System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input basic requirements for an advertising campaign; means for automatically generating advertising content based on the input data provided by the user; means for automatically reviewing the generated advertising content based on internal content policies; means for recommending the reviewed content to the user; means for automatically placing the selected advertising content on each advertising platform; and means for automatically generating and providing detailed reports to the user after completion of the advertising campaign.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the online advertising market, advertising agencies' work involves a large burden, primarily in the creation and optimization of advertising content. Creating advertising content in particular requires specialized knowledge, and the subsequent review and optimization process is time-consuming and costly. This process is difficult for small and medium-sized enterprises, hindering efficient advertising operations. Therefore, a system is needed that reduces the burden on advertising clients and agencies and enables fast and efficient advertising operations. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: A system including a means for a user to input basic requirements for an advertising campaign, a means for automatically generating advertising content based on the input data provided by the user, a means for automatically reviewing the generated advertising content based on an internal content policy, a means for recommending content that passes the review to the user, a means for automatically publishing the selected advertising content on each advertising platform, and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. The system also includes a means for monitoring the generated advertising content in real time and automatically optimizing the advertising campaign as needed, and a means for allowing the user to review the generated advertising content and enter feedback. This automates the entire process from generating advertising content to reviewing, publishing, operating, and evaluating it, significantly reducing the workload of users and advertising agencies.

[0006] "User" means an individual or entity that inputs basic requirements and feedback for an advertising campaign.

[0007] "Advertising campaign" refers to a series of advertising events planned for a specific purpose.

[0008] "Basic Requirements" refers to the essential information required to launch an advertising campaign, including the ad content, target audience, budget, duration, etc.

[0009] "Advertising content" means all materials, including text, images, and video, used to convey an advertising message.

[0010] A "generative AI model" refers to an artificial intelligence algorithm that automatically generates content based on user input data.

[0011] "Content policies" refer to rules and guidelines that ensure ads meet specific standards, such as legal compliance, brand guidelines, and social media policies.

[0012] "Automatic review" refers to the process by which the system automatically determines whether generated advertising content complies with content policies.

[0013] "Recommendation" refers to the act of recommending advertising content that is deemed appropriate to a user.

[0014] "Advertising Platform" refers to a website or application that provides services and tools for delivering and managing advertising.

[0015] "Placing" means that the advertisement is actually distributed on the advertising platform.

[0016] "Monitoring" refers to the real-time monitoring of the progress and effectiveness of an advertising campaign.

[0017] "Optimization" refers to the process of adjusting parameters such as budget and targeting to maximize the effectiveness of an advertising campaign.

[0018] A "report" refers to a document that summarizes the results and effectiveness of an advertising campaign.

[0019] "Feedback" refers to evaluations and suggestions for corrections provided by users regarding generated advertising content. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The system of the present invention automates each step of web advertising content creation, screening, posting, operation, and evaluation. A specific embodiment of the system will be described below.

[0042] 1. Enter user advertising requirements

[0043] The user logs into the system and inputs the following basic requirements from their terminal: the purpose of the advertising campaign, the target audience, the budget, the period, etc. This information is sent to the server and used for subsequent processing.

[0044] example:

[0045] "A user wants to drive traffic to a landing page for a new product. They specify a target audience of women in their 20s and 30s, a budget of ¥50,000, and a time period of one month."

[0046] 2. Automatic generation of advertising content

[0047] The server calls up a generative AI model based on the data entered by the user and automatically generates advertising content. Specifically, advertising materials such as headlines, body text, images, and videos are generated. This allows users to obtain high-quality advertising content even if they do not have specialized advertising knowledge.

[0048] example:

[0049] "The server uses generative AI models to generate headlines that highlight the new product's features and compelling copy for the target audience."

[0050] 3. Automated content review

[0051] The server automatically reviews the generated ad content based on internal content policies. This review process includes legal compliance, brand guideline compatibility, and social media policy compatibility. Content that passes the review proceeds to the next step, but if it fails, it instructs the content to be regenerated or corrected.

[0052] example:

[0053] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[0054] 4. Recommendation function

[0055] The server recommends advertising content that has passed the screening process to the user. The user can then check the recommended content on their device and provide corrections or feedback as necessary, thereby further improving the quality of the content.

[0056] example:

[0057] "The user checks the 'most effective headlines and body text' recommended by the server and makes fine adjustments if necessary."

[0058] 5. Automating advertising operations

[0059] The server automatically posts the advertising content selected by the user to each advertising platform. The server uses each platform's API to send and post the content. In addition, the server monitors the progress of the advertising campaign in real time and optimizes it as necessary.

[0060] example:

[0061] The server uses the APIs of Google Ads and Facebook Ads to automatically publish the ad content selected by the user and monitors success indicators (click-through rate, conversion rate, etc.) in real time.

[0062] 6. Automatic report generation

[0063] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed report, which is then uploaded to the user's dashboard and can be viewed from their device.

[0064] example:

[0065] The server generates detailed performance reports based on data such as click-through rates and conversion rates over the life of the advertising campaign and displays them on the user's dashboard.

[0066] In this way, the system of the present invention automatically generates advertising content based on the basic requirements entered by the user, streamlining the entire process from review, submission, operation, and evaluation, thereby significantly reducing the burden on advertising clients and agencies.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] A user logs into the system using a terminal. The login information is authenticated by the server and the user is redirected to their account page.

[0070] Step 2:

[0071] The user enters the basic requirements for their advertising campaign, including the advertising objective, target audience, budget, time frame, desired advertising style and tone, etc. Once complete, they click the submit button.

[0072] Step 3:

[0073] The server receives the data sent by the user and stores it in a database, which is used in the next process of generating advertising content.

[0074] Step 4:

[0075] The server invokes the generative AI model to automatically generate ad content based on the input data provided by the user. This generation process generates ad headlines, body text, images, videos, etc.

[0076] Step 5:

[0077] The server sends the generated ad content to an internal content policy engine for automated review, which checks for compliance with laws and regulations, brand guidelines, and social media policies.

[0078] Step 6:

[0079] The server judges the results of the review and adds content that passes the review to the recommendation list, while issuing instructions to regenerate content that fails the review.

[0080] Step 7:

[0081] The server recommends content that passes the screening to the user, and the recommended content is displayed on the user's management screen.

[0082] Step 8:

[0083] The user reviews the recommended ad content, makes corrections and provides feedback as needed, and once the changes are confirmed, the user clicks the save button.

[0084] Step 9:

[0085] The server stores the modified advertisement content in a final database and prepares the selected advertisement content to be automatically posted to each advertisement platform.

[0086] Step 10:

[0087] The server calls the API of each advertising platform and automatically places ads on multiple platforms, including Google Ads, Facebook Ads, and Twitter Ads.

[0088] Step 11:

[0089] The server monitors the progress of the advertising campaign in real time and collects data such as click-through rates, conversion rates, and revenue.

[0090] Step 12:

[0091] The server then optimizes the advertising campaign based on the collected data, which may include reallocating advertising budgets and adjusting targeting.

[0092] Step 13:

[0093] After the advertising campaign ends, the server consolidates the collected data and automatically generates a detailed performance report.

[0094] Step 14:

[0095] The server uploads the generated report to the user's dashboard, allowing the user to view the report through their terminal.

[0096] Example 1

[0097] 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."

[0098] In the past, each step of advertising campaigns—creating advertising content, reviewing, publishing, managing, and evaluating it—was often done manually, requiring a high level of expertise and a significant amount of time. This placed a heavy burden on small businesses and individuals when running advertising campaigns, making it difficult to manage advertising effectively. It also made it difficult to optimize advertising content in real time or automatically generate detailed performance reports.

[0099] 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.

[0100] In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content using an artificial intelligence model based on the input data provided by the user; a means for automatically reviewing the generated advertising content based on an internal content policy; a means for recommending content that passes the review to the user; a means for automatically submitting the selected advertising content to each advertising distribution platform; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This makes it possible to streamline the entire advertising campaign process and significantly reduce the burden on advertising clients and agencies.

[0101] "User" means an individual or organization that utilizes the system to conduct advertising campaigns.

[0102] An "advertising campaign" is a series of advertising activities undertaken to achieve a specific marketing objective.

[0103] "Basic requirements" are key pieces of information needed to run an advertising campaign, such as objectives, target audience, budget, and duration.

[0104] "Input means" refers to a device or interface that allows a user to input basic requirements for an advertising campaign into the system.

[0105] An "artificial intelligence model" is a machine learning algorithm or system that automatically generates advertising content based on user-provided input data.

[0106] "Automatic generation means" means a function or process that automatically generates advertising content using an artificial intelligence model based on input data.

[0107] "Content policies" are internal rules and standards that advertising content must follow, such as legal compliance, brand guidelines, and social media policies.

[0108] The "automatic review means" refers to a device or system that determines whether the generated advertising content complies with the content policy.

[0109] "Means of recommendation" refers to the functions and processes for proposing advertising content that has passed screening to users.

[0110] The "means for placing advertisements" refers to a function or system for automatically sending the selected advertisement content to each advertisement distribution platform and starting distribution.

[0111] A "platform" is a medium such as a website or application for delivering advertisements.

[0112] A "report" is a document that details the results of an advertising campaign, including metrics such as click-through rate and conversion rate.

[0113] "Means for automatically generating and providing" refers to a function or system that automatically creates detailed reports based on data collected after an advertising campaign ends and provides them to users.

[0114] The system of the present invention automates each step of an advertising campaign, allowing users to efficiently manage their advertising. Specific embodiments are described below.

[0115] First, the user logs into the system using a terminal. After logging in, the user enters the basic requirements for the advertising campaign, such as the purpose, target audience, budget, and period. This information is sent to the server when the user enters it into a form on the terminal and presses the submit button. As a specific example, the user might enter requirements such as "the goal is to increase traffic to the landing page for a new product, with a target audience of women in their 20s and 30s, a budget of 50,000 yen, and a period of one month."

[0116] Next, the server calls a generative AI model based on the data entered by the user. For example, GPT-3 is used as the generative AI model. The server generates and sends a prompt to this AI model. A specific example of a prompt might be, "Please generate ad copy aimed at women in their 20s and 30s to increase traffic to our landing page." Based on this prompt, the AI ​​model generates content for the ad, such as the headline, body text, images, and video.

[0117] The generated advertising content is then automatically reviewed by the server. This review process includes legal compliance, brand guideline compatibility, and social media policy compatibility. Specifically, the server uses an review engine to check whether the generated content violates regulations or internal policies. Content that passes the review proceeds to the next step, while content that fails the review is instructed to be regenerated or corrected.

[0118] Ad content that passes the screening process is recommended to the user by the server. The user can review this content on their device and provide corrections or feedback as necessary. For example, the user can review and fine-tune the headline and body text recommended by the server.

[0119] The server then automatically posts the advertising content selected by the user to each advertising distribution platform. Specifically, the server uses the API of each platform (e.g., Google Ads, Facebook Ads) to send the content. Furthermore, the server monitors the progress of the advertising campaign in real time and optimizes it as necessary. During this process, the server obtains and analyzes data such as click-through rates and conversion rates.

[0120] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed report. The report is uploaded to the user's dashboard and can be viewed from their device. For example, the server generates a report that provides a detailed analysis of the campaign's performance based on metrics such as click-through rate and conversion rate.

[0121] In this way, the system automatically generates advertising content based on the basic requirements entered by the user, streamlining the entire process from review, submission, operation, and evaluation, and significantly reducing the burden on advertising clients and agencies.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] A user logs into the system using a terminal. After logging in, the user enters the basic requirements for the advertising campaign, such as the objective, target audience, budget, and period. This input data is sent from the terminal to the server. Specifically, the user enters information such as "increase traffic to the landing page for a new product" into a form on the terminal and presses the "Submit" button. The terminal then sends this as an HTTP request to the server.

[0125] Input: The basic requirement of an advertising campaign is that users fill out a form on their device.

[0126] Output: Basic requirements data for the ad campaign sent to the server

[0127] Step 2:

[0128] The server receives the basic requirement data entered by the user and calls a generative AI model based on that data. Specifically, it uses a generative AI model (e.g., GPT-3) to create a prompt based on the input data and sends it to the AI ​​model. The prompt is in the format, "Please generate ad copy aimed at women in their 20s and 30s to increase traffic to the landing page." The AI ​​model generates ad content based on this prompt.

[0129] Input: Basic requirements data for the ad campaign sent to the server

[0130] Output: Ad content returned by the generative AI model

[0131] Step 3:

[0132] The server automatically reviews the generated ad content based on its internal content policies. This review process checks whether it complies with standards such as legal compliance, brand guidelines, and social media policies. Specifically, the server uses a review engine to search for specific keywords and phrases in the content using regular expressions to check for inappropriate content. If there are no problems with the review, it proceeds to the next step.

[0133] Input: Ad content obtained from a generative AI model

[0134] Output: Ad content that has passed the review

[0135] Step 4:

[0136] The server recommends advertising content that has passed the screening process to the user. The user then uses their device to review this content and make corrections or provide feedback as necessary. Specifically, the server generates a preview of the recommended content and sends it to the device. The user then reviews the preview, enters corrections if necessary, and sends it back to the server.

[0137] Input: Ad content that has passed the review

[0138] Output: Ad content recommended to the user

[0139] Step 5:

[0140] The server automatically places the ad content selected by the user on each ad distribution platform. Specifically, the server uses the APIs of Google Ads and Facebook Ads to send the selected ad content to each platform. It also monitors the progress of the ad campaign in real time and optimizes it as needed. For example, it continuously checks click-through rates and conversion rates and changes optimization settings based on that data.

[0141] Input: User selected ad content

[0142] Output: Advertising content posted on the advertising platform and its monitoring data

[0143] Step 6:

[0144] After the ad campaign ends, the server consolidates the data collected from each advertising platform and automatically generates a detailed report. The generated report is uploaded to the user's dashboard, where the user can view it from their device. Specifically, the server aggregates metrics such as click-through rate and conversion rate, and creates a detailed performance report based on this.

[0145] Input: Monitoring data and final results of advertising campaigns

[0146] Output: A detailed report displayed on the user's dashboard

[0147] (Application example 1)

[0148] 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."

[0149] Traditional advertising campaign management requires many manual steps and specialized knowledge to create, review, publish, operate, and evaluate advertising content. Furthermore, optimizing advertising effectiveness and real-time monitoring are complex and require quick responses. This creates problems for advertising agencies and companies, requiring significant effort and costs.

[0150] 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.

[0151] In this invention, the server includes: means for a user to input basic requirements for an advertising campaign; means for automatically generating advertising content based on the input data provided by the user; means for automatically reviewing the generated advertising content based on an internal content policy; means for recommending content that passes the review to the user; means for automatically publishing the selected advertising content on each advertising platform; means for automatically generating and providing a detailed report to the user after the advertising campaign ends; and means for generating advertising content using a generative AI model and making the generated advertising content available through a smartphone application that reviews, recommends, optimizes, publishes, and generates reports on the generated advertising content. This automates the entire advertising campaign process, allowing users to easily manage advertising campaigns and effectively manage advertising without requiring specialized knowledge or significant effort.

[0152] "User" refers to an advertising agency, company, or other user who utilizes the system to input basic requirements for an advertising campaign and then manages the process.

[0153] "Basic requirements for advertising campaign" means basic information required to carry out an advertising campaign, such as the purpose of the advertisement, target audience, budget, and period.

[0154] "Advertising content" refers to advertising materials such as headlines, text, images, and videos, and is material generated as content.

[0155] "Automatic generation" is the process of using artificial intelligence to generate advertising content based on user-provided data.

[0156] "Internal content policies" refer to internal regulations that advertising content must follow, such as legal compliance, brand guidelines, and platform policies.

[0157] "Automatic review" is the process by which the system checks the advertising content generated based on internal content policies to determine whether it complies.

[0158] "Recommendation" is a feature in which the system suggests optimal advertising content to users, allowing for feedback and fine-tuning.

[0159] "Each advertising platform" refers to an online advertising platform for placing advertisements, such as Google Ads or Facebook Ads.

[0160] "Automatic posting" is the process by which the system posts advertising content directly to each advertising platform based on user specifications.

[0161] A "detailed report" is a report that summarizes the progress and success metrics of an advertising campaign and is automatically generated after the campaign has ended.

[0162] A "generative AI model" is a machine learning model that uses generative artificial intelligence (such as GPT) to generate advertising content.

[0163] A "smartphone application" is software that runs on a smartphone and allows users to manage advertising campaigns and generate content.

[0164] The system of the present invention automates the entire advertising campaign process through a smartphone application. The system includes a generative AI model that generates advertising content based on user input data, and multiple functions for reviewing, recommending, optimizing, publishing, and evaluating the generated advertising content.

[0165] System configuration and programs

[0166] This system is realized by combining the following hardware and software.

[0167] Hardware: Smartphone (iPhone, Android)

[0168] software:

[0169] Generative AI models: OpenAI GPT, DALL-E

[0170] Database: MySQL

[0171] Frontend: React Native

[0172] Backend: Node.js + Express

[0173] Advertising API: Google Ads API, Facebook Ads API

[0174] Data processing and calculation

[0175] 1. Obtaining user input data

[0176] Through the smartphone application, users input the basic requirements of their advertising campaign (advertising objectives, target audience, budget, period, etc.) This input data is collected on the server side and used for subsequent processing.

[0177] 2. Automatic generation of advertising content

[0178] The server calls a generative AI model (OpenAI GPT) based on the input data and automatically generates advertising content (headline, body text, images, videos, etc.). This process is an example of evaluation and generation based on prompt text.

[0179] Example prompt sentence:

[0180] "Goal of advertising campaign: Increase traffic to new product landing page

[0181] Target audience: Women in their 20s and 30s

[0182] Budget: 50,000 yen

[0183] Duration: 1 month

[0184] Request: Please generate advertising copy that highlights the appeal of a new product aimed at women in their 20s and 30s.

[0185] 3. Automated review of generated content

[0186] The server then reviews the generated ad content based on its internal content policies, checking for compliance with legal regulations, brand guidelines, and platform policies, and instructs the content to be regenerated or corrected if it does not comply.

[0187] 4. User Recommendations

[0188] Advertising content that passes the screening process is recommended to users, who can then check it on their smartphone application and provide feedback if necessary.

[0189] 5. Advertising content

[0190] The server automatically posts the ad content selected by the user to each advertising platform using the Google Ads API or Facebook Ads API. After posting, the server monitors the progress of the ad campaign in real time and optimizes it as necessary.

[0191] 6. Automatic report generation

[0192] After the ad campaign is over, the server generates a detailed report based on the collected data and uploads it to the user's dashboard, allowing the user to evaluate the effectiveness of the ad campaign.

[0193] In this way, the system of the present invention efficiently automates the entire process of an advertising campaign, allowing users to effectively manage advertising without requiring specialized knowledge or a great deal of effort.

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1:

[0196] Users input the basic requirements of an advertising campaign (objective, target audience, budget, period) using a smartphone application. The input data is sent to the server in real time and stored in a database (MySQL), where it is also validated to ensure that the input data is correct.

[0197] Step 2:

[0198] The server calls a generative AI model (OpenAI GPT) based on the requirements information for the ad campaign sent by the user and generates a prompt. Specifically, the following prompt is used:

[0199] "Goal of advertising campaign: Increase traffic to new product landing page

[0200] Target audience: Women in their 20s and 30s

[0201] Budget: 50,000 yen

[0202] Duration: 1 month

[0203] Request: Please generate advertising copy that highlights the appeal of a new product aimed at women in their 20s and 30s.

[0204] The generated prompt text is analyzed by an AI model to automatically generate advertising content such as headlines and body text, and the generated content is temporarily stored on the server.

[0205] Step 3:

[0206] The server automatically reviews the temporarily stored ad content based on its internal content policies. This review process checks for compliance with specific regulations, platform policies, and brand guidelines. If the content complies, it moves on to the next step, and if it doesn't, it instructs the content to be regenerated or corrected.

[0207] Step 4:

[0208] Advertising content that passes the screening process is recommended to smartphone application users from the server. Users can review the recommended content on their devices and provide feedback if necessary. This feedback is sent to the server, and the content may be further fine-tuned.

[0209] Step 5:

[0210] The ad content finally selected by the user is automatically published by the server using the Google Ads API or Facebook Ads API. During the publishing process to each platform, the ad content is sent via the API, and the status of each platform is updated in real time.

[0211] Step 6:

[0212] The server monitors the progress of the ad campaign in real time and optimizes it as needed, for example by revisiting the generative AI model and fine-tuning the content if click-through or conversion rates are low.

