system

Abstract

We provide the system. [Solution] A means for automatically generating design data for purchase location signs using image generation technology, A means to confirm whether the content of the promotional materials conforms to pre-established regulations based on the aforementioned design data, A means for analyzing design element data received from a terminal device and extracting the necessary parameters for performing the image generation technology and verification, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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] The conventional process for creating a purchase location display item required manual design and confirmation in multiple departments, which was time-consuming and labor-intensive. Therefore, a lot of resources and time were required from creation to deployment, and there was a problem that it was difficult to quickly respond to the market.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates design data for purchase location signs using image generation technology. This system analyzes design element data received from a terminal device and verifies whether the design data generated by the image generation technology conforms to pre-set specifications. Furthermore, by transmitting the generated design data to the terminal device and providing an interface that allows the user to review and save the results, it enables the rapid and effective creation and deployment of promotional materials.

[0006] "Image generation technology" is a technology that uses computer algorithms to generate visual images based on input data.

[0007] "Point of purchase signage" refers to signs installed for the purpose of advertising or providing information for sales promotion, and is generally used within stores to inform customers about products and services.

[0008] "Design data" refers to digital data that incorporates the information necessary to create a purchase location sign, and includes specific layout and design elements.

[0009] A "terminal device" is an electronic device used by a user to input information and receive output from a system, and includes computers and smartphones.

[0010] "Design element data" refers to input information such as text, images, and color schemes necessary for creating purchase location signs, and serves as the basic information for generating the design.

[0011] A "secure communication protocol" is a set of rules for securely sending and receiving data over a network, and it commonly uses encryption technology to prevent information tampering and leakage.

[0012] An "interface" is a point of contact or means for a user to interact with a system, and is a mechanism for inputting and outputting information through on-screen operations and displays. [Brief explanation of the drawing]

[0013] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is an automatic generation system for purchase location indicators utilizing image generation technology, and a specific embodiment thereof is described below.

[0035] The user first accesses a dedicated application or web interface on their device and inputs the design element data necessary to create the purchase location display. This data includes product promotional messages and images, as well as the selected color scheme. Users can also specify their preferences regarding the font and layout to be used. Once the user has completed the input, the device converts this input information into a data structure and sends it to the server via a secure communication protocol.

[0036] The server receives input data sent from the terminal and automatically generates design data using image generation technology. The server also includes a creative check function to verify that the generated design conforms to pre-defined specifications. If any parts violate the specifications, the server automatically corrects them or sends feedback to the user regarding the necessary modifications.

[0037] The completed design data for the purchase location display is sent to the terminal, where the user can review it and download it in high resolution if needed. This system makes it possible to create promotional materials quickly and efficiently, in significantly less time than traditional manual processes.

[0038] As a concrete example, when creating a point-of-purchase display to quickly announce the launch of a new product, the user inputs messages such as "New Release" and "Special Price" along with high-resolution photos of the product into the system. The server then uses image generation technology based on this input to create a professional design, which is then sent to the terminal after undergoing a creative check according to established procedures. The user can obtain promotional materials for stores in as little as a few minutes and immediately use them in promotional activities.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user opens a dedicated application or web interface using their device. The user enters the design element data necessary to create the purchase location display into a form. This includes text messages, images, color schemes, fonts, and layout settings.

[0042] Step 2:

[0043] The terminal receives user input data and converts it into an appropriate data structure (e.g., JSON format). It performs error checking as needed to verify that the input data is correctly formatted.

[0044] Step 3:

[0045] The terminal uses a secure communication protocol (e.g., HTTPS) to send the converted data to the server. The data is encrypted to prevent unauthorized external access.

[0046] Step 4:

[0047] The server analyzes the data received from the terminal and extracts the necessary parameters based on the design elements. Based on this data, it invokes image generation technology to automatically generate the design of the purchase location display.

[0048] Step 5:

[0049] The server performs a creative check on the generated designs. This process verifies that the generated promotional materials comply with established design guidelines and advertising regulations. If problems are found, it either automatically corrects them or generates a message prompting the user to make corrections.

[0050] Step 6:

[0051] The server then sends the completed design data back to the terminal. The data sent includes the generated final design and, if necessary, suggested revisions.

[0052] Step 7:

[0053] The terminal displays the design received from the server to the user. The user reviews the design and downloads it if necessary. If the user is not satisfied with the design, they can return to step 1, modify the design elements, and repeat the process.

[0054] (Example 1)

[0055] 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."

[0056] Currently, creating promotional materials, including purchase location indicators, requires design skills and time, consuming significant resources. In particular, the process of verifying and revising designs to comply with company guidelines is time-consuming and hinders efficient promotional activities. There is a need to streamline these manual processes and generate high-quality designs quickly.

[0057] 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.

[0058] This invention includes means for a server to analyze design element data entered by a user, convert it into a data structure, and then transmit it to the server using a secure communication protocol; means for the server to automatically generate the design element data received from a terminal device using image generation technology; means for the server to check whether the generated design data conforms to pre-set guidelines, correct any non-conforming parts, or provide feedback to the user; and means for constructing a design based on prompt text using a generation AI model. This enables users to quickly and efficiently generate high-quality purchase point displays that conform to the guidelines and immediately utilize them in promotional activities.

[0059] "User" refers to a person who uses the system to input design element data and to review and use the generated promotional materials.

[0060] A "terminal device" refers to an input device used by users to input design element data and to equipment that communicates data with the server.

[0061] "Data structure" refers to a formatted form of information used to organize design element data and facilitate communication with the server.

[0062] A "secure communication protocol" refers to a means of communication used to transmit user data to a server while maintaining security.

[0063] A "server" refers to a central processing unit that processes data received from terminal devices and designs promotional materials using image generation technology.

[0064] "Image generation technology" refers to the technology that generates new images or designs based on set prompt text or input data.

[0065] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to automatically generate images and designs.

[0066] A "prompt statement" refers to an instruction given to a generative AI model, used to specify the content and style of the generated design.

[0067] "Complying with guidelines" means ensuring that the generated design adheres to pre-defined standards and requirements.

[0068] "Purchase location displays" refer to design materials used for product promotion and advertising at stores and sales locations.

[0069] This invention describes an embodiment of the automatic generation system for purchase location indicators.

[0070] First, the user accesses a dedicated application or web interface using their device and inputs design element data. Specifically, they configure the product's promotional message, images, color scheme, fonts, and layout. For example, the user might enter text such as "Design to announce the launch of a new product" and select product photos using the image upload function.

[0071] The terminal converts this data into a data structure. The data is organized into a standard format such as JSON. The terminal sends this organized data to the server using a secure communication protocol (e.g., HTTPS).

[0072] The server processes the received data and automatically generates design data using image generation technology. Specifically, the server uses a generation AI model (e.g., Stable Diffusion or DALL-E) to generate designs based on prompts. For example, by instructing the server to "Generate a promotional design using high-resolution images of the new product, including a message about a special price," the AI ​​model will generate a professional design in accordance with the instructions.

[0073] The server uses a creative check function to verify that the generated design conforms to the company's guidelines. If there are any parts that do not conform to the guidelines, it will either automatically correct them or send feedback to the user about the necessary revisions.

[0074] The completed design is sent from the server to the terminal, where the user can review it. The terminal provides the user with an interface that allows for review and high-resolution download. This system enables users to quickly generate high-quality promotional materials and immediately use them in in-store promotional activities.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user activates their device and accesses a dedicated application or web interface. The user inputs design element data, specifically promotional messages, product images, color schemes, fonts, and layout settings, and then submits them. As input, detailed information tailored to the user's promotional objectives is received, and this information is composed of design element data.

[0078] Step 2:

[0079] The terminal converts the received design element data into a data structure. The system facilitates transmission to the server by converting the input information into a format such as JSON. Specifically, it organizes all data, including product image URLs and messages, into a unified format. The converted data is then sent to the server using a secure communication protocol.

[0080] Step 3:

[0081] The server receives data sent from the terminal and analyzes it. Here, each data element is extracted and classified into appropriate parameters. For example, messages are used as prompts to input to the generating AI model, while images are linked as reference files. This analyzed data is then prepared to be input to the generating AI model along with the prompts.

[0082] Step 4:

[0083] The server uses a generative AI model to generate promotional designs. It constructs appropriate prompts from the analyzed data and passes them to the model. For example, using a prompt such as "Generate promotional images for a new product," the model generates a design that meets the requirements. The output is a high-quality image design that conforms to specific specifications.

[0084] Step 5:

[0085] The server performs a guideline compliance check on the generated design. Here, it verifies it against pre-defined criteria and automatically corrects any non-compliance. If corrections are needed, it generates feedback indicating which parts have been corrected or what the user should manually correct. It then generates compliant design data and prepares it for transmission to the terminal.

[0086] Step 6:

[0087] The server sends the final design data to the terminal. The terminal receives it and displays it to the user. The user can review the design and save it as a high-resolution file or download it for printing. As output, the final version of the promotional material, tailored to the user's preferences, is provided and ready for immediate use in promotional activities.

[0088] (Application Example 1)

[0089] 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."

