the means that have the function of obtaining data using the advertising platform's API and storing it in a database.

[1257] "Analysis means" refers to a means that has the function of analyzing collected data using an analysis tool and identifying highly effective features of advertising.

[1258] "Automatic generation means" refers to a means that has the function of generating the optimal banner using a generative AI model based on user input data and analysis results.

[1259] "Display means" refers to a means having a function for previewing an automatically generated banner on a user's terminal.

[1260] The "modification request receiving means" refers to a means having a function of receiving a modification request from a user in real time.

[1261] The "regeneration means" refers to a means having a function of regenerating a banner based on a user's correction request and displaying a preview again.

[1262] The "means for providing after final confirmation" refers to a means having a function of saving the banner that the user finally approves, generating a download link, and providing it to the user.

[1263] A "generative AI model" refers to an artificial intelligence model that generates images based on prompts.

[1264] "Chat Interface" means an interactive interface through which a user can input banner requirements and communicate with a server in real time.

[1265] "Preview" refers to a temporary display of an automatically generated banner that allows a user to review the content and make correction requests.

[1266] A "terminal" is a device that allows a user to access the system and has the function of displaying a preview and receiving user input.

[1267] As an embodiment of the present invention, a system for collecting and analyzing advertising data and automatically generating optimal banners will be described in detail. This system is composed of three main elements: a server, a terminal, and a user.

[1268] System configuration

[1269] This system consists of a server that collects and analyzes advertising data and generates banners, a terminal that provides an interface with users, and a user that inputs requirements for creating banners.

[1270] Advertising data collection

[1271] The server periodically collects advertising data from the past year using the advertising platform's API and stores it in a database. The collected data includes click-through rates, conversion rates, number of impressions, and detailed creative information. For example, data on "advertisements with a click-through rate of 5% or more" and "advertisements with a conversion rate of 10% or more" is collected from a specific platform.

[1272] Data analysis

[1273] The server analyzes the collected data using analytical tools such as Python's Pandas and SciPy to identify the characteristics of banners with high click-through rates and conversion rates. Through this analysis process, highly effective creative patterns such as "blue background" and "300x250 pixels" are extracted.

[1274] User requirements input

[1275] Through a chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time. For example, they can make specific requests such as "banner size 300x250, background color blue, and tagline simple."

[1276] Automatic banner generation

[1277] The server combines the user's input data with the results of the analysis described above to automatically generate the optimal banner. A generative AI model (e.g., DALL-E or GAN model) is used to create a banner that reflects the characteristics of highly effective creative. At this time, prompts are sent to the generative AI model based on the user's requirements. Possible prompts include "banner size 300x250, background color blue, and catchy copy simple."

[1278] View banner preview

[1279] The device displays a preview of the generated banner to the user in real time. The user can check this preview and send correction requests via chat if necessary. For example, if the generated banner has a product photo, a blue background, and a simple tagline, the user can request that the tagline color be changed to red.

[1280] Fix and Regenerate

[1281] The server receives the user's modification request and regenerates the banner to reflect the changes. This process is repeated until the user is satisfied. The regenerated banner is then displayed again as a preview on the user's device.

[1282] Final confirmation and banner provision

[1283] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link that is displayed on the device, allowing the user to download the final version of the banner.

[1284] In this way, by using this system, it is possible to efficiently create highly effective banners and maximize the effectiveness of advertising.

[1285] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1286] Step 1: Collect advertising data

[1287] The server uses the advertising platform's API to periodically collect advertising data from the past year and store it in a database. The input is data obtained from the advertising platform (click-through rate, conversion rate, number of impressions, and creative details), and the output is a database in which the collected data is stored. Specifically, the server accesses the advertising platform's API at a set time every day, establishes a connection to the database, and stores the data.

[1288] Step 2: Analyze the data

[1289] The server analyzes the advertising data stored in the database using analysis tools such as Python's Pandas and SciPy. The input is the advertising data read from the database, and the output is the analysis results, i.e., the characteristics of highly effective banners (e.g., blue background, 300x250 pixels, etc.). Specifically, the server periodically runs analysis scripts, filters data whose click-through rate or conversion rate exceeds a certain threshold, and performs cluster analysis to extract commonalities among highly effective banners.

[1290] Step 3: Enter user requirements

[1291] The user uses the chat interface to input the requirements for the banner (size, color, image, text, etc.). The input is the banner requirements entered by the user in the chat, and the output is the formatted requirement data sent to the server. Specifically, the user enters instructions to the chatbot on the web browser, such as "banner size 300x250, background color blue, catchy copy simple," and the chatbot confirms the instructions and sends them to the server.

[1292] Step 4: Auto-generate a banner

[1293] The server combines the user's input data with the aforementioned analysis results and automatically generates the optimal banner using a generative AI model. The input is the user's requirement data and the analysis results, and the output is the generated banner image. Specifically, the server sends a prompt to the generative AI model (e.g., DALL-E or GAN model), and the model generates an image based on the specified requirements and saves it in a temporary folder.