[0213] Step 7:

[0214] After the ad campaign ends, the server automatically generates a detailed report based on the collected data. This report includes performance indicators such as click-through rate, conversion rate, and return on investment, and the report is uploaded to the user's dashboard. Users can review the report on their dashboard and use it to improve their next ad campaign.

[0215] This automates the entire advertising campaign process, allowing users to effectively manage and operate ads without requiring specialized knowledge or effort.

[0216] 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.

[0217] The system of the present invention automates each step of web advertising content creation, review, submission, operation, and evaluation. Furthermore, by combining it with an emotion engine that recognizes user emotions, the system customizes and optimizes advertising content. Specific embodiments of the system are described below.

[0218] 1. Enter user advertising requirements

[0219] Users log in to the system and input basic information from their devices, including the purpose of the advertising campaign, target audience, budget, duration, and desired advertising style and tone. This information is sent to the server for subsequent processing. As users input this information, the emotion engine recognizes and analyzes their emotions in real time.

[0220] example:

[0221] "The user aims to increase traffic to a landing page for a new product. They specify a target audience of women in their 20s and 30s, a budget of 50,000 yen, and a time period of one month. The emotion engine then recognizes emotions such as joy and anticipation from the user's input."

[0222] 2. Automatic generation of advertising content

[0223] The server then calls a generative AI model based on the user's input data and the recognized emotions to automatically generate ad content. The generation process generates ad materials such as headlines, body text, images, and videos. The tone and style are also adjusted to fit the emotion based on the emotion engine data.

[0224] example:

[0225] "The server uses generative AI models to generate headlines that highlight the new product's features and compelling copy aimed at the target audience, referencing data from the emotion engine to emphasize an upbeat and hopeful tone."

[0226] 3. Automated content review

[0227] The server then sends the generated ad content to an internal content policy engine for automated review, which includes checking compliance with regulations, brand guidelines, and social media policies.

[0228] example:

[0229] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[0230] 4. Recommendation function

[0231] The server recommends advertising content that passes the screening process to the user. The user can then review the recommended content on their device and make corrections or provide feedback as necessary. At this time, the emotion engine recognizes the user's emotions from the feedback and makes correction suggestions based on that.

[0232] example:

[0233] "The user reviews the 'most effective headlines and body copy' recommended by the server and makes fine adjustments if necessary. The emotion engine recognizes any anxieties or concerns expressed in the user's feedback and suggests revisions to address them."

[0234] 5. Automating advertising operations

[0235] The server automatically posts the ad content selected by the user to each advertising platform. The server uses each platform's API to send and post the content. In addition, an emotion engine is used to monitor the progress of the ad campaign in real time and optimize it as needed.

[0236] example:

[0237] "The server uses the APIs of Google Ads and Facebook Ads to automatically publish the advertising content selected by the user and monitors success indicators (click-through rate, conversion rate, etc.) in real time. During monitoring, the emotion engine takes into account the user's emotional data and suggests more effective adjustments to advertising budget allocation and targeting."

[0238] 6. Automatic report generation

[0239] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed performance report, which is then uploaded to the user's dashboard and can be viewed from their device.

[0240] example:

[0241] "The server generates a detailed performance report based on data such as click-through rates and conversion rates during the advertising campaign period and displays it on the user's dashboard. In addition, an emotion engine includes improvement suggestions in the report based on the user's emotional tendencies."

[0242] In this way, the system of the present invention automatically generates advertising content based on the basic requirements and emotions entered by the user, streamlining the entire process from screening, publishing, operation, and evaluation, and significantly reducing the burden on advertising clients and agencies. In particular, the introduction of an emotion engine makes personalization and optimization of advertising content more effective.

[0243] The processing flow will be explained below.

[0244] Step 1:

[0245] A user logs into the system using a terminal. The login information is authenticated by the server and the user is redirected to their account page.

[0246] Step 2:

[0247] The user enters the basic requirements for their advertising campaign, including the advertising objective, target audience, budget, time frame, desired advertising style and tone, etc. Once complete, they click the submit button.

[0248] Step 3:

[0249] The server receives the data sent by the user and stores it in a database. The stored data is used in the next process of generating advertising content. Here, an emotion engine is activated to recognize emotions from the user's input data and analyze the user's emotions.

[0250] Step 4:

[0251] The server invokes the generative AI model to automatically generate ad content based on the input data and recognized emotions provided by the user. This generation process generates ad materials such as headlines, body text, images, and videos. Based on the data from the emotion engine, the tone and style are also adjusted to match the emotion.

[0252] Step 5:

[0253] The server sends the generated ad content to an internal content policy engine for automated review, which checks for compliance with laws and regulations, brand guidelines, and social media policies.

[0254] Step 6:

[0255] The server judges the results of the review and adds content that passes the review to the recommendation list, while issuing instructions to regenerate content that fails the review.

[0256] Step 7:

[0257] The server recommends content that passes the screening to the user, and the recommended content is displayed on the user's management screen.

[0258] Step 8:

[0259] The user checks the recommended advertising content and makes corrections or inputs feedback as necessary. At this time, the emotion engine recognizes the emotions from the user's feedback and makes correction suggestions based on that.

[0260] Step 9:

[0261] The server stores the modified advertisement content in a final database and prepares the selected advertisement content to be automatically posted to each advertisement platform.

[0262] Step 10:

[0263] The server calls the API of each advertising platform and automatically places ads on multiple platforms, including Google Ads, Facebook Ads, and Twitter Ads.

[0264] Step 11:

[0265] The server monitors the progress of the advertising campaign in real time and collects data such as click-through rates, conversion rates, and revenue.

[0266] Step 12:

[0267] The server optimizes the advertising campaign based on the collected data, which includes reallocating advertising budgets and adjusting targeting. Additionally, the emotion engine takes user emotion data into account and makes optimization suggestions for the advertising campaign.

[0268] Step 13:

[0269] After the advertising campaign ends, the server consolidates the collected data and automatically generates a detailed performance report.

[0270] Step 14:

[0271] The server uploads the generated report to the user's dashboard, allowing the user to view the report through their device. The emotion engine includes improvement suggestions in the report based on the user's emotional tendencies.

[0272] Example 2

[0273] 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."

[0274] The problem with traditional advertising campaigns is that it takes a lot of time and effort to create, review, publish, manage, and evaluate advertising content. Also, because the same advertising content is delivered without considering user emotions, advertising effectiveness may not be maximized.

[0275] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0276] In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content based on the input data provided by the user and user emotion data; a means for automatically reviewing the generated advertising content based on internal content policies such as compliance with laws and regulations and guideline compatibility; a means for recommending content that passes the review to the user and suggesting modifications based on user feedback; a means for automatically submitting the selected advertising content to each advertising platform and monitoring progress in real time; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This streamlines the entire advertising campaign process and enables content generation and optimization based on user emotions.

[0277] "Means for inputting basic requirements for an advertising campaign" refers to a function that allows a user to input information regarding the advertising campaign, such as the objectives, target audience, budget, duration, and desired style and tone of the advertisement.

[0278] "Emotion data" refers to the user's emotional information that is recognized and collected in real time by the emotion engine during user input.

[0279] "Means for automatically generating advertising content" refers to a function that uses a generative AI model to automatically create advertising materials such as headlines, body text, images, and videos based on input data and emotional data provided by users.

[0280] The "internal content policy" is a set of standards for reviewing generated advertising content, including compliance with laws and regulations, brand guidelines, and social media policies.

[0281] The "means for automatically reviewing advertising content" is a function that automatically checks whether generated advertising content complies with internal content policies.

[0282] The "recommendation means" is a function that recommends advertising content that has passed screening to users, and is used by users to confirm and suggest corrections.

[0283] "Feedback" refers to the act of a user providing comments or instructions for corrections to recommended advertising content.

[0284] "Modification Suggestion" is a function in which the emotion engine suggests improvements to advertising content based on user feedback.

[0285] An "advertising platform" is an online service for delivering advertising content, such as Google Ads or Facebook Ads.

[0286] "Means for placing ads" refers to a function that automatically distributes selected advertising content to each advertising platform.

[0287] "Means for monitoring progress" means the ability to monitor real-time performance data (e.g., click-through rate, conversion rate) of advertising campaigns.

[0288] "Means for automatic generation" refers to a function that creates a detailed performance report after the end of an advertising campaign and provides it to the user.

[0289] The system of the present invention automates each step of an advertising campaign and provides optimized advertising content by utilizing user emotional data. This system consists of the following main steps: user input of advertising requirements, content generation, automatic review, recommendation, publication, and evaluation.

[0290] 1. Enter user advertising requirements

[0291] Users log in to the system using a device (PC, smartphone, etc.) and enter the basic requirements for their advertising campaign (purpose, target audience, budget, period, desired advertising style and tone, etc.). This input data is sent to the server, and the emotion engine simultaneously recognizes and analyzes the user's emotions in real time as they enter their data. This allows for the collection of emotional data such as the user's expectations and excitement.

[0292] Specific examples

[0293] To increase traffic to a landing page for a new product, a user inputs the target audience as women in their 20s and 30s, a budget of 50,000 yen, and a period of one month. At this time, the emotion engine recognizes emotions such as joy and anticipation from the user's input.

[0294] 2. Automatic generation of advertising content

[0295] The server then calls a generative AI model to automatically generate ad content based on user-entered data and recognized emotional data. The generation process creates ad materials such as headlines, body text, images, and videos. Additionally, the emotional data is used to adjust tone and style.

[0296] Specific examples

[0297] The server uses a generative AI model to generate headlines that highlight the new product's features and compelling copy aimed at the target audience, emphasizing a positive and hopeful tone based on data from the emotion engine.

[0298] Prompt Sentence Examples

[0299] "Generate an ad aimed at women in their 20s and 30s to drive traffic to a new product landing page. The tone should emphasize anticipation."

[0300] 3. Automated content review

[0301] The server then sends the generated ad content to an internal content policy engine for automated review, which checks for legal compliance, brand guideline compliance, and social media policy compliance.

[0302] Specific examples

[0303] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[0304] 4. Recommendation function

[0305] The server recommends advertising content that has passed the screening process to the user. The user can then use their device to check the recommended content and provide corrections or feedback as necessary. At this time, the emotion engine recognizes the user's emotions when providing feedback and makes correction suggestions based on those emotions.

[0306] Specific examples

[0307] The user can review the "most effective headlines and body text" recommended by the server and make adjustments if necessary. The emotion engine recognizes the anxieties and concerns expressed in the user's feedback and suggests revisions to address them.

[0308] 5. Automating advertising operations

[0309] The server automatically posts the user-selected advertising content to each advertising platform (e.g., Google Ads, Facebook Ads, etc.) using the platform's API. Furthermore, an emotion engine is used to monitor the progress of the advertising campaign in real time and optimize it as necessary.

[0310] Specific examples

[0311] The server uses the APIs of Google Ads and Facebook Ads to automatically publish the ad content selected by the user and monitors metrics such as click-through rate and conversion rate in real time. During monitoring, an emotion engine takes into account the user's emotional data and suggests more effective adjustments to advertising budget allocation and targeting.

[0312] 6. Automatic report generation

[0313] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed performance report, which is then uploaded to the user's dashboard and can be viewed from their device.

[0314] Specific examples

[0315] The server generates detailed performance reports based on data such as click rates and conversion rates during the advertising campaign period and displays them on the user's dashboard. In addition, an emotion engine includes improvement suggestions in the report based on the user's emotional tendencies.

[0316] As a result, the system of the present invention streamlines the entire advertising campaign process, enabling content generation and optimization based on user emotions, and providing users with a more effective and personalized advertising experience.

[0317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0318] Step 1:

[0319] A user logs into the system using a terminal and enters the basic requirements for the advertising campaign.

[0320] What it does: A user uses a computer or smartphone to input the objectives of the advertising campaign, the target audience, the budget, the time frame, the desired style and tone of the ad, etc.

[0321] Inputs: Campaign objectives, target audience, budget, duration, ad style and tone

[0322] Output: Basic requirements data for the input ad campaign

[0323] Step 2:

[0324] The emotion engine recognizes and analyzes the user's emotions in real time as they type.

[0325] How it works: While the user is entering information about an advertising campaign, the emotion engine detects emotions such as joy or anticipation in real time and sends that data to the server.

[0326] Input: Information about the ad campaign entered by the user

[0327] Output: User emotion data

[0328] Step 3:

[0329] The server calls up a generative AI model based on the input data and emotion data, and automatically generates advertising content.

[0330] How it works: The server generates ad materials such as headlines, body text, images, and videos, and adjusts tone and style based on emotional data.

[0331] Input: Basic requirements data for advertising campaigns, user sentiment data

[0332] Output: Generated ad content

[0333] Step 4:

[0334] The server sends the generated advertising content to a content policy engine for automatic review.

[0335] Specific operation: The server will conduct an inspection to check for legal compliance, conformance with brand guidelines, and conformance with social media policies.

[0336] Input: Generated ad content

[0337] Output: Examination result (pass / fail)

[0338] Step 5:

[0339] The server recommends the advertisement content that has passed the screening to the user.

[0340] Specific operation: The server presents the recommended content to the user, who then uses the device to confirm and provide feedback.

[0341] Input: Ad content that has passed the review

[0342] Output: User feedback

[0343] Step 6:

[0344] The emotion engine recognizes the user's emotions when giving feedback and makes suggestions based on them.

[0345] Specific operation: The server analyzes the emotional data in the user's feedback and makes suggestions for modifying the advertising content.

[0346] Input: User feedback, emotional data

[0347] Output: Modified suggested ad content

[0348] Step 7:

[0349] The server automatically posts the selected advertising content to each advertising platform and monitors the progress in real time.

[0350] Specific operation: The server uses the APIs of Google Ads and Facebook Ads to send content and monitor metrics such as click-through rate and conversion rate in real time.

[0351] Input: Selected ad content

[0352] Output: Ad performance data

[0353] Step 8:

[0354] After the advertising campaign ends, the server consolidates the data collected from each advertising platform and automatically generates a detailed performance report.

[0355] Specific operation: The server creates a performance report based on data from the advertising campaign period and uploads it to a dashboard for users to view on their devices.

[0356] Input: Performance data collected from each advertising platform

[0357] Output: Detailed performance report

[0358] The above processing steps streamline the entire process of advertising campaigns and provide optimized advertising content based on user sentiment.

[0359] (Application example 2)

[0360] 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."

[0361] Modern advertising campaigns require a wide range of processes, including the creation, review, submission, operation, and evaluation of advertising content, and performing these processes manually is time-consuming and labor-intensive. While it is also important to incorporate user sentiment data and optimize campaigns in real time to maximize advertising effectiveness, there are currently no efficient ways to do this. Therefore, there is a need for a system that automates the entire advertising operation process and enables customization based on user sentiment.

[0362] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content based on the input data provided by the user and emotion data recognized by an emotion recognition engine; a means for automatically reviewing the generated advertising content based on an internal content policy; a means for recommending content that passes the review to the user and readjusting it based on user feedback; a means for automatically submitting the selected advertising content to each advertising platform and monitoring it in real time; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This makes it possible to efficiently automate the entire advertising operation process and further customize and optimize advertising content based on user emotions.

[0363] "User" refers to the entity that operates and manages an advertising campaign, including advertisers and marketers.

[0364] "Basic requirements for advertising campaign" refers to the initial information required for advertising operations, such as advertising objectives, target audience, budget, duration, desired advertising style and tone, etc.

[0365] An "emotion recognition engine" is a technological element that analyzes and visualizes the emotions contained in user input and feedback in real time.

[0366] "Advertising Content" refers to advertising materials, including headlines, body text, images, and videos, that are automatically generated by a generative AI model.

[0367] "Content Policy" refers to the review criteria for advertising content, including compliance with laws and regulations, conformity with brand guidelines, and conformity with social media policies.

[0368] "Feedback" refers to evaluations and suggestions for corrections that users enter regarding advertising content.

[0369] "Report" means a document detailing the performance of an Advertising Campaign, including metrics such as click-through rates and conversion rates.

[0370] A "generative AI model" is an artificial intelligence technology element that automatically generates advertising content based on information and emotional data provided by users.

[0371] "Prompt" refers to a textual instruction that provides input data to a generative AI model.

[0372] The system of the present invention is implemented as a smartphone app and automates the creation, review, submission, operation, and evaluation of advertising campaigns. The system consists of the following main components:

[0373] 1. User Input and Emotion Recognition

[0374] Using a smartphone app, users input the basic requirements for their advertising campaign (purpose, target audience, budget, period, style, tone, etc.) This input data is sent to a server, and at the same time, an emotion recognition engine analyzes emotions from the user's input in real time.

[0375] 2. Automatic generation of advertising content

[0376] The server receives user input data and emotion recognition engine data, then calls a generative AI model to automatically generate advertising content. This generative AI model receives a text prompt as input and generates advertising materials such as headlines, body text, images, and videos.

[0377] Example prompt sentence:

[0378] "Advertising requirements: We want to promote a new smartwatch to IT engineers in their 20s and 30s. The budget is 100,000 yen, and the duration is two months. The emotions are joy and anticipation. Please generate advertising content based on this."

[0379] 3. Automated review of advertising content

[0380] The generated ad content is automatically sent to an internal content policy engine to check for legal compliance, brand guideline compatibility, and social media policy compatibility, and the engine returns the review results to the server.

[0381] 4. Recommendation and Feedback Collection

[0382] The server recommends advertising content that passes the screening process to the user, who then checks the content on their smartphone. When the user enters feedback, the emotion recognition engine analyzes their emotions again, and the generative AI model readjusts the content based on the analysis results.

[0383] 5. Automatic advertising and real-time monitoring

[0384] The selected advertising content is automatically published through the API of each advertising platform (e.g., Google Ads, Facebook Ads). The server monitors the advertising campaign in real time and optimizes it as needed using emotion recognition data.

[0385] 6. Automatic report generation and provision

[0386] After the ad campaign ends, the server collects data from each ad platform and automatically generates a detailed performance report, which is uploaded to the user's dashboard and can be viewed on their smartphone.

[0387] Hardware and software used

[0388] Hardware: Smartphone

[0389] software:

[0390] Flask: A framework that provides server-side APIs

[0391] EmotionEngine: An emotion recognition engine for recognizing user emotions

[0392] OpenAI API: An API that provides generative AI models, particularly the "davinci-codex" engine.

[0393] Advertising platform APIs: Google Ads, Facebook Ads, etc.

[0394] Examples of specific examples and prompts

[0395] Examples:

[0396] If a user enters the following advertising requirements:

[0397] "I want to promote our new smartwatch to IT engineers in their 20s and 30s. My budget is 100,000 yen and I have two months."

[0398] The emotion recognition engine recognizes "joy and anticipation" from the user's input and generates the following prompt:

[0399] "Advertising requirements: We want to promote a new smartwatch to IT engineers in their 20s and 30s. The budget is 100,000 yen, and the duration is two months. The emotions are joy and anticipation. Please generate advertising content based on this."

[0400] Based on this, advertising content is generated and recommended to users, and necessary feedback is reflected. Furthermore, advertising campaigns are automatically posted to multiple advertising platforms, and results are monitored and optimized in real time to maximize advertising effectiveness.

[0401] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0402] Step 1:

[0403] The user uses a smartphone to input the basic requirements of the advertising campaign (purpose, target audience, budget, period, style, tone, etc.). The input data is immediately sent to the server. The emotion recognition engine then analyzes the user's emotions from this input and sends the analysis results to the server. This allows the user's advertising requirements and accompanying emotional data to be obtained.

[0404] Step 2:

[0405] The server calls the generative AI model based on the acquired user input data and emotion data. The server converts this data into a prompt text and sends this prompt text to the generative AI model. The generative AI model receives the prompt text, generates advertising content (headline, body text, images, videos, etc.), and returns this to the server. This generates advertising content based on the user's requirements.

[0406] Step 3:

[0407] The server sends the generated ad content to an internal content policy engine for automatic review. The review checks for legal compliance, brand guideline compatibility, and social media policy compatibility. The review results are returned to the server, which determines whether the content is deemed eligible.

[0408] Step 4:

[0409] The server recommends advertising content that passes the screening process to the user. The user can then use their smartphone to review the recommended advertising content and provide feedback. This feedback is also analyzed by the emotion recognition engine, and the analysis results are sent back to the server. The generative AI model is then readjusted based on the user's feedback and emotional data.

[0410] Step 5:

[0411] The ad content ultimately selected by the user is automatically published from the server via the API of each ad platform (e.g., Google Ads, Facebook Ads). The server sends the ad content to each platform and monitors it in real time. Depending on the results of the monitoring, adjustments are made to optimize the ad campaign.

