system

Abstract

We provide the system. [Solution] A means for receiving image data and product information and automatically generating sales promotion materials using a generation model, A verification means for evaluating the generated sales promotion materials and determining whether or not revisions are necessary based on pre-set criteria, The sales promotion materials that have been determined to be free of problems by the verification means are displayed on the terminal, and adjustment means are provided to allow for fine-tuning. A means for outputting the aforementioned sales promotion materials in various information media formats, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 the conventional process of creating sales promotion materials, there were problems of significant time and labor consumption due to manual design creation and creative checks in different departments. Also, subjectivity entered the checking process, there were no objective criteria, and variations in judgment sometimes occurred. As a result, there is a demand for providing sales promotion materials quickly and consistently.

Means for Solving the Problems

[0005] This invention provides a system that receives image data and automatically generates promotional materials using a generation model. This system also includes a function to evaluate the content of the generated promotional materials according to pre-set criteria and determine whether revisions are necessary, thereby reducing user effort and enabling the rapid output of high-quality promotional materials. Furthermore, by adding functions to verify data during information input and to allow users to fine-tune the generated products, the system achieves an efficient production process while ensuring design consistency.

[0006] "Image data" refers to information used to generate sales promotion materials, and is electronic data that includes text and images.

[0007] A "generative model" is a machine learning model that has algorithms and methods for automatically creating promotional materials from input image data.

[0008] "Sales promotion materials" refer to printed or digital media used to appeal to customers about products or services, and include POP displays, posters, and flyers.

[0009] "Evaluation means" refers to a process and system for judging the quality of generated promotional materials and determining the need for correction using pre-established criteria.

[0010] A "user" is an entity that operates the system to create and adjust sales promotion materials, and refers to customers, company representatives, or designers who use the system.

[0011] "Standards" refer to the rules and guidelines used to evaluate promotional materials, including design consistency, legal requirements, and visual effectiveness. [Brief explanation of the drawing]

[0012] [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system for users to quickly generate promotional materials via a terminal. First, the user inputs product information and related image data through the terminal's interface. This input data is sent from the terminal to a server, which uses this data to automatically create promotional materials using a generation AI model. The generation model combines design templates with the input data to instantly generate high-quality promotional materials.

[0034] The generated promotional materials are evaluated on the server using a creative checking system. Evaluation criteria include compliance with brand guidelines, legal requirements, and visual consistency. Promotional materials that pass the check are returned to the user's terminal for review and adjustments if necessary. This includes correcting text, adjusting font size, and adjusting color tones.

[0035] Once the user completes the final confirmation, the terminal outputs the promotional materials in a format suitable for saving, such as PDF or image. Throughout this process, the series of operations the user performs using the program are simple and intuitive. All processes are efficiently linked, providing a platform for quickly and effectively creating promotional materials. This establishes a system that significantly reduces traditional time and effort while easily ensuring quality.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] Users use their devices to input product information and image data, such as pictures, that they want to display on promotional materials. Input is done using text boxes and image upload functions.

[0039] Step 2:

[0040] The terminal converts the input data into the appropriate format and sends it to the server. A secure protocol is used for this transmission, ensuring data confidentiality.

[0041] Step 3:

[0042] The server processes the received data and passes it to the generative AI model. The generative model automatically generates sales promotion materials based on this data. A template engine is used to determine the layout and design.

[0043] Step 4:

[0044] The generated promotional materials are evaluated by a creative checking system on the server. This evaluation includes design consistency and compliance with brand guidelines.

[0045] Step 5:

[0046] Promotional materials that pass the check are sent from the server to the terminal. The user reviews these promotional materials on the terminal and makes minor adjustments to the text and layout as needed.

[0047] Step 6:

[0048] Once the user has reviewed and made corrections, the device prepares to save or print the completed promotional material in the selected format, which includes PDF and high-resolution image formats.

[0049] (Example 1)

[0050] 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."

[0051] Traditionally, creating advertising materials required specialized knowledge and skills, and was time-consuming and laborious. Furthermore, ensuring compliance with brand standards and legal requirements was often complex, potentially compromising the quality and consistency of the advertising materials. Additionally, the lack of user-friendly controls for fine-tuning designs and final output made it difficult to efficiently generate advertising materials.

[0052] 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.

[0053] In this invention, the server includes means for receiving image data and information data and automatically generating advertising materials using a generative model; evaluation means for verifying the content of the generated advertising materials and determining whether adjustments are necessary based on pre-set criteria; and means for presenting the advertising materials to the user and reflecting fine-tuning based on the user's requests. This enables users to quickly create efficient, consistent, and high-quality advertising materials without requiring specialized knowledge.

[0054] "Image data" refers to digital data containing visual information used in creating advertising materials.

[0055] "Information data" refers to digital data that includes details such as product information and text related to advertising materials.

[0056] A "generative model" refers to an algorithm or system that automatically generates advertising materials based on input data.

[0057] "Advertising materials" are visual or text-based content intended to promote the sale of products or services.

[0058] "Evaluation methods" refer to a system for checking the quality and compliance with standards of generated advertising materials and determining whether or not there are any problems.

[0059] "User" refers to the entity that creates, reviews, and adjusts advertising materials through the system.

[0060] This invention is a system for users to efficiently and effectively generate high-quality advertising materials via their devices. Specific embodiments are described below.

[0061] First, the user uses the device's interface to input image and informational data for the product being advertised. This interface is designed to allow users to easily and intuitively provide the necessary data. After input, the device sends this data to the server.

[0062] The server creates and sends prompts to the generative AI model based on the received data. These generative AI models include, for example, natural language processing models and image generation algorithms. This integrates design templates with user input to automatically generate advertising materials. This process transforms data using specific algorithms to produce unique outputs. Often, the generative AI model used is a modern model trained on a wide range of datasets.

[0063] The generated advertising materials are then verified by an evaluation system on the server. This process verifies that the advertising materials comply with the brand's guidelines and legal standards. Advertising materials that pass the evaluation are then sent back to the terminal.

[0064] Users review the generated advertising materials using their devices and make adjustments as needed. These adjustments include changing font sizes and adjusting colors. Once the user has completed their final adjustments, the device saves and outputs the advertising materials in digital formats such as PDF and JPEG. At this stage, the advertising materials are easily formatted for digital distribution and printing.

[0065] As a concrete example, when a user creates advertising materials for a new beverage, they input a photo of the product and information such as "new flavor" and "refreshing effect" into the terminal's interface. An example of a prompt sentence that would be input to the generation AI model is, "Create an advertisement for a new beverage highlighting its refreshing effects and unique flavor." In this example, advertising materials that accurately reflect the user's intent are quickly generated.

[0066] This system enables the seamless and efficient generation of advertising materials without relying on the specialized design skills that were previously required.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The user inputs image and informational data about the product using the terminal's interface. This includes product photos and text data describing the product's features. The input data is formatted internally within the terminal and prepared for transmission to the server.

[0070] Step 2:

[0071] The terminal transmits image and information data received from the user to the server. During this process, the data is encrypted and securely sent to the server. The server stores product information and image data, and prepares it for the next stage of processing.

[0072] Step 3:

[0073] The server creates and inputs prompts to the generation AI model based on the received data. Specifically, it analyzes the input data and automatically generates prompt sentences to produce appropriate advertising materials. For example, a prompt such as "Create an advertisement for a new beverage highlighting its refreshing effects and unique flavor." is generated and input to the AI ​​model. The generation model uses this prompt to automatically generate advertising materials. The output is the generated advertising materials.

[0074] Step 4:

[0075] The server validates the generated advertising materials. This evaluation process assesses whether the materials comply with brand standards and legal requirements. Specifically, it analyzes image content and checks text to confirm compliance. Once the evaluation is complete, compliant materials proceed to the next step. The output is the advertising materials that have passed the validation process.

[0076] Step 5:

[0077] The terminal displays verified advertising materials sent from the server to the user. The user can review the materials and make minor adjustments to font size and color balance as needed. Specifically, the user intuitively makes visual adjustments using the operation menu on the terminal. Once adjustments are complete, the revised materials are prepared for saving.

[0078] Step 6:

[0079] The terminal allows users to save and print advertising materials in digital format after making final adjustments. Output formats include PDF and JPEG, and the materials are saved for distribution or printing depending on the user's selection. The final advertising materials are provided as output in the required format.

[0080] (Application Example 1)

[0081] 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."

[0082] In traditional sales promotion material creation, the time and effort required for design creation, review, and adjustment was a significant problem. In particular, efficiently preparing materials applicable to various media required advanced expertise and skills, which was a burden for many users. Therefore, there is a need for a system that allows even non-experts to easily create high-quality sales promotion materials and output them quickly.

[0083] 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.

[0084] In this invention, the server includes means for receiving image data and product information and automatically generating sales promotion materials using a generation model; verification means for evaluating the generated sales promotion materials and determining whether corrections are necessary based on pre-set criteria; and adjustment means for displaying the sales promotion materials deemed problem-free by the verification means on a terminal and enabling fine-tuning. As a result, users can efficiently and quickly generate high-quality sales promotion materials and provide them in their desired information media format without requiring specialized knowledge.

[0085] "Image data" refers to data stored in a digital format that includes visual information, and is used as a design element in sales promotion materials.

[0086] "Product information" refers to detailed data about the product being traded, providing specific explanations and characteristics in sales promotion materials.

[0087] A "generative model" is an algorithm or system that automatically generates output based on input data, and primarily uses AI technology.

[0088] "Sales promotion materials" refer to digital or printed advertising and promotional materials created to promote the sale of specific products or services.

[0089] A "verification tool" is a device that automatically checks whether the generated information or data conforms to the set standards and prompts for corrections as necessary.

[0090] "Adjustment tools" are features that allow users to fine-tune the promotional materials they generate, particularly assisting with modifications to design elements and content.

[0091] "Information media format" refers to a method of representing information in digital or physical form, and includes different formats such as PDFs and image files.

[0092] This invention is a system that efficiently generates high-quality sales promotion materials by allowing users to input product information and image data via a dedicated application, which are then processed on a server.

[0093] First, users input product information and related image data using an application on their smartphone. This data is then transmitted to a server via an internet connection. The application's front-end employs cross-platform technologies such as React Native to ensure seamless data manipulation for users.

[0094] On the server side, a backend platform using Node.js and Express is running, receiving data submitted by users and performing data integrity checks. At this time, a function is activated to verify that there are no missing or inconsistent input data. Once integrity is confirmed, the server uses OpenAI's GPT-4 model to automatically generate sales promotion materials combined with design templates. The generating AI model creates the materials according to predetermined criteria and judges whether the design is consistent and the content is appropriate.

[0095] The generated materials are verified to ensure they comply with brand guidelines and legal standards. This involves using image processing techniques with a Python environment and the OpenCV library to check for visual consistency. After verification, the materials are returned to the user's device, where the user can make minor adjustments to the displayed materials.

[0096] For example, the owner of a small handmade accessories brand might create an advertisement to promote an event. Users simply take product photos within the application and enter the event date and location, and a professional advertisement is instantly generated. This material can be easily shared on social media, enabling direct promotion.

[0097] Furthermore, an example of a prompt message for a generative AI model is as follows:

[0098] Product Information: Handmade square pendant, silver, special event price ¥2980

[0099] Image: event_banner.jpg

[0100] Concept: Weekend pop-up shop event

[0101] Template style: Elegant and modern.

[0102] Based on this prompt, a visually appealing advertising design is generated.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user launches the application on their smartphone and enters product information and image data. The entered information is structured in JSON format and sent from the device to the server. When sending data, an input form is created using React Native, providing an interface that prioritizes user experience.

[0106] Step 2:

[0107] The server analyzes the received data in a Node.js environment to verify product details and image data. First, it checks for inconsistencies. This verification includes checking data format and any missing required information. If there are no problems with the data, it proceeds to the next step. If inconsistencies are found, an error message is generated and resent to the terminal.

[0108] Step 3:

[0109] The server generates prompt messages for the AI ​​model based on data whose integrity has been verified. This AI model uses OpenAI's GPT-4 and creates prompt messages by combining product information and images into a template. The prompt messages, which include instructions for design generation, are sent from the server to the AI ​​model.

[0110] Step 4:

[0111] The generation AI model automatically generates sales promotion materials based on the received prompt text. In this process, the AI ​​utilizes design templates to create visually consistent materials. The generated materials are returned to the server in digital format (JPEG or PDF).