[1294] Step 5: View the banner preview

[1295] The terminal displays the generated banner to the user as a preview in real time. The input is the generated banner image, and the output is a preview screen that the user can view. Specifically, the terminal uses technologies such as JavaScript to display the image file, and the preview screen displays an "Approve" button and a "Request correction" button.

[1296] Step 6: Fix and Regenerate

[1297] The user issues a request for correction to the preview. For example, they send a specific instruction via chat, such as "I want the catchphrase color to be changed to red." The input is the user's request for correction, and the output is a new banner image that reflects the correction. Specifically, when the server receives the request for correction, it again sends a prompt to the generation AI model, which generates a new banner. Once the new banner is generated, it is again displayed as a preview on the device.

[1298] Step 7: Final confirmation and banner submission

[1299] Once the user finally approves the banner, the server saves the final version of the banner and generates a download link. The input is the banner image approved by the user, and the output is the download link. Specifically, the server saves the final version of the banner in a database or file system and provides the generated download link through the user's chat interface. The user clicks the provided link to download the final version of the banner.

[1300] (Application example 1)

[1301] 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."

[1302] Conventional banner ad generation systems require a lot of time and effort to create effective banners. In particular, the process of users inputting banner requirements and then reviewing and modifying the generated banner is cumbersome, making it difficult to efficiently create highly effective banners. Furthermore, the lack of a convenient banner generation process that can be used on smartphones makes it difficult for advertisers to easily and quickly generate banners.

[1303] 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.

[1304] In this invention, the server includes means for collecting and saving information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for displaying the automatically generated banner to the user and receiving a request for modification, means for regenerating the banner based on the requested modification, means for providing the banner after final confirmation by the user, means for inputting requirements for banner generation using a smartphone and previewing the generated banner in real time, and means for providing an interactive interface for the user to input modifications to the previewed banner. This enables a user to efficiently and quickly generate highly effective banners using a smartphone and make modifications in real time.

[1305] "Information about previously placed advertisements" refers to data including click-through rates, conversion rates, number of impressions, and creative details of advertisements previously placed on advertising platforms and websites.

[1306] "Analyzing information about advertisements" refers to a process of analyzing collected advertisement data and identifying the characteristics of banners with high click rates and conversion rates.

[1307] "Highly effective features" refer to elements such as color, size, text, and images that are common to banners that have higher click-through rates and conversion rates than other ads.

[1308] "User input data" refers to information including user requirements for creating a banner advertisement (eg, size, color, text content, etc.).

[1309] "Means for automatically generating banners" refers to a system that combines collected and analyzed data with user input data to automatically create banner advertisements using a program.

[1310] The "means for displaying to the user and receiving a request for modification" is an interface for displaying the automatically generated banner advertisement on the user's device and receiving a request for modification from the user.

[1311] The "means for regenerating a banner" is a mechanism for recreating an already generated banner advertisement based on a user's request for modification.

[1312] "Means for providing a banner after final confirmation by the user" refers to a mechanism in which, after the user gives final approval, the banner advertisement is saved as a final version and provided to the user in the form of a download link or the like.

[1313] "Inputting requirements for banner generation using a smartphone" refers to a user inputting requirements such as size, color, and text content of a banner ad using a smartphone.

[1314] The "means for displaying a preview of the generated banner in real time" refers to a display device or software that allows the user to instantly check the automatically generated banner advertisement.

[1315] The "interactive interface" is an interface that allows a user to input correction requests and send feedback on a generated banner preview using a device such as a smartphone.

[1316] As an embodiment of the present invention, a system consisting of three main elements: a server, a terminal, and a user will be described. Below, the program processing of this system will be explained in natural language, with examples and prompt sentences included.

[1317] System Configuration

[1318] The system mainly includes the following elements:

[1319] 1. Server: This is responsible for collecting, storing, and analyzing data, and creating automatically generated banner ads. Specifically, it consists of a server using Python and Flask, and an SQLite database for storing advertising data.

[1320] 2. Device: Provides the user interface for inputting requirements, previewing banners, and sending correction requests. Smartphones are assumed to be the primary device.

[1321] 3. User: Enters requirements for banner generation, reviews generated banners, requests corrections, and final approval.

[1322] Program processing

[1323] The server uses an API to collect information about past ads and stores it in an SQLite database. This data includes click-through rates, conversion rates, number of impressions, and creative details. A Python analysis tool analyzes the collected data and identifies the characteristics of highly effective banners.

[1324] Users input the requirements for banner generation (size, background color, catchy copy, etc.) using their smartphones, and this data is sent to the server in real time. The server combines the received requirements with the characteristics of highly effective banners and uses an automatic generation algorithm to generate the optimal banner.

[1325] The generated banner is instantly displayed as a preview screen on the device (smartphone), allowing the user to check its contents. The user can then input further correction requests for the preview, which are also sent to the server in real time. The server receives the correction requests, regenerates the banner, and displays the updated preview on the device. This process can be repeated until the user is satisfied.