[0412] Step 6:

[0413] After the ad campaign ends, the server consolidates data collected from each ad platform (e.g., click-through rate, conversion rate, etc.) and automatically generates a detailed performance report. This report is uploaded to the user's dashboard and can be viewed by the user on their smartphone. The report also includes suggestions for improvement based on the user's emotional data.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Second embodiment]

[0418] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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."

[0430] The system of the present invention automates each step of web advertising content creation, screening, posting, operation, and evaluation. A specific embodiment of the system will be described below.

[0431] 1. Enter user advertising requirements

[0432] The user logs into the system and inputs the following basic requirements from their terminal: the purpose of the advertising campaign, the target audience, the budget, the period, etc. This information is sent to the server and used for subsequent processing.

[0433] example:

[0434] "A user wants to drive traffic to a landing page for a new product. They specify a target audience of women in their 20s and 30s, a budget of ¥50,000, and a time period of one month."

[0435] 2. Automatic generation of advertising content

[0436] The server calls up a generative AI model based on the data entered by the user and automatically generates advertising content. Specifically, advertising materials such as headlines, body text, images, and videos are generated. This allows users to obtain high-quality advertising content even if they do not have specialized advertising knowledge.

[0437] example:

[0438] "The server uses generative AI models to generate headlines that highlight the new product's features and compelling copy for the target audience."

[0439] 3. Automated content review

[0440] The server automatically reviews the generated ad content based on internal content policies. This review process includes legal compliance, brand guideline compatibility, and social media policy compatibility. Content that passes the review proceeds to the next step, but if it fails, it instructs the content to be regenerated or corrected.

[0441] example:

[0442] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[0443] 4. Recommendation function

[0444] The server recommends advertising content that has passed the screening process to the user. The user can then check the recommended content on their device and provide corrections or feedback as necessary, thereby further improving the quality of the content.

[0445] example:

[0446] "The user checks the 'most effective headlines and body text' recommended by the server and makes fine adjustments if necessary."

[0447] 5. Automating advertising operations

[0448] The server automatically posts the advertising content selected by the user to each advertising platform. The server uses each platform's API to send and post the content. In addition, the server monitors the progress of the advertising campaign in real time and optimizes it as necessary.

[0449] example:

[0450] The server uses the APIs of Google Ads and Facebook Ads to automatically publish the ad content selected by the user and monitors success indicators (click-through rate, conversion rate, etc.) in real time.

[0451] 6. Automatic report generation

[0452] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed report, which is then uploaded to the user's dashboard and can be viewed from their device.

[0453] example:

[0454] The server generates detailed performance reports based on data such as click-through rates and conversion rates over the life of the advertising campaign and displays them on the user's dashboard.

[0455] In this way, the system of the present invention automatically generates advertising content based on the basic requirements entered by the user, streamlining the entire process from review, submission, operation, and evaluation, thereby significantly reducing the burden on advertising clients and agencies.

[0456] The processing flow will be explained below.

[0457] Step 1:

[0458] A user logs into the system using a terminal. The login information is authenticated by the server and the user is redirected to their account page.

[0459] Step 2:

[0460] The user enters the basic requirements for their advertising campaign, including the advertising objective, target audience, budget, time frame, desired advertising style and tone, etc. Once complete, they click the submit button.

[0461] Step 3:

[0462] The server receives the data sent by the user and stores it in a database, which is used in the next process of generating advertising content.

[0463] Step 4:

[0464] The server invokes the generative AI model to automatically generate ad content based on the input data provided by the user. This generation process generates ad headlines, body text, images, videos, etc.

[0465] Step 5:

[0466] The server sends the generated ad content to an internal content policy engine for automated review, which checks for compliance with laws and regulations, brand guidelines, and social media policies.

[0467] Step 6:

[0468] The server judges the results of the review and adds content that passes the review to the recommendation list, while issuing instructions to regenerate content that fails the review.

[0469] Step 7:

[0470] The server recommends content that passes the screening to the user, and the recommended content is displayed on the user's management screen.

[0471] Step 8:

[0472] The user reviews the recommended ad content, makes corrections and provides feedback as needed, and once the changes are confirmed, the user clicks the save button.

[0473] Step 9:

[0474] The server stores the modified advertisement content in a final database and prepares the selected advertisement content to be automatically posted to each advertisement platform.

[0475] Step 10:

[0476] The server calls the API of each advertising platform and automatically places ads on multiple platforms, including Google Ads, Facebook Ads, and Twitter Ads.

[0477] Step 11:

[0478] The server monitors the progress of the advertising campaign in real time and collects data such as click-through rates, conversion rates, and revenue.

[0479] Step 12:

[0480] The server then optimizes the advertising campaign based on the collected data, which may include reallocating advertising budgets and adjusting targeting.

[0481] Step 13:

[0482] After the advertising campaign ends, the server consolidates the collected data and automatically generates a detailed performance report.

[0483] Step 14:

[0484] The server uploads the generated report to the user's dashboard, allowing the user to view the report through their terminal.

[0485] Example 1

[0486] 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."

[0487] In the past, each step of advertising campaigns—creating advertising content, reviewing, publishing, managing, and evaluating it—was often done manually, requiring a high level of expertise and a significant amount of time. This placed a heavy burden on small businesses and individuals when running advertising campaigns, making it difficult to manage advertising effectively. It also made it difficult to optimize advertising content in real time or automatically generate detailed performance reports.

[0488] 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.

[0489] In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content using an artificial intelligence model based on the input data provided by the user; a means for automatically reviewing the generated advertising content based on an internal content policy; a means for recommending content that passes the review to the user; a means for automatically submitting the selected advertising content to each advertising distribution platform; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This makes it possible to streamline the entire advertising campaign process and significantly reduce the burden on advertising clients and agencies.

[0490] "User" means an individual or organization that utilizes the system to conduct advertising campaigns.

[0491] An "advertising campaign" is a series of advertising activities undertaken to achieve a specific marketing objective.

[0492] "Basic requirements" are key pieces of information needed to run an advertising campaign, such as objectives, target audience, budget, and duration.

[0493] "Input means" refers to a device or interface that allows a user to input basic requirements for an advertising campaign into the system.

[0494] An "artificial intelligence model" is a machine learning algorithm or system that automatically generates advertising content based on user-provided input data.

[0495] "Automatic generation means" means a function or process that automatically generates advertising content using an artificial intelligence model based on input data.

[0496] "Content policies" are internal rules and standards that advertising content must follow, such as legal compliance, brand guidelines, and social media policies.

[0497] The "automatic review means" refers to a device or system that determines whether the generated advertising content complies with the content policy.

[0498] "Means of recommendation" refers to the functions and processes for proposing advertising content that has passed screening to users.

[0499] The "means for placing advertisements" refers to a function or system for automatically sending the selected advertisement content to each advertisement distribution platform and starting distribution.

[0500] A "platform" is a medium such as a website or application for delivering advertisements.

[0501] A "report" is a document that details the results of an advertising campaign, including metrics such as click-through rate and conversion rate.

[0502] "Means for automatically generating and providing" refers to a function or system that automatically creates detailed reports based on data collected after an advertising campaign ends and provides them to users.

[0503] The system of the present invention automates each step of an advertising campaign, allowing users to efficiently manage their advertising. Specific embodiments are described below.

[0504] First, the user logs into the system using a terminal. After logging in, the user enters the basic requirements for the advertising campaign, such as the purpose, target audience, budget, and period. This information is sent to the server when the user enters it into a form on the terminal and presses the submit button. As a specific example, the user might enter requirements such as "the goal is to increase traffic to the landing page for a new product, with a target audience of women in their 20s and 30s, a budget of 50,000 yen, and a period of one month."

[0505] Next, the server calls a generative AI model based on the data entered by the user. For example, GPT-3 is used as the generative AI model. The server generates and sends a prompt to this AI model. A specific example of a prompt might be, "Please generate ad copy aimed at women in their 20s and 30s to increase traffic to our landing page." Based on this prompt, the AI ​​model generates content for the ad, such as the headline, body text, images, and video.

[0506] The generated advertising content is then automatically reviewed by the server. This review process includes legal compliance, brand guideline compatibility, and social media policy compatibility. Specifically, the server uses an review engine to check whether the generated content violates regulations or internal policies. Content that passes the review proceeds to the next step, while content that fails the review is instructed to be regenerated or corrected.

[0507] Ad content that passes the screening process is recommended to the user by the server. The user can review this content on their device and provide corrections or feedback as necessary. For example, the user can review and fine-tune the headline and body text recommended by the server.

[0508] The server then automatically posts the advertising content selected by the user to each advertising distribution platform. Specifically, the server uses the API of each platform (e.g., Google Ads, Facebook Ads) to send the content. Furthermore, the server monitors the progress of the advertising campaign in real time and optimizes it as necessary. During this process, the server obtains and analyzes data such as click-through rates and conversion rates.

[0509] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed report. The report is uploaded to the user's dashboard and can be viewed from their device. For example, the server generates a report that provides a detailed analysis of the campaign's performance based on metrics such as click-through rate and conversion rate.

[0510] In this way, the system automatically generates advertising content based on the basic requirements entered by the user, streamlining the entire process from review, submission, operation, and evaluation, and significantly reducing the burden on advertising clients and agencies.

[0511] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0512] Step 1:

[0513] A user logs into the system using a terminal. After logging in, the user enters the basic requirements for the advertising campaign, such as the objective, target audience, budget, and period. This input data is sent from the terminal to the server. Specifically, the user enters information such as "increase traffic to the landing page for a new product" into a form on the terminal and presses the "Submit" button. The terminal then sends this as an HTTP request to the server.

[0514] Input: The basic requirement of an advertising campaign is that users fill out a form on their device.

[0515] Output: Basic requirements data for the ad campaign sent to the server

[0516] Step 2:

[0517] The server receives the basic requirement data entered by the user and calls a generative AI model based on that data. Specifically, it uses a generative AI model (e.g., GPT-3) to create a prompt based on the input data and sends it to the AI ​​model. The prompt is in the format, "Please generate ad copy aimed at women in their 20s and 30s to increase traffic to the landing page." The AI ​​model generates ad content based on this prompt.

[0518] Input: Basic requirements data for the ad campaign sent to the server

[0519] Output: Ad content returned by the generative AI model

[0520] Step 3:

[0521] The server automatically reviews the generated ad content based on its internal content policies. This review process checks whether it complies with standards such as legal compliance, brand guidelines, and social media policies. Specifically, the server uses a review engine to search for specific keywords and phrases in the content using regular expressions to check for inappropriate content. If there are no problems with the review, it proceeds to the next step.

[0522] Input: Ad content obtained from a generative AI model

[0523] Output: Ad content that has passed the review

[0524] Step 4:

[0525] The server recommends advertising content that has passed the screening process to the user. The user then uses their device to review this content and make corrections or provide feedback as necessary. Specifically, the server generates a preview of the recommended content and sends it to the device. The user then reviews the preview, enters corrections if necessary, and sends it back to the server.

[0526] Input: Ad content that has passed the review

[0527] Output: Ad content recommended to the user

[0528] Step 5:

[0529] The server automatically places the ad content selected by the user on each ad distribution platform. Specifically, the server uses the APIs of Google Ads and Facebook Ads to send the selected ad content to each platform. It also monitors the progress of the ad campaign in real time and optimizes it as needed. For example, it continuously checks click-through rates and conversion rates and changes optimization settings based on that data.

[0530] Input: User selected ad content

[0531] Output: Advertising content posted on the advertising platform and its monitoring data

[0532] Step 6:

[0533] After the ad campaign ends, the server consolidates the data collected from each advertising platform and automatically generates a detailed report. The generated report is uploaded to the user's dashboard, where the user can view it from their device. Specifically, the server aggregates metrics such as click-through rate and conversion rate, and creates a detailed performance report based on this.

[0534] Input: Monitoring data and final results of advertising campaigns

[0535] Output: A detailed report displayed on the user's dashboard

[0536] (Application example 1)

[0537] 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."

[0538] Traditional advertising campaign management requires many manual steps and specialized knowledge to create, review, publish, operate, and evaluate advertising content. Furthermore, optimizing advertising effectiveness and real-time monitoring are complex and require quick responses. This creates problems for advertising agencies and companies, requiring significant effort and costs.

[0539] 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.

[0540] In this invention, the server includes: means for a user to input basic requirements for an advertising campaign; means for automatically generating advertising content based on the input data provided by the user; means for automatically reviewing the generated advertising content based on an internal content policy; means for recommending content that passes the review to the user; means for automatically publishing the selected advertising content on each advertising platform; means for automatically generating and providing a detailed report to the user after the advertising campaign ends; and means for generating advertising content using a generative AI model and making the generated advertising content available through a smartphone application that reviews, recommends, optimizes, publishes, and generates reports on the generated advertising content. This automates the entire advertising campaign process, allowing users to easily manage advertising campaigns and effectively manage advertising without requiring specialized knowledge or significant effort.

[0541] "User" refers to an advertising agency, company, or other user who utilizes the system to input basic requirements for an advertising campaign and then manages the process.

[0542] "Basic requirements for advertising campaign" means basic information required to carry out an advertising campaign, such as the purpose of the advertisement, target audience, budget, and period.

[0543] "Advertising content" refers to advertising materials such as headlines, text, images, and videos, and is material generated as content.

[0544] "Automatic generation" is the process of using artificial intelligence to generate advertising content based on user-provided data.

[0545] "Internal content policies" refer to internal regulations that advertising content must follow, such as legal compliance, brand guidelines, and platform policies.

[0546] "Automatic review" is the process by which the system checks the advertising content generated based on internal content policies to determine whether it complies.

[0547] "Recommendation" is a feature in which the system suggests optimal advertising content to users, allowing for feedback and fine-tuning.

[0548] "Each advertising platform" refers to an online advertising platform for placing advertisements, such as Google Ads or Facebook Ads.

[0549] "Automatic posting" is the process by which the system posts advertising content directly to each advertising platform based on user specifications.

[0550] A "detailed report" is a report that summarizes the progress and success metrics of an advertising campaign and is automatically generated after the campaign has ended.

[0551] A "generative AI model" is a machine learning model that uses generative artificial intelligence (such as GPT) to generate advertising content.

[0552] A "smartphone application" is software that runs on a smartphone and allows users to manage advertising campaigns and generate content.

[0553] The system of the present invention automates the entire advertising campaign process through a smartphone application. The system includes a generative AI model that generates advertising content based on user input data, and multiple functions for reviewing, recommending, optimizing, publishing, and evaluating the generated advertising content.

[0554] System configuration and programs

[0555] This system is realized by combining the following hardware and software.

[0556] Hardware: Smartphone (iPhone, Android)

[0557] software:

[0558] Generative AI models: OpenAI GPT, DALL-E

[0559] Database: MySQL

[0560] Frontend: React Native

[0561] Backend: Node.js + Express

[0562] Advertising API: Google Ads API, Facebook Ads API

[0563] Data processing and calculation

[0564] 1. Obtaining user input data

[0565] Through the smartphone application, users input the basic requirements of their advertising campaign (advertising objectives, target audience, budget, period, etc.) This input data is collected on the server side and used for subsequent processing.

[0566] 2. Automatic generation of advertising content

[0567] The server calls a generative AI model (OpenAI GPT) based on the input data and automatically generates advertising content (headline, body text, images, videos, etc.). This process is an example of evaluation and generation based on prompt text.

[0568] Example prompt sentence:

[0569] "Goal of advertising campaign: Increase traffic to new product landing page

[0570] Target audience: Women in their 20s and 30s

[0571] Budget: 50,000 yen

[0572] Duration: 1 month

[0573] Request: Please generate advertising copy that highlights the appeal of a new product aimed at women in their 20s and 30s.

[0574] 3. Automated review of generated content

[0575] The server then reviews the generated ad content based on its internal content policies, checking for compliance with legal regulations, brand guidelines, and platform policies, and instructs the content to be regenerated or corrected if it does not comply.

[0576] 4. User Recommendations

[0577] Advertising content that passes the screening process is recommended to users, who can then check it on their smartphone application and provide feedback if necessary.

[0578] 5. Advertising content

[0579] The server automatically posts the ad content selected by the user to each advertising platform using the Google Ads API or Facebook Ads API. After posting, the server monitors the progress of the ad campaign in real time and optimizes it as necessary.

[0580] 6. Automatic report generation

[0581] After the ad campaign is over, the server generates a detailed report based on the collected data and uploads it to the user's dashboard, allowing the user to evaluate the effectiveness of the ad campaign.

[0582] In this way, the system of the present invention efficiently automates the entire process of an advertising campaign, allowing users to effectively manage advertising without requiring specialized knowledge or a great deal of effort.

[0583] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0584] Step 1:

[0585] Users input the basic requirements of an advertising campaign (objective, target audience, budget, period) using a smartphone application. The input data is sent to the server in real time and stored in a database (MySQL), where it is also validated to ensure that the input data is correct.

[0586] Step 2:

[0587] The server calls a generative AI model (OpenAI GPT) based on the requirements information for the ad campaign sent by the user and generates a prompt. Specifically, the following prompt is used:

[0588] "Goal of advertising campaign: Increase traffic to new product landing page

[0589] Target audience: Women in their 20s and 30s

[0590] Budget: 50,000 yen

[0591] Duration: 1 month

[0592] Request: Please generate advertising copy that highlights the appeal of a new product aimed at women in their 20s and 30s.

[0593] The generated prompt text is analyzed by an AI model to automatically generate advertising content such as headlines and body text, and the generated content is temporarily stored on the server.

[0594] Step 3:

[0595] The server automatically reviews the temporarily stored ad content based on its internal content policies. This review process checks for compliance with specific regulations, platform policies, and brand guidelines. If the content complies, it moves on to the next step, and if it doesn't, it instructs the content to be regenerated or corrected.

[0596] Step 4:

[0597] Advertising content that passes the screening process is recommended to smartphone application users from the server. Users can review the recommended content on their devices and provide feedback if necessary. This feedback is sent to the server, and the content may be further fine-tuned.

[0598] Step 5:

[0599] The ad content finally selected by the user is automatically published by the server using the Google Ads API or Facebook Ads API. During the publishing process to each platform, the ad content is sent via the API, and the status of each platform is updated in real time.

[0600] Step 6:

[0601] The server monitors the progress of the ad campaign in real time and optimizes it as needed, for example by revisiting the generative AI model and fine-tuning the content if click-through or conversion rates are low.

[0602] Step 7:

[0603] After the ad campaign ends, the server automatically generates a detailed report based on the collected data. This report includes performance indicators such as click-through rate, conversion rate, and return on investment, and the report is uploaded to the user's dashboard. Users can review the report on their dashboard and use it to improve their next ad campaign.

[0604] This automates the entire advertising campaign process, allowing users to effectively manage and operate ads without requiring specialized knowledge or effort.

[0605] 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.

[0606] The system of the present invention automates each step of web advertising content creation, review, submission, operation, and evaluation. Furthermore, by combining it with an emotion engine that recognizes user emotions, the system customizes and optimizes advertising content. Specific embodiments of the system are described below.

[0607] 1. Enter user advertising requirements

[0608] Users log in to the system and input basic information from their devices, including the purpose of the advertising campaign, target audience, budget, duration, and desired advertising style and tone. This information is sent to the server for subsequent processing. As users input this information, the emotion engine recognizes and analyzes their emotions in real time.

[0609] example:

[0610] "The user aims to increase traffic to a landing page for a new product. They specify a target audience of women in their 20s and 30s, a budget of 50,000 yen, and a time period of one month. The emotion engine then recognizes emotions such as joy and anticipation from the user's input."

[0611] 2. Automatic generation of advertising content

[0612] The server then calls a generative AI model based on the user's input data and the recognized emotions to automatically generate ad content. The generation process generates ad materials such as headlines, body text, images, and videos. The tone and style are also adjusted to fit the emotion based on the emotion engine data.

[0613] example:

[0614] "The server uses generative AI models to generate headlines that highlight the new product's features and compelling copy aimed at the target audience, referencing data from the emotion engine to emphasize an upbeat and hopeful tone."

[0615] 3. Automated content review

[0616] The server then sends the generated ad content to an internal content policy engine for automated review, which includes checking compliance with regulations, brand guidelines, and social media policies.

[0617] example:

[0618] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[0619] 4. Recommendation function

[0620] The server recommends advertising content that passes the screening process to the user. The user can then review the recommended content on their device and make corrections or provide feedback as necessary. At this time, the emotion engine recognizes the user's emotions from the feedback and makes correction suggestions based on that.