[0112] Step 5:

[0113] The server evaluates the generated promotional materials through verification mechanisms. Using Python and OpenCV libraries, it checks whether the materials conform to pre-configured criteria (brand guidelines, legal requirements). Only materials that pass this evaluation proceed to the next step.

[0114] Step 6:

[0115] The verified documents are sent to the device and displayed to the user. The user can review the displayed documents and make adjustments as needed. Adjustments include text corrections and changes to fonts and colors.

[0116] Step 7:

[0117] Once the user completes the final review, the device saves the document in the selected format and also provides a direct sharing function to social media. Saving is done using the application's file management system.

[0118] 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.

[0119] This invention combines an emotion engine with a system for automatically generating sales promotion materials. In this system, the user first inputs product information and image data through a terminal. The terminal converts this data and sends it to a server, which uses a generative model to create sales promotion materials. The generated sales promotion materials are evaluated through a creative check process and sent to the user if deemed appropriate.

[0120] Furthermore, this system incorporates an emotion engine that can recognize the user's emotions. The user's emotions are captured from their facial expressions and voice using the camera and microphone on the device. The server uses the emotion engine to analyze the user's emotional data and adjusts design elements according to those emotions. For example, if it is determined that the user is surprised, the system changes the colors and font styles of promotional materials to provide a more dynamic design.

[0121] The final promotional materials are returned to the terminal and displayed to the user. The user reviews the design suggested by the emotion engine and re-evaluates how much satisfaction the emotion-based changes have increased. This process enables the delivery of promotional materials that resonate with the user's emotions, resulting in more effective customer communication. The entire system features a user-friendly interface and is designed for intuitive use.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] Users input product information and images they want to include in promotional materials via their device. The device interface includes text boxes and image upload functions, allowing users to select and input the necessary information.

[0125] Step 2:

[0126] The terminal sends the data entered by the user to the server. The data is converted to JSON format and transmitted using a secure communication protocol.

[0127] Step 3:

[0128] The server passes the received data to a generative model, which automatically generates promotional materials. This generative model utilizes design trends and templates to select the optimal layout and design based on the input data.

[0129] Step 4:

[0130] The generated promotional materials are evaluated by a creative check function on the server. Evaluation criteria include consistency, legal requirements, and visual effectiveness, and based on these, it is determined whether the promotional materials need to be modified.

[0131] Step 5:

[0132] Promotional materials that pass the creative check are sent from the server to the terminal. The terminal displays them to the user, who then reviews the design.

[0133] Step 6:

[0134] The device captures the user's facial expressions and voice using its camera and microphone, and sends the emotional data to a server. This data is processed in real time.

[0135] Step 7:

[0136] The server uses an emotion engine to recognize the user's emotions. For example, if it detects that the user is happy, it will generate suggestions to add messages or images with brighter colors.

[0137] Step 8:

[0138] Users can review changes to design elements based on sentiment data and make desired modifications via their device if additional input or fine-tuning is required.

[0139] Step 9:

[0140] The finalized promotional materials can be saved or printed in PDF or image format depending on the device, resulting in the final output.

[0141] (Example 2)

[0142] 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".

[0143] In the automated generation of marketing materials, there is a need for a system that can adjust the design to take user emotions into consideration. Conventional systems have struggled to automatically generate designs that reflect user emotions, resulting in the creation of materials with low appeal. Furthermore, checking whether the generated materials meet the standards often relies on manual processes, making efficient evaluation difficult.

[0144] 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.

[0145] In this invention, the server includes means for receiving digital images and automatically generating marketing materials using a generation algorithm; means for evaluating the content of the generated materials and determining whether modifications are necessary based on pre-set criteria; and means for analyzing user emotional data and adjusting design elements according to those emotions. This makes it possible to efficiently and automatically generate attractive marketing materials that take user emotions into consideration.

[0146] A "digital image" is visual information represented in a format that can be processed by a computer system.

[0147] A "generative algorithm" is a set of procedures or methods for generating a desired output based on specific input data.

[0148] A "marketing medium" is a document containing information and content intended for the promotion of a product.

[0149] A "verification mechanism" is a function that checks and determines whether the generated content conforms to predetermined standards.

[0150] "Emotional data" refers to data that shows information about a user's emotions and psychological state.

[0151] A "design element" is a visual or sensory component that forms part of the overall design.

[0152] This invention is a system that enables the automatic generation of marketing materials using digital information. A specific embodiment of this system is described below.

[0153] The user first inputs product-related information using a terminal. This includes the product name, features, and image data. The terminal converts this input data into an appropriate digital format (e.g., JSON format) and sends it to the server via a communication protocol. The terminal is a standard digital input device, and the UI is designed using HTML, CSS, and JavaScript®.

[0154] The server first stores the received data in a database and performs data analysis to process it. The server uses a common neural network model as a generative AI model. This enables the automatic generation of promotional materials. For example, it applies a common generative algorithm as a generative model to integrate text generation and design creation based on the input information.

[0155] The server further analyzes facial and voice data obtained from the device's camera and microphone to acquire user emotional data. This analysis utilizes speech recognition and image analysis technologies. Based on this emotional data, the server dynamically adjusts design elements. For example, if the user is showing surprise, the colors and font styles are changed more dynamically.

[0156] The final generated marketing materials are sent from the server to the terminal and displayed to the user. The user can review these materials and request further refinements. This feedback loop allows the system to provide promotional materials that better suit the user's needs.

[0157] For example, if a user wants to create promotional material for a new pair of sports shoes, they would input information such as "Product name: Speed ​​Runner, Features: Lightweight, breathable, waterproof" into the terminal. If, in addition to this input data, emotional data indicating the user is smiling is obtained, the server will use a generative model to automatically generate an engaging and lively advertisement.

[0158] Example of a prompt:

[0159] "Please generate compelling ad copy based on the following information: Product: Speed ​​Runner, Features: Lightweight, breathable, waterproof. The user is smiling."

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user inputs product information (e.g., product name, features, image data) via the terminal. The terminal converts this input data into JSON format and prepares it for transmission to the server. Here, an HTML form and JavaScript are used to accept data input, and the JSON.stringify() method is used in the conversion process. The input is product information, and the output is formatted data.

[0163] Step 2:

[0164] The terminal sends the converted data to the server using a secure protocol (e.g., HTTPS). This communication utilizes an appropriate API endpoint and an HTTP client library such as axios. The input is in JSON format, and the output is the success status of the transmission to the server.

[0165] Step 3:

[0166] The server stores the data received from the terminal in a database and prepares it for analysis. The server parses the received JSON data (e.g., JSON.parse()) and stores it in a database (e.g., MySQL®) using SQL queries. The input is a JSON data stream, and the output is the stored data entries.

[0167] Step 4:

[0168] The server automatically generates promotional materials using a generative AI model. The server uses received data as a prompt to input into the generative AI model (e.g., a neural network) and obtains the generation result. During this process, model inference is performed, and the generated text and design proposals are output. The input is a prompt sentence, and the output is a proposal for promotional materials.

[0169] Step 5:

[0170] The server evaluates the generated media based on pre-defined criteria. This includes quality checks by AI or human reviewers. It compares the generated results to the criteria and determines whether corrections are needed. The input is the generated material, and the output is the evaluation result.

[0171] Step 6:

[0172] User emotional data is acquired through the device's camera and microphone. The device prepares to transmit the user's facial expressions and voice data to the server in real time. JPEG (image) and WAV (audio) file formats are used. Input is the user's real-time media, and output is media data ready for transmission.

[0173] Step 7:

[0174] The server uses an emotion engine to analyze the user's emotional data and adjust the design elements of the promotional materials. The server uses the analysis results to change, for example, the colors and font styles according to the emotion. An emotion recognition API is used in this processing. The input is the user's emotional data, and the output is the adjusted design elements.

[0175] Step 8:

[0176] The final marketing materials are sent from the server to the terminal and presented to the user. The terminal displays the received data to the user using a GUI. The user can review this display and provide feedback for further adjustments. The input is the adjusted promotional material, and the output is the user's feedback.

[0177] (Application Example 2)

[0178] 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".

[0179] In recent years, there has been a growing demand for personalized promotions based on the emotions of target customers in sales promotion activities, but this is difficult to achieve with current methods. In particular, design adjustments that take user emotions into consideration are inefficient because they are done manually, and there are problems with efficiently incorporating feedback. As a result, an adequate approach to improving customer satisfaction has not been achieved.

[0180] 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.

[0181] In this invention, the server includes means for receiving image information and automatically generating sales promotion materials using a generation artificial intelligence model; means for evaluating the content of the generated sales promotion materials using evaluation means and determining whether modifications are necessary based on pre-set criteria; and means for acquiring the user's emotions using emotion recognition means and adjusting design elements based on the acquired emotions. This enables efficient design adjustments that take into account the user's emotions and incorporates their feedback.

[0182] "Image information" refers to data that represents the visual characteristics of a product or service, and forms the basis of promotional materials.

[0183] A "generative artificial intelligence model" is a technology that generates optimal promotional materials based on diverse information, and is a form of artificial intelligence that utilizes machine learning algorithms.

[0184] "Sales promotion materials" are visual or auditory content used to effectively appeal to customers about products or services.

[0185] "Evaluation method" refers to a process or system for determining whether the generated promotional materials are appropriately designed, based on predetermined criteria.

[0186] "Emotion recognition means" refers to technologies that acquire emotional information from a user's facial expressions and voice, analyze it, and then understand the user's feelings.

[0187] "Design elements" refer to the visual components of sales promotion materials, such as colors, fonts, and layouts, which should be adjusted according to the user's emotions.

[0188] The system for realizing this invention begins with a user-owned terminal. The user inputs image information and product information on the terminal, and this data is sent to a server. The server uses a generative artificial intelligence model to automatically generate sales promotion materials from the input image information. In this process, the server analyzes and generates data, and as a result outputs visual or auditory promotional materials.

[0189] Next, the server analyzes the generated promotional materials using an evaluation tool to determine whether they are properly designed according to pre-set criteria. Materials deemed acceptable are then output to the user.

[0190] Furthermore, emotion recognition technology is used to capture the user's facial expressions and voice via the device's camera and microphone. This emotion information is sent to a server, which analyzes the user's emotions based on this data. Based on the analyzed emotions, the server adjusts the design elements of the promotional materials to provide the user with more personalized content.

[0191] Through the above process, the generated promotional materials are fed back to users, and further optimization is performed based on that feedback. Implementation requires a smartphone as a terminal and backend processing by a server. The server requires the implementation of emotion analysis using Azure®'s Emotion API and a generative AI model such as GPT-4.

[0192] As a concrete example, when promoting new sports shoes, the user inputs product information along with data of their smiling expression. The server analyzes the user's emotions and generates and presents a lively design and slogan. An example of a prompt to the generating AI model might be a request such as, "Please propose an advertising design for new sports shoes. The user is currently showing a smiling emotion. The design should incorporate elements that give a fresh and energetic impression."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] Users use their smartphones to input image and product information to be used in promotions. This data forms the basis for generating sales promotion materials and is sent from the device to the server once input is complete. Input includes image files and text information, and output is data converted into a format that the server can prepare for reception.

[0196] Step 2:

[0197] The server analyzes the received image information and automatically generates sales promotion materials using a generative artificial intelligence model. Specifically, based on the input images and text information, the AI ​​generates designs while considering the product's characteristics. In terms of data processing, the generative AI model outputs promotional materials that combine the most suitable design elements from each input data.

[0198] Step 3:

[0199] The server evaluates the generated promotional materials using evaluation tools based on pre-defined criteria. The evaluation determines whether the generated design is appropriate and conforms to the criteria. The input is the generated promotional material, and the output is the appropriate promotional material that has passed verification. Specifically, it checks for inconsistencies with the criteria and evaluates whether the requirements are met.

[0200] Step 4:

[0201] The user uses their device's camera and microphone to input facial expressions and voice into the server. This emotional data is necessary to interpret the user's feelings, and the data acquired by the device is sent to the server. The input includes raw facial and voice data, and the output is this data in a format optimized for emotion recognition.

[0202] Step 5:

[0203] The server analyzes the user's emotional data through emotion recognition mechanisms. Specifically, it uses Azure's Emotion API to analyze facial expressions and vocal characteristics, quantifying the user's emotional state. This analysis is fed into a generative AI model, which adjusts design elements to match the user's mood. The output is the analyzed emotional data.

[0204] Step 6:

[0205] The server adjusts design elements based on emotional data to generate optimized promotional materials. The generating artificial intelligence model uses prompts to adjust the design, changing colors and font styles according to the emotional state. The generated promotional materials are output and provided to the user.