[1326] Specific examples

[1327] For example, if a user inputs "banner size 300x250, background color blue, catchphrase 'Quick & Easy'", the server will automatically generate a banner based on this and display it on the smartphone. The user can check this preview and input a request to modify the banner, such as "change the color of the catchphrase to red". The server can then generate a new banner that reflects this modification and display it again.

[1328] Prompt Sentence Examples

[1329] Enter the prompt for the generative AI model as follows:

[1330] Generate your banner ad based on the following requirements:

[1331] Size:300x250

[1332] Background color: blue

[1333] Catchphrase: 'Quick & Easy'

[1334] In this way, the present invention allows users to efficiently generate highly effective banners using their smartphones and make real-time modifications.

[1335] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1336] Step 1:

[1337] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) from the past year using the API. It receives the advertising data obtained from the API as input, processes it by storing it in an SQLite database, and outputs the stored data.

[1338] Step 2:

[1339] The server analyzes the advertising data stored in the SQLite database. It takes the advertising data stored as input, performs data calculations using Python analysis tools to identify the characteristics of banners with high click-through rates and conversion rates, and outputs the characteristics of highly effective banners.

[1340] Step 3:

[1341] The user inputs the requirements for the banner (size, background color, catchy copy, etc.) using a smartphone. The requirements specified by the user are entered through the smartphone interface, and this requirement data is sent to the server.

[1342] Step 4:

[1343] The server combines user input data with the characteristics of highly effective banners to automatically generate optimal banners. It receives user requirement data and analyzed highly effective banner characteristics as input, performs data calculations to generate banners using an automatic generation algorithm based on this, and outputs the generated banner data.

[1344] Step 5:

[1345] The terminal displays a preview of the generated banner data sent from the server in real time, passes the generated banner data received from the server as input to the display device, and outputs a preview screen that allows the user to visually check the generated banner.

[1346] Step 6:

[1347] The user inputs a correction request for the previewed banner. The correction request is input as specified by the user through the interactive interface of the smartphone, and this correction request data is sent to the server.

[1348] Step 7:

[1349] The server regenerates the banner based on the user's revision request. It receives the user's revision request data and the original banner data as input, performs data calculations to regenerate the banner that reflects the revision request using the automatic generation algorithm, and outputs the new banner data.

[1350] Step 8:

[1351] The terminal again previews the regenerated banner data, passes the new banner data sent from the server as input to the display device, and outputs a preview screen that allows the user to visually confirm the modified banner.

[1352] Step 9:

[1353] The user finally approves the banner, and inputs an operation to confirm the approved banner, which is then sent to the server.

[1354] Step 10:

[1355] The server stores the final banner data approved by the user and generates a download link. It receives the user's approval data as input, processes the data by storing the final banner in a database, and generates a download link, which it then outputs to the device.

[1356] Step 11:

[1357] The terminal displays the generated download link, passes the download link received from the server as input to the display device, and visually displays a link to download the final banner to the user.

[1358] 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.

[1359] As an embodiment of the present invention, we will explain a system that automatically generates banners by collecting and analyzing advertising data and combining it with an emotion engine that recognizes user emotions. Below, we will explain the program processing of this system in natural language, and also provide concrete examples.

[1360] System configuration

[1361] This system consists of three main components: a server, a terminal, and a user. The server mainly collects data, analyzes it, recognizes emotions, and generates banners. The terminal provides an interface with the user, allowing the user to input requirements for banner creation and provide the data necessary for emotion recognition.

[1362] Explanation of program processing

[1363] 1. Advertising Data Collection

[1364] The server periodically collects advertising data (click-through rate, conversion rate, number of impressions, and creative details) from the past year using the advertising platform's API and stores it in a database.

[1365] 2. Data Analysis

[1366] The server analyzes the advertising data stored in the database and identifies the characteristics of highly effective banners. The analysis results include common characteristics of banners with high click-through rates and conversion rates.

[1367] 3. User requirements input

[1368] Through a chat interface, users input their banner requirements (size, color, image, text, etc.), which are then sent to the server in real time.

[1369] 4. User Emotion Recognition

[1370] The server uses an emotion engine to analyze the user's emotions, using text and voice data entered by the user in the chat.

[1371] The emotion engine determines the user's emotional state (e.g., joy, surprise, sadness, anger, etc.) and adjusts the banner design and copy accordingly.

[1372] 5. Automatic banner generation

[1373] The server combines the user's requirements, analysis results, and emotion recognition results to automatically generate the optimal banner. For example, if the emotion engine determines that the user's emotional state is "joy," it will use bright colors and a positive catchy copy.

[1374] 6. Display banner preview

[1375] The terminal displays the generated banner as a preview to the user, who then checks the preview and sends a request for correction if necessary.

[1376] 7. Modify and Regenerate

[1377] The server receives a modification request from the user and regenerates a new banner, reflecting the user's request, for example, by changing the color of the tagline.

[1378] The revised banner will be displayed again as a preview on the device.

[1379] 8. Final confirmation and banner submission

[1380] The user finally checks and approves the banner preview.