[0621] example:

[0622] "The user reviews the 'most effective headlines and body copy' recommended by the server and makes fine adjustments if necessary. The emotion engine recognizes any anxieties or concerns expressed in the user's feedback and suggests revisions to address them."

[0623] 5. Automating advertising operations

[0624] The server automatically posts the ad content selected by the user to each advertising platform. The server uses each platform's API to send and post the content. In addition, an emotion engine is used to monitor the progress of the ad campaign in real time and optimize it as needed.

[0625] example:

[0626] "The server uses the APIs of Google Ads and Facebook Ads to automatically publish the advertising content selected by the user and monitors success indicators (click-through rate, conversion rate, etc.) in real time. During monitoring, the emotion engine takes into account the user's emotional data and suggests more effective adjustments to advertising budget allocation and targeting."

[0627] 6. Automatic report generation

[0628] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed performance report, which is then uploaded to the user's dashboard and can be viewed from their device.

[0629] example:

[0630] "The server generates a detailed performance report based on data such as click-through rates and conversion rates during the advertising campaign period and displays it on the user's dashboard. In addition, an emotion engine includes improvement suggestions in the report based on the user's emotional tendencies."

[0631] In this way, the system of the present invention automatically generates advertising content based on the basic requirements and emotions entered by the user, streamlining the entire process from screening, publishing, operation, and evaluation, and significantly reducing the burden on advertising clients and agencies. In particular, the introduction of an emotion engine makes personalization and optimization of advertising content more effective.

[0632] The processing flow will be explained below.

[0633] Step 1:

[0634] A user logs into the system using a terminal. The login information is authenticated by the server and the user is redirected to their account page.

[0635] Step 2:

[0636] The user enters the basic requirements for their advertising campaign, including the advertising objective, target audience, budget, time frame, desired advertising style and tone, etc. Once complete, they click the submit button.

[0637] Step 3:

[0638] The server receives the data sent by the user and stores it in a database. The stored data is used in the next process of generating advertising content. Here, an emotion engine is activated to recognize emotions from the user's input data and analyze the user's emotions.

[0639] Step 4:

[0640] The server invokes the generative AI model to automatically generate ad content based on the input data and recognized emotions provided by the user. This generation process generates ad materials such as headlines, body text, images, and videos. Based on the data from the emotion engine, the tone and style are also adjusted to match the emotion.

[0641] Step 5:

[0642] The server sends the generated ad content to an internal content policy engine for automated review, which checks for compliance with laws and regulations, brand guidelines, and social media policies.

[0643] Step 6:

[0644] The server judges the results of the review and adds content that passes the review to the recommendation list, while issuing instructions to regenerate content that fails the review.

[0645] Step 7:

[0646] The server recommends content that passes the screening to the user, and the recommended content is displayed on the user's management screen.

[0647] Step 8:

[0648] The user checks the recommended advertising content and makes corrections or inputs feedback as necessary. At this time, the emotion engine recognizes the emotions from the user's feedback and makes correction suggestions based on that.

[0649] Step 9:

[0650] The server stores the modified advertisement content in a final database and prepares the selected advertisement content to be automatically posted to each advertisement platform.

[0651] Step 10:

[0652] The server calls the API of each advertising platform and automatically places ads on multiple platforms, including Google Ads, Facebook Ads, and Twitter Ads.

[0653] Step 11:

[0654] The server monitors the progress of the advertising campaign in real time and collects data such as click-through rates, conversion rates, and revenue.

[0655] Step 12:

[0656] The server optimizes the advertising campaign based on the collected data, which includes reallocating advertising budgets and adjusting targeting. Additionally, the emotion engine takes user emotion data into account and makes optimization suggestions for the advertising campaign.

[0657] Step 13:

[0658] After the advertising campaign ends, the server consolidates the collected data and automatically generates a detailed performance report.

[0659] Step 14:

[0660] The server uploads the generated report to the user's dashboard, allowing the user to view the report through their device. The emotion engine includes improvement suggestions in the report based on the user's emotional tendencies.

[0661] Example 2

[0662] 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."

[0663] The problem with traditional advertising campaigns is that it takes a lot of time and effort to create, review, publish, manage, and evaluate advertising content. Also, because the same advertising content is delivered without considering user emotions, advertising effectiveness may not be maximized.

[0664] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0665] In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content based on the input data provided by the user and user emotion data; a means for automatically reviewing the generated advertising content based on internal content policies such as compliance with laws and regulations and guideline compatibility; a means for recommending content that passes the review to the user and suggesting modifications based on user feedback; a means for automatically submitting the selected advertising content to each advertising platform and monitoring progress in real time; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This streamlines the entire advertising campaign process and enables content generation and optimization based on user emotions.

[0666] "Means for inputting basic requirements for an advertising campaign" refers to a function that allows a user to input information regarding the advertising campaign, such as the objectives, target audience, budget, duration, and desired style and tone of the advertisement.

[0667] "Emotion data" refers to the user's emotional information that is recognized and collected in real time by the emotion engine during user input.

[0668] "Means for automatically generating advertising content" refers to a function that uses a generative AI model to automatically create advertising materials such as headlines, body text, images, and videos based on input data and emotional data provided by users.

[0669] The "internal content policy" is a set of standards for reviewing generated advertising content, including compliance with laws and regulations, brand guidelines, and social media policies.

[0670] The "means for automatically reviewing advertising content" is a function that automatically checks whether generated advertising content complies with internal content policies.

[0671] The "recommendation means" is a function that recommends advertising content that has passed screening to users, and is used by users to confirm and suggest corrections.

[0672] "Feedback" refers to the act of a user providing comments or instructions for corrections to recommended advertising content.

[0673] "Modification Suggestion" is a function in which the emotion engine suggests improvements to advertising content based on user feedback.

[0674] An "advertising platform" is an online service for delivering advertising content, such as Google Ads or Facebook Ads.

[0675] "Means for placing ads" refers to a function that automatically distributes selected advertising content to each advertising platform.

[0676] "Means for monitoring progress" means the ability to monitor real-time performance data (e.g., click-through rate, conversion rate) of advertising campaigns.

[0677] "Means for automatic generation" refers to a function that creates a detailed performance report after the end of an advertising campaign and provides it to the user.

[0678] The system of the present invention automates each step of an advertising campaign and provides optimized advertising content by utilizing user emotional data. This system consists of the following main steps: user input of advertising requirements, content generation, automatic review, recommendation, publication, and evaluation.

[0679] 1. Enter user advertising requirements

[0680] Users log in to the system using a device (PC, smartphone, etc.) and enter the basic requirements for their advertising campaign (purpose, target audience, budget, period, desired advertising style and tone, etc.). This input data is sent to the server, and the emotion engine simultaneously recognizes and analyzes the user's emotions in real time as they enter their data. This allows for the collection of emotional data such as the user's expectations and excitement.

[0681] Specific examples

[0682] To increase traffic to a landing page for a new product, a user inputs the target audience as women in their 20s and 30s, a budget of 50,000 yen, and a period of one month. At this time, the emotion engine recognizes emotions such as joy and anticipation from the user's input.

[0683] 2. Automatic generation of advertising content

[0684] The server then calls a generative AI model to automatically generate ad content based on user-entered data and recognized emotional data. The generation process creates ad materials such as headlines, body text, images, and videos. Additionally, the emotional data is used to adjust tone and style.

[0685] Specific examples

[0686] The server uses a generative AI model to generate headlines that highlight the new product's features and compelling copy aimed at the target audience, emphasizing a positive and hopeful tone based on data from the emotion engine.

[0687] Prompt Sentence Examples

[0688] "Generate an ad aimed at women in their 20s and 30s to drive traffic to a new product landing page. The tone should emphasize anticipation."

[0689] 3. Automated content review

[0690] The server then sends the generated ad content to an internal content policy engine for automated review, which checks for legal compliance, brand guideline compliance, and social media policy compliance.

[0691] Specific examples

[0692] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[0693] 4. Recommendation function

[0694] The server recommends advertising content that has passed the screening process to the user. The user can then use their device to check the recommended content and provide corrections or feedback as necessary. At this time, the emotion engine recognizes the user's emotions when providing feedback and makes correction suggestions based on those emotions.

[0695] Specific examples

[0696] The user can review the "most effective headlines and body text" recommended by the server and make adjustments if necessary. The emotion engine recognizes the anxieties and concerns expressed in the user's feedback and suggests revisions to address them.

[0697] 5. Automating advertising operations

[0698] The server automatically posts the user-selected advertising content to each advertising platform (e.g., Google Ads, Facebook Ads, etc.) using the platform's API. Furthermore, an emotion engine is used to monitor the progress of the advertising campaign in real time and optimize it as necessary.

[0699] Specific examples

[0700] The server uses the APIs of Google Ads and Facebook Ads to automatically publish the ad content selected by the user and monitors metrics such as click-through rate and conversion rate in real time. During monitoring, an emotion engine takes into account the user's emotional data and suggests more effective adjustments to advertising budget allocation and targeting.

[0701] 6. Automatic report generation

[0702] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed performance report, which is then uploaded to the user's dashboard and can be viewed from their device.

[0703] Specific examples

[0704] The server generates detailed performance reports based on data such as click rates and conversion rates during the advertising campaign period and displays them on the user's dashboard. In addition, an emotion engine includes improvement suggestions in the report based on the user's emotional tendencies.

[0705] As a result, the system of the present invention streamlines the entire advertising campaign process, enabling content generation and optimization based on user emotions, and providing users with a more effective and personalized advertising experience.

[0706] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0707] Step 1:

[0708] A user logs into the system using a terminal and enters the basic requirements for the advertising campaign.

[0709] What it does: A user uses a computer or smartphone to input the objectives of the advertising campaign, the target audience, the budget, the time frame, the desired style and tone of the ad, etc.

[0710] Inputs: Campaign objectives, target audience, budget, duration, ad style and tone

[0711] Output: Basic requirements data for the input ad campaign

[0712] Step 2:

[0713] The emotion engine recognizes and analyzes the user's emotions in real time as they type.

[0714] How it works: While the user is entering information about an advertising campaign, the emotion engine detects emotions such as joy or anticipation in real time and sends that data to the server.

[0715] Input: Information about the ad campaign entered by the user

[0716] Output: User emotion data

[0717] Step 3:

[0718] The server calls up a generative AI model based on the input data and emotion data, and automatically generates advertising content.

[0719] How it works: The server generates ad materials such as headlines, body text, images, and videos, and adjusts tone and style based on emotional data.

[0720] Input: Basic requirements data for advertising campaigns, user sentiment data

[0721] Output: Generated ad content

[0722] Step 4:

[0723] The server sends the generated advertising content to a content policy engine for automatic review.

[0724] Specific operation: The server will conduct an inspection to check for legal compliance, conformance with brand guidelines, and conformance with social media policies.

[0725] Input: Generated ad content

[0726] Output: Examination result (pass / fail)

[0727] Step 5:

[0728] The server recommends the advertisement content that has passed the screening to the user.

[0729] Specific operation: The server presents the recommended content to the user, who then uses the device to confirm and provide feedback.

[0730] Input: Ad content that has passed the review

[0731] Output: User feedback

[0732] Step 6:

[0733] The emotion engine recognizes the user's emotions when giving feedback and makes suggestions based on them.

[0734] Specific operation: The server analyzes the emotional data in the user's feedback and makes suggestions for modifying the advertising content.

[0735] Input: User feedback, emotional data

[0736] Output: Modified suggested ad content

[0737] Step 7:

[0738] The server automatically posts the selected advertising content to each advertising platform and monitors the progress in real time.

[0739] Specific operation: The server uses the APIs of Google Ads and Facebook Ads to send content and monitor metrics such as click-through rate and conversion rate in real time.

[0740] Input: Selected ad content

[0741] Output: Ad performance data

[0742] Step 8:

[0743] After the advertising campaign ends, the server consolidates the data collected from each advertising platform and automatically generates a detailed performance report.

[0744] Specific operation: The server creates a performance report based on data from the advertising campaign period and uploads it to a dashboard for users to view on their devices.

[0745] Input: Performance data collected from each advertising platform

[0746] Output: Detailed performance report

[0747] The above processing steps streamline the entire process of advertising campaigns and provide optimized advertising content based on user sentiment.

[0748] (Application example 2)

[0749] 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."

[0750] Modern advertising campaigns require a wide range of processes, including the creation, review, submission, operation, and evaluation of advertising content, and performing these processes manually is time-consuming and labor-intensive. While it is also important to incorporate user sentiment data and optimize campaigns in real time to maximize advertising effectiveness, there are currently no efficient ways to do this. Therefore, there is a need for a system that automates the entire advertising operation process and enables customization based on user sentiment.

[0751] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content based on the input data provided by the user and emotion data recognized by an emotion recognition engine; a means for automatically reviewing the generated advertising content based on an internal content policy; a means for recommending content that passes the review to the user and readjusting it based on user feedback; a means for automatically submitting the selected advertising content to each advertising platform and monitoring it in real time; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This makes it possible to efficiently automate the entire advertising operation process and further customize and optimize advertising content based on user emotions.

[0752] "User" refers to the entity that operates and manages an advertising campaign, including advertisers and marketers.

[0753] "Basic requirements for advertising campaign" refers to the initial information required for advertising operations, such as advertising objectives, target audience, budget, duration, desired advertising style and tone, etc.

[0754] An "emotion recognition engine" is a technological element that analyzes and visualizes the emotions contained in user input and feedback in real time.

[0755] "Advertising Content" refers to advertising materials, including headlines, body text, images, and videos, that are automatically generated by a generative AI model.

[0756] "Content Policy" refers to the review criteria for advertising content, including compliance with laws and regulations, conformity with brand guidelines, and conformity with social media policies.

[0757] "Feedback" refers to evaluations and suggestions for corrections that users enter regarding advertising content.

[0758] "Report" means a document detailing the performance of an Advertising Campaign, including metrics such as click-through rates and conversion rates.

[0759] A "generative AI model" is an artificial intelligence technology element that automatically generates advertising content based on information and emotional data provided by users.

[0760] "Prompt" refers to a textual instruction that provides input data to a generative AI model.

[0761] The system of the present invention is implemented as a smartphone app and automates the creation, review, submission, operation, and evaluation of advertising campaigns. The system consists of the following main components:

[0762] 1. User Input and Emotion Recognition

[0763] Using a smartphone app, users input the basic requirements for their advertising campaign (purpose, target audience, budget, period, style, tone, etc.) This input data is sent to a server, and at the same time, an emotion recognition engine analyzes emotions from the user's input in real time.

[0764] 2. Automatic generation of advertising content

[0765] The server receives user input data and emotion recognition engine data, then calls a generative AI model to automatically generate advertising content. This generative AI model receives a text prompt as input and generates advertising materials such as headlines, body text, images, and videos.

[0766] Example prompt sentence:

[0767] "Advertising requirements: We want to promote a new smartwatch to IT engineers in their 20s and 30s. The budget is 100,000 yen, and the duration is two months. The emotions are joy and anticipation. Please generate advertising content based on this."

[0768] 3. Automated review of advertising content

[0769] The generated ad content is automatically sent to an internal content policy engine to check for legal compliance, brand guideline compatibility, and social media policy compatibility, and the engine returns the review results to the server.

[0770] 4. Recommendation and Feedback Collection

[0771] The server recommends advertising content that passes the screening process to the user, who then checks the content on their smartphone. When the user enters feedback, the emotion recognition engine analyzes their emotions again, and the generative AI model readjusts the content based on the analysis results.

[0772] 5. Automatic advertising and real-time monitoring

[0773] The selected advertising content is automatically published through the API of each advertising platform (e.g., Google Ads, Facebook Ads). The server monitors the advertising campaign in real time and optimizes it as needed using emotion recognition data.

[0774] 6. Automatic report generation and provision

[0775] After the ad campaign ends, the server collects data from each ad platform and automatically generates a detailed performance report, which is uploaded to the user's dashboard and can be viewed on their smartphone.

[0776] Hardware and software used

[0777] Hardware: Smartphone

[0778] software:

[0779] Flask: A framework that provides server-side APIs

[0780] EmotionEngine: An emotion recognition engine for recognizing user emotions

[0781] OpenAI API: An API that provides generative AI models, particularly the "davinci-codex" engine.

[0782] Advertising platform APIs: Google Ads, Facebook Ads, etc.

[0783] Examples of specific examples and prompts

[0784] Examples:

[0785] If a user enters the following advertising requirements:

[0786] "I want to promote our new smartwatch to IT engineers in their 20s and 30s. My budget is 100,000 yen and I have two months."

[0787] The emotion recognition engine recognizes "joy and anticipation" from the user's input and generates the following prompt:

[0788] "Advertising requirements: We want to promote a new smartwatch to IT engineers in their 20s and 30s. The budget is 100,000 yen, and the duration is two months. The emotions are joy and anticipation. Please generate advertising content based on this."

[0789] Based on this, advertising content is generated and recommended to users, and necessary feedback is reflected. Furthermore, advertising campaigns are automatically posted to multiple advertising platforms, and results are monitored and optimized in real time to maximize advertising effectiveness.

[0790] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0791] Step 1:

[0792] The user uses a smartphone to input the basic requirements of the advertising campaign (purpose, target audience, budget, period, style, tone, etc.). The input data is immediately sent to the server. The emotion recognition engine then analyzes the user's emotions from this input and sends the analysis results to the server. This allows the user's advertising requirements and accompanying emotional data to be obtained.

[0793] Step 2:

[0794] The server calls the generative AI model based on the acquired user input data and emotion data. The server converts this data into a prompt text and sends this prompt text to the generative AI model. The generative AI model receives the prompt text, generates advertising content (headline, body text, images, videos, etc.), and returns this to the server. This generates advertising content based on the user's requirements.

[0795] Step 3:

[0796] The server sends the generated ad content to an internal content policy engine for automatic review. The review checks for legal compliance, brand guideline compatibility, and social media policy compatibility. The review results are returned to the server, which determines whether the content is deemed eligible.

[0797] Step 4:

[0798] The server recommends advertising content that passes the screening process to the user. The user can then use their smartphone to review the recommended advertising content and provide feedback. This feedback is also analyzed by the emotion recognition engine, and the analysis results are sent back to the server. The generative AI model is then readjusted based on the user's feedback and emotional data.

[0799] Step 5:

[0800] The ad content ultimately selected by the user is automatically published from the server via the API of each ad platform (e.g., Google Ads, Facebook Ads). The server sends the ad content to each platform and monitors it in real time. Depending on the results of the monitoring, adjustments are made to optimize the ad campaign.

[0801] Step 6:

[0802] After the ad campaign ends, the server consolidates data collected from each ad platform (e.g., click-through rate, conversion rate, etc.) and automatically generates a detailed performance report. This report is uploaded to the user's dashboard and can be viewed by the user on their smartphone. The report also includes suggestions for improvement based on the user's emotional data.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] [Third embodiment]

[0807] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0808] 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.

[0809] 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).

[0810] 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.

[0811] 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.

[0812] 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).

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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."

[0819] The system of the present invention automates each step of web advertising content creation, screening, posting, operation, and evaluation. A specific embodiment of the system will be described below.

[0820] 1. Enter user advertising requirements

[0821] The user logs into the system and inputs the following basic requirements from their terminal: the purpose of the advertising campaign, the target audience, the budget, the period, etc. This information is sent to the server and used for subsequent processing.

[0822] example:

[0823] "A user wants to drive traffic to a landing page for a new product. They specify a target audience of women in their 20s and 30s, a budget of ¥50,000, and a time period of one month."

[0824] 2. Automatic generation of advertising content

[0825] The server calls up a generative AI model based on the data entered by the user and automatically generates advertising content. Specifically, advertising materials such as headlines, body text, images, and videos are generated. This allows users to obtain high-quality advertising content even if they do not have specialized advertising knowledge.

[0826] example:

[0827] "The server uses generative AI models to generate headlines that highlight the new product's features and compelling copy for the target audience."

[0828] 3. Automated content review

[0829] The server automatically reviews the generated ad content based on internal content policies. This review process includes legal compliance, brand guideline compatibility, and social media policy compatibility. Content that passes the review proceeds to the next step, but if it fails, it instructs the content to be regenerated or corrected.

[0830] example:

[0831] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[0832] 4. Recommendation function

[0833] The server recommends advertising content that has passed the screening process to the user. The user can then check the recommended content on their device and provide corrections or feedback as necessary, thereby further improving the quality of the content.

[0834] example:

[0835] "The user checks the 'most effective headlines and body text' recommended by the server and makes fine adjustments if necessary."