[0090] Traditional processes for creating product labels required specialized knowledge and design skills, making it difficult to keep up with rapidly changing market conditions. Furthermore, inefficient communication and verification processes hindered the rapid deployment of promotional materials. Therefore, there is a need for a method that allows users to easily and quickly generate professional, standard-compliant designs that can be immediately used in physical spaces such as stores.

[0091] 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.

[0092] In this invention, the server includes means for automatically generating design information for an item display using image generation technology, means for verifying whether the content of the public medium conforms to pre-set standards based on the design information, means for analyzing design element information received from an information processing device and extracting necessary variables for executing the image generation technology and verification, and means for analyzing visual information acquired from the information processing device to drive an image generation model and output the generated design to a physical medium. As a result, users can efficiently generate visually appealing item displays that meet the standards without relying on specialized technical knowledge, and immediately use them in a physical space.

[0093] "Image generation technology" is a technology that automatically generates new images based on input data or visual information.

[0094] "Item display materials" are physical media used in stores and product promotions to display information in a way that is visually appealing and attracts attention.

[0095] "Design information" refers to data that includes elements and attributes related to the visual appearance and structure of an item's display.

[0096] A "public medium" is a medium used to visually present information to the general public or users.

[0097] A "standard" is a predetermined set of quality and form criteria that the generated design must meet.

[0098] An "information processing device" is a device used for inputting, processing, transmitting, storing, and displaying data.

[0099] "Design element information" refers to information such as images, text, and layouts that users input when designing product displays.

[0100] "Information structure" refers to a format or framework in which design element information is organized in a way that allows for efficient processing.

[0101] An "image generation model" is a computer program that generates new images from input data using specific algorithms or neural networks.

[0102] "Physical media" refers to tangible media such as paper or digital signage used to actually display generated information or designs.

[0103] This system utilizes image generation technology to quickly generate product labels. The program processing required to implement this system is explained below in natural language.

[0104] The server analyzes design element information received from the user via an information processing device. This information includes elements such as text, images, and layout of the product display. The server uses this input information to extract necessary parameters and drive an image generation model. Specifically, it utilizes generative AI models (e.g., GANs and DALL-E) running in a Python environment to generate new designs.

[0105] The terminal receives the generated design from the server via a secure communication protocol and presents it to the user. After the user confirms the design, they can immediately begin promotional activities in physical stores by printing it out on physical media (e.g., printed materials or digital signage). The hardware used includes computers and smart devices with internet connectivity.

[0106] For example, when rapidly deploying promotional activities for a new product, the user inputs a prompt into the system to generate a promotional design for the "newly released fruit tea." An example of a prompt is as follows:

[0107] "Generate a promotional design for a newly released fruit tea. Message: 'Summer Special Price!' Image: High-resolution photo of the fruit tea. Color scheme: Refreshing summer colors. Font: Easy-to-read modern style."

[0108] This allows users to efficiently generate professional designs without specialized design knowledge and use them immediately in the physical space.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user accesses a dedicated application or web interface using an information processing device. The user inputs prompts, images, text, and layout details. These entries are converted into a data structure as design element information. The converted data is sent to the server via a secure communication protocol (HTTPS).

[0112] Step 2:

[0113] The server analyzes the received design element information. This analysis extracts the parameters necessary for image generation. Based on the extracted parameters, it prepares the generation AI model. For example, data calculations are performed regarding specific colors, layout styles, font types, etc.

[0114] Step 3:

[0115] The server drives an image generation model using the extracted parameters. Using a generative AI model (e.g., GAN or DALL-E), it generates the design desired by the user. During this process, new visual information is synthesized based on the input data. The generated design is saved on the server as a temporary file.

[0116] Step 4:

[0117] The generated design is checked on the server to ensure it conforms to pre-defined criteria. This step verifies color consistency, font size, and layout balance. If any parts do not meet the criteria, automatic corrections are made, and the user is provided with feedback.

[0118] Step 5:

[0119] Designs that meet the standards are transmitted to the terminal via a secure communication protocol. The user reviews the design and provides feedback as needed. If the user is satisfied, the design data can be output in high resolution to physical media or digital signage.

[0120] Step 6:

[0121] Users print or display the final design in a format suitable for in-store promotional activities. This allows for a quick and efficient transition of promotional materials into practical use.

[0122] 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.

[0123] This invention is an automated system for generating purchase location indicators that combines image generation technology with an emotion engine that recognizes user emotions. Specific embodiments are described below.

[0124] First, the user inputs design element data for the purchase location display using a dedicated application or web interface on their device. This data includes product promotional messages and images, recommended color schemes, fonts, etc. The device converts this data into a data structure and supplies it to the emotion engine.

[0125] The emotion engine analyzes user input data and infers the user's emotional state based on text analysis and user interaction history on the interface. Based on this emotional state, suggestions are generated to adjust the design of purchase location displays. For example, if the user is showing positive emotions, brighter colors and more positive copy may be recommended.

[0126] The server analyzes the data received from the terminal and, combined with the emotion recognition results, uses image generation technology to automatically generate design data for the purchase location display. Furthermore, it performs a creative check to ensure that the generated design data conforms to pre-set guidelines and regulations. If a violation is detected, the server either automatically corrects it or sends a suggestion for correction to the user.

[0127] The completed design data is sent to the terminal, and the user can view the results through the interface. If the user is not satisfied with the displayed design, the system can use its emotion engine again to propose a newly adjusted design, providing a final design that is closer to the user's wishes. This allows users to efficiently and quickly create professional promotional materials and use them in actual promotional activities.

[0128] For example, when a user preparing a promotional campaign for a new product uses the emotion engine, the system senses the user's positive motivation and recommends a visually impactful design. The server generates a design tailored to this emotional state and sends it to the terminal, allowing the user to obtain a market-responsive point-of-purchase indicator that responds immediately.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users use their devices to open a dedicated application or web interface and input design element data for the purchase location display. This input includes product promotional messages, related images, desired color schemes, and font information.

[0132] Step 2:

[0133] The terminal validates the input design element data and converts it into a standard data structure such as JSON. The terminal then prepares this data for transfer to the emotion engine.

[0134] Step 3:

[0135] The device invokes an emotion engine to analyze user input data. The emotion engine utilizes natural language processing technology to identify the user's emotions from their input text and past interaction history. Based on this identified emotion data, it generates appropriate design arrangement instructions.

[0136] Step 4:

[0137] The device sends design element data, including emotional data, to the server using an encrypted communication protocol. This protocol uses a reliable method such as HTTPS.

[0138] Step 5:

[0139] The server analyzes the data received from the terminal and applies image generation technology to generate design data that reflects specific design instructions tailored to the user's emotional state. During this process, the server performs a creative check to ensure the quality of the generated design.

[0140] Step 6:

[0141] The server generates and sends the completed design data to the terminal, which then displays the design to the user. The data from the server also includes customization information based on the user's emotions.

[0142] Step 7:

[0143] The user reviews the design of the presented purchase location display. If the design is deemed to match their intentions, the user downloads a high-resolution version for use in promotional activities. If further customization is required, the process is restarted from the beginning and continued until the result meets the user's expectations.

[0144] (Example 2)

[0145] 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".

[0146] In today's highly competitive market environment, users are under pressure to create effective promotional materials quickly, efficiently, and effectively. However, conventional systems struggle to provide flexible design suggestions that incorporate user emotions, and require manual correction of designs that violate guidelines. Therefore, there is a need for a system that can automatically and quickly generate designs that reflect user emotions and can correct guideline violations.

[0147] 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.

[0148] In this invention, the server includes means for automatically generating design data for purchase location displays using image generation technology, means for inferring the user's emotional state and generating design adjustment suggestions based on the analyzed emotional state, and means for automatically correcting the generated design or suggesting corrections to the user if the generated design violates guidelines. This makes it possible to quickly generate professional designs that reflect the user's emotions and provide results that comply with the regulations.

[0149] "Image generation technology" refers to the technology used to automatically generate images using computer algorithms.

[0150] "Purchase location indicators" refer to promotional materials placed at the point of sale of a product, used to increase consumer purchasing intent.

[0151] "Design data" refers to information used to design the purchase location signage, and includes elements such as graphics, text, color schemes, and fonts.

[0152] An "emotion engine" is a software component that infers a user's emotional state based on text analysis and user interaction patterns.

[0153] A "guideline violation" refers to a situation where the generated design does not conform to the standards or rules set by the company or industry.

[0154] "Automatic correction" refers to the process by which the system detects guideline violations and makes corrections to the design to conform to the regulations without user intervention.

[0155] This invention provides an automated design generation system equipped with emotion recognition capabilities, and aims to dynamically generate designs based on user input.

[0156] The user first inputs the design element data necessary to create the purchase location display through a dedicated application or web interface installed on their device. This includes product promotional messages, image data, recommended color schemes and fonts, etc. The device converts this data into a data structure and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0157] The server analyzes the received data and uses an emotion engine to infer the user's emotional state. The emotion engine utilizes text analysis technology to evaluate the user's input data and interface operation history. Based on the emotional state, design adjustment suggestions are generated, and design data for purchase location displays is automatically generated using image generation technology. The image generation technology used here is based on a generation AI model and determines various design elements using prompt text and other means.