[1381] The server saves the approved banner in its final form and generates a download link that is displayed on the device, allowing the user to download the banner.

[1382] Specific examples

[1383] 1. Advertising Data Collection

[1384] The server collects advertising data from a specific advertising platform over the past year (e.g., ads with a click-through rate of 5% or more and a conversion rate of 10% or more) via API and stores it in a database.

[1385] 2. Data Analysis

[1386] The server analyzes the stored advertising data using a Python analysis library to identify the characteristics of effective banners, such as a blue background and a size of 300x250 pixels.

[1387] 3. User requirements input

[1388] Through a chat interface, users input information such as "banner size 300x250, background color blue, catchy slogan simple."

[1389] 4. User Emotion Recognition

[1390] The server analyzes the text and voice data entered by the user using an emotion engine and determines that the user is happy.

[1391] The server takes into account your emotional state and suggests banners with positive slogans and upbeat designs.

[1392] 5. Automatic banner generation

[1393] The server runs a banner generation algorithm based on the user's requirements and emotion recognition results to automatically generate the optimal banner.

[1394] 6. Display banner preview

[1395] The device displays a preview of the generated banner, showing the user a banner with a "product photo, a blue background, and a simple, positive tagline."

[1396] 7. Modify and Regenerate

[1397] The user sends a correction request saying, "I want the color of the catchphrase to be changed to red."

[1398] The server reflects this modification request, generates a new banner, and displays the preview again.

[1399] 8. Final confirmation and banner submission

[1400] The user finally checks the preview and clicks the "Approve" button.

[1401] The server saves the final banner and generates a download link that is displayed on the device for the user to download the banner.

[1402] In this way, by using this system, it is possible to efficiently create highly effective banners and provide optimal advertisements that match the user's emotions.

[1403] The processing flow will be explained below.

[1404] Step 1: Collect advertising data

[1405] The server periodically calls the advertising platform's API to collect data on ads that have been displayed over the past year (e.g., click-through rate, conversion rate, number of impressions, creative details, etc.).

[1406] The server stores the collected data in a database and sets the appropriate fields (e.g., banner size, color, text content, etc.).

[1407] Step 2: Analyze the data

[1408] The server analyzes the advertising data stored in the database and uses a Python analysis library to extract the characteristics of banners with high click-through rates and conversion rates.

[1409] Based on the analysis results, the server identifies common characteristics of effective banners (e.g., specific size, color, placement, etc.).

[1410] Step 3: Enter user requirements

[1411] Users input their banner requirements (size, color, image, text, etc.) through a chat interface.

[1412] The terminal transmits the input user data to the server in real time.

[1413] Step 4: Recognizing user emotions

[1414] The server receives text and voice data for analyzing the user's emotions using an emotion engine.

[1415] The emotion engine analyzes input text and voice data to determine the user's emotional state (e.g., joy, surprise, sadness, anger, etc.).

[1416] The server adjusts the banner design and copy taking into account the emotional state identified by the emotion engine.

[1417] Step 5: Auto-generating a banner

[1418] The server combines the requirements entered by the user, the analysis results, and the emotion recognition results to automatically generate the optimal banner.

[1419] For example, if the emotion engine determines that the user's emotion is "joy," it will generate a banner with bright colors and positive copy.

[1420] The server converts the generated banner into an image format and saves it.

[1421] Step 6: View the banner preview

[1422] The terminal displays the generated banner preview to the user.

[1423] The user checks the displayed preview and, if necessary, sends a request for corrections through the chat interface.

[1424] Step 7: Fix and Regenerate

[1425] The server receives a modification request from the user and regenerates a new banner.

[1426] For example, the banner is adjusted in response to a user's request for modification, such as changing the color of the catchphrase to red.

[1427] The server transmits the regenerated banner to the terminal again as a preview.

[1428] Step 8: Final confirmation and banner submission

[1429] The user reviews and approves the final banner preview.

[1430] The server saves the approved banner as the final version and generates a download link.

[1431] The terminal displays the generated download link, allowing the user to download the banner.

[1432] The above steps complete a series of processes, from collecting advertising data to creating and providing optimal banners. By combining this with an emotion recognition engine, it is possible to provide optimal advertising creatives that match the user's emotions.

[1433] Example 2

[1434] 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."

[1435] Conventional banner ad generation systems lack sufficient analysis to maximize advertising effectiveness, making it difficult to provide banner designs that reflect the user's emotional state. As a result, advertising effectiveness is reduced and users are unable to fully respond.

[1436] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and saving information about advertisements that have been posted in the past, means for analyzing information about the advertisements and identifying highly effective features, means for recognizing user input data and emotions, means for automatically generating a banner by combining the identified features with the user's emotional information, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the request for revision, and means for providing the banner after final confirmation by the user. This makes it possible to maximize advertising effectiveness and efficiently generate banners with designs that reflect the user's emotional state.

[1437] "Advertising information" refers to data related to marketing materials that users view and interact with, including click rates, conversion rates, impressions, creative details, and the like.