[0206] Step 7:

[0207] Finally, users review the promotional materials generated on their devices and provide feedback on the design. This user feedback is used for further design optimization and is considered on the server side to enhance the promotional effect. The input is the user's feedback, and the output is the improved promotional material incorporating that feedback.

[0208] 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.

[0209] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention is a system for users to quickly generate promotional materials via a terminal. First, the user inputs product information and related image data through the terminal's interface. This input data is sent from the terminal to a server, which uses this data to automatically create promotional materials using a generation AI model. The generation model combines design templates with the input data to instantly generate high-quality promotional materials.

[0225] The generated promotional materials are evaluated on the server using a creative checking system. Evaluation criteria include compliance with brand guidelines, legal requirements, and visual consistency. Promotional materials that pass the check are returned to the user's terminal for review and adjustments if necessary. This includes correcting text, adjusting font size, and adjusting color tones.

[0226] Once the user completes the final confirmation, the terminal outputs the promotional materials in a format suitable for saving, such as PDF or image. Throughout this process, the series of operations the user performs using the program are simple and intuitive. All processes are efficiently linked, providing a platform for quickly and effectively creating promotional materials. This establishes a system that significantly reduces traditional time and effort while easily ensuring quality.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] Users use their devices to input product information and image data, such as pictures, that they want to display on promotional materials. Input is done using text boxes and image upload functions.

[0230] Step 2:

[0231] The terminal converts the input data into the appropriate format and sends it to the server. A secure protocol is used for this transmission, ensuring data confidentiality.

[0232] Step 3:

[0233] The server processes the received data and passes it to the generative AI model. The generative model automatically generates sales promotion materials based on this data. A template engine is used to determine the layout and design.

[0234] Step 4:

[0235] The generated promotional materials are evaluated by a creative checking system on the server. This evaluation includes design consistency and compliance with brand guidelines.

[0236] Step 5:

[0237] Promotional materials that pass the check are sent from the server to the terminal. The user reviews these promotional materials on the terminal and makes minor adjustments to the text and layout as needed.

[0238] Step 6:

[0239] Once the user has reviewed and made corrections, the device prepares to save or print the completed promotional material in the selected format, which includes PDF and high-resolution image formats.

[0240] (Example 1)

[0241] 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."

[0242] Traditionally, creating advertising materials required specialized knowledge and skills, and was time-consuming and laborious. Furthermore, ensuring compliance with brand standards and legal requirements was often complex, potentially compromising the quality and consistency of the advertising materials. Additionally, the lack of user-friendly controls for fine-tuning designs and final output made it difficult to efficiently generate advertising materials.

[0243] 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.

[0244] In this invention, the server includes means for receiving image data and information data and automatically generating advertising materials using a generative model; evaluation means for verifying the content of the generated advertising materials and determining whether adjustments are necessary based on pre-set criteria; and means for presenting the advertising materials to the user and reflecting fine-tuning based on the user's requests. This enables users to quickly create efficient, consistent, and high-quality advertising materials without requiring specialized knowledge.

[0245] "Image data" refers to digital data containing visual information used in creating advertising materials.

[0246] "Information data" refers to digital data that includes details such as product information and text related to advertising materials.

[0247] A "generative model" refers to an algorithm or system that automatically generates advertising materials based on input data.

[0248] "Advertising materials" are visual or text-based content intended to promote the sale of products or services.

[0249] "Evaluation methods" refer to a system for checking the quality and compliance with standards of generated advertising materials and determining whether or not there are any problems.

[0250] "User" refers to the entity that creates, reviews, and adjusts advertising materials through the system.

[0251] This invention is a system for users to efficiently and effectively generate high-quality advertising materials via their devices. Specific embodiments are described below.

[0252] First, the user uses the device's interface to input image and informational data for the product being advertised. This interface is designed to allow users to easily and intuitively provide the necessary data. After input, the device sends this data to the server.

[0253] The server creates and sends prompts to the generative AI model based on the received data. These generative AI models include, for example, natural language processing models and image generation algorithms. This integrates design templates with user input to automatically generate advertising materials. This process transforms data using specific algorithms to produce unique outputs. Often, the generative AI model used is a modern model trained on a wide range of datasets.

[0254] The generated advertising materials are then verified by an evaluation system on the server. This process verifies that the advertising materials comply with the brand's guidelines and legal standards. Advertising materials that pass the evaluation are then sent back to the terminal.

[0255] Users review the generated advertising materials using their devices and make adjustments as needed. These adjustments include changing font sizes and adjusting colors. Once the user has completed their final adjustments, the device saves and outputs the advertising materials in digital formats such as PDF and JPEG. At this stage, the advertising materials are easily formatted for digital distribution and printing.

[0256] As a concrete example, when a user creates advertising materials for a new beverage, they input a photo of the product and information such as "new flavor" and "refreshing effect" into the terminal's interface. An example of a prompt sentence that would be input to the generation AI model is, "Create an advertisement for a new beverage highlighting its refreshing effects and unique flavor." In this example, advertising materials that accurately reflect the user's intent are quickly generated.

[0257] This system enables the seamless and efficient generation of advertising materials without relying on the specialized design skills that were previously required.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The user inputs image and informational data about the product using the terminal's interface. This includes product photos and text data describing the product's features. The input data is formatted internally within the terminal and prepared for transmission to the server.

[0261] Step 2:

[0262] The terminal transmits image and information data received from the user to the server. During this process, the data is encrypted and securely sent to the server. The server stores product information and image data, and prepares it for the next stage of processing.

[0263] Step 3:

[0264] The server creates and inputs prompts to the generation AI model based on the received data. Specifically, it analyzes the input data and automatically generates prompt sentences to produce appropriate advertising materials. For example, a prompt such as "Create an advertisement for a new beverage highlighting its refreshing effects and unique flavor." is generated and input to the AI ​​model. The generation model uses this prompt to automatically generate advertising materials. The output is the generated advertising materials.

[0265] Step 4:

[0266] The server validates the generated advertising materials. This evaluation process assesses whether the materials comply with brand standards and legal requirements. Specifically, it analyzes image content and checks text to confirm compliance. Once the evaluation is complete, compliant materials proceed to the next step. The output is the advertising materials that have passed the validation process.

[0267] Step 5:

[0268] The terminal displays verified advertising materials sent from the server to the user. The user can review the materials and make minor adjustments to font size and color balance as needed. Specifically, the user intuitively makes visual adjustments using the operation menu on the terminal. Once adjustments are complete, the revised materials are prepared for saving.

[0269] Step 6:

[0270] The terminal allows users to save and print advertising materials in digital format after making final adjustments. Output formats include PDF and JPEG, and the materials are saved for distribution or printing depending on the user's selection. The final advertising materials are provided as output in the required format.

[0271] (Application Example 1)

[0272] 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."

[0273] In traditional sales promotion material creation, the time and effort required for design creation, review, and adjustment was a significant problem. In particular, efficiently preparing materials applicable to various media required advanced expertise and skills, which was a burden for many users. Therefore, there is a need for a system that allows even non-experts to easily create high-quality sales promotion materials and output them quickly.

[0274] 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.

[0275] In this invention, the server includes means for receiving image data and product information and automatically generating sales promotion materials using a generation model; verification means for evaluating the generated sales promotion materials and determining whether corrections are necessary based on pre-set criteria; and adjustment means for displaying the sales promotion materials deemed problem-free by the verification means on a terminal and enabling fine-tuning. As a result, users can efficiently and quickly generate high-quality sales promotion materials and provide them in their desired information media format without requiring specialized knowledge.

[0276] "Image data" refers to data stored in a digital format that includes visual information, and is used as a design element in sales promotion materials.

[0277] "Product information" refers to detailed data about the product being traded, providing specific explanations and characteristics in sales promotion materials.

[0278] A "generative model" is an algorithm or system that automatically generates output based on input data, and primarily uses AI technology.

[0279] "Sales promotion materials" refer to digital or printed advertising and promotional materials created to promote the sale of specific products or services.

[0280] A "verification tool" is a device that automatically checks whether the generated information or data conforms to the set standards and prompts for corrections as necessary.

[0281] "Adjustment tools" are features that allow users to fine-tune the promotional materials they generate, particularly assisting with modifications to design elements and content.

[0282] "Information media format" refers to a method for expressing information in digital or physical form, including different formats such as PDF and image files.

[0283] This invention is a system that enables users to input product information and image data via a dedicated application and have it processed by a server to efficiently generate high-quality sales promotion materials.

[0284] First, the user uses an application on their smartphone to input product information and related image data. These data are transmitted to the server via an internet connection. The front end of the application used at this time adopts cross-platform technologies such as React Native, taking into account that users can seamlessly manipulate the data.

[0285] On the server side, a backend platform using Node.js and Express is running. It receives the data sent by the user and performs data integrity checks. At this time, a function to verify whether there are any deficiencies or inconsistencies in the input data is in operation. Once the integrity is confirmed, the server utilizes OpenAI's GPT-4 model to automatically generate sales promotion materials combined with design templates. The generative AI model creates materials according to predefined criteria and determines whether the design consistency and content are appropriate.

[0286] The generated materials are verified by verification means to ensure compliance with brand guidelines and legal standards. In this case, image processing technologies using a Python environment and the OpenCV library are utilized to check visual consistency. After verification, the materials are returned to the user's terminal, and the user makes fine adjustments to the displayed materials.

[0287] For example, the owner of a small handmade accessories brand might create an advertisement to promote an event. Users simply take product photos within the application and enter the event date and location, and a professional advertisement is instantly generated. This material can be easily shared on social media, enabling direct promotion.

[0288] Furthermore, an example of a prompt message for a generative AI model is as follows:

[0289] Product Information: Handmade square pendant, silver, special event price ¥2980

[0290] Image: event_banner.jpg

[0291] Concept: Weekend pop-up shop event

[0292] Template style: Elegant and modern.

[0293] Based on this prompt, a visually appealing advertising design is generated.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user launches the application on their smartphone and enters product information and image data. The entered information is structured in JSON format and sent from the device to the server. When sending data, an input form is created using React Native, providing an interface that prioritizes user experience.

[0297] Step 2:

[0298] The server analyzes the received data in a Node.js environment to verify product details and image data. First, it checks for inconsistencies. This verification includes checking data format and any missing required information. If there are no problems with the data, it proceeds to the next step. If inconsistencies are found, an error message is generated and resent to the terminal.

[0299] Step 3:

[0300] The server generates prompt messages for the AI ​​model based on data whose integrity has been verified. This AI model uses OpenAI's GPT-4 and creates prompt messages by combining product information and images into a template. The prompt messages, which include instructions for design generation, are sent from the server to the AI ​​model.

[0301] Step 4:

[0302] The generation AI model automatically generates sales promotion materials based on the received prompt text. In this process, the AI ​​utilizes design templates to create visually consistent materials. The generated materials are returned to the server in digital format (JPEG or PDF).

[0303] Step 5:

[0304] The server evaluates the generated promotional materials through verification mechanisms. Using Python and OpenCV libraries, it checks whether the materials conform to pre-configured criteria (brand guidelines, legal requirements). Only materials that pass this evaluation proceed to the next step.

[0305] Step 6:

[0306] The verified documents are sent to the device and displayed to the user. The user can review the displayed documents and make adjustments as needed. Adjustments include text corrections and changes to fonts and colors.

[0307] Step 7:

[0308] When the user completes the final confirmation, the terminal saves the material in the selected format and also provides a direct sharing function to SNS. The saving is performed using the file management system within the application.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0310] The present invention combines an emotion engine with a system for automatically generating sales promotion materials. In this system, first, the user inputs product information and image data through a terminal. The terminal converts this data and transmits it to a server, and the server creates sales promotion materials using a generation model. The generated sales promotion materials are evaluated by a creative check process and sent to the user if judged to be appropriate.

[0311] Furthermore, an emotion engine is incorporated into this system, and it is possible to recognize the user's emotion. The user's emotion is obtained from expressions and voices using a camera and a microphone on the terminal. The server analyzes the user's emotion data by making full use of the emotion engine and adjusts design elements according to the emotion. For example, if the user is judged to be surprised, the color and font style of the sales promotion material may be changed to provide a more dynamic design.

