system

Abstract

We provide the system. [Solution] A device for receiving and analyzing characteristic information of the target population, A device that uses a generative model based on analyzed target group information to generate advertising concepts related to the aforementioned target group, A terminal device equipped with an interface for displaying the generated advertising concept, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern marketing, it is difficult to quickly generate an effective advertising concept that meets the diverse needs of the target population. Conventional methods require a great deal of time and effort to produce novel and creative ideas, and lack reliable means to obtain specific concepts that appeal to the target population. To solve such problems, there is a need for a system that accurately analyzes the characteristics of the target population and generates an effective advertising concept based on it.

Means for Solving the Problems

[0005] This invention provides a device for receiving and analyzing characteristic information of an input target group. It also includes a device for generating advertising concepts using a generative model based on the analysis results. Furthermore, it features an interface for displaying the generated advertising concepts, providing an interactive interface that allows users to intuitively evaluate and provide feedback, thereby enabling the rapid creation of creative advertising concepts that appeal to the target group. This system can also compare the analyzed data with a market trend database to propose highly innovative advertising concepts, supporting effective marketing in increasingly competitive markets.

[0006] "Inputted characteristic information of the target group" refers to data provided by the user regarding attributes of a specific group, such as age group, interests, lifestyle, and purchasing behavior.

[0007] An "analysis device" is a device or program that processes received data and extracts useful information according to a specific purpose.

[0008] A "generative model" is a computational model that uses algorithms or AI to create new concepts or ideas based on given data.

[0009] An "advertising concept" is a collection of elements that constitute an idea or theme for marketing a specific product or service.

[0010] A "display interface" is a method or area on a screen for a user to interact visually with a computer system.

[0011] A "terminal device" is an electronic device used by users to input information through an interface and receive responses from a server.

[0012] An "interactive interface" is an interface that allows users to interact with a system, provide real-time feedback, and control the system's operation.

[0013] A "market trend database" is a collection of data that accumulates sales trends, consumer behavior, and other related information for a specific market. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system for generating and managing advertising concepts using multiple devices and interfaces. This system enables the creation of novel and effective advertising concepts using a generative model based on characteristic information of the target group provided by the user.

[0036] Server execution

[0037] The server receives characteristic information about the target group submitted by the user. The received data is formatted and analyzed within the server. Next, the server supplies the analyzed data to a generative model to generate advertising concepts. This generative model uses an AI algorithm and creates more effective concepts by comparing them with information from a market trend database.

[0038] Implementation of the terminal

[0039] The device features an interface for presenting generated advertising concepts sent from the server to the user. Users can visually review the advertising concepts on the device, evaluate them in an editable environment, and make modifications if necessary. The device also has the functionality to collect user feedback and return it to the server.

[0040] User interaction

[0041] Users input characteristic information through their devices and evaluate the resulting advertising concepts. For example, if a user is planning an advertising campaign for a beverage product, they might input details such as "health-conscious," "younger demographic," and "environmentally friendly" as characteristic information. Based on this information, the server can propose an advertising concept such as "Eco-friendly packaging to support an active lifestyle."

[0042] In this way, this system aims to create rapid and effective advertising concepts based on user-provided characteristic information, and to optimize them through user evaluation and feedback.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input characteristic information about the target group using their own devices. This information includes age group, interests, lifestyle, and purchasing behavior. After completing the input, the user presses the "Submit" button to send the information to the server.

[0046] Step 2:

[0047] The server receives characteristic information sent by the user. The received data is first checked and prepared for analysis, and then converted into a format suitable for analysis.

[0048] Step 3:

[0049] The server inputs the organized characteristic information of the target group into an AI model for analysis. The AI ​​model generates novel and highly relevant advertising concepts by comparing the characteristics of the target group with a market trend database.

[0050] Step 4:

[0051] The server sends the generated advertising concept to the device. At that time, the concept is formatted in a way that the user can easily understand and interact with.

[0052] Step 5:

[0053] The device displays the advertising concept received from the server in the user interface. Through this interface, the user can review the details of the concept and edit or modify it as needed.

[0054] Step 6:

[0055] Users evaluate the displayed ad concepts and provide feedback. User feedback is provided through comment sections and rating functions on the interface.

[0056] Step 7:

[0057] The device sends the user's feedback to the server.

[0058] Step 8:

[0059] The server analyzes the received feedback in conjunction with an AI model, makes necessary adjustments, generates a new advertising concept, and presents it to the user again. This process is repeated until a concept that satisfies the user is obtained.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In generating advertising concepts, there is a need to efficiently utilize characteristic information of the target group and quickly produce ideas with high novelty and effectiveness. However, current methods make it difficult to generate concepts that appropriately reflect market trends, and there are challenges in evaluating the generated ideas and utilizing feedback.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for receiving and processing characteristic information of an input target group, performing data preparation and analysis, means for using a generation AI model based on the analyzed characteristic information, and terminal device means for visually presenting the generated advertising concept to the user and having an editable interface. This enables the generation of highly novel advertising concepts that efficiently utilize characteristic information and market trends, and the optimization of the generation process based on user feedback.

[0065] "Inputted target group characteristic information" refers to information provided by the user as basic data for generating advertising concepts, indicating the attributes and preferences of a specific group.

[0066] "Data preparation" refers to the process of formatting received characteristic information to make it easier to analyze.

[0067] "Analysis" is the process of examining compiled characteristic information in detail and extracting useful information.

[0068] A "generative AI model" is an artificial intelligence algorithm used to create advertising concepts from input data.

[0069] The "Market Trend Data Set" is a dataset containing market demand, trends, and competitive information, providing useful information for generating advertising concepts.

[0070] A "prompt statement" is an input statement used to instruct an AI model to generate a specific output.

[0071] A "visually presented interface" is an interface that displays the generated advertising concept to the user in a viewable format on the screen.

[0072] An "editable interface" is a user interface that allows users to make changes and adjustments to the presented advertising concept.

[0073] This invention is a system in which a server, terminal, and user work together to process information in order to effectively and efficiently generate advertising concepts. This system is implemented as follows.

[0074] Server execution

[0075] The server receives characteristic information of the target group entered by the user. The server uses a data preparation module to format this information so that it is in a standardized and analyzable state. The prepared data is analyzed by an analysis module, and the analysis results are sent to a generative AI model. The generative AI model matches the analyzed characteristic information with market trend data based on the prompt "Propose an advertising concept with the following characteristics: health-conscious, young people, environmentally friendly" and generates a highly novel advertising concept.

[0076] Implementation of the terminal

[0077] The device presents the advertising concept sent from the server in an interface that allows the user to visually review it. Through this interface, the user can review the generated advertising concept in detail and utilize editing functions that allow for modifications. For example, the user can fine-tune the text and images of the advertising concept.

[0078] User interaction

[0079] Users input characteristic information using their devices. Specific examples of this information include characteristics such as "health-conscious," "young," and "environmentally friendly." Users evaluate the generated ad concept on their devices and modify the ad elements as needed. Along with the modifications, users provide feedback, which is then returned to the server, allowing this feedback to be used in the next generation process.

[0080] This patent makes the advertising concept generation process faster and more effective, enabling the creation of unique advertisements that meet market demands.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] User input of characteristic information

[0084] Users use their device's interface to input characteristic information about the target audience for advertisements. Specific examples include forms for inputting characteristics such as "health-conscious," "young people," and "environmentally friendly." The entered data is temporarily stored on the device and then sent to the server in a structured format.

[0085] Step 2:

[0086] Data reception and processing by the server

[0087] The server receives characteristic information sent from the user. The received data is then processed by a data preparation module into a format that facilitates analysis. This process includes supplementing missing data and standardizing the format. The prepared data is then ready to be passed on to the next analysis module.

[0088] Step 3:

[0089] Server-based data analysis and supply to generative models.

[0090] The server uses a data analysis module to analyze the compiled characteristic information in detail. The analysis results are supplied to the generation AI model as foundational data for generating advertising concepts. Specifically, characteristic information associated with market trend data sets is matched with prompt text.

[0091] Step 4:

[0092] Generating advertising concepts using a generative AI model

[0093] The server's AI model generates advertising concepts based on the analysis data and prompt text. For example, using the prompt text "Please propose an advertising concept with the following characteristics: health-conscious, youthful, and environmentally friendly," the model generates "A concept that supports an active lifestyle with eco-friendly packaging." The generated concept is stored on the server and ready to be presented to the user.

[0094] Step 5:

[0095] Display and edit ad concepts on your device

[0096] The device receives advertising concepts sent from the server and displays them visually to the user. Through the interface, the user can provide feedback on the concepts they view and edit the text and visual elements of the concepts as needed.

[0097] Step 6:

[0098] Collecting and saving feedback

[0099] Once users have completed their evaluation and modifications of a concept, they send their feedback from their device to the server. The server stores the received feedback in a database and uses it to improve future concept generation. This feedback information is also used as training data for the AI ​​model, leading to an overall improvement in the system's accuracy.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] In today's advertising industry, there is a demand for the rapid and effective generation of advertising concepts tailored to the target customer base, and for flexible optimization based on user feedback. However, this process is time-consuming and labor-intensive with traditional methods, making efficient and highly accurate advertising concept generation and management essential.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for receiving and analyzing characteristic information of an input target group, means for generating advertising concepts using a generative model based on the analyzed target group information, and terminal means for providing an interface that allows the user to visually confirm and edit the generated advertising concepts. This enables the rapid and effective generation of advertising concepts in a smartphone environment for advertising agency services and marketing operations, and allows for optimization through real-time editing and feedback.

[0105] "Inputted target group characteristics information" refers to information about the attributes and preferences of the target customer group, which is necessary when generating an advertising concept.

[0106] The "means of analysis" refer to a processing device that organizes the characteristic information of the target group received and converts it into a format useful for generating specific advertising concepts.

[0107] "Methods using generative models" refer to devices that utilize AI algorithms to create advertising concepts from analyzed target group information.

[0108] "Terminal means providing an interface" refers to a terminal device having a user interface that allows users to directly view generated advertising concepts and perform visual editing and evaluation.

[0109] "Means for receiving feedback again and optimizing" refers to a device equipped with the function of receiving user reactions and opinions, improving the advertising concept generated based on them, and ultimately refining it into a more suitable concept.

[0110] An "application that operates in a smartphone environment" is software that runs on a smartphone and is developed to streamline advertising agency and marketing-related tasks.

[0111] A "market trend database" is a data storage system that accumulates information on current market trends and consumer behavior, and is used to evaluate the novelty of advertising concepts.

[0112] This invention relates to an advertising concept generation system that operates in a smartphone environment and consists of a server, a terminal, and a user.

[0113] The server receives and analyzes characteristic information of the target group entered by the user. This analysis includes information about the attributes and preferences of the customer segment specified by the user. Using the analyzed information, the server generates advertising concepts using a generative AI model. This generative model utilizes AI algorithms (e.g., OpenAI® GPT-3®) to compare the analyzed data with a market trend database and generate effective advertising concepts that meet the user's needs.

[0114] The terminal features an interface for presenting generated advertising concepts to the user, providing an environment where the user can visually review and edit them. The user can evaluate the advertising concept displayed on the terminal and make modifications on the spot if necessary. For example, if the user's target customer base is "people interested in environmentally friendly lifestyles," an example of a prompt message to enter into the server might be, "Generate an advertising concept for a beverage suitable for eco-conscious customers aged 25 to 35."

[0115] User feedback is sent back to the server, which helps optimize the advertising concept. Generative concepts, born from concrete examples of prompt text, enable a quick and effective response to the target audience's needs.

[0116] As described above, this system enables the rapid and effective generation and management of advertising concepts, supporting more flexible advertising activities.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] Users input characteristic information about their target customer base using a dedicated smartphone application. This information includes age group, interests, and behavioral patterns. The input information is output as data to create prompt messages for the server.

[0120] Step 2:

[0121] The server analyzes the characteristic information received from the user. This analysis formats the input information into a format suitable for the system and converts it into a format that can be input into the generating AI model. Specifically, it performs filtering and normalization of text data. Based on the analysis results, it outputs data to be input into the generating AI model.