[1438] "Analysis" is the process of analyzing data and extracting useful information or features from it, and in the case of advertising data in particular, it refers to evaluating performance metrics such as click-through rates and conversion rates.

[1439] "Means for recognizing emotions" refers to technology that analyzes user input data and voice data to determine the user's emotional state (for example, joy, surprise, sadness, anger, etc.).

[1440] A "banner" is an advertising image or animation that appears on a web page and contains a specific marketing message or visual element.

[1441] "Automatic generation means" refers to the process of using programs and algorithms to automatically create advertising banners based on specific input data.

[1442] A "request for modification" is a request for modification made by a user to a generated banner, including partial modifications to the design or text.

[1443] The "regeneration means" is a process for regenerating a banner based on a user's modification request.

[1444] "Final confirmation" is the act of the user checking and approving the final version of the banner.

[1445] The "means of providing" refers to the technology by which the final approved banner is provided to the user and made available for download and use.

[1446] This invention relates to a system that automatically generates banners by collecting advertising data, analyzing it, and combining it with an emotion engine that recognizes user emotions. This system consists of three main elements: a server, a terminal, and a user.

[1447] System configuration

[1448] server

[1449] The server is the heart of the system and performs the following main functions:

[1450] 1. Data collection: The server uses the advertising platform's API to collect advertising data (click-through rate, conversion rate, number of impressions, creative details) from the past year and saves it in a database.

[1451] 2. Data analysis: Analyze the data using Python analysis libraries (e.g., pandas, numpy, scikit-learn) to identify the characteristics of effective banners.

[1452] 3. Emotion recognition: Analyze user input data and voice data using an emotion engine (e.g., Google Cloud Natural Language API, IBM Watson) to determine the user's emotional state.

[1453] 4. Automatic banner generation: By combining user requirements and emotion recognition results, banners are automatically generated using a generative AI model (e.g., GAN, VQ-VAE).

[1454] 5. Banner regeneration: Receives correction requests from users and regenerates banners that reflect the content.

[1455] 6. Banner submission: Save the final approved banner and generate a download link.

[1456] Terminal

[1457] The terminal is responsible for providing the interface with the user:

[1458] 1. Input interface: A chat interface (e.g., a React-based chatbot) for users to input their banner requirements.

[1459] 2. Preview: The generated banner is displayed to the user as a preview and has the function of receiving correction requests.

[1460] 3. Download: Provides a link to download the final version of the banner.

[1461] User

[1462] Users have the following roles:

[1463] 1. Input requirements: Input requirements such as banner size, color, image, text, etc. through the chat interface.

[1464] 2. Providing emotional data: Provide input text and voice data and have the server analyze your emotional state.

[1465] 3. Preview: Check the preview of the generated banner and make any necessary corrections.

[1466] 4. Final Review: Review and approve the final banner.

[1467] Specific examples

[1468] Here's a concrete example of how the system works:

[1469] 1. Collecting advertising data: The server collects advertising data with a click rate of 5% or more and a conversion rate of 10% or more over the past year through the API of a specific advertising platform and stores it in a database.

[1470] 2. Data Analysis: The server analyzes the stored advertising data and identifies the characteristics of effective banners, such as a blue background and a size of 300x250 pixels.

[1471] 3. User requirements input: The user inputs the following through the chat interface: "Banner size 300x250, background color blue, catchy copy simple."

[1472] 4. User emotion recognition: The server analyzes the text and voice data entered by the user using an emotion engine and determines that the user is happy.

[1473] 5. Automatic banner generation: The server runs a banner generation algorithm based on the user requirements and emotion recognition results to automatically generate the optimal banner.

[1474] 6. Display banner preview: The device displays a preview of the generated banner, showing the user a banner with a product photo, a blue background, and a simple, positive tagline.

[1475] 7. Modify and regenerate: The user sends a modification request, such as "I want the tagline color to be changed to red." The server reflects this modification request, generates a new banner, and displays the preview again.

[1476] 8. Final confirmation and banner delivery: The user finally checks the preview and clicks the "Approve" button. The server saves the final version of the banner and generates a download link. This link is displayed on the device, and the user can download the banner.

[1477] Prompt Sentence Examples

[1478] The banner size should be 300x250, with a blue background. The tagline should be simple. Also, please analyze the sentiment of the text entered by the user and suggest a design that matches that sentiment.

[1479] This system makes it possible to maximize advertising effectiveness and efficiently generate optimal banners that reflect the user's emotional state.

[1480] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1481] Step 1: Collect advertising data

[1482] The server accesses the advertising platform's API and periodically collects advertising data from the past year. This data includes click-through rates, conversion rates, number of impressions, and creative details. The collected data is sent to the server in JSON format and stored in a database. The input is the raw data obtained from the advertising platform's API, and the output is the advertising data stored in the database.