[0312] The final sales promotion material is returned to the terminal and displayed to the user. The user checks the design proposed by the adjustment of the emotion engine and re-evaluates how much the emotion-based changes have enhanced the satisfaction. Through this process, it becomes possible to provide sales promotion materials that conform to the user's mood, and more effective communication with customers is realized. The entire system features a user-friendly interface and has a design that can be used intuitively.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] Users input product information and images they want to include in promotional materials via their device. The device interface includes text boxes and image upload functions, allowing users to select and input the necessary information.

[0316] Step 2:

[0317] The terminal sends the data entered by the user to the server. The data is converted to JSON format and transmitted using a secure communication protocol.

[0318] Step 3:

[0319] The server passes the received data to a generative model, which automatically generates promotional materials. This generative model utilizes design trends and templates to select the optimal layout and design based on the input data.

[0320] Step 4:

[0321] The generated promotional materials are evaluated by a creative check function on the server. Evaluation criteria include consistency, legal requirements, and visual effectiveness, and based on these, it is determined whether the promotional materials need to be modified.

[0322] Step 5:

[0323] Promotional materials that pass the creative check are sent from the server to the terminal. The terminal displays them to the user, who then reviews the design.

[0324] Step 6:

[0325] The device captures the user's facial expressions and voice using its camera and microphone, and sends the emotional data to a server. This data is processed in real time.

[0326] Step 7:

[0327] The server uses an emotion engine to recognize the user's emotions. For example, if it detects that the user is happy, it will generate suggestions to add messages or images with brighter colors.

[0328] Step 8:

[0329] Users can review changes to design elements based on sentiment data and make desired modifications via their device if additional input or fine-tuning is required.

[0330] Step 9:

[0331] The finalized promotional materials can be saved or printed in PDF or image format depending on the device, resulting in the final output.

[0332] (Example 2)

[0333] 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".

[0334] In the automated generation of marketing materials, there is a need for a system that can adjust the design to take user emotions into consideration. Conventional systems have struggled to automatically generate designs that reflect user emotions, resulting in the creation of materials with low appeal. Furthermore, checking whether the generated materials meet the standards often relies on manual processes, making efficient evaluation difficult.

[0335] 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.

[0336] In this invention, the server includes means for receiving digital images and automatically generating marketing materials using a generation algorithm; means for evaluating the content of the generated materials and determining whether modifications are necessary based on pre-set criteria; and means for analyzing user emotional data and adjusting design elements according to those emotions. This makes it possible to efficiently and automatically generate attractive marketing materials that take user emotions into consideration.

[0337] A "digital image" is visual information represented in a format that can be processed by a computer system.

[0338] A "generative algorithm" is a set of procedures or methods for generating a desired output based on specific input data.

[0339] A "marketing medium" is a document containing information and content intended for the promotion of a product.

[0340] A "verification mechanism" is a function that checks and determines whether the generated content conforms to predetermined standards.

[0341] "Emotional data" refers to data that shows information about a user's emotions and psychological state.

[0342] A "design element" is a visual or sensory component that forms part of the overall design.

[0343] This invention is a system that enables the automatic generation of marketing materials using digital information. A specific embodiment of this system is described below.

[0344] The user first inputs product-related information using a terminal. This includes the product name, features, and image data. The terminal converts this input data into an appropriate digital format (e.g., JSON) and sends it to the server via a communication protocol. The terminal is a standard digital input device, and the UI is designed using HTML, CSS, and JavaScript.

[0345] The server first stores the received data in a database and performs data analysis to process it. The server uses a common neural network model as a generative AI model. This enables the automatic generation of promotional materials. For example, it applies a common generative algorithm as a generative model to integrate text generation and design creation based on the input information.

[0346] The server further analyzes facial and voice data obtained from the device's camera and microphone to acquire user emotional data. This analysis utilizes speech recognition and image analysis technologies. Based on this emotional data, the server dynamically adjusts design elements. For example, if the user is showing surprise, the colors and font styles are changed more dynamically.

[0347] The final generated marketing materials are sent from the server to the terminal and displayed to the user. The user can review these materials and request further refinements. This feedback loop allows the system to provide promotional materials that better suit the user's needs.

[0348] For example, if a user wants to create promotional material for a new pair of sports shoes, they would input information such as "Product name: Speed ​​Runner, Features: Lightweight, breathable, waterproof" into the terminal. If, in addition to this input data, emotional data indicating the user is smiling is obtained, the server will use a generative model to automatically generate an engaging and lively advertisement.

[0349] Example of a prompt:

[0350] "Please generate compelling ad copy based on the following information: Product: Speed ​​Runner, Features: Lightweight, breathable, waterproof. The user is smiling."

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The user inputs product information (e.g., product name, features, image data) via the terminal. The terminal converts this input data into JSON format and prepares it for transmission to the server. Here, an HTML form and JavaScript are used to accept data input, and the JSON.stringify() method is used in the conversion process. The input is product information, and the output is formatted data.

[0354] Step 2:

[0355] The terminal sends the converted data to the server using a secure protocol (e.g., HTTPS). This communication utilizes an appropriate API endpoint and an HTTP client library such as axios. The input is in JSON format, and the output is the success status of the transmission to the server.

[0356] Step 3:

[0357] The server stores the data received from the terminal in a database and prepares it for analysis. The server parses the received JSON data (e.g., JSON.parse()) and stores it in the database (e.g., MySQL) using SQL queries. The input is a JSON data stream, and the output is the stored data entry.

[0358] Step 4:

[0359] The server automatically generates promotional materials using a generative AI model. The server uses received data as a prompt to input into the generative AI model (e.g., a neural network) and obtains the generation result. During this process, model inference is performed, and the generated text and design proposals are output. The input is a prompt sentence, and the output is a proposal for promotional materials.

[0360] Step 5:

[0361] The server evaluates the generated media based on pre-defined criteria. This includes quality checks by AI or human reviewers. It compares the generated results to the criteria and determines whether corrections are needed. The input is the generated material, and the output is the evaluation result.

[0362] Step 6:

[0363] User emotional data is acquired through the device's camera and microphone. The device prepares to transmit the user's facial expressions and voice data to the server in real time. JPEG (image) and WAV (audio) file formats are used. Input is the user's real-time media, and output is media data ready for transmission.

[0364] Step 7:

[0365] The server uses an emotion engine to analyze the user's emotional data and adjust the design elements of the promotional materials. The server uses the analysis results to change, for example, the colors and font styles according to the emotion. An emotion recognition API is used in this processing. The input is the user's emotional data, and the output is the adjusted design elements.

[0366] Step 8:

[0367] The final marketing materials are sent from the server to the terminal and presented to the user. The terminal displays the received data to the user using a GUI. The user can review this display and provide feedback for further adjustments. The input is the adjusted promotional material, and the output is the user's feedback.

[0368] (Application Example 2)

[0369] 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."

[0370] In recent years, there has been a growing demand for personalized promotions based on the emotions of target customers in sales promotion activities, but this is difficult to achieve with current methods. In particular, design adjustments that take user emotions into consideration are inefficient because they are done manually, and there are problems with efficiently incorporating feedback. As a result, an adequate approach to improving customer satisfaction has not been achieved.

[0371] 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.

[0372] In this invention, the server includes means for receiving image information and automatically generating sales promotion materials using a generation artificial intelligence model; means for evaluating the content of the generated sales promotion materials using evaluation means and determining whether modifications are necessary based on pre-set criteria; and means for acquiring the user's emotions using emotion recognition means and adjusting design elements based on the acquired emotions. This enables efficient design adjustments that take into account the user's emotions and incorporates their feedback.

[0373] "Image information" refers to data that represents the visual characteristics of a product or service, and forms the basis of promotional materials.

[0374] A "generative artificial intelligence model" is a technology that generates optimal promotional materials based on diverse information, and is a form of artificial intelligence that utilizes machine learning algorithms.

[0375] "Sales promotion materials" are visual or auditory content used to effectively appeal to customers about products or services.

[0376] "Evaluation method" refers to a process or system for determining whether the generated promotional materials are appropriately designed, based on predetermined criteria.

[0377] "Emotion recognition means" refers to technologies that acquire emotional information from a user's facial expressions and voice, analyze it, and then understand the user's feelings.

[0378] "Design elements" refer to the visual components of sales promotion materials, such as colors, fonts, and layouts, which should be adjusted according to the user's emotions.

[0379] The system for realizing this invention begins with a user-owned terminal. The user inputs image information and product information on the terminal, and this data is sent to a server. The server uses a generative artificial intelligence model to automatically generate sales promotion materials from the input image information. In this process, the server analyzes and generates data, and as a result outputs visual or auditory promotional materials.

[0380] Next, the server analyzes the generated promotional materials using an evaluation tool to determine whether they are properly designed according to pre-set criteria. Materials deemed acceptable are then output to the user.

[0381] Furthermore, emotion recognition technology is used to capture the user's facial expressions and voice via the device's camera and microphone. This emotion information is sent to a server, which analyzes the user's emotions based on this data. Based on the analyzed emotions, the server adjusts the design elements of the promotional materials to provide the user with more personalized content.

[0382] Through the above process, the generated promotional materials are fed back to users, and further optimization is performed based on that feedback. Implementation requires a smartphone as a terminal and backend processing by a server. The server will need to implement emotion analysis using Azure's Emotion API and a generative AI model such as GPT-4.

[0383] As a concrete example, when promoting new sports shoes, the user inputs product information along with data of their smiling expression. The server analyzes the user's emotions and generates and presents a lively design and slogan. An example of a prompt to the generating AI model might be a request such as, "Please propose an advertising design for new sports shoes. The user is currently showing a smiling emotion. The design should incorporate elements that give a fresh and energetic impression."

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] Users use their smartphones to input image and product information to be used in promotions. This data forms the basis for generating sales promotion materials and is sent from the device to the server once input is complete. Input includes image files and text information, and output is data converted into a format that the server can prepare for reception.

[0387] Step 2:

[0388] The server analyzes the received image information and automatically generates sales promotion materials using a generative artificial intelligence model. Specifically, based on the input images and text information, the AI ​​generates designs while considering the product's characteristics. In terms of data processing, the generative AI model outputs promotional materials that combine the most suitable design elements from each input data.

[0389] Step 3:

[0390] The server evaluates the generated promotional materials using evaluation tools based on pre-defined criteria. The evaluation determines whether the generated design is appropriate and conforms to the criteria. The input is the generated promotional material, and the output is the appropriate promotional material that has passed verification. Specifically, it checks for inconsistencies with the criteria and evaluates whether the requirements are met.

[0391] Step 4:

[0392] The user uses their device's camera and microphone to input facial expressions and voice into the server. This emotional data is necessary to interpret the user's feelings, and the data acquired by the device is sent to the server. The input includes raw facial and voice data, and the output is this data in a format optimized for emotion recognition.

[0393] Step 5:

[0394] The server analyzes the user's emotional data through emotion recognition mechanisms. Specifically, it uses Azure's Emotion API to analyze facial expressions and vocal characteristics, quantifying the user's emotional state. This analysis is fed into a generative AI model, which adjusts design elements to match the user's mood. The output is the analyzed emotional data.

[0395] Step 6:

[0396] The server adjusts design elements based on emotional data to generate optimized promotional materials. The generating artificial intelligence model uses prompts to adjust the design, changing colors and font styles according to the emotional state. The generated promotional materials are output and provided to the user.

[0397] Step 7:

[0398] Finally, users review the promotional materials generated on their devices and provide feedback on the design. This user feedback is used for further design optimization and is considered on the server side to enhance the promotional effect. The input is the user's feedback, and the output is the improved promotional material incorporating that feedback.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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".

[0415] This invention is a system for users to quickly generate promotional materials via a terminal. First, the user inputs product information and related image data through the terminal's interface. This input data is sent from the terminal to a server, which uses this data to automatically create promotional materials using a generation AI model. The generation model combines design templates with the input data to instantly generate high-quality promotional materials.

[0416] The generated promotional materials are evaluated on the server using a creative checking system. Evaluation criteria include compliance with brand guidelines, legal requirements, and visual consistency. Promotional materials that pass the check are returned to the user's terminal for review and adjustments if necessary. This includes correcting text, adjusting font size, and adjusting color tones.

[0417] Once the user completes the final confirmation, the terminal outputs the promotional materials in a format suitable for saving, such as PDF or image. Throughout this process, the series of operations the user performs using the program are simple and intuitive. All processes are efficiently linked, providing a platform for quickly and effectively creating promotional materials. This establishes a system that significantly reduces traditional time and effort while easily ensuring quality.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] Users use their devices to input product information and image data, such as pictures, that they want to display on promotional materials. Input is done using text boxes and image upload functions.