[0836] 5. Automating advertising operations

[0837] The server automatically posts the advertising content selected by the user to each advertising platform. The server uses each platform's API to send and post the content. In addition, the server monitors the progress of the advertising campaign in real time and optimizes it as necessary.

[0838] example:

[0839] The server uses the APIs of Google Ads and Facebook Ads to automatically publish the ad content selected by the user and monitors success indicators (click-through rate, conversion rate, etc.) in real time.

[0840] 6. Automatic report generation

[0841] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed report, which is then uploaded to the user's dashboard and can be viewed from their device.

[0842] example:

[0843] The server generates detailed performance reports based on data such as click-through rates and conversion rates over the life of the advertising campaign and displays them on the user's dashboard.

[0844] In this way, the system of the present invention automatically generates advertising content based on the basic requirements entered by the user, streamlining the entire process from review, submission, operation, and evaluation, thereby significantly reducing the burden on advertising clients and agencies.

[0845] The processing flow will be explained below.

[0846] Step 1:

[0847] A user logs into the system using a terminal. The login information is authenticated by the server and the user is redirected to their account page.

[0848] Step 2:

[0849] The user enters the basic requirements for their advertising campaign, including the advertising objective, target audience, budget, time frame, desired advertising style and tone, etc. Once complete, they click the submit button.

[0850] Step 3:

[0851] The server receives the data sent by the user and stores it in a database, which is used in the next process of generating advertising content.

[0852] Step 4:

[0853] The server invokes the generative AI model to automatically generate ad content based on the input data provided by the user. This generation process generates ad headlines, body text, images, videos, etc.

[0854] Step 5:

[0855] The server sends the generated ad content to an internal content policy engine for automated review, which checks for compliance with laws and regulations, brand guidelines, and social media policies.

[0856] Step 6:

[0857] The server judges the results of the review and adds content that passes the review to the recommendation list, while issuing instructions to regenerate content that fails the review.

[0858] Step 7:

[0859] The server recommends content that passes the screening to the user, and the recommended content is displayed on the user's management screen.

[0860] Step 8:

[0861] The user reviews the recommended ad content, makes corrections and provides feedback as needed, and once the changes are confirmed, the user clicks the save button.

[0862] Step 9:

[0863] The server stores the modified advertisement content in a final database and prepares the selected advertisement content to be automatically posted to each advertisement platform.

[0864] Step 10:

[0865] The server calls the API of each advertising platform and automatically places ads on multiple platforms, including Google Ads, Facebook Ads, and Twitter Ads.

[0866] Step 11:

[0867] The server monitors the progress of the advertising campaign in real time and collects data such as click-through rates, conversion rates, and revenue.

[0868] Step 12:

[0869] The server then optimizes the advertising campaign based on the collected data, which may include reallocating advertising budgets and adjusting targeting.

[0870] Step 13:

[0871] After the advertising campaign ends, the server consolidates the collected data and automatically generates a detailed performance report.

[0872] Step 14:

[0873] The server uploads the generated report to the user's dashboard, allowing the user to view the report through their terminal.

[0874] Example 1

[0875] 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."

[0876] In the past, each step of advertising campaigns—creating advertising content, reviewing, publishing, managing, and evaluating it—was often done manually, requiring a high level of expertise and a significant amount of time. This placed a heavy burden on small businesses and individuals when running advertising campaigns, making it difficult to manage advertising effectively. It also made it difficult to optimize advertising content in real time or automatically generate detailed performance reports.

[0877] 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.

[0878] In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content using an artificial intelligence model based on the input data provided by the user; a means for automatically reviewing the generated advertising content based on an internal content policy; a means for recommending content that passes the review to the user; a means for automatically submitting the selected advertising content to each advertising distribution platform; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This makes it possible to streamline the entire advertising campaign process and significantly reduce the burden on advertising clients and agencies.

[0879] "User" means an individual or organization that utilizes the system to conduct advertising campaigns.

[0880] An "advertising campaign" is a series of advertising activities undertaken to achieve a specific marketing objective.

[0881] "Basic requirements" are key pieces of information needed to run an advertising campaign, such as objectives, target audience, budget, and duration.

[0882] "Input means" refers to a device or interface that allows a user to input basic requirements for an advertising campaign into the system.

[0883] An "artificial intelligence model" is a machine learning algorithm or system that automatically generates advertising content based on user-provided input data.

[0884] "Automatic generation means" means a function or process that automatically generates advertising content using an artificial intelligence model based on input data.

[0885] "Content policies" are internal rules and standards that advertising content must follow, such as legal compliance, brand guidelines, and social media policies.

[0886] The "automatic review means" refers to a device or system that determines whether the generated advertising content complies with the content policy.

[0887] "Means of recommendation" refers to the functions and processes for proposing advertising content that has passed screening to users.

[0888] The "means for placing advertisements" refers to a function or system for automatically sending the selected advertisement content to each advertisement distribution platform and starting distribution.

[0889] A "platform" is a medium such as a website or application for delivering advertisements.

[0890] A "report" is a document that details the results of an advertising campaign, including metrics such as click-through rate and conversion rate.

[0891] "Means for automatically generating and providing" refers to a function or system that automatically creates detailed reports based on data collected after an advertising campaign ends and provides them to users.

[0892] The system of the present invention automates each step of an advertising campaign, allowing users to efficiently manage their advertising. Specific embodiments are described below.

[0893] First, the user logs into the system using a terminal. After logging in, the user enters the basic requirements for the advertising campaign, such as the purpose, target audience, budget, and period. This information is sent to the server when the user enters it into a form on the terminal and presses the submit button. As a specific example, the user might enter requirements such as "the goal is to increase traffic to the landing page for a new product, with a target audience of women in their 20s and 30s, a budget of 50,000 yen, and a period of one month."

[0894] Next, the server calls a generative AI model based on the data entered by the user. For example, GPT-3 is used as the generative AI model. The server generates and sends a prompt to this AI model. A specific example of a prompt might be, "Please generate ad copy aimed at women in their 20s and 30s to increase traffic to our landing page." Based on this prompt, the AI ​​model generates content for the ad, such as the headline, body text, images, and video.

[0895] The generated advertising content is then automatically reviewed by the server. This review process includes legal compliance, brand guideline compatibility, and social media policy compatibility. Specifically, the server uses an review engine to check whether the generated content violates regulations or internal policies. Content that passes the review proceeds to the next step, while content that fails the review is instructed to be regenerated or corrected.

[0896] Ad content that passes the screening process is recommended to the user by the server. The user can review this content on their device and provide corrections or feedback as necessary. For example, the user can review and fine-tune the headline and body text recommended by the server.

[0897] The server then automatically posts the advertising content selected by the user to each advertising distribution platform. Specifically, the server uses the API of each platform (e.g., Google Ads, Facebook Ads) to send the content. Furthermore, the server monitors the progress of the advertising campaign in real time and optimizes it as necessary. During this process, the server obtains and analyzes data such as click-through rates and conversion rates.

[0898] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed report. The report is uploaded to the user's dashboard and can be viewed from their device. For example, the server generates a report that provides a detailed analysis of the campaign's performance based on metrics such as click-through rate and conversion rate.

[0899] In this way, the system automatically generates advertising content based on the basic requirements entered by the user, streamlining the entire process from review, submission, operation, and evaluation, and significantly reducing the burden on advertising clients and agencies.

[0900] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0901] Step 1:

[0902] A user logs into the system using a terminal. After logging in, the user enters the basic requirements for the advertising campaign, such as the objective, target audience, budget, and period. This input data is sent from the terminal to the server. Specifically, the user enters information such as "increase traffic to the landing page for a new product" into a form on the terminal and presses the "Submit" button. The terminal then sends this as an HTTP request to the server.

[0903] Input: The basic requirement of an advertising campaign is that users fill out a form on their device.

[0904] Output: Basic requirements data for the ad campaign sent to the server

[0905] Step 2:

[0906] The server receives the basic requirement data entered by the user and calls a generative AI model based on that data. Specifically, it uses a generative AI model (e.g., GPT-3) to create a prompt based on the input data and sends it to the AI ​​model. The prompt is in the format, "Please generate ad copy aimed at women in their 20s and 30s to increase traffic to the landing page." The AI ​​model generates ad content based on this prompt.

[0907] Input: Basic requirements data for the ad campaign sent to the server

[0908] Output: Ad content returned by the generative AI model

[0909] Step 3:

[0910] The server automatically reviews the generated ad content based on its internal content policies. This review process checks whether it complies with standards such as legal compliance, brand guidelines, and social media policies. Specifically, the server uses a review engine to search for specific keywords and phrases in the content using regular expressions to check for inappropriate content. If there are no problems with the review, it proceeds to the next step.

[0911] Input: Ad content obtained from a generative AI model

[0912] Output: Ad content that has passed the review

[0913] Step 4:

[0914] The server recommends advertising content that has passed the screening process to the user. The user then uses their device to review this content and make corrections or provide feedback as necessary. Specifically, the server generates a preview of the recommended content and sends it to the device. The user then reviews the preview, enters corrections if necessary, and sends it back to the server.

[0915] Input: Ad content that has passed the review

[0916] Output: Ad content recommended to the user

[0917] Step 5:

[0918] The server automatically places the ad content selected by the user on each ad distribution platform. Specifically, the server uses the APIs of Google Ads and Facebook Ads to send the selected ad content to each platform. It also monitors the progress of the ad campaign in real time and optimizes it as needed. For example, it continuously checks click-through rates and conversion rates and changes optimization settings based on that data.

[0919] Input: User selected ad content

[0920] Output: Advertising content posted on the advertising platform and its monitoring data

[0921] Step 6:

[0922] After the ad campaign ends, the server consolidates the data collected from each advertising platform and automatically generates a detailed report. The generated report is uploaded to the user's dashboard, where the user can view it from their device. Specifically, the server aggregates metrics such as click-through rate and conversion rate, and creates a detailed performance report based on this.

[0923] Input: Monitoring data and final results of advertising campaigns

[0924] Output: A detailed report displayed on the user's dashboard

[0925] (Application example 1)

[0926] 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."

[0927] Traditional advertising campaign management requires many manual steps and specialized knowledge to create, review, publish, operate, and evaluate advertising content. Furthermore, optimizing advertising effectiveness and real-time monitoring are complex and require quick responses. This creates problems for advertising agencies and companies, requiring significant effort and costs.

[0928] 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.

[0929] In this invention, the server includes: means for a user to input basic requirements for an advertising campaign; means for automatically generating advertising content based on the input data provided by the user; means for automatically reviewing the generated advertising content based on an internal content policy; means for recommending content that passes the review to the user; means for automatically publishing the selected advertising content on each advertising platform; means for automatically generating and providing a detailed report to the user after the advertising campaign ends; and means for generating advertising content using a generative AI model and making the generated advertising content available through a smartphone application that reviews, recommends, optimizes, publishes, and generates reports on the generated advertising content. This automates the entire advertising campaign process, allowing users to easily manage advertising campaigns and effectively manage advertising without requiring specialized knowledge or significant effort.

[0930] "User" refers to an advertising agency, company, or other user who utilizes the system to input basic requirements for an advertising campaign and then manages the process.

[0931] "Basic requirements for advertising campaign" means basic information required to carry out an advertising campaign, such as the purpose of the advertisement, target audience, budget, and period.

[0932] "Advertising content" refers to advertising materials such as headlines, text, images, and videos, and is material generated as content.

[0933] "Automatic generation" is the process of using artificial intelligence to generate advertising content based on user-provided data.

[0934] "Internal content policies" refer to internal regulations that advertising content must follow, such as legal compliance, brand guidelines, and platform policies.

[0935] "Automatic review" is the process by which the system checks the advertising content generated based on internal content policies to determine whether it complies.

[0936] "Recommendation" is a feature in which the system suggests optimal advertising content to users, allowing for feedback and fine-tuning.

[0937] "Each advertising platform" refers to an online advertising platform for placing advertisements, such as Google Ads or Facebook Ads.

[0938] "Automatic posting" is the process by which the system posts advertising content directly to each advertising platform based on user specifications.

[0939] A "detailed report" is a report that summarizes the progress and success metrics of an advertising campaign and is automatically generated after the campaign has ended.

[0940] A "generative AI model" is a machine learning model that uses generative artificial intelligence (such as GPT) to generate advertising content.

[0941] A "smartphone application" is software that runs on a smartphone and allows users to manage advertising campaigns and generate content.

[0942] The system of the present invention automates the entire advertising campaign process through a smartphone application. The system includes a generative AI model that generates advertising content based on user input data, and multiple functions for reviewing, recommending, optimizing, publishing, and evaluating the generated advertising content.

[0943] System configuration and programs

[0944] This system is realized by combining the following hardware and software.

[0945] Hardware: Smartphone (iPhone, Android)

[0946] software:

[0947] Generative AI models: OpenAI GPT, DALL-E

[0948] Database: MySQL

[0949] Frontend: React Native

[0950] Backend: Node.js + Express

[0951] Advertising API: Google Ads API, Facebook Ads API

[0952] Data processing and calculation

[0953] 1. Obtaining user input data

[0954] Through the smartphone application, users input the basic requirements of their advertising campaign (advertising objectives, target audience, budget, period, etc.) This input data is collected on the server side and used for subsequent processing.

[0955] 2. Automatic generation of advertising content

[0956] The server calls a generative AI model (OpenAI GPT) based on the input data and automatically generates advertising content (headline, body text, images, videos, etc.). This process is an example of evaluation and generation based on prompt text.

[0957] Example prompt sentence:

[0958] "Goal of advertising campaign: Increase traffic to new product landing page

[0959] Target audience: Women in their 20s and 30s

[0960] Budget: 50,000 yen

[0961] Duration: 1 month

[0962] Request: Please generate advertising copy that highlights the appeal of a new product aimed at women in their 20s and 30s.

[0963] 3. Automated review of generated content

[0964] The server then reviews the generated ad content based on its internal content policies, checking for compliance with legal regulations, brand guidelines, and platform policies, and instructs the content to be regenerated or corrected if it does not comply.

[0965] 4. User Recommendations

[0966] Advertising content that passes the screening process is recommended to users, who can then check it on their smartphone application and provide feedback if necessary.

[0967] 5. Advertising content

[0968] The server automatically posts the ad content selected by the user to each advertising platform using the Google Ads API or Facebook Ads API. After posting, the server monitors the progress of the ad campaign in real time and optimizes it as necessary.

[0969] 6. Automatic report generation

[0970] After the ad campaign is over, the server generates a detailed report based on the collected data and uploads it to the user's dashboard, allowing the user to evaluate the effectiveness of the ad campaign.

[0971] In this way, the system of the present invention efficiently automates the entire process of an advertising campaign, allowing users to effectively manage advertising without requiring specialized knowledge or a great deal of effort.

[0972] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0973] Step 1:

[0974] Users input the basic requirements of an advertising campaign (objective, target audience, budget, period) using a smartphone application. The input data is sent to the server in real time and stored in a database (MySQL), where it is also validated to ensure that the input data is correct.

[0975] Step 2:

[0976] The server calls a generative AI model (OpenAI GPT) based on the requirements information for the ad campaign sent by the user and generates a prompt. Specifically, the following prompt is used:

[0977] "Goal of advertising campaign: Increase traffic to new product landing page

[0978] Target audience: Women in their 20s and 30s

[0979] Budget: 50,000 yen

[0980] Duration: 1 month

[0981] Request: Please generate advertising copy that highlights the appeal of a new product aimed at women in their 20s and 30s.

[0982] The generated prompt text is analyzed by an AI model to automatically generate advertising content such as headlines and body text, and the generated content is temporarily stored on the server.

[0983] Step 3:

[0984] The server automatically reviews the temporarily stored ad content based on its internal content policies. This review process checks for compliance with specific regulations, platform policies, and brand guidelines. If the content complies, it moves on to the next step, and if it doesn't, it instructs the content to be regenerated or corrected.

[0985] Step 4:

[0986] Advertising content that passes the screening process is recommended to smartphone application users from the server. Users can review the recommended content on their devices and provide feedback if necessary. This feedback is sent to the server, and the content may be further fine-tuned.

[0987] Step 5:

[0988] The ad content finally selected by the user is automatically published by the server using the Google Ads API or Facebook Ads API. During the publishing process to each platform, the ad content is sent via the API, and the status of each platform is updated in real time.

[0989] Step 6:

[0990] The server monitors the progress of the ad campaign in real time and optimizes it as needed, for example by revisiting the generative AI model and fine-tuning the content if click-through or conversion rates are low.

[0991] Step 7:

[0992] After the ad campaign ends, the server automatically generates a detailed report based on the collected data. This report includes performance indicators such as click-through rate, conversion rate, and return on investment, and the report is uploaded to the user's dashboard. Users can review the report on their dashboard and use it to improve their next ad campaign.

[0993] This automates the entire advertising campaign process, allowing users to effectively manage and operate ads without requiring specialized knowledge or effort.

[0994] 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.

[0995] The system of the present invention automates each step of web advertising content creation, review, submission, operation, and evaluation. Furthermore, by combining it with an emotion engine that recognizes user emotions, the system customizes and optimizes advertising content. Specific embodiments of the system are described below.

[0996] 1. Enter user advertising requirements

[0997] Users log in to the system and input basic information from their devices, including the purpose of the advertising campaign, target audience, budget, duration, and desired advertising style and tone. This information is sent to the server for subsequent processing. As users input this information, the emotion engine recognizes and analyzes their emotions in real time.

[0998] example:

[0999] "The user aims to increase traffic to a landing page for a new product. They specify a target audience of women in their 20s and 30s, a budget of 50,000 yen, and a time period of one month. The emotion engine then recognizes emotions such as joy and anticipation from the user's input."

[1000] 2. Automatic generation of advertising content

[1001] The server then calls a generative AI model based on the user's input data and the recognized emotions to automatically generate ad content. The generation process generates ad materials such as headlines, body text, images, and videos. The tone and style are also adjusted to fit the emotion based on the emotion engine data.

[1002] example:

[1003] "The server uses generative AI models to generate headlines that highlight the new product's features and compelling copy aimed at the target audience, referencing data from the emotion engine to emphasize an upbeat and hopeful tone."

[1004] 3. Automated content review

[1005] The server then sends the generated ad content to an internal content policy engine for automated review, which includes checking compliance with regulations, brand guidelines, and social media policies.

[1006] example:

[1007] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[1008] 4. Recommendation function

[1009] The server recommends advertising content that passes the screening process to the user. The user can then review the recommended content on their device and make corrections or provide feedback as necessary. At this time, the emotion engine recognizes the user's emotions from the feedback and makes correction suggestions based on that.

[1010] example:

[1011] "The user reviews the 'most effective headlines and body copy' recommended by the server and makes fine adjustments if necessary. The emotion engine recognizes any anxieties or concerns expressed in the user's feedback and suggests revisions to address them."

[1012] 5. Automating advertising operations

[1013] The server automatically posts the ad content selected by the user to each advertising platform. The server uses each platform's API to send and post the content. In addition, an emotion engine is used to monitor the progress of the ad campaign in real time and optimize it as needed.

[1014] example:

[1015] "The server uses the APIs of Google Ads and Facebook Ads to automatically publish the advertising content selected by the user and monitors success indicators (click-through rate, conversion rate, etc.) in real time. During monitoring, the emotion engine takes into account the user's emotional data and suggests more effective adjustments to advertising budget allocation and targeting."

[1016] 6. Automatic report generation

[1017] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed performance report, which is then uploaded to the user's dashboard and can be viewed from their device.

[1018] example:

[1019] "The server generates a detailed performance report based on data such as click-through rates and conversion rates during the advertising campaign period and displays it on the user's dashboard. In addition, an emotion engine includes improvement suggestions in the report based on the user's emotional tendencies."

[1020] In this way, the system of the present invention automatically generates advertising content based on the basic requirements and emotions entered by the user, streamlining the entire process from screening, publishing, operation, and evaluation, and significantly reducing the burden on advertising clients and agencies. In particular, the introduction of an emotion engine makes personalization and optimization of advertising content more effective.

[1021] The processing flow will be explained below.

[1022] Step 1:

[1023] A user logs into the system using a terminal. The login information is authenticated by the server and the user is redirected to their account page.

[1024] Step 2:

[1025] The user enters the basic requirements for their advertising campaign, including the advertising objective, target audience, budget, time frame, desired advertising style and tone, etc. Once complete, they click the submit button.

[1026] Step 3:

[1027] The server receives the data sent by the user and stores it in a database. The stored data is used in the next process of generating advertising content. Here, an emotion engine is activated to recognize emotions from the user's input data and analyze the user's emotions.