[1483] Step 2: Analyze the data

[1484] The server uses Python analysis libraries (e.g., pandas, numpy, scikit-learn) to analyze the advertising data stored in the database. The analysis script is run periodically to identify the characteristics of highly effective banners. Specifically, it extracts common characteristics (e.g., background color, font style, image placement) of banners with high click-through rates and conversion rates. The input is the advertising data stored in the database, and the output is the identified characteristics of highly effective banners.

[1485] Step 3: Enter user requirements

[1486] Users input their banner requirements through a chat interface on their web browser. For example, they can enter specific requests such as "I want the banner size to be 300x250 pixels, the background color to be blue, and the tagline to be simple." The input information is sent to the server in real time and recorded in a database. The input is the banner requirements provided by the user through the chat interface, and the output is the input data stored in the database.

[1487] Step 4: Recognizing user emotions

[1488] The server sends the text and voice data entered by the user to an emotion recognition engine (e.g., Google Cloud Natural Language API, IBM Watson) to analyze the user's emotions. The emotion recognition engine uses text analysis technology to determine the emotion the user is feeling (e.g., joy, surprise, sadness, anger). The analysis results are stored in a database and reflected in the banner design. The input is the text and voice data entered by the user, and the output is the recognized user's emotional information.

[1489] Step 5: Auto-generating a banner

[1490] The server combines the user's requirements, the analyzed features of effective banners, and emotion recognition results to automatically generate a banner using a generative AI model (e.g., GAN, VQ-VAE). This algorithm creates an optimal banner that matches the input conditions. Specifically, if the user's requirements are "joy," a banner size of 300x250 pixels, and a blue background color, a corresponding bright banner design will be generated. The input is the user's requirements, the features of effective banners, and the emotion recognition results, and the output is the generated banner image.

[1491] Step 6: View the banner preview

[1492] The terminal receives the banner image sent from the server and displays it as a preview to the user. Because it is displayed in real time on the web browser, the user can immediately check the banner. For example, a banner with a "product photo, blue background, and simple, positive tagline" is presented to the user. The input is the generated banner image, and the output is the preview screen that the user checks.

[1493] Step 7: Fix and Regenerate

[1494] The user views the preview and sends a request for revisions, such as "change the color of the catchphrase to red," through the chat interface. The server receives this request and runs the generative AI model again to generate a new banner. The revised banner is then sent back to the device and displayed as a preview to the user. The input is the user's request for revisions, and the output is the revised new banner image.

[1495] Step 8: Final confirmation and banner submission

[1496] The user checks the final preview and clicks the "Approve" button to finalize the banner. The server saves the final banner and generates a download link. This link is displayed on the device, and the user can click it to download the banner. The input is the approval result of the final preview, and the output is the final banner and its download link.

[1497] In this way, by combining advertising data analysis and user emotion recognition, this system can efficiently generate highly effective banners and provide them to users.

[1498] (Application example 2)

[1499] 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."

[1500] While conventional banner generation systems provide a means for generating highly effective banners based on the analysis of advertising data, they lack the ability to adjust banners in response to user emotions and real-time requests. This hinders users' ability to create more effective and emotionally appealing banner ads. Furthermore, the lack of a means for flexibly collecting user requirements via voice or text and generating and adjusting banners based on those requirements limits the user experience.

[1501] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1502] In this invention, the server includes means for collecting and saving information about advertisements that have been previously published, means for analyzing information about the advertisements and identifying highly effective features, means for receiving user input data and combining the identified features to automatically generate a banner, means for analyzing the user's emotions and adjusting the banner design and catchy copy based on the user's emotional state, means for displaying the automatically generated banner to the user and receiving a request for revision, means for regenerating the banner based on the request for revision, and means for providing the banner after final confirmation by the user. This makes it possible to automatically generate, display, modify, and finally provide banners flexibly according to the user's emotional state and specific requirements.

[1503] "Advertising Information" refers to data regarding advertisements that have been previously published, including click-through rates, conversion rates, number of impressions, and creative details.

[1504] "Analysis" refers to the process of analyzing collected data using statistical methods and algorithms to identify highly effective features.

[1505] A "banner" refers to an advertising image or text that appears on a digital medium such as a web page or application.

[1506] "User-input data" refers to various requirements and information provided by the user to the system, including banner size, color, image, text, etc.

[1507] "Emotion analysis" is a technology that recognizes a user's emotional state (joy, surprise, sadness, anger, etc.) based on their voice and text data.

[1508] "Automatic banner generation" is the process by which the system algorithmically generates appropriate banner ads based on collected data and user requirements.

[1509] A "request for modification" refers to a user viewing a banner preview and requesting the system to make changes to the design, text, etc.

[1510] "Regeneration" is the process by which the system regenerates the banner based on the user's modification requests.

[1511] "Final confirmation" refers to the process in which the generated banner is presented to the user for final approval.

[1512] "Storage" refers to recording the generated banners and analysis data in a database or storage so that they can be accessed later.

[1513] The system for implementing this invention consists of three main components: a server, a terminal, and a user. The server mainly collects and analyzes advertising data, recognizes emotions, and automatically generates and modifies banners. The terminal provides an interface with the user, allowing the user to input requirements for banner creation and provide the data necessary for emotion recognition.