[0122] Step 3:

[0123] The server uses the analyzed data to generate advertising concepts using an AI algorithm (e.g., OpenAI GPT-3). During this process, the analyzed data is used as input to form prompt statements, which are then compared against a market trend database. The generated advertising concepts are output, taking into account novelty and effectiveness.

[0124] Step 4:

[0125] The device presents the generated advertising concept to the user. The user visually reviews this concept through the smartphone interface and provides feedback as text if necessary. The device outputs data collected from the presented concept and the user's feedback.

[0126] Step 5:

[0127] The server executes a process to optimize the advertising concept based on feedback received from users. This process analyzes the feedback and regenerates an optimized concept using a generative AI model. The optimization results are then output and finalized as the final advertising concept.

[0128] These processing steps enable the system to quickly and effectively generate advertising concepts and optimize them through user feedback.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention is a system that generates advertising concepts based on the characteristics of a target group and integrates an emotion engine for real-time recognition of user emotions. Based on user feedback and emotion analysis, it dynamically adjusts advertising concepts, enabling the proposal of more effective and appealing advertisements.

[0131] Server execution

[0132] The server has the function of receiving and analyzing characteristic information sent from the user. This analyzed data is converted into advertising concepts using an AI generation model. At the same time, the server understands the user's emotions based on data from the emotion engine and uses this to evaluate and improve the generated concepts. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy and interest.

[0133] Implementation of the terminal

[0134] The device features an interface that displays generated advertising concepts sent from the server. Through this interface, users can review the content of the advertising concepts and provide feedback based on their own emotions. Furthermore, emotional information acquired by the emotion engine adjusts the interface on the device, providing the most relevant information based on the emotions the user is feeling.

[0135] User interaction

[0136] Users can react naturally when viewing advertising concepts displayed on their devices. For example, when viewing an advertisement for a beverage product, if the user shows a satisfied expression, the emotion engine recognizes that emotion and reflects it in specific elements of the concept. If there are elements that surprise the user, the emotion engine captures that information and uses it for further analysis and optimization.

[0137] In this way, the system captures user emotions in real time, enabling it to more effectively optimize and dynamically update advertising concepts.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] Users input characteristic information about the target group using their own devices. This information includes age group, interests, lifestyle, etc., and they send the data to the server by pressing the "Submit" button.

[0141] Step 2:

[0142] The server receives characteristic information sent from the user, formats it, and analyzes it. This analyzed data is then supplied to an AI model for generating advertising concepts.

[0143] Step 3:

[0144] A generative model built into the server automatically generates advertising concepts based on user characteristics. The generated concepts are then compared against a market trend database to verify their novelty and effectiveness.

[0145] Step 4:

[0146] The server sends a request to the sentiment engine, along with the generated ad concept, to analyze the user's real-time emotions.

[0147] Step 5:

[0148] The emotion engine analyzes the user's facial expressions and voice tone to identify their emotional state at that time. This allows it to categorize emotions into states such as joy, surprise, and interest.

[0149] Step 6:

[0150] The device displays advertising concepts sent from the server in the user interface. Feedback from the emotion engine is also visualized simultaneously, and adjustments are made according to the user's emotions.

[0151] Step 7:

[0152] Users review the displayed ad concept and express their emotions. Based on the user's natural response, if they wish to provide further feedback, this is captured by the emotion engine and sent to the server.

[0153] Step 8:

[0154] The server readjusts the advertising concept based on analysis results from the emotion engine and user feedback. New information is used to improve the concept, and the final proposal is shaped.

[0155] Step 9:

[0156] The device will again display the improved advertising concept, maintaining an environment where users can review and provide further feedback. This cycle will continue until optimization is confirmed.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0159] Traditional advertising systems have a problem in that they generate advertising concepts based on the characteristics of the target group in a static manner, making it difficult to respond to users' real-time emotions and create effective advertisements. Therefore, in order to improve the appeal of advertisements, there is a need for technology that can dynamically optimize advertising concepts effectively by reflecting users' emotions.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0161] In this invention, the server includes means for receiving and analyzing characteristic information of an input group; means for using a generative model based on the analyzed information to generate an advertising concept related to the group; and means for analyzing the user's emotional information using an emotional engine and evaluating and improving the advertising concept generated based on that information. This enables the optimization of effective and dynamic advertising by taking the user's emotions into consideration in real time.

[0162] "Group characteristic information" refers to general characteristics of a specific group, such as age, gender, and hobbies, and is data used for generating advertising concepts.

[0163] A "generative model" is an algorithm or program used to generate new advertising concepts based on input data.

[0164] A "communication device" is a device equipped with display and input interfaces that allow users to review generated advertising concepts and provide feedback as needed.

[0165] The "emotion engine" is a system that analyzes users' emotional states in real time by analyzing their facial expressions, tone of voice, and other factors, and uses this information to evaluate and improve advertising concepts.

[0166] A "market trends database" is a database that compiles information on current market consumer interests and purchasing behavior, and is used to propose novel advertising concepts.

[0167] This invention provides a system that maximizes the effectiveness of advertising by utilizing user characteristic information. This system mainly consists of a server and terminals and is realized through advanced data analysis technology using a generative AI model and an emotion engine.

[0168] Server execution

[0169] The server has the function of receiving characteristic information of groups sent by users and analyzing that information. An AI generative model is used for the analysis, which dynamically generates advertising concepts. In this process, the generated advertising concepts are realized by supplying the generative model with prompt sentences that are precisely set based on the characteristic information. An example of a prompt sentence would be "a beverage advertisement with a relaxation theme, aimed at women in their 20s." In addition, the server uses emotion engine data to analyze the emotional state of users and evaluate and improve the advertising concepts.

[0170] Implementation of the terminal

[0171] The device features an interface that provides users with advertising concepts sent from the server. Through this interface, users can view the advertising concepts and provide feedback. The emotion engine analyzes the user's facial expressions and tone of voice on the device and sends the results to the server. This allows the device to adjust the interface based on the user's emotion information, providing more personalized information.

[0172] User interaction

[0173] Users can react naturally to the advertising concepts displayed on their devices. For example, if a user smiles while watching a beverage advertisement, the emotion engine captures this information and processes it as positive feedback. This information is stored on the server and used to generate and optimize future advertising concepts.

[0174] This invention enables effective ad delivery that reflects user emotions in real time, thereby improving the persuasive power of the ads.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server receives characteristic information about a group entered by users. Specifically, this includes information such as age, gender, hobbies, and advertising preferences. This input data is analyzed to prepare the foundational data for use in the next step. By receiving data and performing initial analysis, the server creates the foundation for generating advertising concepts.

[0178] Step 2:

[0179] The server generates prompt text to be fed into the generative AI model based on the analyzed characteristics of the group. Here, guidelines are set to generate appropriate advertising concepts, taking into account the characteristics of the users. Specifically, prompt text is created such as "a beverage advertisement with a relaxation theme, aimed at women in their 20s." This prompt text is input into the generative AI model to generate advertising concepts.

[0180] Step 3:

[0181] The server uses a generative AI model to generate advertising concepts based on the input prompt text. It outputs the generated advertising concepts and prepares them for presentation to the user in the next step. The data calculations performed here are to extract the optimal concept based on a large training dataset.

[0182] Step 4:

[0183] The device receives the advertising concept sent from the server and displays it to the user. Specifically, it visually presents the advertising concept on the device's display, allowing the user to review its content. This process enables the user to give their initial reaction to the generated concept.

[0184] Step 5:

[0185] The emotion engine analyzes the user's facial expressions and tone of voice when they view the advertisement concept on their device. Specifically, it collects and analyzes the user's emotional information in real time using the built-in camera and microphone. This emotional information is then sent to the server in the next step.

[0186] Step 6:

[0187] The server receives emotional information transmitted from the terminal and uses it to evaluate and improve the advertising concept. The input here is user emotional data, and the output is data for extracting points for improving the advertisement. This allows the advertising concept to adapt to the user's emotions and be dynamically optimized.

[0188] Step 7:

[0189] Users can submit feedback through their devices. Specifically, they can rate their favorability of an ad using a scale and leave comments suggesting areas for improvement. This feedback information is sent to the server and used to improve future ad generation and optimization processes.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] To enhance the effectiveness of an advertising concept, it is necessary to accurately capture consumer emotions and dynamically adjust the ad content based on those emotions. However, conventional ad generation methods have made it difficult to dynamically adjust ads based on real-time emotional feedback from users. Therefore, there is a need to provide a method for realizing effective advertising campaigns.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes means for receiving and analyzing characteristic information of an input user group; means for using a generation model based on the analyzed user group information to generate an advertising concept related to the user group; a user terminal device equipped with an interface for displaying the generated advertising concept; emotion analysis means for analyzing the user's facial expressions and voice and recognizing emotions; and means for using a model that dynamically adjusts the advertising content based on emotion information acquired in real time. This makes it possible to optimize advertisements in real time based on the user's emotions and dynamically provide more appealing advertising concepts.

[0195] A "user group" is a group of people who have been designated as the target audience for a specific advertisement, and their characteristic information forms the basis of the data used for analysis.

[0196] "Characteristic information" refers to information that shows attribute data, behavioral patterns, interests, and preferences related to a group of users.

[0197] A "generative model" is an algorithmic model used to automatically construct advertising concepts based on analyzed characteristic information.

[0198] An "interface" is a visual display device that allows users to review generated advertising concepts and provide feedback.

[0199] An "emotion analysis tool" is an analytical mechanism that processes a user's facial expressions and voice data to identify their emotions.

[0200] A "dynamically adjusting model" is an algorithm that uses real-time sentiment information to instantly modify advertising concepts in response to user feedback.

[0201] "Real-time" refers to a state where there is virtually no time delay between input and output, enabling immediate feedback and adjustments.

[0202] This invention provides a system for dynamically optimizing advertising concepts. A server receives and analyzes characteristic information of a user group. The analyzed information is converted into advertising concepts by a generative AI model. User group characteristic information may include age, gender, interests, purchase history, etc., and this data forms the basis for increasing users' interest in the advertising concepts.

[0203] The device acquires the user's facial expressions and voice tone in real time and analyzes the user's emotions based on this data. Machine learning libraries such as TENSORFLOW® and PyTorch are used for emotion analysis, and facial and voice data are collected using the device's camera and microphone. This data is processed immediately, and an advertising concept that matches the user's emotions is selected.

[0204] For example, when a user is watching a video ad for pet supplies, if the user smiles with satisfaction, the emotion analysis system detects this as positive feedback and adjusts the ad concept to present a more appealing ad. In this way, the persuasiveness of the ad can be increased.

[0205] Examples of prompt messages include, "Develop an AI model that adjusts ad content in real time using user facial expression data. Inputs are image data of facial expressions and audio data of voice." This enables immediate adjustment of ads based on user feedback.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The server receives characteristic information sent from users. This characteristic information for a user group includes age, gender, interests, and purchase history. This information is stored in a database and prepared for analysis.

[0209] Step 2:

[0210] The server analyzes the received characteristic information. Machine learning algorithms are used for the analysis, and the analysis results are input into a generating AI model. Based on these results, an advertising concept is generated. The generated advertising concept is adjusted to match the characteristic information.

[0211] Step 3:

[0212] The device provides an interface for displaying the generated advertising concept to the user. The advertising concept is displayed on the screen to create an environment where the user can view the advertisement.

[0213] Step 4:

[0214] The device acquires the user's facial expression and voice data in real time. This is done using the device's camera and microphone. The acquired data is sent to an emotion analysis system.

[0215] Step 5:

[0216] The device analyzes the user's emotions using emotion analysis tools. TensorFlow and PyTorch are used for emotion analysis, and data processing is performed to determine the user's satisfaction level and interest.

[0217] Step 6:

[0218] The server receives the analyzed sentiment data and inputs it into a model to dynamically adjust the advertising concept. The model optimizes the ad content in real time and generates ads that are more suitable for the user. This process is continuous based on user feedback.

[0219] Step 7:

[0220] Users can re-view the adjusted ad concept and provide final feedback. This feedback is used to improve the entire system and contribute to increasing the effectiveness of the ads.

[0221] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0228] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0230] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0232] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0234] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0237] This invention relates to a system for generating and managing advertising concepts using multiple devices and interfaces. This system enables the creation of novel and effective advertising concepts using a generative model based on characteristic information of the target group provided by the user.