[1028] Step 4:

[1029] The server invokes the generative AI model to automatically generate ad content based on the input data and recognized emotions provided by the user. This generation process generates ad materials such as headlines, body text, images, and videos. Based on the data from the emotion engine, the tone and style are also adjusted to match the emotion.

[1030] Step 5:

[1031] The server sends the generated ad content to an internal content policy engine for automated review, which checks for compliance with laws and regulations, brand guidelines, and social media policies.

[1032] Step 6:

[1033] The server judges the results of the review and adds content that passes the review to the recommendation list, while issuing instructions to regenerate content that fails the review.

[1034] Step 7:

[1035] The server recommends content that passes the screening to the user, and the recommended content is displayed on the user's management screen.

[1036] Step 8:

[1037] The user checks the recommended advertising content and makes corrections or inputs feedback as necessary. At this time, the emotion engine recognizes the emotions from the user's feedback and makes correction suggestions based on that.

[1038] Step 9:

[1039] The server stores the modified advertisement content in a final database and prepares the selected advertisement content to be automatically posted to each advertisement platform.

[1040] Step 10:

[1041] The server calls the API of each advertising platform and automatically places ads on multiple platforms, including Google Ads, Facebook Ads, and Twitter Ads.

[1042] Step 11:

[1043] The server monitors the progress of the advertising campaign in real time and collects data such as click-through rates, conversion rates, and revenue.

[1044] Step 12:

[1045] The server optimizes the advertising campaign based on the collected data, which includes reallocating advertising budgets and adjusting targeting. Additionally, the emotion engine takes user emotion data into account and makes optimization suggestions for the advertising campaign.

[1046] Step 13:

[1047] After the advertising campaign ends, the server consolidates the collected data and automatically generates a detailed performance report.

[1048] Step 14:

[1049] The server uploads the generated report to the user's dashboard, allowing the user to view the report through their device. The emotion engine includes improvement suggestions in the report based on the user's emotional tendencies.

[1050] Example 2

[1051] 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."

[1052] The problem with traditional advertising campaigns is that it takes a lot of time and effort to create, review, publish, manage, and evaluate advertising content. Also, because the same advertising content is delivered without considering user emotions, advertising effectiveness may not be maximized.

[1053] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1054] In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content based on the input data provided by the user and user emotion data; a means for automatically reviewing the generated advertising content based on internal content policies such as compliance with laws and regulations and guideline compatibility; a means for recommending content that passes the review to the user and suggesting modifications based on user feedback; a means for automatically submitting the selected advertising content to each advertising platform and monitoring progress in real time; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This streamlines the entire advertising campaign process and enables content generation and optimization based on user emotions.

[1055] "Means for inputting basic requirements for an advertising campaign" refers to a function that allows a user to input information regarding the advertising campaign, such as the objectives, target audience, budget, duration, and desired style and tone of the advertisement.

[1056] "Emotion data" refers to the user's emotional information that is recognized and collected in real time by the emotion engine during user input.

[1057] "Means for automatically generating advertising content" refers to a function that uses a generative AI model to automatically create advertising materials such as headlines, body text, images, and videos based on input data and emotional data provided by users.

[1058] The "internal content policy" is a set of standards for reviewing generated advertising content, including compliance with laws and regulations, brand guidelines, and social media policies.

[1059] The "means for automatically reviewing advertising content" is a function that automatically checks whether generated advertising content complies with internal content policies.

[1060] The "recommendation means" is a function that recommends advertising content that has passed screening to users, and is used by users to confirm and suggest corrections.

[1061] "Feedback" refers to the act of a user providing comments or instructions for corrections to recommended advertising content.

[1062] "Modification Suggestion" is a function in which the emotion engine suggests improvements to advertising content based on user feedback.

[1063] An "advertising platform" is an online service for delivering advertising content, such as Google Ads or Facebook Ads.

[1064] "Means for placing ads" refers to a function that automatically distributes selected advertising content to each advertising platform.

[1065] "Means for monitoring progress" means the ability to monitor real-time performance data (e.g., click-through rate, conversion rate) of advertising campaigns.

[1066] "Means for automatic generation" refers to a function that creates a detailed performance report after the end of an advertising campaign and provides it to the user.

[1067] The system of the present invention automates each step of an advertising campaign and provides optimized advertising content by utilizing user emotional data. This system consists of the following main steps: user input of advertising requirements, content generation, automatic review, recommendation, publication, and evaluation.

[1068] 1. Enter user advertising requirements

[1069] Users log in to the system using a device (PC, smartphone, etc.) and enter the basic requirements for their advertising campaign (purpose, target audience, budget, period, desired advertising style and tone, etc.). This input data is sent to the server, and the emotion engine simultaneously recognizes and analyzes the user's emotions in real time as they enter their data. This allows for the collection of emotional data such as the user's expectations and excitement.

[1070] Specific examples

[1071] To increase traffic to a landing page for a new product, a user inputs the target audience as women in their 20s and 30s, a budget of 50,000 yen, and a period of one month. At this time, the emotion engine recognizes emotions such as joy and anticipation from the user's input.

[1072] 2. Automatic generation of advertising content

[1073] The server then calls a generative AI model to automatically generate ad content based on user-entered data and recognized emotional data. The generation process creates ad materials such as headlines, body text, images, and videos. Additionally, the emotional data is used to adjust tone and style.

[1074] Specific examples

[1075] The server uses a generative AI model to generate headlines that highlight the new product's features and compelling copy aimed at the target audience, emphasizing a positive and hopeful tone based on data from the emotion engine.

[1076] Prompt Sentence Examples

[1077] "Generate an ad aimed at women in their 20s and 30s to drive traffic to a new product landing page. The tone should emphasize anticipation."

[1078] 3. Automated content review

[1079] The server then sends the generated ad content to an internal content policy engine for automated review, which checks for legal compliance, brand guideline compliance, and social media policy compliance.

[1080] Specific examples

[1081] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[1082] 4. Recommendation function

[1083] The server recommends advertising content that has passed the screening process to the user. The user can then use their device to check the recommended content and provide corrections or feedback as necessary. At this time, the emotion engine recognizes the user's emotions when providing feedback and makes correction suggestions based on those emotions.

[1084] Specific examples

[1085] The user can review the "most effective headlines and body text" recommended by the server and make adjustments if necessary. The emotion engine recognizes the anxieties and concerns expressed in the user's feedback and suggests revisions to address them.

[1086] 5. Automating advertising operations

[1087] The server automatically posts the user-selected advertising content to each advertising platform (e.g., Google Ads, Facebook Ads, etc.) using the platform's API. Furthermore, an emotion engine is used to monitor the progress of the advertising campaign in real time and optimize it as necessary.

[1088] Specific examples

[1089] The server uses the APIs of Google Ads and Facebook Ads to automatically publish the ad content selected by the user and monitors metrics such as click-through rate and conversion rate in real time. During monitoring, an emotion engine takes into account the user's emotional data and suggests more effective adjustments to advertising budget allocation and targeting.

[1090] 6. Automatic report generation

[1091] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed performance report, which is then uploaded to the user's dashboard and can be viewed from their device.

[1092] Specific examples

[1093] The server generates detailed performance reports based on data such as click rates and conversion rates during the advertising campaign period and displays them on the user's dashboard. In addition, an emotion engine includes improvement suggestions in the report based on the user's emotional tendencies.

[1094] As a result, the system of the present invention streamlines the entire advertising campaign process, enabling content generation and optimization based on user emotions, and providing users with a more effective and personalized advertising experience.

[1095] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1096] Step 1:

[1097] A user logs into the system using a terminal and enters the basic requirements for the advertising campaign.

[1098] What it does: A user uses a computer or smartphone to input the objectives of the advertising campaign, the target audience, the budget, the time frame, the desired style and tone of the ad, etc.

[1099] Inputs: Campaign objectives, target audience, budget, duration, ad style and tone

[1100] Output: Basic requirements data for the input ad campaign

[1101] Step 2:

[1102] The emotion engine recognizes and analyzes the user's emotions in real time as they type.

[1103] How it works: While the user is entering information about an advertising campaign, the emotion engine detects emotions such as joy or anticipation in real time and sends that data to the server.

[1104] Input: Information about the ad campaign entered by the user

[1105] Output: User emotion data

[1106] Step 3:

[1107] The server calls up a generative AI model based on the input data and emotion data, and automatically generates advertising content.

[1108] How it works: The server generates ad materials such as headlines, body text, images, and videos, and adjusts tone and style based on emotional data.

[1109] Input: Basic requirements data for advertising campaigns, user sentiment data

[1110] Output: Generated ad content

[1111] Step 4:

[1112] The server sends the generated advertising content to a content policy engine for automatic review.

[1113] Specific operation: The server will conduct an inspection to check for legal compliance, conformance with brand guidelines, and conformance with social media policies.

[1114] Input: Generated ad content

[1115] Output: Examination result (pass / fail)

[1116] Step 5:

[1117] The server recommends the advertisement content that has passed the screening to the user.

[1118] Specific operation: The server presents the recommended content to the user, who then uses the device to confirm and provide feedback.

[1119] Input: Ad content that has passed the review

[1120] Output: User feedback

[1121] Step 6:

[1122] The emotion engine recognizes the user's emotions when giving feedback and makes suggestions based on them.

[1123] Specific operation: The server analyzes the emotional data in the user's feedback and makes suggestions for modifying the advertising content.

[1124] Input: User feedback, emotional data

[1125] Output: Modified suggested ad content

[1126] Step 7:

[1127] The server automatically posts the selected advertising content to each advertising platform and monitors the progress in real time.

[1128] Specific operation: The server uses the APIs of Google Ads and Facebook Ads to send content and monitor metrics such as click-through rate and conversion rate in real time.

[1129] Input: Selected ad content

[1130] Output: Ad performance data

[1131] Step 8:

[1132] After the advertising campaign ends, the server consolidates the data collected from each advertising platform and automatically generates a detailed performance report.

[1133] Specific operation: The server creates a performance report based on data from the advertising campaign period and uploads it to a dashboard for users to view on their devices.

[1134] Input: Performance data collected from each advertising platform

[1135] Output: Detailed performance report

[1136] The above processing steps streamline the entire process of advertising campaigns and provide optimized advertising content based on user sentiment.

[1137] (Application example 2)

[1138] 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."

[1139] Modern advertising campaigns require a wide range of processes, including the creation, review, submission, operation, and evaluation of advertising content, and performing these processes manually is time-consuming and labor-intensive. While it is also important to incorporate user sentiment data and optimize campaigns in real time to maximize advertising effectiveness, there are currently no efficient ways to do this. Therefore, there is a need for a system that automates the entire advertising operation process and enables customization based on user sentiment.

[1140] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content based on the input data provided by the user and emotion data recognized by an emotion recognition engine; a means for automatically reviewing the generated advertising content based on an internal content policy; a means for recommending content that passes the review to the user and readjusting it based on user feedback; a means for automatically submitting the selected advertising content to each advertising platform and monitoring it in real time; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This makes it possible to efficiently automate the entire advertising operation process and further customize and optimize advertising content based on user emotions.

[1141] "User" refers to the entity that operates and manages an advertising campaign, including advertisers and marketers.

[1142] "Basic requirements for advertising campaign" refers to the initial information required for advertising operations, such as advertising objectives, target audience, budget, duration, desired advertising style and tone, etc.

[1143] An "emotion recognition engine" is a technological element that analyzes and visualizes the emotions contained in user input and feedback in real time.

[1144] "Advertising Content" refers to advertising materials, including headlines, body text, images, and videos, that are automatically generated by a generative AI model.

[1145] "Content Policy" refers to the review criteria for advertising content, including compliance with laws and regulations, conformity with brand guidelines, and conformity with social media policies.

[1146] "Feedback" refers to evaluations and suggestions for corrections that users enter regarding advertising content.

[1147] "Report" means a document detailing the performance of an Advertising Campaign, including metrics such as click-through rates and conversion rates.

[1148] A "generative AI model" is an artificial intelligence technology element that automatically generates advertising content based on information and emotional data provided by users.

[1149] "Prompt" refers to a textual instruction that provides input data to a generative AI model.

[1150] The system of the present invention is implemented as a smartphone app and automates the creation, review, submission, operation, and evaluation of advertising campaigns. The system consists of the following main components:

[1151] 1. User Input and Emotion Recognition

[1152] Using a smartphone app, users input the basic requirements for their advertising campaign (purpose, target audience, budget, period, style, tone, etc.) This input data is sent to a server, and at the same time, an emotion recognition engine analyzes emotions from the user's input in real time.

[1153] 2. Automatic generation of advertising content

[1154] The server receives user input data and emotion recognition engine data, then calls a generative AI model to automatically generate advertising content. This generative AI model receives a text prompt as input and generates advertising materials such as headlines, body text, images, and videos.

[1155] Example prompt sentence:

[1156] "Advertising requirements: We want to promote a new smartwatch to IT engineers in their 20s and 30s. The budget is 100,000 yen, and the duration is two months. The emotions are joy and anticipation. Please generate advertising content based on this."

[1157] 3. Automated review of advertising content

[1158] The generated ad content is automatically sent to an internal content policy engine to check for legal compliance, brand guideline compatibility, and social media policy compatibility, and the engine returns the review results to the server.

[1159] 4. Recommendation and Feedback Collection

[1160] The server recommends advertising content that passes the screening process to the user, who then checks the content on their smartphone. When the user enters feedback, the emotion recognition engine analyzes their emotions again, and the generative AI model readjusts the content based on the analysis results.

[1161] 5. Automatic advertising and real-time monitoring

[1162] The selected advertising content is automatically published through the API of each advertising platform (e.g., Google Ads, Facebook Ads). The server monitors the advertising campaign in real time and optimizes it as needed using emotion recognition data.

[1163] 6. Automatic report generation and provision

[1164] After the ad campaign ends, the server collects data from each ad platform and automatically generates a detailed performance report, which is uploaded to the user's dashboard and can be viewed on their smartphone.

[1165] Hardware and software used

[1166] Hardware: Smartphone

[1167] software:

[1168] Flask: A framework that provides server-side APIs

[1169] EmotionEngine: An emotion recognition engine for recognizing user emotions

[1170] OpenAI API: An API that provides generative AI models, particularly the "davinci-codex" engine.

[1171] Advertising platform APIs: Google Ads, Facebook Ads, etc.

[1172] Examples of specific examples and prompts

[1173] Examples:

[1174] If a user enters the following advertising requirements:

[1175] "I want to promote our new smartwatch to IT engineers in their 20s and 30s. My budget is 100,000 yen and I have two months."

[1176] The emotion recognition engine recognizes "joy and anticipation" from the user's input and generates the following prompt:

[1177] "Advertising requirements: We want to promote a new smartwatch to IT engineers in their 20s and 30s. The budget is 100,000 yen, and the duration is two months. The emotions are joy and anticipation. Please generate advertising content based on this."

[1178] Based on this, advertising content is generated and recommended to users, and necessary feedback is reflected. Furthermore, advertising campaigns are automatically posted to multiple advertising platforms, and results are monitored and optimized in real time to maximize advertising effectiveness.

[1179] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1180] Step 1:

[1181] The user uses a smartphone to input the basic requirements of the advertising campaign (purpose, target audience, budget, period, style, tone, etc.). The input data is immediately sent to the server. The emotion recognition engine then analyzes the user's emotions from this input and sends the analysis results to the server. This allows the user's advertising requirements and accompanying emotional data to be obtained.

[1182] Step 2:

[1183] The server calls the generative AI model based on the acquired user input data and emotion data. The server converts this data into a prompt text and sends this prompt text to the generative AI model. The generative AI model receives the prompt text, generates advertising content (headline, body text, images, videos, etc.), and returns this to the server. This generates advertising content based on the user's requirements.

[1184] Step 3:

[1185] The server sends the generated ad content to an internal content policy engine for automatic review. The review checks for legal compliance, brand guideline compatibility, and social media policy compatibility. The review results are returned to the server, which determines whether the content is deemed eligible.

[1186] Step 4:

[1187] The server recommends advertising content that passes the screening process to the user. The user can then use their smartphone to review the recommended advertising content and provide feedback. This feedback is also analyzed by the emotion recognition engine, and the analysis results are sent back to the server. The generative AI model is then readjusted based on the user's feedback and emotional data.

[1188] Step 5:

[1189] The ad content ultimately selected by the user is automatically published from the server via the API of each ad platform (e.g., Google Ads, Facebook Ads). The server sends the ad content to each platform and monitors it in real time. Depending on the results of the monitoring, adjustments are made to optimize the ad campaign.

[1190] Step 6:

[1191] After the ad campaign ends, the server consolidates data collected from each ad platform (e.g., click-through rate, conversion rate, etc.) and automatically generates a detailed performance report. This report is uploaded to the user's dashboard and can be viewed by the user on their smartphone. The report also includes suggestions for improvement based on the user's emotional data.

[1192] 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.

[1193] 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.

[1194] 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.

[1195] [Fourth embodiment]

[1196] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1197] 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.

[1198] 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).

[1199] 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.

[1200] 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.

[1201] 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).

[1202] 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.

[1203] 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.

[1204] 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.

[1205] 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.

[1206] 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.

[1207] 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.

[1208] 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."

[1209] The system of the present invention automates each step of web advertising content creation, screening, posting, operation, and evaluation. A specific embodiment of the system will be described below.

[1210] 1. Enter user advertising requirements

[1211] The user logs into the system and inputs the following basic requirements from their terminal: the purpose of the advertising campaign, the target audience, the budget, the period, etc. This information is sent to the server and used for subsequent processing.

[1212] example:

[1213] "A user wants to drive traffic to a landing page for a new product. They specify a target audience of women in their 20s and 30s, a budget of ¥50,000, and a time period of one month."

[1214] 2. Automatic generation of advertising content

[1215] The server calls up a generative AI model based on the data entered by the user and automatically generates advertising content. Specifically, advertising materials such as headlines, body text, images, and videos are generated. This allows users to obtain high-quality advertising content even if they do not have specialized advertising knowledge.

[1216] example:

[1217] "The server uses generative AI models to generate headlines that highlight the new product's features and compelling copy for the target audience."

[1218] 3. Automated content review

[1219] The server automatically reviews the generated ad content based on internal content policies. This review process includes legal compliance, brand guideline compatibility, and social media policy compatibility. Content that passes the review proceeds to the next step, but if it fails, it instructs the content to be regenerated or corrected.

[1220] example:

[1221] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[1222] 4. Recommendation function

[1223] The server recommends advertising content that has passed the screening process to the user. The user can then check the recommended content on their device and provide corrections or feedback as necessary, thereby further improving the quality of the content.

[1224] example:

[1225] "The user checks the 'most effective headlines and body text' recommended by the server and makes fine adjustments if necessary."

[1226] 5. Automating advertising operations

[1227] The server automatically posts the advertising content selected by the user to each advertising platform. The server uses each platform's API to send and post the content. In addition, the server monitors the progress of the advertising campaign in real time and optimizes it as necessary.

[1228] example:

[1229] The server uses the APIs of Google Ads and Facebook Ads to automatically publish the ad content selected by the user and monitors success indicators (click-through rate, conversion rate, etc.) in real time.

[1230] 6. Automatic report generation

[1231] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed report, which is then uploaded to the user's dashboard and can be viewed from their device.

[1232] example:

[1233] The server generates detailed performance reports based on data such as click-through rates and conversion rates over the life of the advertising campaign and displays them on the user's dashboard.

[1234] In this way, the system of the present invention automatically generates advertising content based on the basic requirements entered by the user, streamlining the entire process from review, submission, operation, and evaluation, thereby significantly reducing the burden on advertising clients and agencies.

[1235] The processing flow will be explained below.

[1236] Step 1:

[1237] A user logs into the system using a terminal. The login information is authenticated by the server and the user is redirected to their account page.

[1238] Step 2:

[1239] The user enters the basic requirements for their advertising campaign, including the advertising objective, target audience, budget, time frame, desired advertising style and tone, etc. Once complete, they click the submit button.

[1240] Step 3:

[1241] The server receives the data sent by the user and stores it in a database, which is used in the next process of generating advertising content.

[1242] Step 4:

[1243] The server invokes the generative AI model to automatically generate ad content based on the input data provided by the user. This generation process generates ad headlines, body text, images, videos, etc.

[1244] Step 5:

[1245] The server sends the generated ad content to an internal content policy engine for automated review, which checks for compliance with laws and regulations, brand guidelines, and social media policies.

[1246] Step 6:

[1247] The server judges the results of the review and adds content that passes the review to the recommendation list, while issuing instructions to regenerate content that fails the review.

[1248] Step 7:

[1249] The server recommends content that passes the screening to the user, and the recommended content is displayed on the user's management screen.