[0158] The generated designs undergo further creative checks to ensure they comply with pre-defined guidelines and regulations. The server has the capability to automatically correct guideline violations or, if necessary, send correction suggestions to the user.

[0159] The final design data is sent to the device, and the user checks the results through the interface. If the user is not satisfied with the design, the system receives feedback and can readjust the design using the emotion engine. This allows users to efficiently create effective point-of-purchase displays that reflect their own emotions and thoughts.

[0160] As a concrete example, when generating a design for a new product promotion, if the user enters the prompt, "Generate a design for the new product promotion that includes a bright and positive message. The user has motivated and positive emotions," the emotion engine will recognize the user's positive emotions and generate a design that suggests a bright color scheme and a positive message.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user opens a dedicated application or web interface on their device. Here, they input design element data for the purchase location display. This input data includes promotional messages in text format, image files, color schemes, font styles, etc. This action allows the device to receive the raw input data and prepare it for conversion into a data structure.

[0164] Step 2:

[0165] The terminal converts the design element data entered by the user into a consistent data structure and formats it into a format such as JSON. This data is sent to the server via a secure communication protocol. Specifically, the terminal validates the input and sends only valid data to the server.

[0166] Step 3:

[0167] The server receives data sent from the terminal. Based on the received data, it performs text analysis to prepare it for transmission to the emotion engine. Using text analysis techniques (NLP), it extracts features from the input promotional messages, etc., to infer the user's emotional state. This result is then provided to the emotion engine.

[0168] Step 4:

[0169] The emotion engine running on the server infers the user's emotional state based on the analyzed data. It analyzes extracted features and past user interaction history to determine whether the emotion is positive or negative. This information is used to suggest adjustments during the design generation process.

[0170] Step 5:

[0171] The server utilizes the results of the emotion engine and executes the image generation process using a generative AI model. It passes design suggestions based on emotional states as prompts to the AI ​​model, generating a variety of design options. The generated design data is output as a diagram to be used as a purchase location display.

[0172] Step 6:

[0173] The server performs a creative check to ensure the generated design conforms to the company's guidelines and regulations. If any violations are detected during this process, it attempts to make automatic corrections. The design after the creative check is output as final verification data.

[0174] Step 7:

[0175] The server sends the final design, which has passed the creative check, to the terminal. The terminal displays the received design in the user interface. The user can review the displayed design and, if necessary, save it or request further adjustments.

[0176] In this way, users can quickly and efficiently create purchase location indicators.

[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 advertising design, it is difficult to quickly generate effective and appropriate visuals based on user emotions. Traditional methods require manual adjustments and trial and error, which are time-consuming and costly. Furthermore, the process of verifying that the generated designs conform to pre-defined guidelines is also time-consuming, hindering efficient promotional activities.

[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 dynamically adjusting and automatically generating design information for purchase location displays based on the user's emotional state using image generation technology and emotion recognition technology; means for verifying whether the content of advertising materials matches pre-set rules based on the design information; and means for analyzing design element information received from a terminal device and extracting necessary variables for executing the image generation technology, emotion recognition technology, and verification. This enables the generation of effective and rapid advertising designs.

[0182] "Image generation technology" is a technology that uses computer programs to automatically create visual content.

[0183] "Emotion recognition technology" is a technology that analyzes a user's text input and operation history to infer the user's emotional state.

[0184] "Purchase location indicators" refer to visual materials and advertising materials designed to promote products and services.

[0185] "Design information" refers to information containing elemental data necessary for generating purchase location signs, including color, font, and message.

[0186] "Advertising materials" refer to visual or text-based means of communication intended to promote products or services.

[0187] "Guidelines" are pre-established standards or rules that the generated design must follow.

[0188] "Terminal device" refers to an electronic device used by a user to input design information, and includes computers, smartphones, and the like.

[0189] The system for implementing this invention integrates emotion recognition technology and image generation technology to automatically generate effective advertising materials based on design elements input by the user.

[0190] The terminal is responsible for acquiring design element data from the user and converting it into an appropriate data structure. Specifically, a smartphone or computer is used, and this data is transmitted to the server using a secure communication protocol. The design elements entered by the user include product images, promotional messages, color schemes, font information, and so on.

[0191] The server analyzes the user's emotional state based on the received data using the Google Cloud Natural Language API. This allows it to infer emotions from input messages and user interface interaction history, and extract the parameters necessary for design generation. Next, it uses the OpenAI DALL-E API to generate an appropriate image based on the inferred emotions. This process also verifies that the generated image conforms to pre-defined guidelines.

[0192] The generated final design is sent to the device, where the user can review and save the results via the interface. If the user is not satisfied with the design, they can perform sentiment recognition again and propose a new design.

[0193] For example, if a user who wants to create an advertisement for a new product, the "Eco-Friendly Bottle," enters "This bottle is kind to the planet and makes everyday life more sustainable" into the input field, the system will recognize this positive environmental sentiment and generate a design that emphasizes the beauty of nature.

[0194] Examples of prompts for a generative AI model:

[0195] "Generate an advertisement image that represents 'eco-friendly' and 'sustainability'. The design should be bright and hopeful, targeting environmentally conscious consumers."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] Users use a terminal to input design element data such as product images, promotional messages, color schemes, and font information. The input data is converted into a data structure such as JSON format. This prepares the data in a format that can be efficiently processed by the system.

[0199] Step 2:

[0200] The terminal sends the converted data to the server using the secure HTTP protocol. Here, measures are taken to ensure data integrity while preventing unauthorized external access.

[0201] Step 3:

[0202] The server analyzes the received design element data and uses the Google Cloud Natural Language API to infer the user's emotional state. This process takes text information as input and outputs an emotional score and other relevant psychological indicators.

[0203] Step 4:

[0204] The server uses the emotion analysis results to call OpenAI's DALL-E API and generate images that match the emotional state. It provides emotion scores and metrics as input data and receives visually appropriate advertising designs as output. Generative AI models and prompt messages are utilized in this process.

[0205] Step 5:

[0206] The server verifies whether the generated image design matches the pre-defined guidelines. This involves using algorithms to check if the image's color scheme and message content conform to the specified requirements. The output provides feedback on the conformance.

[0207] Step 6:

[0208] Once the image design is confirmed to be suitable, it is sent from the server to the terminal. The user reviews the final design on the interface and saves it to local storage if necessary. The output here is the completed advertisement design.

[0209] Step 7:

[0210] If a user is not satisfied with the design, they can provide feedback again, and the sentiment recognition process will be repeated to propose a new design. By reviewing the readjusted design on their device, it becomes possible to obtain the most suitable advertising materials.

[0211] 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.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention is an automatic generation system for purchase location indicators utilizing image generation technology, and a specific embodiment thereof is described below.

[0228] The user first accesses a dedicated application or web interface on their device and inputs the design element data necessary to create the purchase location display. This data includes product promotional messages and images, as well as the selected color scheme. Users can also specify their preferences regarding the font and layout to be used. Once the user has completed the input, the device converts this input information into a data structure and sends it to the server via a secure communication protocol.

[0229] The server receives input data sent from the terminal and automatically generates design data using image generation technology. The server also includes a creative check function to verify that the generated design conforms to pre-defined specifications. If any parts violate the specifications, the server automatically corrects them or sends feedback to the user regarding the necessary modifications.

[0230] The completed design data for the purchase location display is sent to the terminal, where the user can review it and download it in high resolution if needed. This system makes it possible to create promotional materials quickly and efficiently, in significantly less time than traditional manual processes.

[0231] As a concrete example, when creating a point-of-purchase display to quickly announce the launch of a new product, the user inputs messages such as "New Release" and "Special Price" along with high-resolution photos of the product into the system. The server then uses image generation technology based on this input to create a professional design, which is then sent to the terminal after undergoing a creative check according to established procedures. The user can obtain promotional materials for stores in as little as a few minutes and immediately use them in promotional activities.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user opens a dedicated application or web interface using their device. The user enters the design element data necessary to create the purchase location display into a form. This includes text messages, images, color schemes, fonts, and layout settings.

[0235] Step 2:

[0236] The terminal receives user input data and converts it into an appropriate data structure (e.g., JSON format). It performs error checking as needed to verify that the input data is correctly formatted.

[0237] Step 3:

[0238] The terminal uses a secure communication protocol (e.g., HTTPS) to send the converted data to the server. The data is encrypted to prevent unauthorized external access.

[0239] Step 4:

[0240] The server analyzes the data received from the terminal and extracts the necessary parameters based on the design elements. Based on this data, it invokes image generation technology to automatically generate the design of the purchase location display.

[0241] Step 5:

[0242] The server performs a creative check on the generated designs. This process verifies that the generated promotional materials comply with established design guidelines and advertising regulations. If problems are found, it either automatically corrects them or generates a message prompting the user to make corrections.

[0243] Step 6:

[0244] The server then sends the completed design data back to the terminal. The data sent includes the generated final design and, if necessary, suggested revisions.