[0238] Server execution

[0239] The server receives characteristic information about the target group submitted by the user. The received data is formatted and analyzed within the server. Next, the server supplies the analyzed data to a generative model to generate advertising concepts. This generative model uses an AI algorithm and creates more effective concepts by comparing them with information from a market trend database.

[0240] Implementation of the terminal

[0241] The device features an interface for presenting generated advertising concepts sent from the server to the user. Users can visually review the advertising concepts on the device, evaluate them in an editable environment, and make modifications if necessary. The device also has the functionality to collect user feedback and return it to the server.

[0242] User interaction

[0243] Users input characteristic information through their devices and evaluate the resulting advertising concepts. For example, if a user is planning an advertising campaign for a beverage product, they might input details such as "health-conscious," "younger demographic," and "environmentally friendly" as characteristic information. Based on this information, the server can propose an advertising concept such as "Eco-friendly packaging to support an active lifestyle."

[0244] In this way, this system aims to create rapid and effective advertising concepts based on user-provided characteristic information, and to optimize them through user evaluation and feedback.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] Users input characteristic information about the target group using their own devices. This information includes age group, interests, lifestyle, and purchasing behavior. After completing the input, the user presses the "Submit" button to send the information to the server.

[0248] Step 2:

[0249] The server receives characteristic information sent by the user. The received data is first checked and prepared for analysis, and then converted into a format suitable for analysis.

[0250] Step 3:

[0251] The server inputs the organized characteristic information of the target group into an AI model for analysis. The AI ​​model generates novel and highly relevant advertising concepts by comparing the characteristics of the target group with a market trend database.

[0252] Step 4:

[0253] The server sends the generated advertising concept to the device. At that time, the concept is formatted in a way that the user can easily understand and interact with.

[0254] Step 5:

[0255] The device displays the advertising concept received from the server in the user interface. Through this interface, the user can review the details of the concept and edit or modify it as needed.

[0256] Step 6:

[0257] Users evaluate the displayed ad concepts and provide feedback. User feedback is provided through comment sections and rating functions on the interface.

[0258] Step 7:

[0259] The device sends the user's feedback to the server.

[0260] Step 8:

[0261] The server analyzes the received feedback in conjunction with an AI model, makes necessary adjustments, generates a new advertising concept, and presents it to the user again. This process is repeated until a concept that satisfies the user is obtained.

[0262] (Example 1)

[0263] Next, we will describe Example 1. 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."

[0264] In generating advertising concepts, there is a need to efficiently utilize characteristic information of the target group and quickly produce ideas with high novelty and effectiveness. However, current methods make it difficult to generate concepts that appropriately reflect market trends, and there are challenges in evaluating the generated ideas and utilizing feedback.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes means for receiving and processing characteristic information of an input target group, performing data preparation and analysis, means for using a generation AI model based on the analyzed characteristic information, and terminal device means for visually presenting the generated advertising concept to the user and having an editable interface. This enables the generation of highly novel advertising concepts that efficiently utilize characteristic information and market trends, and the optimization of the generation process based on user feedback.

[0267] "Inputted target group characteristic information" refers to information provided by the user as basic data for generating advertising concepts, indicating the attributes and preferences of a specific group.

[0268] "Data preparation" refers to the process of formatting received characteristic information to make it easier to analyze.

[0269] "Analysis" is the process of examining compiled characteristic information in detail and extracting useful information.

[0270] A "generative AI model" is an artificial intelligence algorithm used to create advertising concepts from input data.

[0271] The "Market Trend Data Set" is a dataset containing market demand, trends, and competitive information, providing useful information for generating advertising concepts.

[0272] A "prompt statement" is an input statement used to instruct an AI model to generate a specific output.

[0273] A "visually presented interface" is an interface that displays the generated advertising concept to the user in a viewable format on the screen.

[0274] An "editable interface" is a user interface that allows users to make changes and adjustments to the presented advertising concept.

[0275] This invention is a system in which a server, terminal, and user work together to process information in order to effectively and efficiently generate advertising concepts. This system is implemented as follows.

[0276] Server execution

[0277] The server receives the characteristic information of the target population input by the user. The server uses the data grooming module to groom this information into a unified and analyzable state. The groomed data is analyzed by the analysis module, and the analysis results are sent to the generative AI model. The generative AI model collates the analyzed characteristic information and the market trend data group based on the prompt sentence "Please propose an advertising concept with the following characteristics: health-oriented, young generation, environmentally friendly", and generates a highly novel advertising concept.

[0278] Implementation of the terminal

[0279] The terminal presents the advertising concept sent from the server through an interface that allows the user to visually confirm it. Through this interface, the user can view the generated advertising concept in detail and utilize an editable function that can be modified. For example, the user can fine-tune the text or image of the advertising concept.

[0280] User interaction

[0281] The user inputs characteristic information using the terminal. Specific examples of the information to be input include characteristics such as "health-oriented", "young generation", and "environmentally friendly". The user evaluates the generated advertising concept on the terminal and modifies the advertising elements as needed. Along with the modification, the user fills in feedback and returns it to the server, enabling this feedback to be utilized in the next generation process.

[0282] With this patent, the process of generating advertising concepts becomes faster and more effective, enabling the creation of unique advertisements that meet the market's demands.

[0283] [[ID=2,4]]The flow of specific processing in Example 1 will be described using Figure 11.

[0284] Step 1:

[0285] Input of characteristic information by the user

[0286] The user uses the interface of the terminal to input characteristic information about the target group of the advertisement. As a specific example, there is a form for inputting characteristics such as "health-oriented", "young people", and "environmentally friendly". The input data is temporarily stored in the terminal and sent to the server in an organized state.

[0287] Step 2:

[0288] Receiving and organizing data by the server

[0289] The server receives the characteristic information sent by the user. The received data is organized by the data organization module into a form that is easy to analyze. In this process, the complement of missing data and the standardization of the format are carried out. The organized data is ready to be passed to the next analysis module.

[0290] Step 3:

[0291] Data analysis by the server and supply to the generation model

[0292] The server uses the data analysis module to analyze the organized characteristic information in detail. The analysis result is supplied to the generation AI model as the base data for generating advertisement concepts. Specifically, the characteristic information associated with the market trend data group is collated with the prompt text.

[0293] Step 4:

[0294] Generation of advertisement concepts by the generation AI model

[0295] The generation AI model of the server generates advertisement concepts based on the analysis data and the prompt text. For example, using the prompt text "Please propose an advertisement concept with the following characteristics: health-oriented, young people, environmentally friendly", the model generates "A concept that supports an active lifestyle with eco-friendly packaging". The generated concept is saved in the server and is ready to be presented to the user.

[0296] Step 5:

[0297] Display and edit ad concepts on your device

[0298] The device receives advertising concepts sent from the server and displays them visually to the user. Through the interface, the user can provide feedback on the concepts they view and edit the text and visual elements of the concepts as needed.

[0299] Step 6:

[0300] Collecting and saving feedback

[0301] Once users have completed their evaluation and modifications of a concept, they send their feedback from their device to the server. The server stores the received feedback in a database and uses it to improve future concept generation. This feedback information is also used as training data for the AI ​​model, leading to an overall improvement in the system's accuracy.

[0302] (Application Example 1)

[0303] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0304] In today's advertising industry, there is a demand for the rapid and effective generation of advertising concepts tailored to the target customer base, and for flexible optimization based on user feedback. However, this process is time-consuming and labor-intensive with traditional methods, making efficient and highly accurate advertising concept generation and management essential.

[0305] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0306] In this invention, the server includes means for receiving and analyzing the characteristic information of the input target population, means for generating an advertising concept using a generation model based on the analyzed target population information, and terminal means for providing an interface through which a user can visually confirm and edit the generated advertising concept. As a result, in advertising agency services and marketing operations, it becomes possible to quickly and effectively generate advertising concepts in a smartphone environment and optimize them through real-time editing and feedback.

[0307] The "characteristic information of the input target population" refers to information regarding the attributes and preferences of the target customer group, which is necessary when generating an advertising concept.

[0308] The "means for analyzing" is a processing device that organizes the received characteristic information of the target population and converts it into a form useful for generating specific advertising concepts.

[0309] The "means for using the generation model" is a device that utilizes an AI algorithm to create an advertising concept from the analyzed target population information.

[0310] The "terminal means for providing an interface" is a terminal device having a user interface through which a user can directly view the generated advertising concept and visually edit and evaluate it.

[0311] The "means for receiving feedback again and optimizing" is a device equipped with a function for receiving reactions and opinions from users, improving the generated advertising concept based on them, and finally finishing it into a more suitable concept.

[0312] The "application operating in a smartphone environment" is software developed to operate on a smartphone and improve the efficiency of advertising agency and marketing-related work.

[0313] A "market trend database" is a data storage system that accumulates information on current market trends and consumer behavior, and is used to evaluate the novelty of advertising concepts.

[0314] This invention relates to an advertising concept generation system that operates in a smartphone environment and consists of a server, a terminal, and a user.

[0315] The server receives and analyzes characteristic information of the target group entered by the user. This analysis includes information about the attributes and preferences of the customer segment specified by the user. Using the analyzed information, the server generates advertising concepts using a generative AI model. This generative model utilizes AI algorithms (e.g., OpenAI GPT-3) to compare the analyzed data with a market trend database and generate effective advertising concepts that meet the user's needs.

[0316] The terminal features an interface for presenting generated advertising concepts to the user, providing an environment where the user can visually review and edit them. The user can evaluate the advertising concept displayed on the terminal and make modifications on the spot if necessary. For example, if the user's target customer base is "people interested in environmentally friendly lifestyles," an example of a prompt message to enter into the server might be, "Generate an advertising concept for a beverage suitable for eco-conscious customers aged 25 to 35."

[0317] User feedback is sent back to the server, which helps optimize the advertising concept. Generative concepts, born from concrete examples of prompt text, enable a quick and effective response to the target audience's needs.

[0318] As described above, this system enables the rapid and effective generation and management of advertising concepts, supporting more flexible advertising activities.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] Users input characteristic information about their target customer base using a dedicated smartphone application. This information includes age group, interests, and behavioral patterns. The input information is output as data to create prompt messages for the server.

[0322] Step 2:

[0323] The server analyzes the characteristic information received from the user. This analysis formats the input information into a format suitable for the system and converts it into a format that can be input into the generating AI model. Specifically, it performs filtering and normalization of text data. Based on the analysis results, it outputs data to be input into the generating AI model.

[0324] Step 3:

[0325] The server uses the analyzed data to generate advertising concepts using an AI algorithm (e.g., OpenAI GPT-3). During this process, the analyzed data is used as input to form prompt statements, which are then compared against a market trend database. The generated advertising concepts are output, taking into account novelty and effectiveness.

[0326] Step 4:

[0327] The device presents the generated advertising concept to the user. The user visually reviews this concept through the smartphone interface and provides feedback as text if necessary. The device outputs data collected from the presented concept and the user's feedback.

[0328] Step 5:

[0329] The server executes a process to optimize the advertising concept based on feedback received from users. This process analyzes the feedback and regenerates an optimized concept using a generative AI model. The optimization results are then output and finalized as the final advertising concept.

[0330] These processing steps enable the system to quickly and effectively generate advertising concepts and optimize them through user feedback.

[0331] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0332] This invention is a system that generates advertising concepts based on the characteristics of a target group and integrates an emotion engine for real-time recognition of user emotions. Based on user feedback and emotion analysis, it dynamically adjusts advertising concepts, enabling the proposal of more effective and appealing advertisements.

[0333] Server execution

[0334] The server has the function of receiving and analyzing characteristic information sent from the user. This analyzed data is converted into advertising concepts using an AI generation model. At the same time, the server understands the user's emotions based on data from the emotion engine and uses this to evaluate and improve the generated concepts. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy and interest.

[0335] Implementation of the terminal

[0336] The device features an interface that displays generated advertising concepts sent from the server. Through this interface, users can review the content of the advertising concepts and provide feedback based on their own emotions. Furthermore, emotional information acquired by the emotion engine adjusts the interface on the device, providing the most relevant information based on the emotions the user is feeling.