[1514] The server uses the advertising platform's API to collect and store information about previously displayed ads, including click-through rates, conversion rates, number of impressions, creative details, etc. The collected information is stored in a database and used for analysis.

[1515] The server analyzes the collected advertising information using statistical methods and algorithms to identify the characteristics of effective banners. This analysis reveals which advertising elements are most effective, such as specific colors, sizes, and text styles.

[1516] Users input their banner requirements through the device's operating interface, using voice and text interfaces, such as "banner size 300x250, background color blue, catchy copy simple."

[1517] The server uses an emotion engine to analyze the user's emotions. The emotion analysis uses the user's voice and text data to determine their emotional state (happiness, surprise, sadness, anger, etc.). Based on the results of this emotion analysis, the banner design and catchy copy are adjusted.

[1518] For example, if the emotion engine determines that the user's emotional state is "joy," a banner using bright colors and a positive catchphrase is automatically generated. This banner is displayed as a preview to the user via their device. The user can check the preview and, if necessary, send a request for corrections by voice or text, such as "I want the catchphrase color to be red."

[1519] The server receives the user's request for corrections and regenerates the banner based on the request. This regenerated banner is then displayed to the user as a preview. Finally, when the user checks the preview and approves it, the server saves the final version of the banner and generates a download link. This link is displayed on the device, allowing the user to download the banner.

[1520] A concrete example of this system is its application using smart glasses. The smart glasses can display banner ads in the user's field of vision in real time and adjust the ad content through emotion recognition. When the user verbally commands, "Show me recommended banners," the system performs emotion analysis and displays banners and designs with positive messages in real time. For example, if the user verbally requests, "Change the catchphrase," the banner will be regenerated and displayed in the user's field of vision.

[1521] Example prompt sentence:

[1522] User: "Show me the recommended banner."

[1523] Program: "We're happy to show you a banner with a positive message."

[1524] User: "Change the tagline."

[1525] Program: "Change the tagline and preview again."

[1526] This system allows for the automatic generation of banners that can be flexibly adapted to the user's emotional state and specific requirements, and then displayed, modified, and finally provided.

[1527] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1528] Step 1:

[1529] The server uses the advertising platform's API to collect and store information about previously displayed ads (click-through rate, conversion rate, number of impressions, creative details, etc.) The server makes an API request and stores the obtained data in a database, where this information is accumulated and used for later analysis.

[1530] Input: Advertising data obtained from advertising platforms

[1531] Output: Advertisement information stored in the database

[1532] Step 2:

[1533] The server analyzes the advertising information stored in the database using statistical methods and algorithms to identify highly effective features. Specifically, it uses a Python analysis library to extract common features of ads that meet certain criteria, such as a click-through rate of 5% or more and a conversion rate of 10% or more.

[1534] Input: Advertisement information stored in the database

[1535] Output: High-impact banner features (e.g. color, size, text style, etc.)

[1536] Step 3:

[1537] Users can input banner requirements using their device's voice assistant or text interface. For example, if a user requests a banner size of 300x250, a blue background, and a simple tagline, these requirements are sent to the server.

[1538] Input: User-entered data (voice or text)

[1539] Output: User's banner requirements are sent to the server

[1540] Step 4:

[1541] The server uses an emotion engine to analyze the user's emotions. The emotion engine determines the user's emotional state (happiness, surprise, sadness, anger, etc.) based on the user's voice data and text data. For example, if the user enters "I'm feeling happy today," the emotion engine will determine that the emotion is "happiness."

[1542] Input: User voice and text data

[1543] Output: User's emotional state

[1544] Step 5:

[1545] The server automatically generates a banner by combining the user's requirements, the characteristics of effective banners, and the results of sentiment analysis. For example, if the sentiment analysis result is "joy," a banner with bright colors and a positive catchphrase will be generated. The generated banner is temporarily stored on the server.

[1546] Input: User banner requirements, characteristics of effective banners, user emotional state

[1547] Output: Auto-generated banner

[1548] Step 6:

[1549] The device displays the generated banner as a preview to the user. The user checks the preview and sends a request for corrections, such as "I want the catchphrase color to be changed to red," to the device via voice or text. The request data is then sent to the server.

[1550] Input: Auto-generated banner, user request for correction

[1551] Output: The modification request is sent to the server

[1552] Step 7:

[1553] The server regenerates the banner based on the user's request for modification. For example, if the user requests that the color of the catchphrase be changed to red, the server regenerates the banner reflecting the request and temporarily saves it again.

[1554] Input: User's correction request

[1555] Output: Regenerated banner

[1556] Step 8:

[1557] The device will then preview the regenerated banner and display it to the user. If the user confirms the final version and approves it, the server will save the final version of the banner and generate a download link. This link will be displayed on the device, allowing the user to download the banner.

[1558] Input: Regenerated banner, final user confirmation

[1559] Output: A download link will be displayed on the terminal.

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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).

[1567] 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.