[0245] Step 7:

[0246] The terminal displays the design received from the server to the user. The user reviews the design and downloads it if necessary. If the user is not satisfied with the design, they can return to step 1, modify the design elements, and repeat the process.

[0247] (Example 1)

[0248] 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."

[0249] Currently, creating promotional materials, including purchase location indicators, requires design skills and time, consuming significant resources. In particular, the process of verifying and revising designs to comply with company guidelines is time-consuming and hinders efficient promotional activities. There is a need to streamline these manual processes and generate high-quality designs quickly.

[0250] 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.

[0251] This invention includes means for a server to analyze design element data entered by a user, convert it into a data structure, and then transmit it to the server using a secure communication protocol; means for the server to automatically generate the design element data received from a terminal device using image generation technology; means for the server to check whether the generated design data conforms to pre-set guidelines, correct any non-conforming parts, or provide feedback to the user; and means for constructing a design based on prompt text using a generation AI model. This enables users to quickly and efficiently generate high-quality purchase point displays that conform to the guidelines and immediately utilize them in promotional activities.

[0252] "User" refers to a person who uses the system to input design element data and to review and use the generated promotional materials.

[0253] A "terminal device" refers to an input device used by users to input design element data and to equipment that communicates data with the server.

[0254] "Data structure" refers to a formatted form of information used to organize design element data and facilitate communication with the server.

[0255] A "secure communication protocol" refers to a means of communication used to transmit user data to a server while maintaining security.

[0256] A "server" refers to a central processing unit that processes data received from terminal devices and designs promotional materials using image generation technology.

[0257] "Image generation technology" refers to the technology that generates new images or designs based on set prompt text or input data.

[0258] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to automatically generate images and designs.

[0259] A "prompt statement" refers to an instruction given to a generative AI model, used to specify the content and style of the generated design.

[0260] "Complying with guidelines" means ensuring that the generated design adheres to pre-defined standards and requirements.

[0261] "Purchase location displays" refer to design materials used for product promotion and advertising at stores and sales locations.

[0262] This invention describes an embodiment of the automatic generation system for purchase location indicators.

[0263] First, the user accesses a dedicated application or web interface using their device and inputs design element data. Specifically, they configure the product's promotional message, images, color scheme, fonts, and layout. For example, the user might enter text such as "Design to announce the launch of a new product" and select product photos using the image upload function.

[0264] The terminal converts this data into a data structure. The data is organized into a standard format such as JSON. The terminal sends this organized data to the server using a secure communication protocol (e.g., HTTPS).

[0265] The server processes the received data and automatically generates design data using image generation technology. Specifically, the server uses a generation AI model (e.g., Stable Diffusion or DALL-E) to generate designs based on prompts. For example, by instructing the server to "Generate a promotional design using high-resolution images of the new product, including a message about a special price," the AI ​​model will generate a professional design in accordance with the instructions.

[0266] The server uses a creative check function to verify that the generated design conforms to the company's guidelines. If there are any parts that do not conform to the guidelines, it will either automatically correct them or send feedback to the user about the necessary revisions.

[0267] The completed design is sent from the server to the terminal, where the user can review it. The terminal provides the user with an interface that allows for review and high-resolution download. This system enables users to quickly generate high-quality promotional materials and immediately use them in in-store promotional activities.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user activates their device and accesses a dedicated application or web interface. The user inputs design element data, specifically promotional messages, product images, color schemes, fonts, and layout settings, and then submits them. As input, detailed information tailored to the user's promotional objectives is received, and this information is composed of design element data.

[0271] Step 2:

[0272] The terminal converts the received design element data into a data structure. The system facilitates transmission to the server by converting the input information into a format such as JSON. Specifically, it organizes all data, including product image URLs and messages, into a unified format. The converted data is then sent to the server using a secure communication protocol.

[0273] Step 3:

[0274] The server receives data sent from the terminal and analyzes it. Here, each data element is extracted and classified into appropriate parameters. For example, messages are used as prompts to input to the generating AI model, while images are linked as reference files. This analyzed data is then prepared to be input to the generating AI model along with the prompts.

[0275] Step 4:

[0276] The server uses a generative AI model to generate promotional designs. It constructs appropriate prompts from the analyzed data and passes them to the model. For example, using a prompt such as "Generate promotional images for a new product," the model generates a design that meets the requirements. The output is a high-quality image design that conforms to specific specifications.

[0277] Step 5:

[0278] The server performs a guideline compliance check on the generated design. Here, it is confirmed by comparing with the pre-set criteria, and if there is non-compliance, automatic correction is performed. If correction is necessary, feedback is generated on which part was corrected or the points that the user should manually correct. Prepare to generate conforming design data and send it to the terminal.

[0279] Step 6:

[0280] The server sends the final design data to the terminal. The terminal receives this and displays it to the user. The user can view the design and save it as a high-resolution file or download it for printing. As output, a final version of the promotional material according to the user's wishes is provided, and it is immediately ready to be used for promotional activities.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] The process of creating a conventional article display required specialized knowledge and design skills and was difficult to cope with rapidly changing market situations. Also, inefficient communication and confirmation work hindered speedy promotion deployment. For this reason, there is a need for a means by which users can easily and quickly generate a professional design that conforms to the standards and can be immediately utilized in a physical space such as a store.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0285] In this invention, the server includes means for automatically generating design information of an article display using image generation technology, means for checking whether the content of a public medium conforms to a pre-set standard based on the design information, means for analyzing design element information received from an information processing device and extracting necessary variables for executing the image generation technology and the checking, and means for analyzing visual information acquired from the information processing device to drive an image generation model and outputting the generated design to a physical medium. As a result, users can efficiently generate visually attractive article displays that meet the standards without relying on specialized technical knowledge and can immediately use them in the physical space.

[0286] "Image generation technology" is a technology for automatically generating new images based on input data and visual information.

[0287] "Article display" is a physical medium for visually prominent information display used in stores and product promotions.

[0288] "Design information" is data including elements and attributes related to the vision and configuration for creating an article display.

[0289] "Public medium" is a medium used for visually presenting information to general customers and users.

[0290] "Standard" is a pre-set standard regarding the quality and form that the generated design should meet.

[0291] "Information processing device" is a device for inputting, processing, transmitting, storing, and displaying data.

[0292] "Design element information" is information such as images, texts, layouts, etc. input by users when designing an article display.

[0293] "Information structure" is a form or framework in which design element information is organized into a state where it can be efficiently processed.

[0294] An "image generation model" is a computer program that generates new images from input data using specific algorithms or neural networks.

[0295] "Physical media" refers to tangible media such as paper or digital signage used to actually display generated information or designs.

[0296] This system utilizes image generation technology to quickly generate product labels. The program processing required to implement this system is explained below in natural language.

[0297] The server analyzes design element information received from the user via an information processing device. This information includes elements such as text, images, and layout of the product display. The server uses this input information to extract necessary parameters and drive an image generation model. Specifically, it utilizes generative AI models (e.g., GANs and DALL-E) running in a Python environment to generate new designs.

[0298] The terminal receives the generated design from the server via a secure communication protocol and presents it to the user. After the user confirms the design, they can immediately begin promotional activities in physical stores by printing it out on physical media (e.g., printed materials or digital signage). The hardware used includes computers and smart devices with internet connectivity.

[0299] For example, when rapidly deploying promotional activities for a new product, the user inputs a prompt into the system to generate a promotional design for the "newly released fruit tea." An example of a prompt is as follows:

[0300] Generate a promotional design for the newly launched fruit tea. Message: 'Special summer price!' Image: High-quality photo of fruit tea. Color scheme: Fresh and summery colors. Font: Readable modern style.

[0301] This enables users to efficiently generate professional designs without specialized design knowledge and immediately use them in the physical space.

[0302] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0303] Step 1:

[0304] The user accesses a dedicated application or web interface using an information processing device. The user inputs prompt sentences, images, text, and layout details. These entries are converted into a data structure as design element information. The converted data is sent to the server via a secure communication protocol (HTTPS).

[0305] Step 2:

[0306] The server analyzes the received design element information. In this analysis, parameters required for image generation are extracted. Based on the extracted parameters, preparation of the generation AI model is carried out. For example, data operations regarding specific hues, layout styles, font types, etc. are performed.

[0307] Step 3:

[0308] The server drives an image generation model using the extracted parameters. Using a generation AI model (e.g., GAN or DALL-E), a design desired by the user is generated. In this process, new visual information is synthesized based on the input data. The generated design is saved on the server as a temporary file.

[0309] Step 4:

[0310] The generated design is checked on the server to ensure it conforms to pre-defined criteria. This step verifies color consistency, font size, and layout balance. If any parts do not meet the criteria, automatic corrections are made, and the user is provided with feedback.

[0311] Step 5:

[0312] Designs that meet the standards are transmitted to the terminal via a secure communication protocol. The user reviews the design and provides feedback as needed. If the user is satisfied, the design data can be output in high resolution to physical media or digital signage.