[0421] Step 2:

[0422] The terminal converts the input data into the appropriate format and sends it to the server. A secure protocol is used for this transmission, ensuring data confidentiality.

[0423] Step 3:

[0424] The server processes the received data and passes it to the generative AI model. The generative model automatically generates sales promotion materials based on this data. A template engine is used to determine the layout and design.

[0425] Step 4:

[0426] The generated promotional materials are evaluated by a creative checking system on the server. This evaluation includes design consistency and compliance with brand guidelines.

[0427] Step 5:

[0428] Promotional materials that pass the check are sent from the server to the terminal. The user reviews these promotional materials on the terminal and makes minor adjustments to the text and layout as needed.

[0429] Step 6:

[0430] Once the user has reviewed and made corrections, the device prepares to save or print the completed promotional material in the selected format, which includes PDF and high-resolution image formats.

[0431] (Example 1)

[0432] 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."

[0433] Traditionally, creating advertising materials required specialized knowledge and skills, and was time-consuming and laborious. Furthermore, ensuring compliance with brand standards and legal requirements was often complex, potentially compromising the quality and consistency of the advertising materials. Additionally, the lack of user-friendly controls for fine-tuning designs and final output made it difficult to efficiently generate advertising materials.

[0434] 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.

[0435] In this invention, the server includes means for receiving image data and information data and automatically generating advertising materials using a generative model; evaluation means for verifying the content of the generated advertising materials and determining whether adjustments are necessary based on pre-set criteria; and means for presenting the advertising materials to the user and reflecting fine-tuning based on the user's requests. This enables users to quickly create efficient, consistent, and high-quality advertising materials without requiring specialized knowledge.

[0436] "Image data" refers to digital data containing visual information used in creating advertising materials.

[0437] "Information data" refers to digital data that includes details such as product information and text related to advertising materials.

[0438] A "generative model" refers to an algorithm or system that automatically generates advertising materials based on input data.

[0439] "Advertising materials" are visual or text-based content intended to promote the sale of products or services.

[0440] "Evaluation methods" refer to a system for checking the quality and compliance with standards of generated advertising materials and determining whether or not there are any problems.

[0441] "User" refers to the entity that creates, reviews, and adjusts advertising materials through the system.

[0442] This invention is a system for users to efficiently and effectively generate high-quality advertising materials via their devices. Specific embodiments are described below.

[0443] First, the user uses the device's interface to input image and informational data for the product being advertised. This interface is designed to allow users to easily and intuitively provide the necessary data. After input, the device sends this data to the server.

[0444] The server creates and sends prompts to the generative AI model based on the received data. These generative AI models include, for example, natural language processing models and image generation algorithms. This integrates design templates with user input to automatically generate advertising materials. This process transforms data using specific algorithms to produce unique outputs. Often, the generative AI model used is a modern model trained on a wide range of datasets.

[0445] The generated advertising materials are then verified by an evaluation system on the server. This process verifies that the advertising materials comply with the brand's guidelines and legal standards. Advertising materials that pass the evaluation are then sent back to the terminal.

[0446] Users review the generated advertising materials using their devices and make adjustments as needed. These adjustments include changing font sizes and adjusting colors. Once the user has completed their final adjustments, the device saves and outputs the advertising materials in digital formats such as PDF and JPEG. At this stage, the advertising materials are easily formatted for digital distribution and printing.

[0447] As a concrete example, when a user creates advertising materials for a new beverage, they input a photo of the product and information such as "new flavor" and "refreshing effect" into the terminal's interface. An example of a prompt sentence that would be input to the generation AI model is, "Create an advertisement for a new beverage highlighting its refreshing effects and unique flavor." In this example, advertising materials that accurately reflect the user's intent are quickly generated.

[0448] This system enables the seamless and efficient generation of advertising materials without relying on the specialized design skills that were previously required.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] The user inputs image and informational data about the product using the terminal's interface. This includes product photos and text data describing the product's features. The input data is formatted internally within the terminal and prepared for transmission to the server.

[0452] Step 2:

[0453] The terminal transmits image and information data received from the user to the server. During this process, the data is encrypted and securely sent to the server. The server stores product information and image data, and prepares it for the next stage of processing.

[0454] Step 3:

[0455] The server creates and inputs prompts to the generation AI model based on the received data. Specifically, it analyzes the input data and automatically generates prompt sentences to produce appropriate advertising materials. For example, a prompt such as "Create an advertisement for a new beverage highlighting its refreshing effects and unique flavor." is generated and input to the AI ​​model. The generation model uses this prompt to automatically generate advertising materials. The output is the generated advertising materials.

[0456] Step 4:

[0457] The server validates the generated advertising materials. This evaluation process assesses whether the materials comply with brand standards and legal requirements. Specifically, it analyzes image content and checks text to confirm compliance. Once the evaluation is complete, compliant materials proceed to the next step. The output is the advertising materials that have passed the validation process.

[0458] Step 5:

[0459] The terminal displays verified advertising materials sent from the server to the user. The user can review the materials and make minor adjustments to font size and color balance as needed. Specifically, the user intuitively makes visual adjustments using the operation menu on the terminal. Once adjustments are complete, the revised materials are prepared for saving.

[0460] Step 6:

[0461] The terminal allows users to save and print advertising materials in digital format after making final adjustments. Output formats include PDF and JPEG, and the materials are saved for distribution or printing depending on the user's selection. The final advertising materials are provided as output in the required format.

[0462] (Application Example 1)

[0463] 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."

[0464] In traditional sales promotion material creation, the time and effort required for design creation, review, and adjustment was a significant problem. In particular, efficiently preparing materials applicable to various media required advanced expertise and skills, which was a burden for many users. Therefore, there is a need for a system that allows even non-experts to easily create high-quality sales promotion materials and output them quickly.

[0465] 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.

[0466] In this invention, the server includes means for receiving image data and product information and automatically generating sales promotion materials using a generation model; verification means for evaluating the generated sales promotion materials and determining whether corrections are necessary based on pre-set criteria; and adjustment means for displaying the sales promotion materials deemed problem-free by the verification means on a terminal and enabling fine-tuning. As a result, users can efficiently and quickly generate high-quality sales promotion materials and provide them in their desired information media format without requiring specialized knowledge.

[0467] "Image data" refers to data stored in a digital format that includes visual information, and is used as a design element in sales promotion materials.

[0468] "Product information" refers to detailed data about the product being traded, providing specific explanations and characteristics in sales promotion materials.

[0469] A "generative model" is an algorithm or system that automatically generates output based on input data, and primarily uses AI technology.

[0470] "Sales promotion materials" refer to digital or printed advertising and promotional materials created to promote the sale of specific products or services.

[0471] A "verification tool" is a device that automatically checks whether the generated information or data conforms to the set standards and prompts for corrections as necessary.

[0472] "Adjustment tools" are features that allow users to fine-tune the promotional materials they generate, particularly assisting with modifications to design elements and content.

[0473] "Information media format" refers to a method of representing information in digital or physical form, and includes different formats such as PDFs and image files.

[0474] This invention is a system that efficiently generates high-quality sales promotion materials by allowing users to input product information and image data via a dedicated application, which are then processed on a server.

[0475] First, users input product information and related image data using an application on their smartphone. This data is then transmitted to a server via an internet connection. The application's front-end employs cross-platform technologies such as React Native to ensure seamless data manipulation for users.

[0476] On the server side, a backend platform using Node.js and Express is running, receiving data submitted by users and performing data integrity checks. At this time, a function is activated to verify that there are no missing or inconsistent input data. Once integrity is confirmed, the server uses OpenAI's GPT-4 model to automatically generate sales promotion materials combined with design templates. The generating AI model creates the materials according to predetermined criteria and judges whether the design is consistent and the content is appropriate.

[0477] The generated materials are verified to ensure they comply with brand guidelines and legal standards. This involves using image processing techniques with a Python environment and the OpenCV library to check for visual consistency. After verification, the materials are returned to the user's device, where the user can make minor adjustments to the displayed materials.

[0478] For example, the owner of a small handmade accessories brand might create an advertisement to promote an event. Users simply take product photos within the application and enter the event date and location, and a professional advertisement is instantly generated. This material can be easily shared on social media, enabling direct promotion.

[0479] Furthermore, an example of a prompt message for a generative AI model is as follows:

[0480] Product Information: Handmade square pendant, silver, special event price ¥2980

[0481] Image: event_banner.jpg

[0482] Concept: Weekend pop-up shop event

[0483] Template style: Elegant and modern.

[0484] Based on this prompt, a visually appealing advertising design is generated.

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The user launches the application on their smartphone and enters product information and image data. The entered information is structured in JSON format and sent from the device to the server. When sending data, an input form is created using React Native, providing an interface that prioritizes user experience.

[0488] Step 2:

[0489] The server analyzes the received data in a Node.js environment to verify product details and image data. First, it checks for inconsistencies. This verification includes checking data format and any missing required information. If there are no problems with the data, it proceeds to the next step. If inconsistencies are found, an error message is generated and resent to the terminal.

[0490] Step 3:

[0491] The server generates prompt messages for the AI ​​model based on data whose integrity has been verified. This AI model uses OpenAI's GPT-4 and creates prompt messages by combining product information and images into a template. The prompt messages, which include instructions for design generation, are sent from the server to the AI ​​model.

[0492] Step 4:

[0493] The generation AI model automatically generates sales promotion materials based on the received prompt text. In this process, the AI ​​utilizes design templates to create visually consistent materials. The generated materials are returned to the server in digital format (JPEG or PDF).

[0494] Step 5:

[0495] The server evaluates the generated promotional materials through verification mechanisms. Using Python and OpenCV libraries, it checks whether the materials conform to pre-configured criteria (brand guidelines, legal requirements). Only materials that pass this evaluation proceed to the next step.

[0496] Step 6:

[0497] The verified documents are sent to the device and displayed to the user. The user can review the displayed documents and make adjustments as needed. Adjustments include text corrections and changes to fonts and colors.

[0498] Step 7:

[0499] Once the user completes the final review, the device saves the document in the selected format and also provides a direct sharing function to social media. Saving is done using the application's file management system.

[0500] 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.

[0501] This invention combines an emotion engine with a system for automatically generating sales promotion materials. In this system, the user first inputs product information and image data through a terminal. The terminal converts this data and sends it to a server, which uses a generative model to create sales promotion materials. The generated sales promotion materials are evaluated through a creative check process and sent to the user if deemed appropriate.

[0502] Furthermore, this system incorporates an emotion engine that can recognize the user's emotions. The user's emotions are captured from their facial expressions and voice using the camera and microphone on the device. The server uses the emotion engine to analyze the user's emotional data and adjusts design elements according to those emotions. For example, if it is determined that the user is surprised, the system changes the colors and font styles of promotional materials to provide a more dynamic design.

[0503] The final promotional materials are returned to the terminal and displayed to the user. The user reviews the design suggested by the emotion engine and re-evaluates how much satisfaction the emotion-based changes have increased. This process enables the delivery of promotional materials that resonate with the user's emotions, resulting in more effective customer communication. The entire system features a user-friendly interface and is designed for intuitive use.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] Users input product information and images they want to include in promotional materials via their device. The device interface includes text boxes and image upload functions, allowing users to select and input the necessary information.

[0507] Step 2:

[0508] The terminal sends the data entered by the user to the server. The data is converted to JSON format and transmitted using a secure communication protocol.

[0509] Step 3:

[0510] The server passes the received data to a generative model, which automatically generates promotional materials. This generative model utilizes design trends and templates to select the optimal layout and design based on the input data.

[0511] Step 4:

[0512] The generated promotional materials are evaluated by a creative check function on the server. Evaluation criteria include consistency, legal requirements, and visual effectiveness, and based on these, it is determined whether the promotional materials need to be modified.

[0513] Step 5:

[0514] Promotional materials that pass the creative check are sent from the server to the terminal. The terminal displays them to the user, who then reviews the design.

[0515] Step 6:

[0516] The device captures the user's facial expressions and voice using its camera and microphone, and sends the emotional data to a server. This data is processed in real time.

[0517] Step 7:

[0518] The server uses an emotion engine to recognize the user's emotions. For example, if it detects that the user is happy, it will generate suggestions to add messages or images with brighter colors.

[0519] Step 8:

[0520] Users can review changes to design elements based on sentiment data and make desired modifications via their device if additional input or fine-tuning is required.

[0521] Step 9:

[0522] The finalized promotional materials can be saved or printed in PDF or image format depending on the device, resulting in the final output.