[0337] User interaction

[0338] Users can react naturally when viewing advertising concepts displayed on their devices. For example, when viewing an advertisement for a beverage product, if the user shows a satisfied expression, the emotion engine recognizes that emotion and reflects it in specific elements of the concept. If there are elements that surprise the user, the emotion engine captures that information and uses it for further analysis and optimization.

[0339] In this way, the system captures user emotions in real time, enabling it to more effectively optimize and dynamically update advertising concepts.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] Users input characteristic information about the target group using their own devices. This information includes age group, interests, lifestyle, etc., and they send the data to the server by pressing the "Submit" button.

[0343] Step 2:

[0344] The server receives characteristic information sent from the user, formats it, and analyzes it. This analyzed data is then supplied to an AI model for generating advertising concepts.

[0345] Step 3:

[0346] A generative model built into the server automatically generates advertising concepts based on user characteristics. The generated concepts are then compared against a market trend database to verify their novelty and effectiveness.

[0347] Step 4:

[0348] The server sends a request to the sentiment engine, along with the generated ad concept, to analyze the user's real-time emotions.

[0349] Step 5:

[0350] The emotion engine analyzes the user's facial expressions and voice tone to identify their emotional state at that time. This allows it to categorize emotions into states such as joy, surprise, and interest.

[0351] Step 6:

[0352] The device displays advertising concepts sent from the server in the user interface. Feedback from the emotion engine is also visualized simultaneously, and adjustments are made according to the user's emotions.

[0353] Step 7:

[0354] Users review the displayed ad concept and express their emotions. Based on the user's natural response, if they wish to provide further feedback, this is captured by the emotion engine and sent to the server.

[0355] Step 8:

[0356] The server readjusts the advertising concept based on analysis results from the emotion engine and user feedback. New information is used to improve the concept, and the final proposal is shaped.

[0357] Step 9:

[0358] The device will again display the improved advertising concept, maintaining an environment where users can review and provide further feedback. This cycle will continue until optimization is confirmed.

[0359] (Example 2)

[0360] Next, we will describe Example 2. 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".

[0361] Traditional advertising systems have a problem in that they generate advertising concepts based on the characteristics of the target group in a static manner, making it difficult to respond to users' real-time emotions and create effective advertisements. Therefore, in order to improve the appeal of advertisements, there is a need for technology that can dynamically optimize advertising concepts effectively by reflecting users' emotions.

[0362] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0363] In this invention, the server includes means for receiving and analyzing characteristic information of an input group; means for using a generative model based on the analyzed information to generate an advertising concept related to the group; and means for analyzing the user's emotional information using an emotional engine and evaluating and improving the advertising concept generated based on that information. This enables the optimization of effective and dynamic advertising by taking the user's emotions into consideration in real time.

[0364] "Group characteristic information" refers to general characteristics of a specific group, such as age, gender, and hobbies, and is data used for generating advertising concepts.

[0365] A "generative model" is an algorithm or program used to generate new advertising concepts based on input data.

[0366] A "communication device" is a device equipped with display and input interfaces that allow users to review generated advertising concepts and provide feedback as needed.

[0367] The "emotion engine" is a system that analyzes users' emotional states in real time by analyzing their facial expressions, tone of voice, and other factors, and uses this information to evaluate and improve advertising concepts.

[0368] A "market trends database" is a database that compiles information on current market consumer interests and purchasing behavior, and is used to propose novel advertising concepts.

[0369] This invention provides a system that maximizes the effectiveness of advertising by utilizing user characteristic information. This system mainly consists of a server and terminals and is realized through advanced data analysis technology using a generative AI model and an emotion engine.

[0370] Server execution

[0371] The server has the function of receiving characteristic information of groups sent by users and analyzing that information. An AI generative model is used for the analysis, which dynamically generates advertising concepts. In this process, the generated advertising concepts are realized by supplying the generative model with prompt sentences that are precisely set based on the characteristic information. An example of a prompt sentence would be "a beverage advertisement with a relaxation theme, aimed at women in their 20s." In addition, the server uses emotion engine data to analyze the emotional state of users and evaluate and improve the advertising concepts.

[0372] Implementation of the terminal

[0373] The device features an interface that provides users with advertising concepts sent from the server. Through this interface, users can view the advertising concepts and provide feedback. The emotion engine analyzes the user's facial expressions and tone of voice on the device and sends the results to the server. This allows the device to adjust the interface based on the user's emotion information, providing more personalized information.

[0374] User interaction

[0375] Users can react naturally to the advertising concepts displayed on their devices. For example, if a user smiles while watching a beverage advertisement, the emotion engine captures this information and processes it as positive feedback. This information is stored on the server and used to generate and optimize future advertising concepts.

[0376] This invention enables effective ad delivery that reflects user emotions in real time, thereby improving the persuasive power of the ads.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] The server receives characteristic information about a group entered by users. Specifically, this includes information such as age, gender, hobbies, and advertising preferences. This input data is analyzed to prepare the foundational data for use in the next step. By receiving data and performing initial analysis, the server creates the foundation for generating advertising concepts.

[0380] Step 2:

[0381] The server generates prompt text to be fed into the generative AI model based on the analyzed characteristics of the group. Here, guidelines are set to generate appropriate advertising concepts, taking into account the characteristics of the users. Specifically, prompt text is created such as "a beverage advertisement with a relaxation theme, aimed at women in their 20s." This prompt text is input into the generative AI model to generate advertising concepts.

[0382] Step 3:

[0383] The server uses a generative AI model to generate advertising concepts based on the input prompt text. It outputs the generated advertising concepts and prepares them for presentation to the user in the next step. The data calculations performed here are to extract the optimal concept based on a large training dataset.

[0384] Step 4:

[0385] The device receives the advertising concept sent from the server and displays it to the user. Specifically, it visually presents the advertising concept on the device's display, allowing the user to review its content. This process enables the user to give their initial reaction to the generated concept.

[0386] Step 5:

[0387] The emotion engine analyzes the user's facial expressions and tone of voice when they view the advertisement concept on their device. Specifically, it collects and analyzes the user's emotional information in real time using the built-in camera and microphone. This emotional information is then sent to the server in the next step.

[0388] Step 6:

[0389] The server receives emotional information transmitted from the terminal and uses it to evaluate and improve the advertising concept. The input here is user emotional data, and the output is data for extracting points for improving the advertisement. This allows the advertising concept to adapt to the user's emotions and be dynamically optimized.

[0390] Step 7:

[0391] Users can submit feedback through their devices. Specifically, they can rate their favorability of an ad using a scale and leave comments suggesting areas for improvement. This feedback information is sent to the server and used to improve future ad generation and optimization processes.

[0392] (Application Example 2)

[0393] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0394] To enhance the effectiveness of an advertising concept, it is necessary to accurately capture consumer emotions and dynamically adjust the ad content based on those emotions. However, conventional ad generation methods have made it difficult to dynamically adjust ads based on real-time emotional feedback from users. Therefore, there is a need to provide a method for realizing effective advertising campaigns.

[0395] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0396] In this invention, the server includes means for receiving and analyzing characteristic information of an input user group; means for using a generation model based on the analyzed user group information to generate an advertising concept related to the user group; a user terminal device equipped with an interface for displaying the generated advertising concept; emotion analysis means for analyzing the user's facial expressions and voice and recognizing emotions; and means for using a model that dynamically adjusts the advertising content based on emotion information acquired in real time. This makes it possible to optimize advertisements in real time based on the user's emotions and dynamically provide more appealing advertising concepts.

[0397] A "user group" is a group of people who have been designated as the target audience for a specific advertisement, and their characteristic information forms the basis of the data used for analysis.

[0398] "Characteristic information" refers to information that shows attribute data, behavioral patterns, interests, and preferences related to a group of users.

[0399] A "generative model" is an algorithmic model used to automatically construct advertising concepts based on analyzed characteristic information.

[0400] An "interface" is a visual display device that allows users to review generated advertising concepts and provide feedback.

[0401] An "emotion analysis tool" is an analytical mechanism that processes a user's facial expressions and voice data to identify their emotions.

[0402] A "dynamically adjusting model" is an algorithm that uses real-time sentiment information to instantly modify advertising concepts in response to user feedback.

[0403] "Real-time" refers to a state where there is virtually no time delay between input and output, enabling immediate feedback and adjustments.

[0404] This invention provides a system for dynamically optimizing advertising concepts. A server receives and analyzes characteristic information of a user group. The analyzed information is converted into advertising concepts by a generative AI model. User group characteristic information may include age, gender, interests, purchase history, etc., and this data forms the basis for increasing users' interest in the advertising concepts.

[0405] The device acquires the user's facial expressions and voice tone in real time and analyzes the user's emotions based on this data. Machine learning libraries such as TensorFlow and PyTorch are used for emotion analysis, and facial and voice data are collected using the device's camera and microphone. This data is processed immediately, and an advertising concept that matches the user's emotions is selected.

[0406] For example, when a user is watching a video ad for pet supplies, if the user smiles with satisfaction, the emotion analysis system detects this as positive feedback and adjusts the ad concept to present a more appealing ad. In this way, the persuasiveness of the ad can be increased.

[0407] Examples of prompt messages include, "Develop an AI model that adjusts ad content in real time using user facial expression data. Inputs are image data of facial expressions and audio data of voice." This enables immediate adjustment of ads based on user feedback.

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The server receives characteristic information sent from users. This characteristic information for a user group includes age, gender, interests, and purchase history. This information is stored in a database and prepared for analysis.

[0411] Step 2:

[0412] The server analyzes the received characteristic information. Machine learning algorithms are used for the analysis, and the analysis results are input into a generating AI model. Based on these results, an advertising concept is generated. The generated advertising concept is adjusted to match the characteristic information.

[0413] Step 3:

[0414] The device provides an interface for displaying the generated advertising concept to the user. The advertising concept is displayed on the screen to create an environment where the user can view the advertisement.

[0415] Step 4:

[0416] The device acquires the user's facial expression and voice data in real time. This is done using the device's camera and microphone. The acquired data is sent to an emotion analysis system.

[0417] Step 5:

[0418] The device analyzes the user's emotions using emotion analysis tools. TensorFlow and PyTorch are used for emotion analysis, and data processing is performed to determine the user's satisfaction level and interest.

[0419] Step 6:

[0420] The server receives the analyzed sentiment data and inputs it into a model to dynamically adjust the advertising concept. The model optimizes the ad content in real time and generates ads that are more suitable for the user. This process is continuous based on user feedback.

[0421] Step 7:

[0422] Users can re-view the adjusted ad concept and provide final feedback. This feedback is used to improve the entire system and contribute to increasing the effectiveness of the ads.

[0423] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0424] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0425] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0429] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0430] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0431] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0432] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0433] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0434] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0435] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0436] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0437] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0438] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0439] This invention relates to a system for generating and managing advertising concepts using multiple devices and interfaces. This system enables the creation of novel and effective advertising concepts using a generative model based on characteristic information of the target group provided by the user.

[0440] Server execution

[0441] The server receives characteristic information about the target group submitted by the user. The received data is formatted and analyzed within the server. Next, the server supplies the analyzed data to a generative model to generate advertising concepts. This generative model uses an AI algorithm and creates more effective concepts by comparing them with information from a market trend database.

[0442] Implementation of the terminal

[0443] The device features an interface for presenting generated advertising concepts sent from the server to the user. Users can visually review the advertising concepts on the device, evaluate them in an editable environment, and make modifications if necessary. The device also has the functionality to collect user feedback and return it to the server.

[0444] User interaction

[0445] Users input characteristic information through their devices and evaluate the resulting advertising concepts. For example, if a user is planning an advertising campaign for a beverage product, they might input details such as "health-conscious," "younger demographic," and "environmentally friendly" as characteristic information. Based on this information, the server can propose an advertising concept such as "Eco-friendly packaging to support an active lifestyle."

[0446] In this way, this system aims to create rapid and effective advertising concepts based on user-provided characteristic information, and to optimize them through user evaluation and feedback.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] Users input characteristic information about the target group using their own devices. This information includes age group, interests, lifestyle, and purchasing behavior. After completing the input, the user presses the "Submit" button to send the information to the server.