[0313] Step 6:

[0314] Users print or display the final design in a format suitable for in-store promotional activities. This allows for a quick and efficient transition of promotional materials into practical use.

[0315] 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.

[0316] This invention is an automated system for generating purchase location indicators that combines image generation technology with an emotion engine that recognizes user emotions. Specific embodiments are described below.

[0317] First, the user inputs design element data for the purchase location display using a dedicated application or web interface on their device. This data includes product promotional messages and images, recommended color schemes, fonts, etc. The device converts this data into a data structure and supplies it to the emotion engine.

[0318] The emotion engine analyzes user input data and infers the user's emotional state based on text analysis and user interaction history on the interface. Based on this emotional state, suggestions are generated to adjust the design of purchase location displays. For example, if the user is showing positive emotions, brighter colors and more positive copy may be recommended.

[0319] The server analyzes the data received from the terminal and, combined with the emotion recognition results, uses image generation technology to automatically generate design data for the purchase location display. Furthermore, it performs a creative check to ensure that the generated design data conforms to pre-set guidelines and regulations. If a violation is detected, the server either automatically corrects it or sends a suggestion for correction to the user.

[0320] The completed design data is sent to the terminal, and the user can view the results through the interface. If the user is not satisfied with the displayed design, the system can use its emotion engine again to propose a newly adjusted design, providing a final design that is closer to the user's wishes. This allows users to efficiently and quickly create professional promotional materials and use them in actual promotional activities.

[0321] For example, when a user preparing a promotional campaign for a new product uses the emotion engine, the system senses the user's positive motivation and recommends a visually impactful design. The server generates a design tailored to this emotional state and sends it to the terminal, allowing the user to obtain a market-responsive point-of-purchase indicator that responds immediately.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] Users use their devices to open a dedicated application or web interface and input design element data for the purchase location display. This input includes product promotional messages, related images, desired color schemes, and font information.

[0325] Step 2:

[0326] The terminal validates the input design element data and converts it into a standard data structure such as JSON. The terminal then prepares this data for transfer to the emotion engine.

[0327] Step 3:

[0328] The device invokes an emotion engine to analyze user input data. The emotion engine utilizes natural language processing technology to identify the user's emotions from their input text and past interaction history. Based on this identified emotion data, it generates appropriate design arrangement instructions.

[0329] Step 4:

[0330] The device sends design element data, including emotional data, to the server using an encrypted communication protocol. This protocol uses a reliable method such as HTTPS.

[0331] Step 5:

[0332] The server analyzes the data received from the terminal and applies image generation technology to generate design data that reflects specific design instructions tailored to the user's emotional state. During this process, the server performs a creative check to ensure the quality of the generated design.

[0333] Step 6:

[0334] The server generates and sends the completed design data to the terminal, which then displays the design to the user. The data from the server also includes customization information based on the user's emotions.

[0335] Step 7:

[0336] The user reviews the design of the presented purchase location display. If the design is deemed to match their intentions, the user downloads a high-resolution version for use in promotional activities. If further customization is required, the process is restarted from the beginning and continued until the result meets the user's expectations.

[0337] (Example 2)

[0338] 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".

[0339] In today's highly competitive market environment, users are under pressure to create effective promotional materials quickly, efficiently, and effectively. However, conventional systems struggle to provide flexible design suggestions that incorporate user emotions, and require manual correction of designs that violate guidelines. Therefore, there is a need for a system that can automatically and quickly generate designs that reflect user emotions and can correct guideline violations.

[0340] 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.

[0341] In this invention, the server includes means for automatically generating design data for purchase location displays using image generation technology, means for inferring the user's emotional state and generating design adjustment suggestions based on the analyzed emotional state, and means for automatically correcting the generated design or suggesting corrections to the user if the generated design violates guidelines. This makes it possible to quickly generate professional designs that reflect the user's emotions and provide results that comply with the regulations.

[0342] "Image generation technology" refers to the technology used to automatically generate images using computer algorithms.

[0343] "Purchase location indicators" refer to promotional materials placed at the point of sale of a product, used to increase consumer purchasing intent.

[0344] "Design data" refers to information used to design the purchase location signage, and includes elements such as graphics, text, color schemes, and fonts.

[0345] An "emotion engine" is a software component that infers a user's emotional state based on text analysis and user interaction patterns.

[0346] A "guideline violation" refers to a situation where the generated design does not conform to the standards or rules set by the company or industry.

[0347] "Automatic correction" refers to the process by which the system detects guideline violations and makes corrections to the design to conform to the regulations without user intervention.

[0348] This invention provides an automated design generation system equipped with emotion recognition capabilities, and aims to dynamically generate designs based on user input.

[0349] The user first inputs the design element data necessary to create the purchase location display through a dedicated application or web interface installed on their device. This includes product promotional messages, image data, recommended color schemes and fonts, etc. The device converts this data into a data structure and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0350] The server analyzes the received data and uses an emotion engine to infer the user's emotional state. The emotion engine utilizes text analysis technology to evaluate the user's input data and interface operation history. Based on the emotional state, design adjustment suggestions are generated, and design data for purchase location displays is automatically generated using image generation technology. The image generation technology used here is based on a generation AI model and determines various design elements using prompt text and other means.

[0351] The generated designs undergo further creative checks to ensure they comply with pre-defined guidelines and regulations. The server has the capability to automatically correct guideline violations or, if necessary, send correction suggestions to the user.

[0352] The final design data is sent to the device, and the user checks the results through the interface. If the user is not satisfied with the design, the system receives feedback and can readjust the design using the emotion engine. This allows users to efficiently create effective point-of-purchase displays that reflect their own emotions and thoughts.

[0353] As a concrete example, when generating a design for a new product promotion, if the user enters the prompt, "Generate a design for the new product promotion that includes a bright and positive message. The user has motivated and positive emotions," the emotion engine will recognize the user's positive emotions and generate a design that suggests a bright color scheme and a positive message.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user opens a dedicated application or web interface on their device. Here, they input design element data for the purchase location display. This input data includes promotional messages in text format, image files, color schemes, font styles, etc. This action allows the device to receive the raw input data and prepare it for conversion into a data structure.

[0357] Step 2:

[0358] The terminal converts the design element data entered by the user into a consistent data structure and formats it into a format such as JSON. This data is sent to the server via a secure communication protocol. Specifically, the terminal validates the input and sends only valid data to the server.

[0359] Step 3:

[0360] The server receives data sent from the terminal. Based on the received data, it performs text analysis to prepare it for transmission to the emotion engine. Using text analysis techniques (NLP), it extracts features from the input promotional messages, etc., to infer the user's emotional state. This result is then provided to the emotion engine.

[0361] Step 4:

[0362] The emotion engine running on the server infers the user's emotional state based on the analyzed data. It analyzes extracted features and past user interaction history to determine whether the emotion is positive or negative. This information is used to suggest adjustments during the design generation process.

[0363] Step 5:

[0364] The server utilizes the results of the emotion engine and executes the image generation process using a generative AI model. It passes design suggestions based on emotional states as prompts to the AI ​​model, generating a variety of design options. The generated design data is output as a diagram to be used as a purchase location display.

[0365] Step 6:

[0366] The server performs a creative check to ensure the generated design conforms to the company's guidelines and regulations. If any violations are detected during this process, it attempts to make automatic corrections. The design after the creative check is output as final verification data.

[0367] Step 7:

[0368] The server sends the final design, which has passed the creative check, to the terminal. The terminal displays the received design in the user interface. The user can review the displayed design and, if necessary, save it or request further adjustments.

[0369] In this way, users can quickly and efficiently create purchase location indicators.

[0370] (Application Example 2)

[0371] 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."

[0372] In advertising design, it is difficult to quickly generate effective and appropriate visuals based on user emotions. Traditional methods require manual adjustments and trial and error, which are time-consuming and costly. Furthermore, the process of verifying that the generated designs conform to pre-defined guidelines is also time-consuming, hindering efficient promotional activities.

[0373] 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.

[0374] In this invention, the server includes means for dynamically adjusting and automatically generating design information for purchase location displays based on the user's emotional state using image generation technology and emotion recognition technology; means for verifying whether the content of advertising materials matches pre-set rules based on the design information; and means for analyzing design element information received from a terminal device and extracting necessary variables for executing the image generation technology, emotion recognition technology, and verification. This enables the generation of effective and rapid advertising designs.

[0375] "Image generation technology" is a technology that uses computer programs to automatically create visual content.

[0376] "Emotion recognition technology" is a technology that analyzes a user's text input and operation history to infer the user's emotional state.

[0377] "Purchase location indicators" refer to visual materials and advertising materials designed to promote products and services.

[0378] "Design information" refers to information containing elemental data necessary for generating purchase location signs, including color, font, and message.

[0379] "Advertising materials" refer to visual or text-based means of communication intended to promote products or services.

[0380] "Guidelines" are pre-established standards or rules that the generated design must follow.

[0381] "Terminal device" refers to an electronic device used by a user to input design information, and includes computers, smartphones, and the like.

[0382] The system for implementing this invention integrates emotion recognition technology and image generation technology to automatically generate effective advertising materials based on design elements input by the user.