[0523] (Example 2)

[0524] 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."

[0525] In the automated generation of marketing materials, there is a need for a system that can adjust the design to take user emotions into consideration. Conventional systems have struggled to automatically generate designs that reflect user emotions, resulting in the creation of materials with low appeal. Furthermore, checking whether the generated materials meet the standards often relies on manual processes, making efficient evaluation difficult.

[0526] 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.

[0527] In this invention, the server includes means for receiving digital images and automatically generating marketing materials using a generation algorithm; means for evaluating the content of the generated materials and determining whether modifications are necessary based on pre-set criteria; and means for analyzing user emotional data and adjusting design elements according to those emotions. This makes it possible to efficiently and automatically generate attractive marketing materials that take user emotions into consideration.

[0528] A "digital image" is visual information represented in a format that can be processed by a computer system.

[0529] A "generative algorithm" is a set of procedures or methods for generating a desired output based on specific input data.

[0530] A "marketing medium" is a document containing information and content intended for the promotion of a product.

[0531] A "verification mechanism" is a function that checks and determines whether the generated content conforms to predetermined standards.

[0532] "Emotional data" refers to data that shows information about a user's emotions and psychological state.

[0533] A "design element" is a visual or sensory component that forms part of the overall design.

[0534] This invention is a system that enables the automatic generation of marketing materials using digital information. A specific embodiment of this system is described below.

[0535] The user first inputs product-related information using a terminal. This includes the product name, features, and image data. The terminal converts this input data into an appropriate digital format (e.g., JSON) and sends it to the server via a communication protocol. The terminal is a standard digital input device, and the UI is designed using HTML, CSS, and JavaScript.

[0536] The server first stores the received data in a database and performs data analysis to process it. The server uses a common neural network model as a generative AI model. This enables the automatic generation of promotional materials. For example, it applies a common generative algorithm as a generative model to integrate text generation and design creation based on the input information.

[0537] The server further analyzes facial and voice data obtained from the device's camera and microphone to acquire user emotional data. This analysis utilizes speech recognition and image analysis technologies. Based on this emotional data, the server dynamically adjusts design elements. For example, if the user is showing surprise, the colors and font styles are changed more dynamically.

[0538] The final generated marketing materials are sent from the server to the terminal and displayed to the user. The user can review these materials and request further refinements. This feedback loop allows the system to provide promotional materials that better suit the user's needs.

[0539] For example, if a user wants to create promotional material for a new pair of sports shoes, they would input information such as "Product name: Speed ​​Runner, Features: Lightweight, breathable, waterproof" into the terminal. If, in addition to this input data, emotional data indicating the user is smiling is obtained, the server will use a generative model to automatically generate an engaging and lively advertisement.

[0540] Example of a prompt:

[0541] "Please generate compelling ad copy based on the following information: Product: Speed ​​Runner, Features: Lightweight, breathable, waterproof. The user is smiling."

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The user inputs product information (e.g., product name, features, image data) via the terminal. The terminal converts this input data into JSON format and prepares it for transmission to the server. Here, an HTML form and JavaScript are used to accept data input, and the JSON.stringify() method is used in the conversion process. The input is product information, and the output is formatted data.

[0545] Step 2:

[0546] The terminal sends the converted data to the server using a secure protocol (e.g., HTTPS). This communication utilizes an appropriate API endpoint and an HTTP client library such as axios. The input is in JSON format, and the output is the success status of the transmission to the server.

[0547] Step 3:

[0548] The server stores the data received from the terminal in a database and prepares it for analysis. The server parses the received JSON data (e.g., JSON.parse()) and stores it in the database (e.g., MySQL) using SQL queries. The input is a JSON data stream, and the output is the stored data entry.

[0549] Step 4:

[0550] The server automatically generates promotional materials using a generative AI model. The server uses received data as a prompt to input into the generative AI model (e.g., a neural network) and obtains the generation result. During this process, model inference is performed, and the generated text and design proposals are output. The input is a prompt sentence, and the output is a proposal for promotional materials.

[0551] Step 5:

[0552] The server evaluates the generated media based on pre-defined criteria. This includes quality checks by AI or human reviewers. It compares the generated results to the criteria and determines whether corrections are needed. The input is the generated material, and the output is the evaluation result.

[0553] Step 6:

[0554] User emotional data is acquired through the device's camera and microphone. The device prepares to transmit the user's facial expressions and voice data to the server in real time. JPEG (image) and WAV (audio) file formats are used. Input is the user's real-time media, and output is media data ready for transmission.

[0555] Step 7:

[0556] The server uses an emotion engine to analyze the user's emotional data and adjust the design elements of the promotional materials. The server uses the analysis results to change, for example, the colors and font styles according to the emotion. An emotion recognition API is used in this processing. The input is the user's emotional data, and the output is the adjusted design elements.

[0557] Step 8:

[0558] The final marketing materials are sent from the server to the terminal and presented to the user. The terminal displays the received data to the user using a GUI. The user can review this display and provide feedback for further adjustments. The input is the adjusted promotional material, and the output is the user's feedback.

[0559] (Application Example 2)

[0560] 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."

[0561] In recent years, there has been a growing demand for personalized promotions based on the emotions of target customers in sales promotion activities, but this is difficult to achieve with current methods. In particular, design adjustments that take user emotions into consideration are inefficient because they are done manually, and there are problems with efficiently incorporating feedback. As a result, an adequate approach to improving customer satisfaction has not been achieved.

[0562] 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.

[0563] In this invention, the server includes means for receiving image information and automatically generating sales promotion materials using a generation artificial intelligence model; means for evaluating the content of the generated sales promotion materials using evaluation means and determining whether modifications are necessary based on pre-set criteria; and means for acquiring the user's emotions using emotion recognition means and adjusting design elements based on the acquired emotions. This enables efficient design adjustments that take into account the user's emotions and incorporates their feedback.

[0564] "Image information" refers to data that represents the visual characteristics of a product or service, and forms the basis of promotional materials.

[0565] A "generative artificial intelligence model" is a technology that generates optimal promotional materials based on diverse information, and is a form of artificial intelligence that utilizes machine learning algorithms.

[0566] "Sales promotion materials" are visual or auditory content used to effectively appeal to customers about products or services.

[0567] "Evaluation method" refers to a process or system for determining whether the generated promotional materials are appropriately designed, based on predetermined criteria.

[0568] "Emotion recognition means" refers to technologies that acquire emotional information from a user's facial expressions and voice, analyze it, and then understand the user's feelings.

[0569] "Design elements" refer to the visual components of sales promotion materials, such as colors, fonts, and layouts, which should be adjusted according to the user's emotions.

[0570] The system for realizing this invention begins with a user-owned terminal. The user inputs image information and product information on the terminal, and this data is sent to a server. The server uses a generative artificial intelligence model to automatically generate sales promotion materials from the input image information. In this process, the server analyzes and generates data, and as a result outputs visual or auditory promotional materials.

[0571] Next, the server analyzes the generated promotional materials using an evaluation tool to determine whether they are properly designed according to pre-set criteria. Materials deemed acceptable are then output to the user.

[0572] Furthermore, emotion recognition technology is used to capture the user's facial expressions and voice via the device's camera and microphone. This emotion information is sent to a server, which analyzes the user's emotions based on this data. Based on the analyzed emotions, the server adjusts the design elements of the promotional materials to provide the user with more personalized content.

[0573] Through the above process, the generated promotional materials are fed back to users, and further optimization is performed based on that feedback. Implementation requires a smartphone as a terminal and backend processing by a server. The server will need to implement emotion analysis using Azure's Emotion API and a generative AI model such as GPT-4.

[0574] As a concrete example, when promoting new sports shoes, the user inputs product information along with data of their smiling expression. The server analyzes the user's emotions and generates and presents a lively design and slogan. An example of a prompt to the generating AI model might be a request such as, "Please propose an advertising design for new sports shoes. The user is currently showing a smiling emotion. The design should incorporate elements that give a fresh and energetic impression."

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] Users use their smartphones to input image and product information to be used in promotions. This data forms the basis for generating sales promotion materials and is sent from the device to the server once input is complete. Input includes image files and text information, and output is data converted into a format that the server can prepare for reception.

[0578] Step 2:

[0579] The server analyzes the received image information and automatically generates sales promotion materials using a generative artificial intelligence model. Specifically, based on the input images and text information, the AI ​​generates designs while considering the product's characteristics. In terms of data processing, the generative AI model outputs promotional materials that combine the most suitable design elements from each input data.

[0580] Step 3:

[0581] The server evaluates the generated promotional materials using evaluation tools based on pre-defined criteria. The evaluation determines whether the generated design is appropriate and conforms to the criteria. The input is the generated promotional material, and the output is the appropriate promotional material that has passed verification. Specifically, it checks for inconsistencies with the criteria and evaluates whether the requirements are met.

[0582] Step 4:

[0583] The user uses their device's camera and microphone to input facial expressions and voice into the server. This emotional data is necessary to interpret the user's feelings, and the data acquired by the device is sent to the server. The input includes raw facial and voice data, and the output is this data in a format optimized for emotion recognition.

[0584] Step 5:

[0585] The server analyzes the user's emotional data through emotion recognition mechanisms. Specifically, it uses Azure's Emotion API to analyze facial expressions and vocal characteristics, quantifying the user's emotional state. This analysis is fed into a generative AI model, which adjusts design elements to match the user's mood. The output is the analyzed emotional data.

[0586] Step 6:

[0587] The server adjusts design elements based on emotional data to generate optimized promotional materials. The generating artificial intelligence model uses prompts to adjust the design, changing colors and font styles according to the emotional state. The generated promotional materials are output and provided to the user.

[0588] Step 7:

[0589] Finally, users review the promotional materials generated on their devices and provide feedback on the design. This user feedback is used for further design optimization and is considered on the server side to enhance the promotional effect. The input is the user's feedback, and the output is the improved promotional material incorporating that feedback.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] [Fourth Embodiment]

[0594] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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".

[0607] This invention is a system for users to quickly generate promotional materials via a terminal. First, the user inputs product information and related image data through the terminal's interface. This input data is sent from the terminal to a server, which uses this data to automatically create promotional materials using a generation AI model. The generation model combines design templates with the input data to instantly generate high-quality promotional materials.

[0608] The generated promotional materials are evaluated on the server using a creative checking system. Evaluation criteria include compliance with brand guidelines, legal requirements, and visual consistency. Promotional materials that pass the check are returned to the user's terminal for review and adjustments if necessary. This includes correcting text, adjusting font size, and adjusting color tones.

[0609] Once the user completes the final confirmation, the terminal outputs the promotional materials in a format suitable for saving, such as PDF or image. Throughout this process, the series of operations the user performs using the program are simple and intuitive. All processes are efficiently linked, providing a platform for quickly and effectively creating promotional materials. This establishes a system that significantly reduces traditional time and effort while easily ensuring quality.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] Users use their devices to input product information and image data, such as pictures, that they want to display on promotional materials. Input is done using text boxes and image upload functions.

[0613] Step 2:

[0614] The terminal converts the input data into the appropriate format and sends it to the server. A secure protocol is used for this transmission, ensuring data confidentiality.

[0615] Step 3:

[0616] The server processes the received data and passes it to the generative AI model. The generative model automatically generates sales promotion materials based on this data. A template engine is used to determine the layout and design.

[0617] Step 4:

[0618] The generated promotional materials are evaluated by a creative checking system on the server. This evaluation includes design consistency and compliance with brand guidelines.

[0619] Step 5:

[0620] Promotional materials that pass the check are sent from the server to the terminal. The user reviews these promotional materials on the terminal and makes minor adjustments to the text and layout as needed.

[0621] Step 6:

[0622] Once the user has reviewed and made corrections, the device prepares to save or print the completed promotional material in the selected format, which includes PDF and high-resolution image formats.

[0623] (Example 1)

[0624] 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".

[0625] Traditionally, creating advertising materials required specialized knowledge and skills, and was time-consuming and laborious. Furthermore, ensuring compliance with brand standards and legal requirements was often complex, potentially compromising the quality and consistency of the advertising materials. Additionally, the lack of user-friendly controls for fine-tuning designs and final output made it difficult to efficiently generate advertising materials.

[0626] 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.

[0627] In this invention, the server includes means for receiving image data and information data and automatically generating advertising materials using a generative model; evaluation means for verifying the content of the generated advertising materials and determining whether adjustments are necessary based on pre-set criteria; and means for presenting the advertising materials to the user and reflecting fine-tuning based on the user's requests. This enables users to quickly create efficient, consistent, and high-quality advertising materials without requiring specialized knowledge.