[0450] Step 2:

[0451] The server receives characteristic information sent by the user. The received data is first checked and prepared for analysis, and then converted into a format suitable for analysis.

[0452] Step 3:

[0453] The server inputs the organized characteristic information of the target group into an AI model for analysis. The AI ​​model generates novel and highly relevant advertising concepts by comparing the characteristics of the target group with a market trend database.

[0454] Step 4:

[0455] The server sends the generated advertising concept to the device. At that time, the concept is formatted in a way that the user can easily understand and interact with.

[0456] Step 5:

[0457] The device displays the advertising concept received from the server in the user interface. Through this interface, the user can review the details of the concept and edit or modify it as needed.

[0458] Step 6:

[0459] Users evaluate the displayed ad concepts and provide feedback. User feedback is provided through comment sections and rating functions on the interface.

[0460] Step 7:

[0461] The device sends the user's feedback to the server.

[0462] Step 8:

[0463] The server analyzes the received feedback in conjunction with an AI model, makes necessary adjustments, generates a new advertising concept, and presents it to the user again. This process is repeated until a concept that satisfies the user is obtained.

[0464] (Example 1)

[0465] Next, we will describe Example 1. 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."

[0466] In generating advertising concepts, there is a need to efficiently utilize characteristic information of the target group and quickly produce ideas with high novelty and effectiveness. However, current methods make it difficult to generate concepts that appropriately reflect market trends, and there are challenges in evaluating the generated ideas and utilizing feedback.

[0467] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0468] In this invention, the server includes means for receiving and processing characteristic information of an input target group, performing data preparation and analysis, means for using a generation AI model based on the analyzed characteristic information, and terminal device means for visually presenting the generated advertising concept to the user and having an editable interface. This enables the generation of highly novel advertising concepts that efficiently utilize characteristic information and market trends, and the optimization of the generation process based on user feedback.

[0469] "Inputted target group characteristic information" refers to information provided by the user as basic data for generating advertising concepts, indicating the attributes and preferences of a specific group.

[0470] "Data preparation" refers to the process of formatting received characteristic information to make it easier to analyze.

[0471] "Analysis" is the process of examining compiled characteristic information in detail and extracting useful information.

[0472] A "generative AI model" is an artificial intelligence algorithm used to create advertising concepts from input data.

[0473] The "Market Trend Data Set" is a dataset containing market demand, trends, and competitive information, providing useful information for generating advertising concepts.

[0474] A "prompt statement" is an input statement used to instruct an AI model to generate a specific output.

[0475] A "visually presented interface" is an interface that displays the generated advertising concept to the user in a viewable format on the screen.

[0476] An "editable interface" is a user interface that allows users to make changes and adjustments to the presented advertising concept.

[0477] This invention is a system in which a server, terminal, and user work together to process information in order to effectively and efficiently generate advertising concepts. This system is implemented as follows.

[0478] Server execution

[0479] The server receives characteristic information of the target group entered by the user. The server uses a data preparation module to format this information so that it is in a standardized and analyzable state. The prepared data is analyzed by an analysis module, and the analysis results are sent to a generative AI model. The generative AI model matches the analyzed characteristic information with market trend data based on the prompt "Propose an advertising concept with the following characteristics: health-conscious, young people, environmentally friendly" and generates a highly novel advertising concept.

[0480] Implementation of the terminal

[0481] The device presents the advertising concept sent from the server in an interface that allows the user to visually review it. Through this interface, the user can review the generated advertising concept in detail and utilize editing functions that allow for modifications. For example, the user can fine-tune the text and images of the advertising concept.

[0482] User interaction

[0483] Users input characteristic information using their devices. Specific examples of this information include characteristics such as "health-conscious," "young," and "environmentally friendly." Users evaluate the generated ad concept on their devices and modify the ad elements as needed. Along with the modifications, users provide feedback, which is then returned to the server, allowing this feedback to be used in the next generation process.

[0484] This patent makes the advertising concept generation process faster and more effective, enabling the creation of unique advertisements that meet market demands.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0486] Step 1:

[0487] User input of characteristic information

[0488] Users use their device's interface to input characteristic information about the target audience for advertisements. Specific examples include forms for inputting characteristics such as "health-conscious," "young people," and "environmentally friendly." The entered data is temporarily stored on the device and then sent to the server in a structured format.

[0489] Step 2:

[0490] Data reception and processing by the server

[0491] The server receives characteristic information sent from the user. The received data is then processed by a data preparation module into a format that facilitates analysis. This process includes supplementing missing data and standardizing the format. The prepared data is then ready to be passed on to the next analysis module.

[0492] Step 3:

[0493] Server-based data analysis and supply to generative models.

[0494] The server uses a data analysis module to analyze the compiled characteristic information in detail. The analysis results are supplied to the generation AI model as foundational data for generating advertising concepts. Specifically, characteristic information associated with market trend data sets is matched with prompt text.

[0495] Step 4:

[0496] Generating advertising concepts using a generative AI model

[0497] The server's AI model generates advertising concepts based on the analysis data and prompt text. For example, using the prompt text "Please propose an advertising concept with the following characteristics: health-conscious, youthful, and environmentally friendly," the model generates "A concept that supports an active lifestyle with eco-friendly packaging." The generated concept is stored on the server and ready to be presented to the user.

[0498] Step 5:

[0499] Display and edit ad concepts on your device

[0500] The device receives advertising concepts sent from the server and displays them visually to the user. Through the interface, the user can provide feedback on the concepts they view and edit the text and visual elements of the concepts as needed.

[0501] Step 6:

[0502] Collecting and saving feedback

[0503] Once users have completed their evaluation and modifications of a concept, they send their feedback from their device to the server. The server stores the received feedback in a database and uses it to improve future concept generation. This feedback information is also used as training data for the AI ​​model, leading to an overall improvement in the system's accuracy.

[0504] (Application Example 1)

[0505] Next, we will explain Application Example 1. In the following explanation, 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."

[0506] In today's advertising industry, there is a demand for the rapid and effective generation of advertising concepts tailored to the target customer base, and for flexible optimization based on user feedback. However, this process is time-consuming and labor-intensive with traditional methods, making efficient and highly accurate advertising concept generation and management essential.

[0507] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0508] In this invention, the server includes means for receiving and analyzing characteristic information of an input target group, means for generating advertising concepts using a generative model based on the analyzed target group information, and terminal means for providing an interface that allows the user to visually confirm and edit the generated advertising concepts. This enables the rapid and effective generation of advertising concepts in a smartphone environment for advertising agency services and marketing operations, and allows for optimization through real-time editing and feedback.

[0509] "Inputted target group characteristics information" refers to information about the attributes and preferences of the target customer group, which is necessary when generating an advertising concept.

[0510] The "means of analysis" refer to a processing device that organizes the characteristic information of the target group received and converts it into a format useful for generating specific advertising concepts.

[0511] "Methods using generative models" refer to devices that utilize AI algorithms to create advertising concepts from analyzed target group information.

[0512] "Terminal means providing an interface" refers to a terminal device having a user interface that allows users to directly view generated advertising concepts and perform visual editing and evaluation.

[0513] "Means for receiving feedback again and optimizing" refers to a device equipped with the function of receiving user reactions and opinions, improving the advertising concept generated based on them, and ultimately refining it into a more suitable concept.

[0514] An "application that operates in a smartphone environment" is software that runs on a smartphone and is developed to streamline advertising agency and marketing-related tasks.

[0515] A "market trend database" is a data storage system that accumulates information on current market trends and consumer behavior, and is used to evaluate the novelty of advertising concepts.

[0516] This invention relates to an advertising concept generation system that operates in a smartphone environment and consists of a server, a terminal, and a user.

[0517] The server receives and analyzes characteristic information of the target group entered by the user. This analysis includes information about the attributes and preferences of the customer segment specified by the user. Using the analyzed information, the server generates advertising concepts using a generative AI model. This generative model utilizes AI algorithms (e.g., OpenAI GPT-3) to compare the analyzed data with a market trend database and generate effective advertising concepts that meet the user's needs.

[0518] The terminal features an interface for presenting generated advertising concepts to the user, providing an environment where the user can visually review and edit them. The user can evaluate the advertising concept displayed on the terminal and make modifications on the spot if necessary. For example, if the user's target customer base is "people interested in environmentally friendly lifestyles," an example of a prompt message to enter into the server might be, "Generate an advertising concept for a beverage suitable for eco-conscious customers aged 25 to 35."

[0519] User feedback is sent back to the server, which helps optimize the advertising concept. Generative concepts, born from concrete examples of prompt text, enable a quick and effective response to the target audience's needs.

[0520] As described above, this system enables the rapid and effective generation and management of advertising concepts, supporting more flexible advertising activities.

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] Users input characteristic information about their target customer base using a dedicated smartphone application. This information includes age group, interests, and behavioral patterns. The input information is output as data to create prompt messages for the server.

[0524] Step 2:

[0525] The server analyzes the characteristic information received from the user. This analysis formats the input information into a format suitable for the system and converts it into a format that can be input into the generating AI model. Specifically, it performs filtering and normalization of text data. Based on the analysis results, it outputs data to be input into the generating AI model.

[0526] Step 3:

[0527] The server uses the analyzed data to generate advertising concepts using an AI algorithm (e.g., OpenAI GPT-3). During this process, the analyzed data is used as input to form prompt statements, which are then compared against a market trend database. The generated advertising concepts are output, taking into account novelty and effectiveness.

[0528] Step 4:

[0529] The device presents the generated advertising concept to the user. The user visually reviews this concept through the smartphone interface and provides feedback as text if necessary. The device outputs data collected from the presented concept and the user's feedback.

[0530] Step 5:

[0531] The server executes a process to optimize the advertising concept based on feedback received from users. This process analyzes the feedback and regenerates an optimized concept using a generative AI model. The optimization results are then output and finalized as the final advertising concept.

[0532] These processing steps enable the system to quickly and effectively generate advertising concepts and optimize them through user feedback.

[0533] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0534] This invention is a system that generates advertising concepts based on the characteristics of a target group and integrates an emotion engine for real-time recognition of user emotions. Based on user feedback and emotion analysis, it dynamically adjusts advertising concepts, enabling the proposal of more effective and appealing advertisements.

[0535] Server execution

[0536] The server has the function of receiving and analyzing characteristic information sent from the user. This analyzed data is converted into advertising concepts using an AI generation model. At the same time, the server understands the user's emotions based on data from the emotion engine and uses this to evaluate and improve the generated concepts. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy and interest.

[0537] Implementation of the terminal

[0538] The device features an interface that displays generated advertising concepts sent from the server. Through this interface, users can review the content of the advertising concepts and provide feedback based on their own emotions. Furthermore, emotional information acquired by the emotion engine adjusts the interface on the device, providing the most relevant information based on the emotions the user is feeling.

[0539] User interaction

[0540] Users can react naturally when viewing advertising concepts displayed on their devices. For example, when viewing an advertisement for a beverage product, if the user shows a satisfied expression, the emotion engine recognizes that emotion and reflects it in specific elements of the concept. If there are elements that surprise the user, the emotion engine captures that information and uses it for further analysis and optimization.

[0541] In this way, the system captures user emotions in real time, enabling it to more effectively optimize and dynamically update advertising concepts.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] Users input characteristic information about the target group using their own devices. This information includes age group, interests, lifestyle, etc., and they send the data to the server by pressing the "Submit" button.

[0545] Step 2:

[0546] The server receives characteristic information sent from the user, formats it, and analyzes it. This analyzed data is then supplied to an AI model for generating advertising concepts.

[0547] Step 3:

[0548] A generative model built into the server automatically generates advertising concepts based on user characteristics. The generated concepts are then compared against a market trend database to verify their novelty and effectiveness.

[0549] Step 4:

[0550] The server sends a request to the sentiment engine, along with the generated ad concept, to analyze the user's real-time emotions.

[0551] Step 5:

[0552] The emotion engine analyzes the user's facial expressions and voice tone to identify their emotional state at that time. This allows it to categorize emotions into states such as joy, surprise, and interest.