[0383] The terminal is responsible for acquiring design element data from the user and converting it into an appropriate data structure. Specifically, a smartphone or computer is used, and this data is transmitted to the server using a secure communication protocol. The design elements entered by the user include product images, promotional messages, color schemes, font information, and so on.

[0384] The server analyzes the user's emotional state using the Google Cloud Natural Language API based on the received data. This allows it to infer emotions from input messages and user interface interaction history, and extract the parameters necessary for design generation. Next, it uses the OpenAI DALL-E API to generate an appropriate image based on the inferred emotions. This process also verifies that the generated image conforms to pre-defined guidelines.

[0385] The generated final design is sent to the device, where the user can review and save the results via the interface. If the user is not satisfied with the design, they can perform sentiment recognition again and propose a new design.

[0386] For example, if a user who wants to create an advertisement for a new product, the "Eco-Friendly Bottle," enters "This bottle is kind to the planet and makes everyday life more sustainable" into the input field, the system will recognize this positive environmental sentiment and generate a design that emphasizes the beauty of nature.

[0387] Examples of prompts for a generative AI model:

[0388] "Generate an advertisement image that represents 'eco-friendly' and 'sustainability'. The design should be bright and hopeful, targeting environmentally conscious consumers."

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] Users use a terminal to input design element data such as product images, promotional messages, color schemes, and font information. The input data is converted into a data structure such as JSON format. This prepares the data in a format that can be efficiently processed by the system.

[0392] Step 2:

[0393] The terminal sends the converted data to the server using the secure HTTP protocol. Here, measures are taken to ensure data integrity while preventing unauthorized external access.

[0394] Step 3:

[0395] The server analyzes the received design element data and uses the Google Cloud Natural Language API to infer the user's emotional state. This process takes text information as input and outputs an emotional score and other relevant psychological indicators.

[0396] Step 4:

[0397] The server uses the emotion analysis results to call OpenAI's DALL-E API and generate images that match the emotional state. It provides emotion scores and metrics as input data and receives visually appropriate advertising designs as output. Generative AI models and prompt messages are utilized in this process.

[0398] Step 5:

[0399] The server verifies whether the generated image design matches the pre-defined guidelines. This involves using algorithms to check if the image's color scheme and message content conform to the specified requirements. The output provides feedback on the conformance.

[0400] Step 6:

[0401] Once the image design is confirmed to be suitable, it is sent from the server to the terminal. The user reviews the final design on the interface and saves it to local storage if necessary. The output here is the completed advertisement design.

[0402] Step 7:

[0403] If a user is not satisfied with the design, they can provide feedback again, and the sentiment recognition process will be repeated to propose a new design. By reviewing the readjusted design on their device, it becomes possible to obtain the most suitable advertising materials.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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".

[0420] This invention is an automatic generation system for purchase location indicators utilizing image generation technology, and a specific embodiment thereof is described below.

[0421] The user first accesses a dedicated application or web interface on their device and inputs the design element data necessary to create the purchase location display. This data includes product promotional messages and images, as well as the selected color scheme. Users can also specify their preferences regarding the font and layout to be used. Once the user has completed the input, the device converts this input information into a data structure and sends it to the server via a secure communication protocol.

[0422] The server receives input data sent from the terminal and automatically generates design data using image generation technology. The server also includes a creative check function to verify that the generated design conforms to pre-defined specifications. If any parts violate the specifications, the server automatically corrects them or sends feedback to the user regarding the necessary modifications.

[0423] The completed design data for the purchase location display is sent to the terminal, where the user can review it and download it in high resolution if needed. This system makes it possible to create promotional materials quickly and efficiently, in significantly less time than traditional manual processes.

[0424] As a concrete example, when creating a point-of-purchase display to quickly announce the launch of a new product, the user inputs messages such as "New Release" and "Special Price" along with high-resolution photos of the product into the system. The server then uses image generation technology based on this input to create a professional design, which is then sent to the terminal after undergoing a creative check according to established procedures. The user can obtain promotional materials for stores in as little as a few minutes and immediately use them in promotional activities.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The user opens a dedicated application or web interface using their device. The user enters the design element data necessary to create the purchase location display into a form. This includes text messages, images, color schemes, fonts, and layout settings.

[0428] Step 2:

[0429] The terminal receives user input data and converts it into an appropriate data structure (e.g., JSON format). It performs error checking as needed to verify that the input data is correctly formatted.

[0430] Step 3:

[0431] The terminal uses a secure communication protocol (e.g., HTTPS) to send the converted data to the server. The data is encrypted to prevent unauthorized external access.

[0432] Step 4:

[0433] The server analyzes the data received from the terminal and extracts the necessary parameters based on the design elements. Based on this data, it invokes image generation technology to automatically generate the design of the purchase location display.

[0434] Step 5:

[0435] The server performs a creative check on the generated designs. This process verifies that the generated promotional materials comply with established design guidelines and advertising regulations. If problems are found, it either automatically corrects them or generates a message prompting the user to make corrections.

[0436] Step 6:

[0437] The server then sends the completed design data back to the terminal. The data sent includes the generated final design and, if necessary, suggested revisions.

[0438] Step 7:

[0439] The terminal displays the design received from the server to the user. The user reviews the design and downloads it if necessary. If the user is not satisfied with the design, they can return to step 1, modify the design elements, and repeat the process.

[0440] (Example 1)

[0441] 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."

[0442] Currently, creating promotional materials, including purchase location indicators, requires design skills and time, consuming significant resources. In particular, the process of verifying and revising designs to comply with company guidelines is time-consuming and hinders efficient promotional activities. There is a need to streamline these manual processes and generate high-quality designs quickly.

[0443] 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.

[0444] This invention includes means for a server to analyze design element data entered by a user, convert it into a data structure, and then transmit it to the server using a secure communication protocol; means for the server to automatically generate the design element data received from a terminal device using image generation technology; means for the server to check whether the generated design data conforms to pre-set guidelines, correct any non-conforming parts, or provide feedback to the user; and means for constructing a design based on prompt text using a generation AI model. This enables users to quickly and efficiently generate high-quality purchase point displays that conform to the guidelines and immediately utilize them in promotional activities.

[0445] "User" refers to a person who uses the system to input design element data and to review and use the generated promotional materials.

[0446] A "terminal device" refers to an input device used by users to input design element data and to equipment that communicates data with the server.

[0447] "Data structure" refers to a formatted form of information used to organize design element data and facilitate communication with the server.

[0448] A "secure communication protocol" refers to a means of communication used to transmit user data to a server while maintaining security.

[0449] A "server" refers to a central processing unit that processes data received from terminal devices and designs promotional materials using image generation technology.

[0450] "Image generation technology" refers to the technology that generates new images or designs based on set prompt text or input data.

[0451] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to automatically generate images and designs.

[0452] A "prompt statement" refers to an instruction given to a generative AI model, used to specify the content and style of the generated design.

[0453] "Complying with guidelines" means ensuring that the generated design adheres to pre-defined standards and requirements.

[0454] "Purchase location displays" refer to design materials used for product promotion and advertising at stores and sales locations.

[0455] This invention describes an embodiment of the automatic generation system for purchase location indicators.

[0456] First, the user accesses a dedicated application or web interface using their device and inputs design element data. Specifically, they configure the product's promotional message, images, color scheme, fonts, and layout. For example, the user might enter text such as "Design to announce the launch of a new product" and select product photos using the image upload function.

[0457] The terminal converts this data into a data structure. The data is organized into a standard format such as JSON. The terminal sends this organized data to the server using a secure communication protocol (e.g., HTTPS).

[0458] The server processes the received data and automatically generates design data using image generation technology. Specifically, the server uses a generation AI model (e.g., Stable Diffusion or DALL-E) to generate designs based on prompts. For example, by instructing the server to "Generate a promotional design using high-resolution images of the new product, including a message about a special price," the AI ​​model will generate a professional design in accordance with the instructions.

[0459] The server uses a creative check function to verify that the generated design conforms to the company's guidelines. If there are any parts that do not conform to the guidelines, it will either automatically correct them or send feedback to the user about the necessary revisions.

[0460] The completed design is sent from the server to the terminal, where the user can review it. The terminal provides the user with an interface that allows for review and high-resolution download. This system enables users to quickly generate high-quality promotional materials and immediately use them in in-store promotional activities.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The user activates their device and accesses a dedicated application or web interface. The user inputs design element data, specifically promotional messages, product images, color schemes, fonts, and layout settings, and then submits them. As input, detailed information tailored to the user's promotional objectives is received, and this information is composed of design element data.

[0464] Step 2:

[0465] The terminal converts the received design element data into a data structure. The system facilitates transmission to the server by converting the input information into a format such as JSON. Specifically, it organizes all data, including product image URLs and messages, into a unified format. The converted data is then sent to the server using a secure communication protocol.

[0466] Step 3:

[0467] The server receives data sent from the terminal and analyzes it. Here, each data element is extracted and classified into appropriate parameters. For example, messages are used as prompts to input to the generating AI model, while images are linked as reference files. This analyzed data is then prepared to be input to the generating AI model along with the prompts.