[1250] Step 8:

[1251] The user reviews the recommended ad content, makes corrections and provides feedback as needed, and once the changes are confirmed, the user clicks the save button.

[1252] Step 9:

[1253] The server stores the modified advertisement content in a final database and prepares the selected advertisement content to be automatically posted to each advertisement platform.

[1254] Step 10:

[1255] The server calls the API of each advertising platform and automatically places ads on multiple platforms, including Google Ads, Facebook Ads, and Twitter Ads.

[1256] Step 11:

[1257] The server monitors the progress of the advertising campaign in real time and collects data such as click-through rates, conversion rates, and revenue.

[1258] Step 12:

[1259] The server then optimizes the advertising campaign based on the collected data, which may include reallocating advertising budgets and adjusting targeting.

[1260] Step 13:

[1261] After the advertising campaign ends, the server consolidates the collected data and automatically generates a detailed performance report.

[1262] Step 14:

[1263] The server uploads the generated report to the user's dashboard, allowing the user to view the report through their terminal.

[1264] Example 1

[1265] 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."

[1266] In the past, each step of advertising campaigns—creating advertising content, reviewing, publishing, managing, and evaluating it—was often done manually, requiring a high level of expertise and a significant amount of time. This placed a heavy burden on small businesses and individuals when running advertising campaigns, making it difficult to manage advertising effectively. It also made it difficult to optimize advertising content in real time or automatically generate detailed performance reports.

[1267] 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.

[1268] In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content using an artificial intelligence model based on the input data provided by the user; a means for automatically reviewing the generated advertising content based on an internal content policy; a means for recommending content that passes the review to the user; a means for automatically submitting the selected advertising content to each advertising distribution platform; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This makes it possible to streamline the entire advertising campaign process and significantly reduce the burden on advertising clients and agencies.

[1269] "User" means an individual or organization that utilizes the system to conduct advertising campaigns.

[1270] An "advertising campaign" is a series of advertising activities undertaken to achieve a specific marketing objective.

[1271] "Basic requirements" are key pieces of information needed to run an advertising campaign, such as objectives, target audience, budget, and duration.

[1272] "Input means" refers to a device or interface that allows a user to input basic requirements for an advertising campaign into the system.

[1273] An "artificial intelligence model" is a machine learning algorithm or system that automatically generates advertising content based on user-provided input data.

[1274] "Automatic generation means" means a function or process that automatically generates advertising content using an artificial intelligence model based on input data.

[1275] "Content policies" are internal rules and standards that advertising content must follow, such as legal compliance, brand guidelines, and social media policies.

[1276] The "automatic review means" refers to a device or system that determines whether the generated advertising content complies with the content policy.

[1277] "Means of recommendation" refers to the functions and processes for proposing advertising content that has passed screening to users.

[1278] The "means for placing advertisements" refers to a function or system for automatically sending the selected advertisement content to each advertisement distribution platform and starting distribution.

[1279] A "platform" is a medium such as a website or application for delivering advertisements.

[1280] A "report" is a document that details the results of an advertising campaign, including metrics such as click-through rate and conversion rate.

[1281] "Means for automatically generating and providing" refers to a function or system that automatically creates detailed reports based on data collected after an advertising campaign ends and provides them to users.

[1282] The system of the present invention automates each step of an advertising campaign, allowing users to efficiently manage their advertising. Specific embodiments are described below.

[1283] First, the user logs into the system using a terminal. After logging in, the user enters the basic requirements for the advertising campaign, such as the purpose, target audience, budget, and period. This information is sent to the server when the user enters it into a form on the terminal and presses the submit button. As a specific example, the user might enter requirements such as "the goal is to increase traffic to the landing page for a new product, with a target audience of women in their 20s and 30s, a budget of 50,000 yen, and a period of one month."

[1284] Next, the server calls a generative AI model based on the data entered by the user. For example, GPT-3 is used as the generative AI model. The server generates and sends a prompt to this AI model. A specific example of a prompt might be, "Please generate ad copy aimed at women in their 20s and 30s to increase traffic to our landing page." Based on this prompt, the AI ​​model generates content for the ad, such as the headline, body text, images, and video.

[1285] The generated advertising content is then automatically reviewed by the server. This review process includes legal compliance, brand guideline compatibility, and social media policy compatibility. Specifically, the server uses an review engine to check whether the generated content violates regulations or internal policies. Content that passes the review proceeds to the next step, while content that fails the review is instructed to be regenerated or corrected.

[1286] Ad content that passes the screening process is recommended to the user by the server. The user can review this content on their device and provide corrections or feedback as necessary. For example, the user can review and fine-tune the headline and body text recommended by the server.

[1287] The server then automatically posts the advertising content selected by the user to each advertising distribution platform. Specifically, the server uses the API of each platform (e.g., Google Ads, Facebook Ads) to send the content. Furthermore, the server monitors the progress of the advertising campaign in real time and optimizes it as necessary. During this process, the server obtains and analyzes data such as click-through rates and conversion rates.

[1288] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed report. The report is uploaded to the user's dashboard and can be viewed from their device. For example, the server generates a report that provides a detailed analysis of the campaign's performance based on metrics such as click-through rate and conversion rate.

[1289] In this way, the system automatically generates advertising content based on the basic requirements entered by the user, streamlining the entire process from review, submission, operation, and evaluation, and significantly reducing the burden on advertising clients and agencies.

[1290] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1291] Step 1:

[1292] A user logs into the system using a terminal. After logging in, the user enters the basic requirements for the advertising campaign, such as the objective, target audience, budget, and period. This input data is sent from the terminal to the server. Specifically, the user enters information such as "increase traffic to the landing page for a new product" into a form on the terminal and presses the "Submit" button. The terminal then sends this as an HTTP request to the server.

[1293] Input: The basic requirement of an advertising campaign is that users fill out a form on their device.

[1294] Output: Basic requirements data for the ad campaign sent to the server

[1295] Step 2:

[1296] The server receives the basic requirement data entered by the user and calls a generative AI model based on that data. Specifically, it uses a generative AI model (e.g., GPT-3) to create a prompt based on the input data and sends it to the AI ​​model. The prompt is in the format, "Please generate ad copy aimed at women in their 20s and 30s to increase traffic to the landing page." The AI ​​model generates ad content based on this prompt.

[1297] Input: Basic requirements data for the ad campaign sent to the server

[1298] Output: Ad content returned by the generative AI model

[1299] Step 3:

[1300] The server automatically reviews the generated ad content based on its internal content policies. This review process checks whether it complies with standards such as legal compliance, brand guidelines, and social media policies. Specifically, the server uses a review engine to search for specific keywords and phrases in the content using regular expressions to check for inappropriate content. If there are no problems with the review, it proceeds to the next step.

[1301] Input: Ad content obtained from a generative AI model

[1302] Output: Ad content that has passed the review

[1303] Step 4:

[1304] The server recommends advertising content that has passed the screening process to the user. The user then uses their device to review this content and make corrections or provide feedback as necessary. Specifically, the server generates a preview of the recommended content and sends it to the device. The user then reviews the preview, enters corrections if necessary, and sends it back to the server.

[1305] Input: Ad content that has passed the review

[1306] Output: Ad content recommended to the user

[1307] Step 5:

[1308] The server automatically places the ad content selected by the user on each ad distribution platform. Specifically, the server uses the APIs of Google Ads and Facebook Ads to send the selected ad content to each platform. It also monitors the progress of the ad campaign in real time and optimizes it as needed. For example, it continuously checks click-through rates and conversion rates and changes optimization settings based on that data.

[1309] Input: User selected ad content

[1310] Output: Advertising content posted on the advertising platform and its monitoring data

[1311] Step 6:

[1312] After the ad campaign ends, the server consolidates the data collected from each advertising platform and automatically generates a detailed report. The generated report is uploaded to the user's dashboard, where the user can view it from their device. Specifically, the server aggregates metrics such as click-through rate and conversion rate, and creates a detailed performance report based on this.

[1313] Input: Monitoring data and final results of advertising campaigns

[1314] Output: A detailed report displayed on the user's dashboard

[1315] (Application example 1)

[1316] 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."

[1317] Traditional advertising campaign management requires many manual steps and specialized knowledge to create, review, publish, operate, and evaluate advertising content. Furthermore, optimizing advertising effectiveness and real-time monitoring are complex and require quick responses. This creates problems for advertising agencies and companies, requiring significant effort and costs.

[1318] 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.

[1319] In this invention, the server includes: means for a user to input basic requirements for an advertising campaign; means for automatically generating advertising content based on the input data provided by the user; means for automatically reviewing the generated advertising content based on an internal content policy; means for recommending content that passes the review to the user; means for automatically publishing the selected advertising content on each advertising platform; means for automatically generating and providing a detailed report to the user after the advertising campaign ends; and means for generating advertising content using a generative AI model and making the generated advertising content available through a smartphone application that reviews, recommends, optimizes, publishes, and generates reports on the generated advertising content. This automates the entire advertising campaign process, allowing users to easily manage advertising campaigns and effectively manage advertising without requiring specialized knowledge or significant effort.

[1320] "User" refers to an advertising agency, company, or other user who utilizes the system to input basic requirements for an advertising campaign and then manages the process.

[1321] "Basic requirements for advertising campaign" means basic information required to carry out an advertising campaign, such as the purpose of the advertisement, target audience, budget, and period.

[1322] "Advertising content" refers to advertising materials such as headlines, text, images, and videos, and is material generated as content.

[1323] "Automatic generation" is the process of using artificial intelligence to generate advertising content based on user-provided data.

[1324] "Internal content policies" refer to internal regulations that advertising content must follow, such as legal compliance, brand guidelines, and platform policies.

[1325] "Automatic review" is the process by which the system checks the advertising content generated based on internal content policies to determine whether it complies.

[1326] "Recommendation" is a feature in which the system suggests optimal advertising content to users, allowing for feedback and fine-tuning.

[1327] "Each advertising platform" refers to an online advertising platform for placing advertisements, such as Google Ads or Facebook Ads.

[1328] "Automatic posting" is the process by which the system posts advertising content directly to each advertising platform based on user specifications.

[1329] A "detailed report" is a report that summarizes the progress and success metrics of an advertising campaign and is automatically generated after the campaign has ended.

[1330] A "generative AI model" is a machine learning model that uses generative artificial intelligence (such as GPT) to generate advertising content.

[1331] A "smartphone application" is software that runs on a smartphone and allows users to manage advertising campaigns and generate content.

[1332] The system of the present invention automates the entire advertising campaign process through a smartphone application. The system includes a generative AI model that generates advertising content based on user input data, and multiple functions for reviewing, recommending, optimizing, publishing, and evaluating the generated advertising content.

[1333] System configuration and programs

[1334] This system is realized by combining the following hardware and software.

[1335] Hardware: Smartphone (iPhone, Android)

[1336] software:

[1337] Generative AI models: OpenAI GPT, DALL-E

[1338] Database: MySQL

[1339] Frontend: React Native

[1340] Backend: Node.js + Express

[1341] Advertising API: Google Ads API, Facebook Ads API

[1342] Data processing and calculation

[1343] 1. Obtaining user input data

[1344] Through the smartphone application, users input the basic requirements of their advertising campaign (advertising objectives, target audience, budget, period, etc.) This input data is collected on the server side and used for subsequent processing.

[1345] 2. Automatic generation of advertising content

[1346] The server calls a generative AI model (OpenAI GPT) based on the input data and automatically generates advertising content (headline, body text, images, videos, etc.). This process is an example of evaluation and generation based on prompt text.

[1347] Example prompt sentence:

[1348] "Goal of advertising campaign: Increase traffic to new product landing page

[1349] Target audience: Women in their 20s and 30s

[1350] Budget: 50,000 yen

[1351] Duration: 1 month

[1352] Request: Please generate advertising copy that highlights the appeal of a new product aimed at women in their 20s and 30s.

[1353] 3. Automated review of generated content

[1354] The server then reviews the generated ad content based on its internal content policies, checking for compliance with legal regulations, brand guidelines, and platform policies, and instructs the content to be regenerated or corrected if it does not comply.

[1355] 4. User Recommendations

[1356] Advertising content that passes the screening process is recommended to users, who can then check it on their smartphone application and provide feedback if necessary.

[1357] 5. Advertising content

[1358] The server automatically posts the ad content selected by the user to each advertising platform using the Google Ads API or Facebook Ads API. After posting, the server monitors the progress of the ad campaign in real time and optimizes it as necessary.

[1359] 6. Automatic report generation

[1360] After the ad campaign is over, the server generates a detailed report based on the collected data and uploads it to the user's dashboard, allowing the user to evaluate the effectiveness of the ad campaign.

[1361] In this way, the system of the present invention efficiently automates the entire process of an advertising campaign, allowing users to effectively manage advertising without requiring specialized knowledge or a great deal of effort.

[1362] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1363] Step 1:

[1364] Users input the basic requirements of an advertising campaign (objective, target audience, budget, period) using a smartphone application. The input data is sent to the server in real time and stored in a database (MySQL), where it is also validated to ensure that the input data is correct.

[1365] Step 2:

[1366] The server calls a generative AI model (OpenAI GPT) based on the requirements information for the ad campaign sent by the user and generates a prompt. Specifically, the following prompt is used:

[1367] "Goal of advertising campaign: Increase traffic to new product landing page

[1368] Target audience: Women in their 20s and 30s

[1369] Budget: 50,000 yen

[1370] Duration: 1 month

[1371] Request: Please generate advertising copy that highlights the appeal of a new product aimed at women in their 20s and 30s.

[1372] The generated prompt text is analyzed by an AI model to automatically generate advertising content such as headlines and body text, and the generated content is temporarily stored on the server.

[1373] Step 3:

[1374] The server automatically reviews the temporarily stored ad content based on its internal content policies. This review process checks for compliance with specific regulations, platform policies, and brand guidelines. If the content complies, it moves on to the next step, and if it doesn't, it instructs the content to be regenerated or corrected.

[1375] Step 4:

[1376] Advertising content that passes the screening process is recommended to smartphone application users from the server. Users can review the recommended content on their devices and provide feedback if necessary. This feedback is sent to the server, and the content may be further fine-tuned.

[1377] Step 5:

[1378] The ad content finally selected by the user is automatically published by the server using the Google Ads API or Facebook Ads API. During the publishing process to each platform, the ad content is sent via the API, and the status of each platform is updated in real time.

[1379] Step 6:

[1380] The server monitors the progress of the ad campaign in real time and optimizes it as needed, for example by revisiting the generative AI model and fine-tuning the content if click-through or conversion rates are low.

[1381] Step 7:

[1382] After the ad campaign ends, the server automatically generates a detailed report based on the collected data. This report includes performance indicators such as click-through rate, conversion rate, and return on investment, and the report is uploaded to the user's dashboard. Users can review the report on their dashboard and use it to improve their next ad campaign.

[1383] This automates the entire advertising campaign process, allowing users to effectively manage and operate ads without requiring specialized knowledge or effort.

[1384] 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.

[1385] The system of the present invention automates each step of web advertising content creation, review, submission, operation, and evaluation. Furthermore, by combining it with an emotion engine that recognizes user emotions, the system customizes and optimizes advertising content. Specific embodiments of the system are described below.

[1386] 1. Enter user advertising requirements

[1387] Users log in to the system and input basic information from their devices, including the purpose of the advertising campaign, target audience, budget, duration, and desired advertising style and tone. This information is sent to the server for subsequent processing. As users input this information, the emotion engine recognizes and analyzes their emotions in real time.

[1388] example:

[1389] "The user aims to increase traffic to a landing page for a new product. They specify a target audience of women in their 20s and 30s, a budget of 50,000 yen, and a time period of one month. The emotion engine then recognizes emotions such as joy and anticipation from the user's input."

[1390] 2. Automatic generation of advertising content

[1391] The server then calls a generative AI model based on the user's input data and the recognized emotions to automatically generate ad content. The generation process generates ad materials such as headlines, body text, images, and videos. The tone and style are also adjusted to fit the emotion based on the emotion engine data.

[1392] example:

[1393] "The server uses generative AI models to generate headlines that highlight the new product's features and compelling copy aimed at the target audience, referencing data from the emotion engine to emphasize an upbeat and hopeful tone."

[1394] 3. Automated content review

[1395] The server then sends the generated ad content to an internal content policy engine for automated review, which includes checking compliance with regulations, brand guidelines, and social media policies.

[1396] example:

[1397] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[1398] 4. Recommendation function

[1399] The server recommends advertising content that passes the screening process to the user. The user can then review the recommended content on their device and make corrections or provide feedback as necessary. At this time, the emotion engine recognizes the user's emotions from the feedback and makes correction suggestions based on that.

[1400] example:

[1401] "The user reviews the 'most effective headlines and body copy' recommended by the server and makes fine adjustments if necessary. The emotion engine recognizes any anxieties or concerns expressed in the user's feedback and suggests revisions to address them."

[1402] 5. Automating advertising operations

[1403] The server automatically posts the ad content selected by the user to each advertising platform. The server uses each platform's API to send and post the content. In addition, an emotion engine is used to monitor the progress of the ad campaign in real time and optimize it as needed.

[1404] example:

[1405] "The server uses the APIs of Google Ads and Facebook Ads to automatically publish the advertising content selected by the user and monitors success indicators (click-through rate, conversion rate, etc.) in real time. During monitoring, the emotion engine takes into account the user's emotional data and suggests more effective adjustments to advertising budget allocation and targeting."

[1406] 6. Automatic report generation

[1407] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed performance report, which is then uploaded to the user's dashboard and can be viewed from their device.

[1408] example:

[1409] "The server generates a detailed performance report based on data such as click-through rates and conversion rates during the advertising campaign period and displays it on the user's dashboard. In addition, an emotion engine includes improvement suggestions in the report based on the user's emotional tendencies."

[1410] In this way, the system of the present invention automatically generates advertising content based on the basic requirements and emotions entered by the user, streamlining the entire process from screening, publishing, operation, and evaluation, and significantly reducing the burden on advertising clients and agencies. In particular, the introduction of an emotion engine makes personalization and optimization of advertising content more effective.

[1411] The processing flow will be explained below.

[1412] Step 1:

[1413] A user logs into the system using a terminal. The login information is authenticated by the server and the user is redirected to their account page.

[1414] Step 2:

[1415] The user enters the basic requirements for their advertising campaign, including the advertising objective, target audience, budget, time frame, desired advertising style and tone, etc. Once complete, they click the submit button.

[1416] Step 3:

[1417] The server receives the data sent by the user and stores it in a database. The stored data is used in the next process of generating advertising content. Here, an emotion engine is activated to recognize emotions from the user's input data and analyze the user's emotions.

[1418] Step 4:

[1419] The server invokes the generative AI model to automatically generate ad content based on the input data and recognized emotions provided by the user. This generation process generates ad materials such as headlines, body text, images, and videos. Based on the data from the emotion engine, the tone and style are also adjusted to match the emotion.

[1420] Step 5:

[1421] The server sends the generated ad content to an internal content policy engine for automated review, which checks for compliance with laws and regulations, brand guidelines, and social media policies.

[1422] Step 6:

[1423] The server judges the results of the review and adds content that passes the review to the recommendation list, while issuing instructions to regenerate content that fails the review.

[1424] Step 7:

[1425] The server recommends content that passes the screening to the user, and the recommended content is displayed on the user's management screen.

[1426] Step 8:

[1427] The user checks the recommended advertising content and makes corrections or inputs feedback as necessary. At this time, the emotion engine recognizes the emotions from the user's feedback and makes correction suggestions based on that.

[1428] Step 9:

[1429] The server stores the modified advertisement content in a final database and prepares the selected advertisement content to be automatically posted to each advertisement platform.

[1430] Step 10:

[1431] The server calls the API of each advertising platform and automatically places ads on multiple platforms, including Google Ads, Facebook Ads, and Twitter Ads.

[1432] Step 11:

[1433] The server monitors the progress of the advertising campaign in real time and collects data such as click-through rates, conversion rates, and revenue.

[1434] Step 12:

[1435] The server optimizes the advertising campaign based on the collected data, which includes reallocating advertising budgets and adjusting targeting. Additionally, the emotion engine takes user emotion data into account and makes optimization suggestions for the advertising campaign.

[1436] Step 13:

[1437] After the advertising campaign ends, the server consolidates the collected data and automatically generates a detailed performance report.

[1438] Step 14:

[1439] The server uploads the generated report to the user's dashboard, allowing the user to view the report through their device. The emotion engine includes improvement suggestions in the report based on the user's emotional tendencies.

[1440] Example 2

[1441] 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."