[0553] Step 6:

[0554] The device displays advertising concepts sent from the server in the user interface. Feedback from the emotion engine is also visualized simultaneously, and adjustments are made according to the user's emotions.

[0555] Step 7:

[0556] Users review the displayed ad concept and express their emotions. Based on the user's natural response, if they wish to provide further feedback, this is captured by the emotion engine and sent to the server.

[0557] Step 8:

[0558] The server readjusts the advertising concept based on analysis results from the emotion engine and user feedback. New information is used to improve the concept, and the final proposal is shaped.

[0559] Step 9:

[0560] The device will again display the improved advertising concept, maintaining an environment where users can review and provide further feedback. This cycle will continue until optimization is confirmed.

[0561] (Example 2)

[0562] Next, we will describe Example 2. 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."

[0563] Traditional advertising systems have a problem in that they generate advertising concepts based on the characteristics of the target group in a static manner, making it difficult to respond to users' real-time emotions and create effective advertisements. Therefore, in order to improve the appeal of advertisements, there is a need for technology that can dynamically optimize advertising concepts effectively by reflecting users' emotions.

[0564] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0565] In this invention, the server includes means for receiving and analyzing characteristic information of an input group; means for using a generative model based on the analyzed information to generate an advertising concept related to the group; and means for analyzing the user's emotional information using an emotional engine and evaluating and improving the advertising concept generated based on that information. This enables the optimization of effective and dynamic advertising by taking the user's emotions into consideration in real time.

[0566] "Group characteristic information" refers to general characteristics of a specific group, such as age, gender, and hobbies, and is data used for generating advertising concepts.

[0567] A "generative model" is an algorithm or program used to generate new advertising concepts based on input data.

[0568] A "communication device" is a device equipped with display and input interfaces that allow users to review generated advertising concepts and provide feedback as needed.

[0569] The "emotion engine" is a system that analyzes users' emotional states in real time by analyzing their facial expressions, tone of voice, and other factors, and uses this information to evaluate and improve advertising concepts.

[0570] A "market trends database" is a database that compiles information on current market consumer interests and purchasing behavior, and is used to propose novel advertising concepts.

[0571] This invention provides a system that maximizes the effectiveness of advertising by utilizing user characteristic information. This system mainly consists of a server and terminals and is realized through advanced data analysis technology using a generative AI model and an emotion engine.

[0572] Server execution

[0573] The server has the function of receiving characteristic information of groups sent by users and analyzing that information. An AI generative model is used for the analysis, which dynamically generates advertising concepts. In this process, the generated advertising concepts are realized by supplying the generative model with prompt sentences that are precisely set based on the characteristic information. An example of a prompt sentence would be "a beverage advertisement with a relaxation theme, aimed at women in their 20s." In addition, the server uses emotion engine data to analyze the emotional state of users and evaluate and improve the advertising concepts.

[0574] Implementation of the terminal

[0575] The device features an interface that provides users with advertising concepts sent from the server. Through this interface, users can view the advertising concepts and provide feedback. The emotion engine analyzes the user's facial expressions and tone of voice on the device and sends the results to the server. This allows the device to adjust the interface based on the user's emotion information, providing more personalized information.

[0576] User interaction

[0577] Users can react naturally to the advertising concepts displayed on their devices. For example, if a user smiles while watching a beverage advertisement, the emotion engine captures this information and processes it as positive feedback. This information is stored on the server and used to generate and optimize future advertising concepts.

[0578] This invention enables effective ad delivery that reflects user emotions in real time, thereby improving the persuasive power of the ads.

[0579] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0580] Step 1:

[0581] The server receives characteristic information about a group entered by users. Specifically, this includes information such as age, gender, hobbies, and advertising preferences. This input data is analyzed to prepare the foundational data for use in the next step. By receiving data and performing initial analysis, the server creates the foundation for generating advertising concepts.

[0582] Step 2:

[0583] The server generates prompt text to be fed into the generative AI model based on the analyzed characteristics of the group. Here, guidelines are set to generate appropriate advertising concepts, taking into account the characteristics of the users. Specifically, prompt text is created such as "a beverage advertisement with a relaxation theme, aimed at women in their 20s." This prompt text is input into the generative AI model to generate advertising concepts.

[0584] Step 3:

[0585] The server uses a generative AI model to generate advertising concepts based on the input prompt text. It outputs the generated advertising concepts and prepares them for presentation to the user in the next step. The data calculations performed here are to extract the optimal concept based on a large training dataset.

[0586] Step 4:

[0587] The device receives the advertising concept sent from the server and displays it to the user. Specifically, it visually presents the advertising concept on the device's display, allowing the user to review its content. This process enables the user to give their initial reaction to the generated concept.

[0588] Step 5:

[0589] The emotion engine analyzes the user's facial expressions and tone of voice when they view the advertisement concept on their device. Specifically, it collects and analyzes the user's emotional information in real time using the built-in camera and microphone. This emotional information is then sent to the server in the next step.

[0590] Step 6:

[0591] The server receives emotional information transmitted from the terminal and uses it to evaluate and improve the advertising concept. The input here is user emotional data, and the output is data for extracting points for improving the advertisement. This allows the advertising concept to adapt to the user's emotions and be dynamically optimized.

[0592] Step 7:

[0593] Users can submit feedback through their devices. Specifically, they can rate their favorability of an ad using a scale and leave comments suggesting areas for improvement. This feedback information is sent to the server and used to improve future ad generation and optimization processes.

[0594] (Application Example 2)

[0595] Next, we will explain application example 2. In the following explanation, 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."

[0596] To enhance the effectiveness of an advertising concept, it is necessary to accurately capture consumer emotions and dynamically adjust the ad content based on those emotions. However, conventional ad generation methods have made it difficult to dynamically adjust ads based on real-time emotional feedback from users. Therefore, there is a need to provide a method for realizing effective advertising campaigns.

[0597] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0598] In this invention, the server includes means for receiving and analyzing characteristic information of an input user group; means for using a generation model based on the analyzed user group information to generate an advertising concept related to the user group; a user terminal device equipped with an interface for displaying the generated advertising concept; emotion analysis means for analyzing the user's facial expressions and voice and recognizing emotions; and means for using a model that dynamically adjusts the advertising content based on emotion information acquired in real time. This makes it possible to optimize advertisements in real time based on the user's emotions and dynamically provide more appealing advertising concepts.

[0599] A "user group" is a group of people who have been designated as the target audience for a specific advertisement, and their characteristic information forms the basis of the data used for analysis.

[0600] "Characteristic information" refers to information that shows attribute data, behavioral patterns, interests, and preferences related to a group of users.

[0601] A "generative model" is an algorithmic model used to automatically construct advertising concepts based on analyzed characteristic information.

[0602] An "interface" is a visual display device that allows users to review generated advertising concepts and provide feedback.

[0603] An "emotion analysis tool" is an analytical mechanism that processes a user's facial expressions and voice data to identify their emotions.

[0604] A "dynamically adjusting model" is an algorithm that uses real-time sentiment information to instantly modify advertising concepts in response to user feedback.

[0605] "Real-time" refers to a state where there is virtually no time delay between input and output, enabling immediate feedback and adjustments.

[0606] This invention provides a system for dynamically optimizing advertising concepts. A server receives and analyzes characteristic information of a user group. The analyzed information is converted into advertising concepts by a generative AI model. User group characteristic information may include age, gender, interests, purchase history, etc., and this data forms the basis for increasing users' interest in the advertising concepts.

[0607] The device acquires the user's facial expressions and voice tone in real time and analyzes the user's emotions based on this data. Machine learning libraries such as TensorFlow and PyTorch are used for emotion analysis, and facial and voice data are collected using the device's camera and microphone. This data is processed immediately, and an advertising concept that matches the user's emotions is selected.

[0608] For example, when a user is watching a video ad for pet supplies, if the user smiles with satisfaction, the emotion analysis system detects this as positive feedback and adjusts the ad concept to present a more appealing ad. In this way, the persuasiveness of the ad can be increased.

[0609] Examples of prompt messages include, "Develop an AI model that adjusts ad content in real time using user facial expression data. Inputs are image data of facial expressions and audio data of voice." This enables immediate adjustment of ads based on user feedback.

[0610] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0611] Step 1:

[0612] The server receives characteristic information sent from users. This characteristic information for a user group includes age, gender, interests, and purchase history. This information is stored in a database and prepared for analysis.

[0613] Step 2:

[0614] The server analyzes the received characteristic information. Machine learning algorithms are used for the analysis, and the analysis results are input into a generating AI model. Based on these results, an advertising concept is generated. The generated advertising concept is adjusted to match the characteristic information.

[0615] Step 3:

[0616] The device provides an interface for displaying the generated advertising concept to the user. The advertising concept is displayed on the screen to create an environment where the user can view the advertisement.

[0617] Step 4:

[0618] The device acquires the user's facial expression and voice data in real time. This is done using the device's camera and microphone. The acquired data is sent to an emotion analysis system.

[0619] Step 5:

[0620] The device analyzes the user's emotions using emotion analysis tools. TensorFlow and PyTorch are used for emotion analysis, and data processing is performed to determine the user's satisfaction level and interest.

[0621] Step 6:

[0622] The server receives the analyzed sentiment data and inputs it into a model to dynamically adjust the advertising concept. The model optimizes the ad content in real time and generates ads that are more suitable for the user. This process is continuous based on user feedback.

[0623] Step 7:

[0624] Users can re-view the adjusted ad concept and provide final feedback. This feedback is used to improve the entire system and contribute to increasing the effectiveness of the ads.

[0625] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0626] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0627] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0628] [Fourth Embodiment]

[0629] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0630] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0631] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0632] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0633] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0634] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0635] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0636] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0637] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0638] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0639] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0640] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0641] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0642] This invention relates to a system for generating and managing advertising concepts using multiple devices and interfaces. This system enables the creation of novel and effective advertising concepts using a generative model based on characteristic information of the target group provided by the user.

[0643] Server execution

[0644] The server receives characteristic information about the target group submitted by the user. The received data is formatted and analyzed within the server. Next, the server supplies the analyzed data to a generative model to generate advertising concepts. This generative model uses an AI algorithm and creates more effective concepts by comparing them with information from a market trend database.

[0645] Implementation of the terminal

[0646] The device features an interface for presenting generated advertising concepts sent from the server to the user. Users can visually review the advertising concepts on the device, evaluate them in an editable environment, and make modifications if necessary. The device also has the functionality to collect user feedback and return it to the server.

[0647] User interaction

[0648] Users input characteristic information through their devices and evaluate the resulting advertising concepts. For example, if a user is planning an advertising campaign for a beverage product, they might input details such as "health-conscious," "younger demographic," and "environmentally friendly" as characteristic information. Based on this information, the server can propose an advertising concept such as "Eco-friendly packaging to support an active lifestyle."

[0649] In this way, this system aims to create rapid and effective advertising concepts based on user-provided characteristic information, and to optimize them through user evaluation and feedback.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] Users input characteristic information about the target group using their own devices. This information includes age group, interests, lifestyle, and purchasing behavior. After completing the input, the user presses the "Submit" button to send the information to the server.

[0653] Step 2:

[0654] The server receives characteristic information sent by the user. The received data is first checked and prepared for analysis, and then converted into a format suitable for analysis.

[0655] Step 3:

[0656] The server inputs the organized characteristic information of the target group into an AI model for analysis. The AI ​​model generates novel and highly relevant advertising concepts by comparing the characteristics of the target group with a market trend database.

[0657] Step 4:

[0658] The server sends the generated advertising concept to the device. At that time, the concept is formatted in a way that the user can easily understand and interact with.

[0659] Step 5:

[0660] The device displays the advertising concept received from the server in the user interface. Through this interface, the user can review the details of the concept and edit or modify it as needed.

[0661] Step 6:

[0662] Users evaluate the displayed ad concepts and provide feedback. User feedback is provided through comment sections and rating functions on the interface.

[0663] Step 7:

[0664] The device sends the user's feedback to the server.

[0665] Step 8:

[0666] The server analyzes the received feedback in conjunction with an AI model, makes necessary adjustments, generates a new advertising concept, and presents it to the user again. This process is repeated until a concept that satisfies the user is obtained.