[0468] Step 4:

[0469] The server uses a generative AI model to generate promotional designs. It constructs appropriate prompts from the analyzed data and passes them to the model. For example, using a prompt such as "Generate promotional images for a new product," the model generates a design that meets the requirements. The output is a high-quality image design that conforms to specific specifications.

[0470] Step 5:

[0471] The server performs a guideline compliance check on the generated design. Here, it verifies it against pre-defined criteria and automatically corrects any non-compliance. If corrections are needed, it generates feedback indicating which parts have been corrected or what the user should manually correct. It then generates compliant design data and prepares it for transmission to the terminal.

[0472] Step 6:

[0473] The server sends the final design data to the terminal. The terminal receives it and displays it to the user. The user can review the design and save it as a high-resolution file or download it for printing. As output, the final version of the promotional material, tailored to the user's preferences, is provided and ready for immediate use in promotional activities.

[0474] (Application Example 1)

[0475] 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."

[0476] Traditional processes for creating product labels required specialized knowledge and design skills, making it difficult to keep up with rapidly changing market conditions. Furthermore, inefficient communication and verification processes hindered the rapid deployment of promotional materials. Therefore, there is a need for a method that allows users to easily and quickly generate professional, standard-compliant designs that can be immediately used in physical spaces such as stores.

[0477] 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.

[0478] In this invention, the server includes means for automatically generating design information for an item display using image generation technology, means for verifying whether the content of the public medium conforms to pre-set standards based on the design information, means for analyzing design element information received from an information processing device and extracting necessary variables for executing the image generation technology and verification, and means for analyzing visual information acquired from the information processing device to drive an image generation model and output the generated design to a physical medium. As a result, users can efficiently generate visually appealing item displays that meet the standards without relying on specialized technical knowledge, and immediately use them in a physical space.

[0479] "Image generation technology" is a technology that automatically generates new images based on input data or visual information.

[0480] "Item display materials" are physical media used in stores and product promotions to display information in a way that is visually appealing and attracts attention.

[0481] "Design information" refers to data that includes elements and attributes related to the visual appearance and structure of an item's display.

[0482] A "public medium" is a medium used to visually present information to the general public or users.

[0483] A "standard" is a predetermined set of quality and form criteria that the generated design must meet.

[0484] An "information processing device" is a device used for inputting, processing, transmitting, storing, and displaying data.

[0485] "Design element information" refers to information such as images, text, and layouts that users input when designing product displays.

[0486] "Information structure" refers to a format or framework in which design element information is organized in a way that allows for efficient processing.

[0487] An "image generation model" is a computer program that generates new images from input data using specific algorithms or neural networks.

[0488] "Physical media" refers to tangible media such as paper or digital signage used to actually display generated information or designs.

[0489] This system utilizes image generation technology to quickly generate product labels. The program processing required to implement this system is explained below in natural language.

[0490] The server analyzes design element information received from the user via an information processing device. This information includes elements such as text, images, and layout of the product display. The server uses this input information to extract necessary parameters and drive an image generation model. Specifically, it utilizes generative AI models (e.g., GANs and DALL-E) running in a Python environment to generate new designs.

[0491] The terminal receives the generated design from the server via a secure communication protocol and presents it to the user. After the user confirms the design, they can immediately begin promotional activities in physical stores by printing it out on physical media (e.g., printed materials or digital signage). The hardware used includes computers and smart devices with internet connectivity.

[0492] For example, when rapidly deploying promotional activities for a new product, the user inputs a prompt into the system to generate a promotional design for the "newly released fruit tea." An example of a prompt is as follows:

[0493] "Generate a promotional design for a newly released fruit tea. Message: 'Summer Special Price!' Image: High-resolution photo of the fruit tea. Color scheme: Refreshing summer colors. Font: Easy-to-read modern style."

[0494] This allows users to efficiently generate professional designs without specialized design knowledge and use them immediately in the physical space.

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The user accesses a dedicated application or web interface using an information processing device. The user inputs prompts, images, text, and layout details. These entries are converted into a data structure as design element information. The converted data is sent to the server via a secure communication protocol (HTTPS).

[0498] Step 2:

[0499] The server analyzes the received design element information. This analysis extracts the parameters necessary for image generation. Based on the extracted parameters, it prepares the generation AI model. For example, data calculations are performed regarding specific colors, layout styles, font types, etc.

[0500] Step 3:

[0501] The server drives an image generation model using the extracted parameters. Using a generative AI model (e.g., GAN or DALL-E), it generates the design desired by the user. During this process, new visual information is synthesized based on the input data. The generated design is saved on the server as a temporary file.

[0502] Step 4:

[0503] The generated design is checked on the server to ensure it conforms to pre-defined criteria. This step verifies color consistency, font size, and layout balance. If any parts do not meet the criteria, automatic corrections are made, and the user is provided with feedback.

[0504] Step 5:

[0505] Designs that meet the standards are transmitted to the terminal via a secure communication protocol. The user reviews the design and provides feedback as needed. If the user is satisfied, the design data can be output in high resolution to physical media or digital signage.

[0506] Step 6:

[0507] Users print or display the final design in a format suitable for in-store promotional activities. This allows for a quick and efficient transition of promotional materials into practical use.

[0508] 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.

[0509] This invention is an automated system for generating purchase location indicators that combines image generation technology with an emotion engine that recognizes user emotions. Specific embodiments are described below.

[0510] First, the user inputs design element data for the purchase location display using a dedicated application or web interface on their device. This data includes product promotional messages and images, recommended color schemes, fonts, etc. The device converts this data into a data structure and supplies it to the emotion engine.

[0511] The emotion engine analyzes user input data and infers the user's emotional state based on text analysis and user interaction history on the interface. Based on this emotional state, suggestions are generated to adjust the design of purchase location displays. For example, if the user is showing positive emotions, brighter colors and more positive copy may be recommended.

[0512] The server analyzes the data received from the terminal and, combined with the emotion recognition results, uses image generation technology to automatically generate design data for the purchase location display. Furthermore, it performs a creative check to ensure that the generated design data conforms to pre-set guidelines and regulations. If a violation is detected, the server either automatically corrects it or sends a suggestion for correction to the user.

[0513] The completed design data is sent to the terminal, and the user can view the results through the interface. If the user is not satisfied with the displayed design, the system can use its emotion engine again to propose a newly adjusted design, providing a final design that is closer to the user's wishes. This allows users to efficiently and quickly create professional promotional materials and use them in actual promotional activities.

[0514] For example, when a user preparing a promotional campaign for a new product uses the emotion engine, the system senses the user's positive motivation and recommends a visually impactful design. The server generates a design tailored to this emotional state and sends it to the terminal, allowing the user to obtain a market-responsive point-of-purchase indicator that responds immediately.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] Users use their devices to open a dedicated application or web interface and input design element data for the purchase location display. This input includes product promotional messages, related images, desired color schemes, and font information.

[0518] Step 2:

[0519] The terminal validates the input design element data and converts it into a standard data structure such as JSON. The terminal then prepares this data for transfer to the emotion engine.

[0520] Step 3:

[0521] The device invokes an emotion engine to analyze user input data. The emotion engine utilizes natural language processing technology to identify the user's emotions from their input text and past interaction history. Based on this identified emotion data, it generates appropriate design arrangement instructions.

[0522] Step 4:

[0523] The device sends design element data, including emotional data, to the server using an encrypted communication protocol. This protocol uses a reliable method such as HTTPS.

[0524] Step 5:

[0525] The server analyzes the data received from the terminal and applies image generation technology to generate design data that reflects specific design instructions tailored to the user's emotional state. During this process, the server performs a creative check to ensure the quality of the generated design.

[0526] Step 6:

[0527] The server generates and sends the completed design data to the terminal, which then displays the design to the user. The data from the server also includes customization information based on the user's emotions.

[0528] Step 7:

[0529] The user reviews the design of the presented purchase location display. If the design is deemed to match their intentions, the user downloads a high-resolution version for use in promotional activities. If further customization is required, the process is restarted from the beginning and continued until the result meets the user's expectations.

[0530] (Example 2)

[0531] 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."

[0532] In today's highly competitive market environment, users are under pressure to create effective promotional materials quickly, efficiently, and effectively. However, conventional systems struggle to provide flexible design suggestions that incorporate user emotions, and require manual correction of designs that violate guidelines. Therefore, there is a need for a system that can automatically and quickly generate designs that reflect user emotions and can correct guideline violations.

[0533] 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.

[0534] In this invention, the server includes means for automatically generating design data for purchase location displays using image generation technology, means for inferring the user's emotional state and generating design adjustment suggestions based on the analyzed emotional state, and means for automatically correcting the generated design or suggesting corrections to the user if the generated design violates guidelines. This makes it possible to quickly generate professional designs that reflect the user's emotions and provide results that comply with the regulations.

[0535] "Image generation technology" refers to the technology used to automatically generate images using computer algorithms.

[0536] "Purchase location indicators" refer to promotional materials placed at the point of sale of a product, used to increase consumer purchasing intent.