[1568] 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."

[1569] 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.

[1570] 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).

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] The following is further disclosed regarding the above embodiment.

[1582] (Claim 1)

[1583] means for collecting and storing information about previously placed advertisements;

[1584] means for analyzing information about the advertisement and identifying highly effective features;

[1585] means for receiving user input data and combining it with the identified characteristics to automatically generate a banner;

[1586] means for displaying the automatically generated banner to a user and receiving a request for modification;

[1587] means for regenerating the banner based on the modification request;

[1588] The system includes means for providing a banner after final confirmation by the user.

[1589] (Claim 2)

[1590] 2. The system of claim 1, wherein the collection of information about advertisements uses an API of an advertising platform.

[1591] (Claim 3)

[1592] 10. The system of claim 1, wherein the user input data is collected through a chat interface.

[1593] "Example 1"

[1594] (Claim 1)

[1595] means for collecting and storing information about previously placed advertisements;

[1596] means for analyzing information about the advertisement and identifying highly effective features;

[1597] means for receiving user input data and combining it with the identified characteristics to automatically generate a banner;

[1598] means for displaying the automatically generated banner to a user and receiving a request for modification;

[1599] means for regenerating the banner based on the modification request;

[1600] means for providing a banner after final confirmation by the user;

[1601] A means to automatically generate banners using generative AI models,

[1602] means for collecting user input data through a chat interface;

[1603] means for displaying a preview of the automatically generated banner on a user's terminal;

[1604] means for transmitting a user's request for modification of said preview to a server in real time;

[1605] means for regenerating a banner that reflects the modification request and displaying a preview of the banner again on the user's terminal;

[1606] A system including:

[1607] (Claim 2)

[1608] 2. The system of claim 1, wherein the collection of information about advertisements uses an API of an advertising platform.

[1609] (Claim 3)

[1610] 10. The system of claim 1, wherein the system sends a prompt to a generative AI model to generate the banner.

[1611] "Application Example 1"

[1612] (Claim 1)

[1613] means for collecting and storing information about previously placed advertisements;

[1614] means for analyzing information about the advertisement and identifying highly effective features;

[1615] means for receiving user input data and combining it with the identified characteristics to automatically generate a banner;

[1616] means for displaying the automatically generated banner to a user and receiving a request for modification;

[1617] means for regenerating the banner based on the modification request;

[1618] means for providing a banner after final confirmation by the user;

[1619] A means for inputting requirements for banner generation using a smartphone and previewing the generated banner in real time;

[1620] The system further includes means for providing an interactive interface for a user to input a request for modification to the previewed banner.

[1621] (Claim 2)

[1622] 2. The system of claim 1, wherein the collection of information about advertisements uses an API of an advertising platform.

[1623] (Claim 3)

[1624] 10. The system of claim 1, wherein the user input data is collected through a chat interface.

[1625] "Example 2: Combining Emotion Engines"

[1626] (Claim 1)

[1627] means for collecting and storing information about previously placed advertisements;

[1628] means for analyzing information about the advertisement and identifying highly effective features;

[1629] means for recognizing user input data and emotions;

[1630] means for automatically generating a banner by combining the identified features and user emotion information;

[1631] means for displaying the automatically generated banner to a user and receiving a request for modification;

[1632] means for regenerating the banner based on the modification request;

[1633] The system includes means for providing a banner after final confirmation by the user.

[1634] (Claim 2)

[1635] The system according to claim 1, wherein the collection of information about advertisements uses an API of the information provider.

[1636] (Claim 3)

[1637] 10. The system of claim 1, wherein the user input data is collected through an interactive interface.

[1638] "Application example 2 when combining emotion engines"

[1639] (Claim 1)

[1640] means for collecting and storing information about previously placed advertisements;

[1641] means for analyzing information about the advertisement and identifying highly effective features;

[1642] means for receiving user input data and combining it with the identified characteristics to automatically generate a banner;

[1643] means for analyzing the user's emotions and adjusting the banner design and catchy copy based on the user's emotional state;

[1644] means for displaying the automatically generated banner to a user and receiving a request for modification;

[1645] means for regenerating the banner based on the modification request;

[1646] The system includes means for providing a banner after final confirmation by the user.

[1647] (Claim 2)

[1648] 2. The system of claim 1, wherein the collection of information about advertisements uses an API of a communication network.

[1649] (Claim 3)

[1650] 10. The system of claim 1, wherein the user input data is collected through a voice and text interface. [Explanation of symbols]

[1651] 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. means for collecting and storing information about previously placed advertisements; means for analyzing information about the advertisement and identifying highly effective features; means for receiving user input data and combining it with the identified characteristics to automatically generate a banner; means for displaying the automatically generated banner to a user and receiving a request for modification; means for regenerating the banner based on the modification request; The system includes means for providing a banner after final confirmation by the user.

2. The system of claim 1, wherein the collection of information about advertisements uses an API of an advertisement platform.

3. 10. The system of claim 1, wherein the user input data is collected through a chat interface.

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