[0667] (Example 1)

[0668] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] In generating advertising concepts, there is a need to efficiently utilize characteristic information of the target group and quickly produce ideas with high novelty and effectiveness. However, current methods make it difficult to generate concepts that appropriately reflect market trends, and there are challenges in evaluating the generated ideas and utilizing feedback.

[0670] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0671] In this invention, the server includes means for receiving and processing characteristic information of an input target group, performing data preparation and analysis, means for using a generation AI model based on the analyzed characteristic information, and terminal device means for visually presenting the generated advertising concept to the user and having an editable interface. This enables the generation of highly novel advertising concepts that efficiently utilize characteristic information and market trends, and the optimization of the generation process based on user feedback.

[0672] "Inputted target group characteristic information" refers to information provided by the user as basic data for generating advertising concepts, indicating the attributes and preferences of a specific group.

[0673] "Data preparation" refers to the process of formatting received characteristic information to make it easier to analyze.

[0674] "Analysis" is the process of examining compiled characteristic information in detail and extracting useful information.

[0675] A "generative AI model" is an artificial intelligence algorithm used to create advertising concepts from input data.

[0676] The "Market Trend Data Set" is a dataset containing market demand, trends, and competitive information, providing useful information for generating advertising concepts.

[0677] A "prompt statement" is an input statement used to instruct an AI model to generate a specific output.

[0678] A "visually presented interface" is an interface that displays the generated advertising concept to the user in a viewable format on the screen.

[0679] An "editable interface" is a user interface that allows users to make changes and adjustments to the presented advertising concept.

[0680] This invention is a system in which a server, terminal, and user work together to process information in order to effectively and efficiently generate advertising concepts. This system is implemented as follows.

[0681] Server execution

[0682] The server receives characteristic information of the target group entered by the user. The server uses a data preparation module to format this information so that it is in a standardized and analyzable state. The prepared data is analyzed by an analysis module, and the analysis results are sent to a generative AI model. The generative AI model matches the analyzed characteristic information with market trend data based on the prompt "Propose an advertising concept with the following characteristics: health-conscious, young people, environmentally friendly" and generates a highly novel advertising concept.

[0683] Implementation of the terminal

[0684] The device presents the advertising concept sent from the server in an interface that allows the user to visually review it. Through this interface, the user can review the generated advertising concept in detail and utilize editing functions that allow for modifications. For example, the user can fine-tune the text and images of the advertising concept.

[0685] User interaction

[0686] Users input characteristic information using their devices. Specific examples of this information include characteristics such as "health-conscious," "young," and "environmentally friendly." Users evaluate the generated ad concept on their devices and modify the ad elements as needed. Along with the modifications, users provide feedback, which is then returned to the server, allowing this feedback to be used in the next generation process.

[0687] This patent makes the advertising concept generation process faster and more effective, enabling the creation of unique advertisements that meet market demands.

[0688] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0689] Step 1:

[0690] User input of characteristic information

[0691] Users use their device's interface to input characteristic information about the target audience for advertisements. Specific examples include forms for inputting characteristics such as "health-conscious," "young people," and "environmentally friendly." The entered data is temporarily stored on the device and then sent to the server in a structured format.

[0692] Step 2:

[0693] Data reception and processing by the server

[0694] The server receives characteristic information sent from the user. The received data is then processed by a data preparation module into a format that facilitates analysis. This process includes supplementing missing data and standardizing the format. The prepared data is then ready to be passed on to the next analysis module.

[0695] Step 3:

[0696] Server-based data analysis and supply to generative models.

[0697] The server uses a data analysis module to analyze the compiled characteristic information in detail. The analysis results are supplied to the generation AI model as foundational data for generating advertising concepts. Specifically, characteristic information associated with market trend data sets is matched with prompt text.

[0698] Step 4:

[0699] Generating advertising concepts using a generative AI model

[0700] The server's AI model generates advertising concepts based on the analysis data and prompt text. For example, using the prompt text "Please propose an advertising concept with the following characteristics: health-conscious, youthful, and environmentally friendly," the model generates "A concept that supports an active lifestyle with eco-friendly packaging." The generated concept is stored on the server and ready to be presented to the user.

[0701] Step 5:

[0702] Display and edit ad concepts on your device

[0703] The device receives advertising concepts sent from the server and displays them visually to the user. Through the interface, the user can provide feedback on the concepts they view and edit the text and visual elements of the concepts as needed.

[0704] Step 6:

[0705] Collecting and saving feedback

[0706] Once users have completed their evaluation and modifications of a concept, they send their feedback from their device to the server. The server stores the received feedback in a database and uses it to improve future concept generation. This feedback information is also used as training data for the AI ​​model, leading to an overall improvement in the system's accuracy.

[0707] (Application Example 1)

[0708] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] In today's advertising industry, there is a demand for the rapid and effective generation of advertising concepts tailored to the target customer base, and for flexible optimization based on user feedback. However, this process is time-consuming and labor-intensive with traditional methods, making efficient and highly accurate advertising concept generation and management essential.

[0710] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0711] In this invention, the server includes means for receiving and analyzing characteristic information of an input target group, means for generating advertising concepts using a generative model based on the analyzed target group information, and terminal means for providing an interface that allows the user to visually confirm and edit the generated advertising concepts. This enables the rapid and effective generation of advertising concepts in a smartphone environment for advertising agency services and marketing operations, and allows for optimization through real-time editing and feedback.

[0712] "Inputted target group characteristics information" refers to information about the attributes and preferences of the target customer group, which is necessary when generating an advertising concept.

[0713] The "means of analysis" refer to a processing device that organizes the characteristic information of the target group received and converts it into a format useful for generating specific advertising concepts.

[0714] "Methods using generative models" refer to devices that utilize AI algorithms to create advertising concepts from analyzed target group information.

[0715] "Terminal means providing an interface" refers to a terminal device having a user interface that allows users to directly view generated advertising concepts and perform visual editing and evaluation.

[0716] "Means for receiving feedback again and optimizing" refers to a device equipped with the function of receiving user reactions and opinions, improving the advertising concept generated based on them, and ultimately refining it into a more suitable concept.

[0717] An "application that operates in a smartphone environment" is software that runs on a smartphone and is developed to streamline advertising agency and marketing-related tasks.

[0718] A "market trend database" is a data storage system that accumulates information on current market trends and consumer behavior, and is used to evaluate the novelty of advertising concepts.

[0719] This invention relates to an advertising concept generation system that operates in a smartphone environment and consists of a server, a terminal, and a user.

[0720] The server receives and analyzes characteristic information of the target group entered by the user. This analysis includes information about the attributes and preferences of the customer segment specified by the user. Using the analyzed information, the server generates advertising concepts using a generative AI model. This generative model utilizes AI algorithms (e.g., OpenAI GPT-3) to compare the analyzed data with a market trend database and generate effective advertising concepts that meet the user's needs.

[0721] The terminal features an interface for presenting generated advertising concepts to the user, providing an environment where the user can visually review and edit them. The user can evaluate the advertising concept displayed on the terminal and make modifications on the spot if necessary. For example, if the user's target customer base is "people interested in environmentally friendly lifestyles," an example of a prompt message to enter into the server might be, "Generate an advertising concept for a beverage suitable for eco-conscious customers aged 25 to 35."

[0722] User feedback is sent back to the server, which helps optimize the advertising concept. Generative concepts, born from concrete examples of prompt text, enable a quick and effective response to the target audience's needs.

[0723] As described above, this system enables the rapid and effective generation and management of advertising concepts, supporting more flexible advertising activities.

[0724] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0725] Step 1:

[0726] Users input characteristic information about their target customer base using a dedicated smartphone application. This information includes age group, interests, and behavioral patterns. The input information is output as data to create prompt messages for the server.

[0727] Step 2:

[0728] The server analyzes the characteristic information received from the user. This analysis formats the input information into a format suitable for the system and converts it into a format that can be input into the generating AI model. Specifically, it performs filtering and normalization of text data. Based on the analysis results, it outputs data to be input into the generating AI model.

[0729] Step 3:

[0730] The server uses the analyzed data to generate advertising concepts using an AI algorithm (e.g., OpenAI GPT-3). During this process, the analyzed data is used as input to form prompt statements, which are then compared against a market trend database. The generated advertising concepts are output, taking into account novelty and effectiveness.

[0731] Step 4:

[0732] The device presents the generated advertising concept to the user. The user visually reviews this concept through the smartphone interface and provides feedback as text if necessary. The device outputs data collected from the presented concept and the user's feedback.

[0733] Step 5:

[0734] The server executes a process to optimize the advertising concept based on feedback received from users. This process analyzes the feedback and regenerates an optimized concept using a generative AI model. The optimization results are then output and finalized as the final advertising concept.

[0735] These processing steps enable the system to quickly and effectively generate advertising concepts and optimize them through user feedback.

[0736] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0737] This invention is a system that generates advertising concepts based on the characteristics of a target group and integrates an emotion engine for real-time recognition of user emotions. Based on user feedback and emotion analysis, it dynamically adjusts advertising concepts, enabling the proposal of more effective and appealing advertisements.

[0738] Server execution

[0739] The server has the function of receiving and analyzing characteristic information sent from the user. This analyzed data is converted into advertising concepts using an AI generation model. At the same time, the server understands the user's emotions based on data from the emotion engine and uses this to evaluate and improve the generated concepts. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy and interest.

[0740] Implementation of the terminal

[0741] The device features an interface that displays generated advertising concepts sent from the server. Through this interface, users can review the content of the advertising concepts and provide feedback based on their own emotions. Furthermore, emotional information acquired by the emotion engine adjusts the interface on the device, providing the most relevant information based on the emotions the user is feeling.

[0742] User interaction

[0743] Users can react naturally when viewing advertising concepts displayed on their devices. For example, when viewing an advertisement for a beverage product, if the user shows a satisfied expression, the emotion engine recognizes that emotion and reflects it in specific elements of the concept. If there are elements that surprise the user, the emotion engine captures that information and uses it for further analysis and optimization.

[0744] In this way, the system captures user emotions in real time, enabling it to more effectively optimize and dynamically update advertising concepts.

[0745] The following describes the processing flow.

[0746] Step 1:

[0747] Users input characteristic information about the target group using their own devices. This information includes age group, interests, lifestyle, etc., and they send the data to the server by pressing the "Submit" button.

[0748] Step 2:

[0749] The server receives characteristic information sent from the user, formats it, and analyzes it. This analyzed data is then supplied to an AI model for generating advertising concepts.

[0750] Step 3:

[0751] A generative model built into the server automatically generates advertising concepts based on user characteristics. The generated concepts are then compared against a market trend database to verify their novelty and effectiveness.

[0752] Step 4:

[0753] The server sends a request to the sentiment engine, along with the generated ad concept, to analyze the user's real-time emotions.

[0754] Step 5:

[0755] The emotion engine analyzes the user's facial expressions and voice tone to identify their emotional state at that time. This allows it to categorize emotions into states such as joy, surprise, and interest.

[0756] Step 6:

[0757] The device displays advertising concepts sent from the server in the user interface. Feedback from the emotion engine is also visualized simultaneously, and adjustments are made according to the user's emotions.

[0758] Step 7:

[0759] Users review the displayed ad concept and express their emotions. Based on the user's natural response, if they wish to provide further feedback, this is captured by the emotion engine and sent to the server.

[0760] Step 8:

[0761] The server readjusts the advertising concept based on analysis results from the emotion engine and user feedback. New information is used to improve the concept, and the final proposal is shaped.

[0762] Step 9:

[0763] The device will again display the improved advertising concept, maintaining an environment where users can review and provide further feedback. This cycle will continue until optimization is confirmed.

[0764] (Example 2)

[0765] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0766] Traditional advertising systems have a problem in that they generate advertising concepts based on the characteristics of the target group in a static manner, making it difficult to respond to users' real-time emotions and create effective advertisements. Therefore, in order to improve the appeal of advertisements, there is a need for technology that can dynamically optimize advertising concepts effectively by reflecting users' emotions.

[0767] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0768] In this invention, the server includes means for receiving and analyzing characteristic information of an input group; means for using a generative model based on the analyzed information to generate an advertising concept related to the group; and means for analyzing the user's emotional information using an emotional engine and evaluating and improving the advertising concept generated based on that information. This enables the optimization of effective and dynamic advertising by taking the user's emotions into consideration in real time.