[0628] "Image data" refers to digital data containing visual information used in creating advertising materials.

[0629] "Information data" refers to digital data that includes details such as product information and text related to advertising materials.

[0630] A "generative model" refers to an algorithm or system that automatically generates advertising materials based on input data.

[0631] "Advertising materials" are visual or text-based content intended to promote the sale of products or services.

[0632] "Evaluation methods" refer to a system for checking the quality and compliance with standards of generated advertising materials and determining whether or not there are any problems.

[0633] "User" refers to the entity that creates, reviews, and adjusts advertising materials through the system.

[0634] This invention is a system for users to efficiently and effectively generate high-quality advertising materials via their devices. Specific embodiments are described below.

[0635] First, the user uses the device's interface to input image and informational data for the product being advertised. This interface is designed to allow users to easily and intuitively provide the necessary data. After input, the device sends this data to the server.

[0636] The server creates and sends prompts to the generative AI model based on the received data. These generative AI models include, for example, natural language processing models and image generation algorithms. This integrates design templates with user input to automatically generate advertising materials. This process transforms data using specific algorithms to produce unique outputs. Often, the generative AI model used is a modern model trained on a wide range of datasets.

[0637] The generated advertising materials are then verified by an evaluation system on the server. This process verifies that the advertising materials comply with the brand's guidelines and legal standards. Advertising materials that pass the evaluation are then sent back to the terminal.

[0638] Users review the generated advertising materials using their devices and make adjustments as needed. These adjustments include changing font sizes and adjusting colors. Once the user has completed their final adjustments, the device saves and outputs the advertising materials in digital formats such as PDF and JPEG. At this stage, the advertising materials are easily formatted for digital distribution and printing.

[0639] As a concrete example, when a user creates advertising materials for a new beverage, they input a photo of the product and information such as "new flavor" and "refreshing effect" into the terminal's interface. An example of a prompt sentence that would be input to the generation AI model is, "Create an advertisement for a new beverage highlighting its refreshing effects and unique flavor." In this example, advertising materials that accurately reflect the user's intent are quickly generated.

[0640] This system enables the seamless and efficient generation of advertising materials without relying on the specialized design skills that were previously required.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The user inputs image and informational data about the product using the terminal's interface. This includes product photos and text data describing the product's features. The input data is formatted internally within the terminal and prepared for transmission to the server.

[0644] Step 2:

[0645] The terminal transmits image and information data received from the user to the server. During this process, the data is encrypted and securely sent to the server. The server stores product information and image data, and prepares it for the next stage of processing.

[0646] Step 3:

[0647] The server creates and inputs prompts to the generation AI model based on the received data. Specifically, it analyzes the input data and automatically generates prompt sentences to produce appropriate advertising materials. For example, a prompt such as "Create an advertisement for a new beverage highlighting its refreshing effects and unique flavor." is generated and input to the AI ​​model. The generation model uses this prompt to automatically generate advertising materials. The output is the generated advertising materials.

[0648] Step 4:

[0649] The server validates the generated advertising materials. This evaluation process assesses whether the materials comply with brand standards and legal requirements. Specifically, it analyzes image content and checks text to confirm compliance. Once the evaluation is complete, compliant materials proceed to the next step. The output is the advertising materials that have passed the validation process.

[0650] Step 5:

[0651] The terminal displays verified advertising materials sent from the server to the user. The user can review the materials and make minor adjustments to font size and color balance as needed. Specifically, the user intuitively makes visual adjustments using the operation menu on the terminal. Once adjustments are complete, the revised materials are prepared for saving.

[0652] Step 6:

[0653] The terminal allows users to save and print advertising materials in digital format after making final adjustments. Output formats include PDF and JPEG, and the materials are saved for distribution or printing depending on the user's selection. The final advertising materials are provided as output in the required format.

[0654] (Application Example 1)

[0655] 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".

[0656] In traditional sales promotion material creation, the time and effort required for design creation, review, and adjustment was a significant problem. In particular, efficiently preparing materials applicable to various media required advanced expertise and skills, which was a burden for many users. Therefore, there is a need for a system that allows even non-experts to easily create high-quality sales promotion materials and output them quickly.

[0657] 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.

[0658] In this invention, the server includes means for receiving image data and product information and automatically generating sales promotion materials using a generation model; verification means for evaluating the generated sales promotion materials and determining whether corrections are necessary based on pre-set criteria; and adjustment means for displaying the sales promotion materials deemed problem-free by the verification means on a terminal and enabling fine-tuning. As a result, users can efficiently and quickly generate high-quality sales promotion materials and provide them in their desired information media format without requiring specialized knowledge.

[0659] "Image data" refers to data stored in a digital format that includes visual information, and is used as a design element in sales promotion materials.

[0660] "Product information" refers to detailed data about the product being traded, providing specific explanations and characteristics in sales promotion materials.

[0661] A "generative model" is an algorithm or system that automatically generates output based on input data, and primarily uses AI technology.

[0662] "Sales promotion materials" refer to digital or printed advertising and promotional materials created to promote the sale of specific products or services.

[0663] A "verification tool" is a device that automatically checks whether the generated information or data conforms to the set standards and prompts for corrections as necessary.

[0664] "Adjustment tools" are features that allow users to fine-tune the promotional materials they generate, particularly assisting with modifications to design elements and content.

[0665] "Information media format" refers to a method of representing information in digital or physical form, and includes different formats such as PDFs and image files.

[0666] This invention is a system that efficiently generates high-quality sales promotion materials by allowing users to input product information and image data via a dedicated application, which are then processed on a server.

[0667] First, users input product information and related image data using an application on their smartphone. This data is then transmitted to a server via an internet connection. The application's front-end employs cross-platform technologies such as React Native to ensure seamless data manipulation for users.

[0668] On the server side, a backend platform using Node.js and Express is running, receiving data submitted by users and performing data integrity checks. At this time, a function is activated to verify that there are no missing or inconsistent input data. Once integrity is confirmed, the server uses OpenAI's GPT-4 model to automatically generate sales promotion materials combined with design templates. The generating AI model creates the materials according to predetermined criteria and judges whether the design is consistent and the content is appropriate.

[0669] The generated materials are verified to ensure they comply with brand guidelines and legal standards. This involves using image processing techniques with a Python environment and the OpenCV library to check for visual consistency. After verification, the materials are returned to the user's device, where the user can make minor adjustments to the displayed materials.

[0670] For example, the owner of a small handmade accessories brand might create an advertisement to promote an event. Users simply take product photos within the application and enter the event date and location, and a professional advertisement is instantly generated. This material can be easily shared on social media, enabling direct promotion.

[0671] Furthermore, an example of a prompt message for a generative AI model is as follows:

[0672] Product Information: Handmade square pendant, silver, special event price ¥2980

[0673] Image: event_banner.jpg

[0674] Concept: Weekend pop-up shop event

[0675] Template style: Elegant and modern.

[0676] Based on this prompt, a visually appealing advertising design is generated.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The user launches the application on their smartphone and enters product information and image data. The entered information is structured in JSON format and sent from the device to the server. When sending data, an input form is created using React Native, providing an interface that prioritizes user experience.

[0680] Step 2:

[0681] The server analyzes the received data in a Node.js environment to verify product details and image data. First, it checks for inconsistencies. This verification includes checking data format and any missing required information. If there are no problems with the data, it proceeds to the next step. If inconsistencies are found, an error message is generated and resent to the terminal.

[0682] Step 3:

[0683] The server generates prompt messages for the AI ​​model based on data whose integrity has been verified. This AI model uses OpenAI's GPT-4 and creates prompt messages by combining product information and images into a template. The prompt messages, which include instructions for design generation, are sent from the server to the AI ​​model.

[0684] Step 4:

[0685] The generation AI model automatically generates sales promotion materials based on the received prompt text. In this process, the AI ​​utilizes design templates to create visually consistent materials. The generated materials are returned to the server in digital format (JPEG or PDF).

[0686] Step 5:

[0687] The server evaluates the generated promotional materials through verification mechanisms. Using Python and OpenCV libraries, it checks whether the materials conform to pre-configured criteria (brand guidelines, legal requirements). Only materials that pass this evaluation proceed to the next step.

[0688] Step 6:

[0689] The verified documents are sent to the device and displayed to the user. The user can review the displayed documents and make adjustments as needed. Adjustments include text corrections and changes to fonts and colors.

[0690] Step 7:

[0691] Once the user completes the final review, the device saves the document in the selected format and also provides a direct sharing function to social media. Saving is done using the application's file management system.

[0692] 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.

[0693] This invention combines an emotion engine with a system for automatically generating sales promotion materials. In this system, the user first inputs product information and image data through a terminal. The terminal converts this data and sends it to a server, which uses a generative model to create sales promotion materials. The generated sales promotion materials are evaluated through a creative check process and sent to the user if deemed appropriate.

[0694] Furthermore, this system incorporates an emotion engine that can recognize the user's emotions. The user's emotions are captured from their facial expressions and voice using the camera and microphone on the device. The server uses the emotion engine to analyze the user's emotional data and adjusts design elements according to those emotions. For example, if it is determined that the user is surprised, the system changes the colors and font styles of promotional materials to provide a more dynamic design.

[0695] The final promotional materials are returned to the terminal and displayed to the user. The user reviews the design suggested by the emotion engine and re-evaluates how much satisfaction the emotion-based changes have increased. This process enables the delivery of promotional materials that resonate with the user's emotions, resulting in more effective customer communication. The entire system features a user-friendly interface and is designed for intuitive use.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] Users input product information and images they want to include in promotional materials via their device. The device interface includes text boxes and image upload functions, allowing users to select and input the necessary information.

[0699] Step 2:

[0700] The terminal sends the data entered by the user to the server. The data is converted to JSON format and transmitted using a secure communication protocol.

[0701] Step 3:

[0702] The server passes the received data to a generative model, which automatically generates promotional materials. This generative model utilizes design trends and templates to select the optimal layout and design based on the input data.

[0703] Step 4:

[0704] The generated promotional materials are evaluated by a creative check function on the server. Evaluation criteria include consistency, legal requirements, and visual effectiveness, and based on these, it is determined whether the promotional materials need to be modified.

[0705] Step 5:

[0706] Promotional materials that pass the creative check are sent from the server to the terminal. The terminal displays them to the user, who then reviews the design.

[0707] Step 6:

[0708] The device captures the user's facial expressions and voice using its camera and microphone, and sends the emotional data to a server. This data is processed in real time.

[0709] Step 7:

[0710] The server uses an emotion engine to recognize the user's emotions. For example, if it detects that the user is happy, it will generate suggestions to add messages or images with brighter colors.

[0711] Step 8:

[0712] Users can review changes to design elements based on sentiment data and make desired modifications via their device if additional input or fine-tuning is required.

[0713] Step 9:

[0714] The finalized promotional materials can be saved or printed in PDF or image format depending on the device, resulting in the final output.

[0715] (Example 2)

[0716] 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".

[0717] In the automated generation of marketing materials, there is a need for a system that can adjust the design to take user emotions into consideration. Conventional systems have struggled to automatically generate designs that reflect user emotions, resulting in the creation of materials with low appeal. Furthermore, checking whether the generated materials meet the standards often relies on manual processes, making efficient evaluation difficult.

[0718] 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.

[0719] In this invention, the server includes means for receiving digital images and automatically generating marketing materials using a generation algorithm; means for evaluating the content of the generated materials and determining whether modifications are necessary based on pre-set criteria; and means for analyzing user emotional data and adjusting design elements according to those emotions. This makes it possible to efficiently and automatically generate attractive marketing materials that take user emotions into consideration.

[0720] A "digital image" is visual information represented in a format that can be processed by a computer system.

[0721] A "generative algorithm" is a set of procedures or methods for generating a desired output based on specific input data.

[0722] A "marketing medium" is a document containing information and content intended for the promotion of a product.

[0723] A "verification mechanism" is a function that checks and determines whether the generated content conforms to predetermined standards.

[0724] "Emotional data" refers to data that shows information about a user's emotions and psychological state.

[0725] A "design element" is a visual or sensory component that forms part of the overall design.

[0726] This invention is a system that enables the automatic generation of marketing materials using digital information. A specific embodiment of this system is described below.

[0727] The user first inputs product-related information using a terminal. This includes the product name, features, and image data. The terminal converts this input data into an appropriate digital format (e.g., JSON) and sends it to the server via a communication protocol. The terminal is a standard digital input device, and the UI is designed using HTML, CSS, and JavaScript.