[1442] The problem with traditional advertising campaigns is that it takes a lot of time and effort to create, review, publish, manage, and evaluate advertising content. Also, because the same advertising content is delivered without considering user emotions, advertising effectiveness may not be maximized.

[1443] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1444] In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content based on the input data provided by the user and user emotion data; a means for automatically reviewing the generated advertising content based on internal content policies such as compliance with laws and regulations and guideline compatibility; a means for recommending content that passes the review to the user and suggesting modifications based on user feedback; a means for automatically submitting the selected advertising content to each advertising platform and monitoring progress in real time; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This streamlines the entire advertising campaign process and enables content generation and optimization based on user emotions.

[1445] "Means for inputting basic requirements for an advertising campaign" refers to a function that allows a user to input information regarding the advertising campaign, such as the objectives, target audience, budget, duration, and desired style and tone of the advertisement.

[1446] "Emotion data" refers to the user's emotional information that is recognized and collected in real time by the emotion engine during user input.

[1447] "Means for automatically generating advertising content" refers to a function that uses a generative AI model to automatically create advertising materials such as headlines, body text, images, and videos based on input data and emotional data provided by users.

[1448] The "internal content policy" is a set of standards for reviewing generated advertising content, including compliance with laws and regulations, brand guidelines, and social media policies.

[1449] The "means for automatically reviewing advertising content" is a function that automatically checks whether generated advertising content complies with internal content policies.

[1450] The "recommendation means" is a function that recommends advertising content that has passed screening to users, and is used by users to confirm and suggest corrections.

[1451] "Feedback" refers to the act of a user providing comments or instructions for corrections to recommended advertising content.

[1452] "Modification Suggestion" is a function in which the emotion engine suggests improvements to advertising content based on user feedback.

[1453] An "advertising platform" is an online service for delivering advertising content, such as Google Ads or Facebook Ads.

[1454] "Means for placing ads" refers to a function that automatically distributes selected advertising content to each advertising platform.

[1455] "Means for monitoring progress" means the ability to monitor real-time performance data (e.g., click-through rate, conversion rate) of advertising campaigns.

[1456] "Means for automatic generation" refers to a function that creates a detailed performance report after the end of an advertising campaign and provides it to the user.

[1457] The system of the present invention automates each step of an advertising campaign and provides optimized advertising content by utilizing user emotional data. This system consists of the following main steps: user input of advertising requirements, content generation, automatic review, recommendation, publication, and evaluation.

[1458] 1. Enter user advertising requirements

[1459] Users log in to the system using a device (PC, smartphone, etc.) and enter the basic requirements for their advertising campaign (purpose, target audience, budget, period, desired advertising style and tone, etc.). This input data is sent to the server, and the emotion engine simultaneously recognizes and analyzes the user's emotions in real time as they enter their data. This allows for the collection of emotional data such as the user's expectations and excitement.

[1460] Specific examples

[1461] To increase traffic to a landing page for a new product, a user inputs the target audience as women in their 20s and 30s, a budget of 50,000 yen, and a period of one month. At this time, the emotion engine recognizes emotions such as joy and anticipation from the user's input.

[1462] 2. Automatic generation of advertising content

[1463] The server then calls a generative AI model to automatically generate ad content based on user-entered data and recognized emotional data. The generation process creates ad materials such as headlines, body text, images, and videos. Additionally, the emotional data is used to adjust tone and style.

[1464] Specific examples

[1465] The server uses a generative AI model to generate headlines that highlight the new product's features and compelling copy aimed at the target audience, emphasizing a positive and hopeful tone based on data from the emotion engine.

[1466] Prompt Sentence Examples

[1467] "Generate an ad aimed at women in their 20s and 30s to drive traffic to a new product landing page. The tone should emphasize anticipation."

[1468] 3. Automated content review

[1469] The server then sends the generated ad content to an internal content policy engine for automated review, which checks for legal compliance, brand guideline compliance, and social media policy compliance.

[1470] Specific examples

[1471] The server uses an inspection engine to check whether the generated ad content violates laws and regulations and complies with brand guidelines.

[1472] 4. Recommendation function

[1473] The server recommends advertising content that has passed the screening process to the user. The user can then use their device to check the recommended content and provide corrections or feedback as necessary. At this time, the emotion engine recognizes the user's emotions when providing feedback and makes correction suggestions based on those emotions.

[1474] Specific examples

[1475] The user can review the "most effective headlines and body text" recommended by the server and make adjustments if necessary. The emotion engine recognizes the anxieties and concerns expressed in the user's feedback and suggests revisions to address them.

[1476] 5. Automating advertising operations

[1477] The server automatically posts the user-selected advertising content to each advertising platform (e.g., Google Ads, Facebook Ads, etc.) using the platform's API. Furthermore, an emotion engine is used to monitor the progress of the advertising campaign in real time and optimize it as necessary.

[1478] Specific examples

[1479] The server uses the APIs of Google Ads and Facebook Ads to automatically publish the ad content selected by the user and monitors metrics such as click-through rate and conversion rate in real time. During monitoring, an emotion engine takes into account the user's emotional data and suggests more effective adjustments to advertising budget allocation and targeting.

[1480] 6. Automatic report generation

[1481] After the ad campaign ends, the server consolidates the data collected from each ad platform and automatically generates a detailed performance report, which is then uploaded to the user's dashboard and can be viewed from their device.

[1482] Specific examples

[1483] The server generates detailed performance reports based on data such as click rates and conversion rates during the advertising campaign period and displays them on the user's dashboard. In addition, an emotion engine includes improvement suggestions in the report based on the user's emotional tendencies.

[1484] As a result, the system of the present invention streamlines the entire advertising campaign process, enabling content generation and optimization based on user emotions, and providing users with a more effective and personalized advertising experience.

[1485] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1486] Step 1:

[1487] A user logs into the system using a terminal and enters the basic requirements for the advertising campaign.

[1488] What it does: A user uses a computer or smartphone to input the objectives of the advertising campaign, the target audience, the budget, the time frame, the desired style and tone of the ad, etc.

[1489] Inputs: Campaign objectives, target audience, budget, duration, ad style and tone

[1490] Output: Basic requirements data for the input ad campaign

[1491] Step 2:

[1492] The emotion engine recognizes and analyzes the user's emotions in real time as they type.

[1493] How it works: While the user is entering information about an advertising campaign, the emotion engine detects emotions such as joy or anticipation in real time and sends that data to the server.

[1494] Input: Information about the ad campaign entered by the user

[1495] Output: User emotion data

[1496] Step 3:

[1497] The server calls up a generative AI model based on the input data and emotion data, and automatically generates advertising content.

[1498] How it works: The server generates ad materials such as headlines, body text, images, and videos, and adjusts tone and style based on emotional data.

[1499] Input: Basic requirements data for advertising campaigns, user sentiment data

[1500] Output: Generated ad content

[1501] Step 4:

[1502] The server sends the generated advertising content to a content policy engine for automatic review.

[1503] Specific operation: The server will conduct an inspection to check for legal compliance, conformance with brand guidelines, and conformance with social media policies.

[1504] Input: Generated ad content

[1505] Output: Examination result (pass / fail)

[1506] Step 5:

[1507] The server recommends the advertisement content that has passed the screening to the user.

[1508] Specific operation: The server presents the recommended content to the user, who then uses the device to confirm and provide feedback.

[1509] Input: Ad content that has passed the review

[1510] Output: User feedback

[1511] Step 6:

[1512] The emotion engine recognizes the user's emotions when giving feedback and makes suggestions based on them.

[1513] Specific operation: The server analyzes the emotional data in the user's feedback and makes suggestions for modifying the advertising content.

[1514] Input: User feedback, emotional data

[1515] Output: Modified suggested ad content

[1516] Step 7:

[1517] The server automatically posts the selected advertising content to each advertising platform and monitors the progress in real time.

[1518] Specific operation: The server uses the APIs of Google Ads and Facebook Ads to send content and monitor metrics such as click-through rate and conversion rate in real time.

[1519] Input: Selected ad content

[1520] Output: Ad performance data

[1521] Step 8:

[1522] After the advertising campaign ends, the server consolidates the data collected from each advertising platform and automatically generates a detailed performance report.

[1523] Specific operation: The server creates a performance report based on data from the advertising campaign period and uploads it to a dashboard for users to view on their devices.

[1524] Input: Performance data collected from each advertising platform

[1525] Output: Detailed performance report

[1526] The above processing steps streamline the entire process of advertising campaigns and provide optimized advertising content based on user sentiment.

[1527] (Application example 2)

[1528] 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."

[1529] Modern advertising campaigns require a wide range of processes, including the creation, review, submission, operation, and evaluation of advertising content, and performing these processes manually is time-consuming and labor-intensive. While it is also important to incorporate user sentiment data and optimize campaigns in real time to maximize advertising effectiveness, there are currently no efficient ways to do this. Therefore, there is a need for a system that automates the entire advertising operation process and enables customization based on user sentiment.

[1530] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input basic requirements for an advertising campaign; a means for automatically generating advertising content based on the input data provided by the user and emotion data recognized by an emotion recognition engine; a means for automatically reviewing the generated advertising content based on an internal content policy; a means for recommending content that passes the review to the user and readjusting it based on user feedback; a means for automatically submitting the selected advertising content to each advertising platform and monitoring it in real time; and a means for automatically generating and providing a detailed report to the user after the advertising campaign ends. This makes it possible to efficiently automate the entire advertising operation process and further customize and optimize advertising content based on user emotions.

[1531] "User" refers to the entity that operates and manages an advertising campaign, including advertisers and marketers.

[1532] "Basic requirements for advertising campaign" refers to the initial information required for advertising operations, such as advertising objectives, target audience, budget, duration, desired advertising style and tone, etc.

[1533] An "emotion recognition engine" is a technological element that analyzes and visualizes the emotions contained in user input and feedback in real time.

[1534] "Advertising Content" refers to advertising materials, including headlines, body text, images, and videos, that are automatically generated by a generative AI model.

[1535] "Content Policy" refers to the review criteria for advertising content, including compliance with laws and regulations, conformity with brand guidelines, and conformity with social media policies.

[1536] "Feedback" refers to evaluations and suggestions for corrections that users enter regarding advertising content.

[1537] "Report" means a document detailing the performance of an Advertising Campaign, including metrics such as click-through rates and conversion rates.

[1538] A "generative AI model" is an artificial intelligence technology element that automatically generates advertising content based on information and emotional data provided by users.

[1539] "Prompt" refers to a textual instruction that provides input data to a generative AI model.

[1540] The system of the present invention is implemented as a smartphone app and automates the creation, review, submission, operation, and evaluation of advertising campaigns. The system consists of the following main components:

[1541] 1. User Input and Emotion Recognition

[1542] Using a smartphone app, users input the basic requirements for their advertising campaign (purpose, target audience, budget, period, style, tone, etc.) This input data is sent to a server, and at the same time, an emotion recognition engine analyzes emotions from the user's input in real time.

[1543] 2. Automatic generation of advertising content

[1544] The server receives user input data and emotion recognition engine data, then calls a generative AI model to automatically generate advertising content. This generative AI model receives a text prompt as input and generates advertising materials such as headlines, body text, images, and videos.

[1545] Example prompt sentence:

[1546] "Advertising requirements: We want to promote a new smartwatch to IT engineers in their 20s and 30s. The budget is 100,000 yen, and the duration is two months. The emotions are joy and anticipation. Please generate advertising content based on this."

[1547] 3. Automated review of advertising content

[1548] The generated ad content is automatically sent to an internal content policy engine to check for legal compliance, brand guideline compatibility, and social media policy compatibility, and the engine returns the review results to the server.

[1549] 4. Recommendation and Feedback Collection

[1550] The server recommends advertising content that passes the screening process to the user, who then checks the content on their smartphone. When the user enters feedback, the emotion recognition engine analyzes their emotions again, and the generative AI model readjusts the content based on the analysis results.

[1551] 5. Automatic advertising and real-time monitoring

[1552] The selected advertising content is automatically published through the API of each advertising platform (e.g., Google Ads, Facebook Ads). The server monitors the advertising campaign in real time and optimizes it as needed using emotion recognition data.

[1553] 6. Automatic report generation and provision

[1554] After the ad campaign ends, the server collects data from each ad platform and automatically generates a detailed performance report, which is uploaded to the user's dashboard and can be viewed on their smartphone.

[1555] Hardware and software used

[1556] Hardware: Smartphone

[1557] software:

[1558] Flask: A framework that provides server-side APIs

[1559] EmotionEngine: An emotion recognition engine for recognizing user emotions

[1560] OpenAI API: An API that provides generative AI models, particularly the "davinci-codex" engine.

[1561] Advertising platform APIs: Google Ads, Facebook Ads, etc.

[1562] Examples of specific examples and prompts

[1563] Examples:

[1564] If a user enters the following advertising requirements:

[1565] "I want to promote our new smartwatch to IT engineers in their 20s and 30s. My budget is 100,000 yen and I have two months."

[1566] The emotion recognition engine recognizes "joy and anticipation" from the user's input and generates the following prompt:

[1567] "Advertising requirements: We want to promote a new smartwatch to IT engineers in their 20s and 30s. The budget is 100,000 yen, and the duration is two months. The emotions are joy and anticipation. Please generate advertising content based on this."

[1568] Based on this, advertising content is generated and recommended to users, and necessary feedback is reflected. Furthermore, advertising campaigns are automatically posted to multiple advertising platforms, and results are monitored and optimized in real time to maximize advertising effectiveness.

[1569] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1570] Step 1:

[1571] The user uses a smartphone to input the basic requirements of the advertising campaign (purpose, target audience, budget, period, style, tone, etc.). The input data is immediately sent to the server. The emotion recognition engine then analyzes the user's emotions from this input and sends the analysis results to the server. This allows the user's advertising requirements and accompanying emotional data to be obtained.

[1572] Step 2:

[1573] The server calls the generative AI model based on the acquired user input data and emotion data. The server converts this data into a prompt text and sends this prompt text to the generative AI model. The generative AI model receives the prompt text, generates advertising content (headline, body text, images, videos, etc.), and returns this to the server. This generates advertising content based on the user's requirements.

[1574] Step 3:

[1575] The server sends the generated ad content to an internal content policy engine for automatic review. The review checks for legal compliance, brand guideline compatibility, and social media policy compatibility. The review results are returned to the server, which determines whether the content is deemed eligible.

[1576] Step 4:

[1577] The server recommends advertising content that passes the screening process to the user. The user can then use their smartphone to review the recommended advertising content and provide feedback. This feedback is also analyzed by the emotion recognition engine, and the analysis results are sent back to the server. The generative AI model is then readjusted based on the user's feedback and emotional data.

[1578] Step 5:

[1579] The ad content ultimately selected by the user is automatically published from the server via the API of each ad platform (e.g., Google Ads, Facebook Ads). The server sends the ad content to each platform and monitors it in real time. Depending on the results of the monitoring, adjustments are made to optimize the ad campaign.

[1580] Step 6:

[1581] After the ad campaign ends, the server consolidates data collected from each ad platform (e.g., click-through rate, conversion rate, etc.) and automatically generates a detailed performance report. This report is uploaded to the user's dashboard and can be viewed by the user on their smartphone. The report also includes suggestions for improvement based on the user's emotional data.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] 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).

[1589] 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.

[1590] 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."

[1591] 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.

[1592] 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).

[1593] 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.

[1594] 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.

[1595] 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.

[1596] 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.

[1597] 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.

[1598] 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.

[1599] 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.

[1600] 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.

[1601] 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.

[1602] 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.

[1603] The following is further disclosed regarding the above embodiment.

[1604] (Claim 1)

[1605] a means for a user to input basic requirements for an advertising campaign;

[1606] means for automatically generating advertising content based on input data provided by a user;

[1607] a means for automatically reviewing the generated advertising content based on an internal content policy;

[1608] A means of recommending content that has passed the screening process to users;

[1609] A means for automatically placing the selected advertising content on each advertising platform;

[1610] A means for automatically generating and providing detailed reports to users after the end of an advertising campaign;

[1611] A system including:

[1612] (Claim 2)

[1613] 10. The system of claim 1, further comprising: monitoring generated advertising content in real time; and automatically optimizing advertising campaigns as needed.

[1614] (Claim 3)

[1615] 10. The system of claim 1, wherein the system allows a user to review the generated advertising content and input feedback.

[1616] "Example 1"

[1617] (Claim 1)

[1618] a means for a user to input basic requirements for an advertising campaign;

[1619] means for automatically generating advertising content using an artificial intelligence model based on user-provided input data;

[1620] a means for automatically reviewing the generated advertising content based on an internal content policy;

[1621] A means of recommending content that has passed the screening process to users;

[1622] A means for automatically placing the selected advertising content on each advertising distribution platform;

[1623] means for automatically generating and providing a detailed report to the user after the end of the advertising campaign;

[1624] A system including:

[1625] (Claim 2)

[1626] 10. The system of claim 1, further comprising: monitoring generated advertising content in real time; and automatically optimizing advertising campaigns as needed.

[1627] (Claim 3)

[1628] 10. The system of claim 1, wherein the system allows a user to review the generated advertising content and input feedback.

[1629] "Application Example 1"

[1630] (Claim 1)

[1631] a means for a user to input basic requirements for an advertising campaign;

[1632] means for automatically generating advertising content based on input data provided by a user;

[1633] a means for automatically reviewing the generated advertising content based on an internal content policy;

[1634] A means of recommending content that has passed the screening process to users;

[1635] A means for automatically placing the selected advertising content on each advertising platform;

[1636] A means for automatically generating and providing detailed reports to users after the end of an advertising campaign;

[1637] A means for generating advertising content using the generative AI model and making it available through a smartphone application that reviews, recommends, optimizes, places, and generates reports on the generated advertising content; and

[1638] A system including:

[1639] (Claim 2)

[1640] 10. The system of claim 1, further comprising: monitoring generated advertising content in real time; and automatically optimizing advertising campaigns as needed.

[1641] (Claim 3)

[1642] 10. The system of claim 1, wherein the system allows a user to review the generated advertising content and input feedback.

[1643] "Example 2: Combining Emotion Engines"

[1644] (Claim 1)

[1645] a means for a user to input basic requirements for an advertising campaign;

[1646] means for automatically generating advertising content based on input data provided by a user and emotional data of the user;

[1647] A means to automatically review the generated advertising content based on internal content policies, such as compliance with laws and guidelines, and

[1648] A means for recommending content that has passed the review to users and making suggestions for revisions based on user feedback;

[1649] A means to automatically publish selected advertising content to each advertising platform and monitor progress in real time;

[1650] A means for automatically generating and providing detailed reports to users after the end of an advertising campaign;

[1651] A system including:

[1652] (Claim 2)

[1653] 10. The system of claim 1, wherein the system monitors generated advertising content in real time and automatically optimizes the advertising campaign as needed, also taking into account user sentiment data.

[1654] (Claim 3)

[1655] 10. The system of claim 1, wherein the system allows a user to review the generated advertising content, and the system inputs feedback and makes suggestions for revision based on emotion data.

[1656] "Application example 2 when combining emotion engines"

[1657] (Claim 1)

[1658] a means for a user to input basic requirements for an advertising campaign;

[1659] means for automatically generating advertising content based on input data provided by a user and emotion data recognized by an emotion recognition engine;

[1660] a means for automatically reviewing the generated advertising content based on an internal content policy;

[1661] A means for recommending content that passes the review to users and readjusting it based on user feedback;

[1662] A means to automatically publish selected advertising content to each advertising platform and monitor it in real time;

[1663] A means for automatically generating and providing detailed reports to users after the end of an advertising campaign;

[1664] A system including:

[1665] (Claim 2)

[1666] 10. The system of claim 1, further comprising: monitoring generated advertising content in real time; and automatically optimizing advertising campaigns as needed.

[1667] (Claim 3)

[1668] 10. The system of claim 1, wherein the generated advertising content is reviewed by a user, the user can input feedback, and the emotion engine analyzes the feedback to readjust the advertising content. [Explanation of symbols]

[1669] 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. a means for a user to input basic requirements for an advertising campaign; means for automatically generating advertising content based on input data provided by a user; a means for automatically reviewing the generated advertising content based on an internal content policy; A means of recommending content that has passed the screening process to users; A means for automatically placing the selected advertising content on each advertising platform; A means for automatically generating and providing detailed reports to users after the end of an advertising campaign; A system including:

2. 10. The system of claim 1, wherein the system monitors generated advertising content in real time and automatically optimizes advertising campaigns as needed.

3. 10. The system of claim 1, wherein the system allows a user to review the generated advertising content and input feedback.

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