[0769] "Group characteristic information" refers to general characteristics of a specific group, such as age, gender, and hobbies, and is data used for generating advertising concepts.

[0770] A "generative model" is an algorithm or program used to generate new advertising concepts based on input data.

[0771] A "communication device" is a device equipped with display and input interfaces that allow users to review generated advertising concepts and provide feedback as needed.

[0772] The "emotion engine" is a system that analyzes users' emotional states in real time by analyzing their facial expressions, tone of voice, and other factors, and uses this information to evaluate and improve advertising concepts.

[0773] A "market trends database" is a database that compiles information on current market consumer interests and purchasing behavior, and is used to propose novel advertising concepts.

[0774] This invention provides a system that maximizes the effectiveness of advertising by utilizing user characteristic information. This system mainly consists of a server and terminals and is realized through advanced data analysis technology using a generative AI model and an emotion engine.

[0775] Server execution

[0776] The server has the function of receiving characteristic information of groups sent by users and analyzing that information. An AI generative model is used for the analysis, which dynamically generates advertising concepts. In this process, the generated advertising concepts are realized by supplying the generative model with prompt sentences that are precisely set based on the characteristic information. An example of a prompt sentence would be "a beverage advertisement with a relaxation theme, aimed at women in their 20s." In addition, the server uses emotion engine data to analyze the emotional state of users and evaluate and improve the advertising concepts.

[0777] Implementation of the terminal

[0778] The device features an interface that provides users with advertising concepts sent from the server. Through this interface, users can view the advertising concepts and provide feedback. The emotion engine analyzes the user's facial expressions and tone of voice on the device and sends the results to the server. This allows the device to adjust the interface based on the user's emotion information, providing more personalized information.

[0779] User interaction

[0780] Users can react naturally to the advertising concepts displayed on their devices. For example, if a user smiles while watching a beverage advertisement, the emotion engine captures this information and processes it as positive feedback. This information is stored on the server and used to generate and optimize future advertising concepts.

[0781] This invention enables effective ad delivery that reflects user emotions in real time, thereby improving the persuasive power of the ads.

[0782] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0783] Step 1:

[0784] The server receives characteristic information about a group entered by users. Specifically, this includes information such as age, gender, hobbies, and advertising preferences. This input data is analyzed to prepare the foundational data for use in the next step. By receiving data and performing initial analysis, the server creates the foundation for generating advertising concepts.

[0785] Step 2:

[0786] The server generates prompt text to be fed into the generative AI model based on the analyzed characteristics of the group. Here, guidelines are set to generate appropriate advertising concepts, taking into account the characteristics of the users. Specifically, prompt text is created such as "a beverage advertisement with a relaxation theme, aimed at women in their 20s." This prompt text is input into the generative AI model to generate advertising concepts.

[0787] Step 3:

[0788] The server uses a generative AI model to generate advertising concepts based on the input prompt text. It outputs the generated advertising concepts and prepares them for presentation to the user in the next step. The data calculations performed here are to extract the optimal concept based on a large training dataset.

[0789] Step 4:

[0790] The device receives the advertising concept sent from the server and displays it to the user. Specifically, it visually presents the advertising concept on the device's display, allowing the user to review its content. This process enables the user to give their initial reaction to the generated concept.

[0791] Step 5:

[0792] The emotion engine analyzes the user's facial expressions and tone of voice when they view the advertisement concept on their device. Specifically, it collects and analyzes the user's emotional information in real time using the built-in camera and microphone. This emotional information is then sent to the server in the next step.

[0793] Step 6:

[0794] The server receives emotional information transmitted from the terminal and uses it to evaluate and improve the advertising concept. The input here is user emotional data, and the output is data for extracting points for improving the advertisement. This allows the advertising concept to adapt to the user's emotions and be dynamically optimized.

[0795] Step 7:

[0796] Users can submit feedback through their devices. Specifically, they can rate their favorability of an ad using a scale and leave comments suggesting areas for improvement. This feedback information is sent to the server and used to improve future ad generation and optimization processes.

[0797] (Application Example 2)

[0798] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0799] To enhance the effectiveness of an advertising concept, it is necessary to accurately capture consumer emotions and dynamically adjust the ad content based on those emotions. However, conventional ad generation methods have made it difficult to dynamically adjust ads based on real-time emotional feedback from users. Therefore, there is a need to provide a method for realizing effective advertising campaigns.

[0800] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0801] In this invention, the server includes means for receiving and analyzing characteristic information of an input user group; means for using a generation model based on the analyzed user group information to generate an advertising concept related to the user group; a user terminal device equipped with an interface for displaying the generated advertising concept; emotion analysis means for analyzing the user's facial expressions and voice and recognizing emotions; and means for using a model that dynamically adjusts the advertising content based on emotion information acquired in real time. This makes it possible to optimize advertisements in real time based on the user's emotions and dynamically provide more appealing advertising concepts.

[0802] A "user group" is a group of people who have been designated as the target audience for a specific advertisement, and their characteristic information forms the basis of the data used for analysis.

[0803] "Characteristic information" refers to information that shows attribute data, behavioral patterns, interests, and preferences related to a group of users.

[0804] A "generative model" is an algorithmic model used to automatically construct advertising concepts based on analyzed characteristic information.

[0805] An "interface" is a visual display device that allows users to review generated advertising concepts and provide feedback.

[0806] An "emotion analysis tool" is an analytical mechanism that processes a user's facial expressions and voice data to identify their emotions.

[0807] A "dynamically adjusting model" is an algorithm that uses real-time sentiment information to instantly modify advertising concepts in response to user feedback.

[0808] "Real-time" refers to a state where there is virtually no time delay between input and output, enabling immediate feedback and adjustments.

[0809] This invention provides a system for dynamically optimizing advertising concepts. A server receives and analyzes characteristic information of a user group. The analyzed information is converted into advertising concepts by a generative AI model. User group characteristic information may include age, gender, interests, purchase history, etc., and this data forms the basis for increasing users' interest in the advertising concepts.

[0810] The device acquires the user's facial expressions and voice tone in real time and analyzes the user's emotions based on this data. Machine learning libraries such as TensorFlow and PyTorch are used for emotion analysis, and facial and voice data are collected using the device's camera and microphone. This data is processed immediately, and an advertising concept that matches the user's emotions is selected.

[0811] For example, when a user is watching a video ad for pet supplies, if the user smiles with satisfaction, the emotion analysis system detects this as positive feedback and adjusts the ad concept to present a more appealing ad. In this way, the persuasiveness of the ad can be increased.

[0812] Examples of prompt messages include, "Develop an AI model that adjusts ad content in real time using user facial expression data. Inputs are image data of facial expressions and audio data of voice." This enables immediate adjustment of ads based on user feedback.

[0813] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0814] Step 1:

[0815] The server receives characteristic information sent from users. This characteristic information for a user group includes age, gender, interests, and purchase history. This information is stored in a database and prepared for analysis.

[0816] Step 2:

[0817] The server analyzes the received characteristic information. Machine learning algorithms are used for the analysis, and the analysis results are input into a generating AI model. Based on these results, an advertising concept is generated. The generated advertising concept is adjusted to match the characteristic information.

[0818] Step 3:

[0819] The device provides an interface for displaying the generated advertising concept to the user. The advertising concept is displayed on the screen to create an environment where the user can view the advertisement.

[0820] Step 4:

[0821] The device acquires the user's facial expression and voice data in real time. This is done using the device's camera and microphone. The acquired data is sent to an emotion analysis system.

[0822] Step 5:

[0823] The device analyzes the user's emotions using emotion analysis tools. TensorFlow and PyTorch are used for emotion analysis, and data processing is performed to determine the user's satisfaction level and interest.

[0824] Step 6:

[0825] The server receives the analyzed sentiment data and inputs it into a model to dynamically adjust the advertising concept. The model optimizes the ad content in real time and generates ads that are more suitable for the user. This process is continuous based on user feedback.

[0826] Step 7:

[0827] Users can re-view the adjusted ad concept and provide final feedback. This feedback is used to improve the entire system and contribute to increasing the effectiveness of the ads.

[0828] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0829] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0830] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0831] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0832] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0833] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0834] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0835] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0836] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0837] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0838] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0839] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0840] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0841] 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.

[0842] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0843] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0844] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0845] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0846] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0847] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0848] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0849] The following is further disclosed regarding the embodiments described above.

[0850] (Claim 1)

[0851] A device for receiving and analyzing characteristic information of the target population,

[0852] A device that uses a generative model based on analyzed target group information to generate advertising concepts related to the aforementioned target group,

[0853] A terminal device equipped with an interface for displaying the generated advertising concept,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which provides an interactive interface for receiving evaluations and feedback on generated advertising concepts.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising a device that compares the analyzed data with a market trend database to propose highly novel advertising concepts.

[0859] "Example 1"

[0860] (Claim 1)

[0861] A means of receiving characteristic information of the target group, preparing the data, and performing analysis,

[0862] In order to generate advertising concepts related to the aforementioned target group, means of using a generation AI model based on analyzed characteristic information,

[0863] A terminal device means that visually presents the generated advertising concept to the user and has an editable interface,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which provides a means for receiving evaluations and feedback on the generated advertising concepts, and then accumulating the feedback to utilize in the next generation process.

[0867] (Claim 3)

[0868] The system according to claim 1, comprising means for proposing a highly novel advertising concept by comparing the analyzed data with a set of market trend data, and for optimizing the generation process using prompt sentences.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A means for receiving and analyzing characteristic information of the target group,

[0872] Means for using a generative model based on analyzed target group information in order to generate advertising concepts related to the aforementioned target group,

[0873] A terminal means that displays the generated advertising concept and provides an interface that allows the user to visually confirm and edit it,

[0874] A means to receive user feedback on the generated ad concepts and optimize them,

[0875] As an application that operates in a smartphone environment, it is a means applicable to advertising agency services and marketing operations,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, which provides an interactive interface for receiving evaluations and feedback on generated advertising concepts, and enables users to edit the concepts in real time using a smartphone.

[0879] (Claim 3)

[0880] The system according to claim 1, comprising means for proposing a highly novel advertising concept by comparing the analyzed data with a market trend database and operating as a marketing application for smartphones.

[0881] "Example 2 of combining an emotion engine"

[0882] (Claim 1)

[0883] A means for receiving and analyzing characteristic information of an input group,

[0884] Means for using a generative model based on analyzed information to generate a concept of propaganda related to the aforementioned group,

[0885] A communication device that displays the generated advertising concept,

[0886] A means for analyzing user emotional information using an emotion engine, and for evaluating and improving advertising concepts generated based on that information,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, providing an interactive communication means for receiving evaluation information and feedback information on a generated advertising concept.

[0890] (Claim 3)

[0891] The system according to claim 1, comprising means for comparing the analyzed data with a market trend database and proposing a novel advertising concept.

[0892] "Application example 2 when combining with an emotional engine"

[0893] (Claim 1)

[0894] A means for receiving and analyzing characteristic information of an input user group,

[0895] Means for using a generative model based on analyzed user group information in order to generate advertising concepts related to the user group,

[0896] A user terminal device equipped with an interface for displaying the generated advertising concept,

[0897] An emotion analysis method for analyzing the user's facial expressions and voice to recognize emotions,

[0898] A method that uses a model to dynamically adjust ad content based on sentiment information acquired in real time,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, which provides an interactive interface for receiving evaluations and feedback on generated advertising concepts in real time.

[0902] (Claim 3)

[0903] The system according to claim 1, further comprising a device that compares the analyzed emotional data with a market trend database to propose highly novel advertising concepts. [Explanation of Symbols]

[0904] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for receiving and analyzing characteristic information of the target population, A device that uses a generative model based on analyzed target group information to generate advertising concepts related to the aforementioned target group, A terminal device equipped with an interface for displaying the generated advertising concept, A system that includes this.

2. The system according to claim 1, which provides an interactive interface for receiving evaluations and feedback on generated advertising concepts.

3. The system according to claim 1, further comprising a device that compares the analyzed data with a market trend database to propose highly novel advertising concepts.

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