[0728] The server first stores the received data in a database and performs data analysis to process it. The server uses a common neural network model as a generative AI model. This enables the automatic generation of promotional materials. For example, it applies a common generative algorithm as a generative model to integrate text generation and design creation based on the input information.

[0729] The server further analyzes facial and voice data obtained from the device's camera and microphone to acquire user emotional data. This analysis utilizes speech recognition and image analysis technologies. Based on this emotional data, the server dynamically adjusts design elements. For example, if the user is showing surprise, the colors and font styles are changed more dynamically.

[0730] The final generated marketing materials are sent from the server to the terminal and displayed to the user. The user can review these materials and request further refinements. This feedback loop allows the system to provide promotional materials that better suit the user's needs.

[0731] For example, if a user wants to create promotional material for a new pair of sports shoes, they would input information such as "Product name: Speed ​​Runner, Features: Lightweight, breathable, waterproof" into the terminal. If, in addition to this input data, emotional data indicating the user is smiling is obtained, the server will use a generative model to automatically generate an engaging and lively advertisement.

[0732] Example of a prompt:

[0733] "Please generate compelling ad copy based on the following information: Product: Speed ​​Runner, Features: Lightweight, breathable, waterproof. The user is smiling."

[0734] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0735] Step 1:

[0736] The user inputs product information (e.g., product name, features, image data) via the terminal. The terminal converts this input data into JSON format and prepares it for transmission to the server. Here, an HTML form and JavaScript are used to accept data input, and the JSON.stringify() method is used in the conversion process. The input is product information, and the output is formatted data.

[0737] Step 2:

[0738] The terminal sends the converted data to the server using a secure protocol (e.g., HTTPS). This communication utilizes an appropriate API endpoint and an HTTP client library such as axios. The input is in JSON format, and the output is the success status of the transmission to the server.

[0739] Step 3:

[0740] The server stores the data received from the terminal in a database and prepares it for analysis. The server parses the received JSON data (e.g., JSON.parse()) and stores it in the database (e.g., MySQL) using SQL queries. The input is a JSON data stream, and the output is the stored data entry.

[0741] Step 4:

[0742] The server automatically generates promotional materials using a generative AI model. The server uses received data as a prompt to input into the generative AI model (e.g., a neural network) and obtains the generation result. During this process, model inference is performed, and the generated text and design proposals are output. The input is a prompt sentence, and the output is a proposal for promotional materials.

[0743] Step 5:

[0744] The server evaluates the generated media based on pre-defined criteria. This includes quality checks by AI or human reviewers. It compares the generated results to the criteria and determines whether corrections are needed. The input is the generated material, and the output is the evaluation result.

[0745] Step 6:

[0746] User emotional data is acquired through the device's camera and microphone. The device prepares to transmit the user's facial expressions and voice data to the server in real time. JPEG (image) and WAV (audio) file formats are used. Input is the user's real-time media, and output is media data ready for transmission.

[0747] Step 7:

[0748] The server uses an emotion engine to analyze the user's emotional data and adjust the design elements of the promotional materials. The server uses the analysis results to change, for example, the colors and font styles according to the emotion. An emotion recognition API is used in this processing. The input is the user's emotional data, and the output is the adjusted design elements.

[0749] Step 8:

[0750] The final marketing materials are sent from the server to the terminal and presented to the user. The terminal displays the received data to the user using a GUI. The user can review this display and provide feedback for further adjustments. The input is the adjusted promotional material, and the output is the user's feedback.

[0751] (Application Example 2)

[0752] 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".

[0753] In recent years, there has been a growing demand for personalized promotions based on the emotions of target customers in sales promotion activities, but this is difficult to achieve with current methods. In particular, design adjustments that take user emotions into consideration are inefficient because they are done manually, and there are problems with efficiently incorporating feedback. As a result, an adequate approach to improving customer satisfaction has not been achieved.

[0754] 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.

[0755] In this invention, the server includes means for receiving image information and automatically generating sales promotion materials using a generation artificial intelligence model; means for evaluating the content of the generated sales promotion materials using evaluation means and determining whether modifications are necessary based on pre-set criteria; and means for acquiring the user's emotions using emotion recognition means and adjusting design elements based on the acquired emotions. This enables efficient design adjustments that take into account the user's emotions and incorporates their feedback.

[0756] "Image information" refers to data that represents the visual characteristics of a product or service, and forms the basis of promotional materials.

[0757] A "generative artificial intelligence model" is a technology that generates optimal promotional materials based on diverse information, and is a form of artificial intelligence that utilizes machine learning algorithms.

[0758] "Sales promotion materials" are visual or auditory content used to effectively appeal to customers about products or services.

[0759] "Evaluation method" refers to a process or system for determining whether the generated promotional materials are appropriately designed, based on predetermined criteria.

[0760] "Emotion recognition means" refers to technologies that acquire emotional information from a user's facial expressions and voice, analyze it, and then understand the user's feelings.

[0761] "Design elements" refer to the visual components of sales promotion materials, such as colors, fonts, and layouts, which should be adjusted according to the user's emotions.

[0762] The system for realizing this invention begins with a user-owned terminal. The user inputs image information and product information on the terminal, and this data is sent to a server. The server uses a generative artificial intelligence model to automatically generate sales promotion materials from the input image information. In this process, the server analyzes and generates data, and as a result outputs visual or auditory promotional materials.

[0763] Next, the server analyzes the generated promotional materials using an evaluation tool to determine whether they are properly designed according to pre-set criteria. Materials deemed acceptable are then output to the user.

[0764] Furthermore, emotion recognition technology is used to capture the user's facial expressions and voice via the device's camera and microphone. This emotion information is sent to a server, which analyzes the user's emotions based on this data. Based on the analyzed emotions, the server adjusts the design elements of the promotional materials to provide the user with more personalized content.

[0765] Through the above process, the generated promotional materials are fed back to users, and further optimization is performed based on that feedback. Implementation requires a smartphone as a terminal and backend processing by a server. The server will need to implement emotion analysis using Azure's Emotion API and a generative AI model such as GPT-4.

[0766] As a concrete example, when promoting new sports shoes, the user inputs product information along with data of their smiling expression. The server analyzes the user's emotions and generates and presents a lively design and slogan. An example of a prompt to the generating AI model might be a request such as, "Please propose an advertising design for new sports shoes. The user is currently showing a smiling emotion. The design should incorporate elements that give a fresh and energetic impression."

[0767] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0768] Step 1:

[0769] Users use their smartphones to input image and product information to be used in promotions. This data forms the basis for generating sales promotion materials and is sent from the device to the server once input is complete. Input includes image files and text information, and output is data converted into a format that the server can prepare for reception.

[0770] Step 2:

[0771] The server analyzes the received image information and automatically generates sales promotion materials using a generative artificial intelligence model. Specifically, based on the input images and text information, the AI ​​generates designs while considering the product's characteristics. In terms of data processing, the generative AI model outputs promotional materials that combine the most suitable design elements from each input data.

[0772] Step 3:

[0773] The server evaluates the generated promotional materials using evaluation tools based on pre-defined criteria. The evaluation determines whether the generated design is appropriate and conforms to the criteria. The input is the generated promotional material, and the output is the appropriate promotional material that has passed verification. Specifically, it checks for inconsistencies with the criteria and evaluates whether the requirements are met.

[0774] Step 4:

[0775] The user uses their device's camera and microphone to input facial expressions and voice into the server. This emotional data is necessary to interpret the user's feelings, and the data acquired by the device is sent to the server. The input includes raw facial and voice data, and the output is this data in a format optimized for emotion recognition.

[0776] Step 5:

[0777] The server analyzes the user's emotional data through emotion recognition mechanisms. Specifically, it uses Azure's Emotion API to analyze facial expressions and vocal characteristics, quantifying the user's emotional state. This analysis is fed into a generative AI model, which adjusts design elements to match the user's mood. The output is the analyzed emotional data.

[0778] Step 6:

[0779] The server adjusts design elements based on emotional data to generate optimized promotional materials. The generating artificial intelligence model uses prompts to adjust the design, changing colors and font styles according to the emotional state. The generated promotional materials are output and provided to the user.

[0780] Step 7:

[0781] Finally, users review the promotional materials generated on their devices and provide feedback on the design. This user feedback is used for further design optimization and is considered on the server side to enhance the promotional effect. The input is the user's feedback, and the output is the improved promotional material incorporating that feedback.

[0782] 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.

[0783] 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.

[0784] 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 robot 414.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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."

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] The following is further disclosed regarding the embodiments described above.

[0804] (Claim 1)

[0805] A means for receiving image data and automatically generating sales promotion materials using a generative model,

[0806] An evaluation means for evaluating the content of the generated sales promotion material and determining whether or not modifications are necessary based on pre-set criteria,

[0807] A means for outputting sales promotion materials that have been determined to be free of problems by the evaluation means,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, further comprising means for verifying information entered by a user to check for any omissions or inconsistencies.

[0811] (Claim 3)

[0812] The system according to claim 1, further comprising means for displaying generated promotional materials to a user and reflecting fine-tuning based on user requests.

[0813] "Example 1"

[0814] (Claim 1)

[0815] A means for receiving image data and information data and automatically generating advertising materials using a generative model,

[0816] An evaluation means for verifying the content of the generated advertising material and determining whether or not adjustments are necessary based on pre-set criteria,

[0817] A means for outputting advertising materials that have been determined to be free of problems by the aforementioned evaluation means,

[0818] A means of presenting the aforementioned advertising material to the user and reflecting fine-tuning based on the user's requests,

[0819] A means of saving and outputting advertising materials in digital format after the user has completed final confirmation,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, further comprising means for verifying information data entered by a user to check for any omissions or inconsistencies.

[0823] (Claim 3)

[0824] The system according to claim 1, further comprising means for adjusting font size and color scheme based on user requirements for the design of advertising materials.

[0825] "Application Example 1"

[0826] (Claim 1)

[0827] A means for receiving image data and product information and automatically generating sales promotion materials using a generation model,

[0828] A verification means for evaluating the generated sales promotion materials and determining whether or not revisions are necessary based on pre-set criteria,

[0829] The sales promotion materials that have been determined to be free of problems by the verification means are displayed on the terminal, and adjustment means are provided to allow for fine-tuning.

[0830] A means for outputting the aforementioned sales promotion materials in various information media formats,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising a function to review information entered by the user and verify that there are no omissions or inconsistencies.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising the function of displaying generated sales promotion materials to the user and reflecting fine-tuning based on the user's selection.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A means for receiving digital images and automatically generating marketing materials using a generation algorithm,

[0839] A verification means for evaluating the content of the generated medium and determining whether or not correction is necessary based on pre-set criteria,

[0840] A means of analyzing user emotional data and adjusting design elements according to those emotions,

[0841] A means for outputting marketing media that have been determined to be free of problems by the aforementioned verification means,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for verifying information entered by a user to check for any omissions or inconsistencies.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for displaying the generated marketing materials to the user and reflecting fine-tuning based on the user's requests.

[0847] "Application example 2 when combining with an emotional engine"

[0848] (Claim 1)

[0849] A means for receiving image information and automatically generating sales promotion materials using a generation artificial intelligence model,

[0850] An evaluation means for evaluating the content of the generated sales promotion material and determining whether or not modifications are necessary based on pre-set criteria,

[0851] A means for outputting sales promotion materials that have been determined to be free of problems by the evaluation means,

[0852] An emotion recognition means for acquiring the user's emotions,

[0853] A means of adjusting design elements based on acquired emotions,

[0854] ...

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising means for verifying information entered by a user to check for any omissions or inconsistencies.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising means for displaying generated promotional materials to users and reflecting adjustments to design elements based on user feedback. [Explanation of Symbols]

[0860] 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 means for receiving image data and product information and automatically generating sales promotion materials using a generative model, A verification means for evaluating the generated sales promotion materials and determining whether or not revisions are necessary based on pre-set criteria, The sales promotion materials that have been determined to be free of problems by the verification means are displayed on the terminal, and adjustment means are provided to allow for fine-tuning. A means for outputting the aforementioned sales promotion materials in various information media formats, A system that includes this.

2. The system according to claim 1, further comprising a function to review information entered by the user and verify that there are no omissions or inconsistencies.

3. The system according to claim 1, further comprising a function to display generated sales promotion materials to the user and to reflect fine-tuning based on the user's selection.

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