[0537] "Design data" refers to information used to design the purchase location signage, and includes elements such as graphics, text, color schemes, and fonts.

[0538] An "emotion engine" is a software component that infers a user's emotional state based on text analysis and user interaction patterns.

[0539] A "guideline violation" refers to a situation where the generated design does not conform to the standards or rules set by the company or industry.

[0540] "Automatic correction" refers to the process by which the system detects guideline violations and makes corrections to the design to conform to the regulations without user intervention.

[0541] This invention provides an automated design generation system equipped with emotion recognition capabilities, and aims to dynamically generate designs based on user input.

[0542] The user first inputs the design element data necessary to create the purchase location display through a dedicated application or web interface installed on their device. This includes product promotional messages, image data, recommended color schemes and fonts, etc. The device converts this data into a data structure and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0543] The server analyzes the received data and uses an emotion engine to infer the user's emotional state. The emotion engine utilizes text analysis technology to evaluate the user's input data and interface operation history. Based on the emotional state, design adjustment suggestions are generated, and design data for purchase location displays is automatically generated using image generation technology. The image generation technology used here is based on a generation AI model and determines various design elements using prompt text and other means.

[0544] The generated designs undergo further creative checks to ensure they comply with pre-defined guidelines and regulations. The server has the capability to automatically correct guideline violations or, if necessary, send correction suggestions to the user.

[0545] The final design data is sent to the device, and the user checks the results through the interface. If the user is not satisfied with the design, the system receives feedback and can readjust the design using the emotion engine. This allows users to efficiently create effective point-of-purchase displays that reflect their own emotions and thoughts.

[0546] As a concrete example, when generating a design for a new product promotion, if the user enters the prompt, "Generate a design for the new product promotion that includes a bright and positive message. The user has motivated and positive emotions," the emotion engine will recognize the user's positive emotions and generate a design that suggests a bright color scheme and a positive message.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The user opens a dedicated application or web interface on their device. Here, they input design element data for the purchase location display. This input data includes promotional messages in text format, image files, color schemes, font styles, etc. This action allows the device to receive the raw input data and prepare it for conversion into a data structure.

[0550] Step 2:

[0551] The terminal converts the design element data entered by the user into a consistent data structure and formats it into a format such as JSON. This data is sent to the server via a secure communication protocol. Specifically, the terminal validates the input and sends only valid data to the server.

[0552] Step 3:

[0553] The server receives data sent from the terminal. Based on the received data, it performs text analysis to prepare it for transmission to the emotion engine. Using text analysis techniques (NLP), it extracts features from the input promotional messages, etc., to infer the user's emotional state. This result is then provided to the emotion engine.

[0554] Step 4:

[0555] The emotion engine running on the server infers the user's emotional state based on the analyzed data. It analyzes extracted features and past user interaction history to determine whether the emotion is positive or negative. This information is used to suggest adjustments during the design generation process.

[0556] Step 5:

[0557] The server utilizes the results of the emotion engine and executes the image generation process using a generative AI model. It passes design suggestions based on emotional states as prompts to the AI ​​model, generating a variety of design options. The generated design data is output as a diagram to be used as a purchase location display.

[0558] Step 6:

[0559] The server performs a creative check to ensure the generated design conforms to the company's guidelines and regulations. If any violations are detected during this process, it attempts to make automatic corrections. The design after the creative check is output as final verification data.

[0560] Step 7:

[0561] The server sends the final design, which has passed the creative check, to the terminal. The terminal displays the received design in the user interface. The user can review the displayed design and, if necessary, save it or request further adjustments.

[0562] In this way, users can quickly and efficiently create purchase location indicators.

[0563] (Application Example 2)

[0564] 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."

[0565] In advertising design, it is difficult to quickly generate effective and appropriate visuals based on user emotions. Traditional methods require manual adjustments and trial and error, which are time-consuming and costly. Furthermore, the process of verifying that the generated designs conform to pre-defined guidelines is also time-consuming, hindering efficient promotional activities.

[0566] 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.

[0567] In this invention, the server includes means for dynamically adjusting and automatically generating design information for purchase location displays based on the user's emotional state using image generation technology and emotion recognition technology; means for verifying whether the content of advertising materials matches pre-set rules based on the design information; and means for analyzing design element information received from a terminal device and extracting necessary variables for executing the image generation technology, emotion recognition technology, and verification. This enables the generation of effective and rapid advertising designs.

[0568] "Image generation technology" is a technology that uses computer programs to automatically create visual content.

[0569] "Emotion recognition technology" is a technology that analyzes a user's text input and operation history to infer the user's emotional state.

[0570] "Purchase location indicators" refer to visual materials and advertising materials designed to promote products and services.

[0571] "Design information" refers to information containing elemental data necessary for generating purchase location signs, including color, font, and message.

[0572] "Advertising materials" refer to visual or text-based means of communication intended to promote products or services.

[0573] "Guidelines" are pre-established standards or rules that the generated design must follow.

[0574] "Terminal device" refers to an electronic device used by a user to input design information, and includes computers, smartphones, and the like.

[0575] The system for implementing this invention integrates emotion recognition technology and image generation technology to automatically generate effective advertising materials based on design elements input by the user.

[0576] The terminal is responsible for acquiring design element data from the user and converting it into an appropriate data structure. Specifically, a smartphone or computer is used, and this data is transmitted to the server using a secure communication protocol. The design elements entered by the user include product images, promotional messages, color schemes, font information, and so on.

[0577] The server analyzes the user's emotional state using the Google Cloud Natural Language API based on the received data. This allows it to infer emotions from input messages and user interface interaction history, and extract the parameters necessary for design generation. Next, it uses the OpenAI DALL-E API to generate an appropriate image based on the inferred emotions. This process also verifies that the generated image conforms to pre-defined guidelines.

[0578] The generated final design is sent to the device, where the user can review and save the results via the interface. If the user is not satisfied with the design, they can perform sentiment recognition again and propose a new design.

[0579] For example, if a user who wants to create an advertisement for a new product, the "Eco-Friendly Bottle," enters "This bottle is kind to the planet and makes everyday life more sustainable" into the input field, the system will recognize this positive environmental sentiment and generate a design that emphasizes the beauty of nature.

[0580] Examples of prompts for a generative AI model:

[0581] "Generate an advertisement image that represents 'eco-friendly' and 'sustainability'. The design should be bright and hopeful, targeting environmentally conscious consumers."

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] Users use a terminal to input design element data such as product images, promotional messages, color schemes, and font information. The input data is converted into a data structure such as JSON format. This prepares the data in a format that can be efficiently processed by the system.

[0585] Step 2:

[0586] The terminal sends the converted data to the server using the secure HTTP protocol. Here, measures are taken to ensure data integrity while preventing unauthorized external access.

[0587] Step 3:

[0588] The server analyzes the received design element data and uses the Google Cloud Natural Language API to infer the user's emotional state. This process takes text information as input and outputs an emotional score and other relevant psychological indicators.

[0589] Step 4:

[0590] The server uses the emotion analysis results to call OpenAI's DALL-E API and generate images that match the emotional state. It provides emotion scores and metrics as input data and receives visually appropriate advertising designs as output. Generative AI models and prompt messages are utilized in this process.

[0591] Step 5:

[0592] The server verifies whether the generated image design matches the pre-defined guidelines. This involves using algorithms to check if the image's color scheme and message content conform to the specified requirements. The output provides feedback on the conformance.

[0593] Step 6:

[0594] Once the image design is confirmed to be suitable, it is sent from the server to the terminal. The user reviews the final design on the interface and saves it to local storage if necessary. The output here is the completed advertisement design.

[0595] Step 7:

[0596] If a user is not satisfied with the design, they can provide feedback again, and the sentiment recognition process will be repeated to propose a new design. By reviewing the readjusted design on their device, it becomes possible to obtain the most suitable advertising materials.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] [Fourth Embodiment]

[0601] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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".

[0614] This invention is an automatic generation system for purchase location indicators utilizing image generation technology, and a specific embodiment thereof is described below.

[0615] The user first accesses a dedicated application or web interface on their device and inputs the design element data necessary to create the purchase location display. This data includes product promotional messages and images, as well as the selected color scheme. Users can also specify their preferences regarding the font and layout to be used. Once the user has completed the input, the device converts this input information into a data structure and sends it to the server via a secure communication protocol.

[0616] The server receives input data sent from the terminal and automatically generates design data using image generation technology. The server also includes a creative check function to verify that the generated design conforms to pre-defined specifications. If any parts violate the specifications, the server automatically corrects them or sends feedback to the user regarding the necessary modifications.

[0617] The completed design data for the purchase location display is sent to the terminal, where the user can review it and download it in high resolution if needed. This system makes it possible to create promotional materials quickly and efficiently, in significantly less time than traditional manual processes.

[0618] As a concrete example, when creating a point-of-purchase display to quickly announce the launch of a new product, the user inputs messages such as "New Release" and "Special Price" along with high-resolution photos of the product into the system. The server then uses image generation technology based on this input to create a professional design, which is then sent to the terminal after undergoing a creative check according to established procedures. The user can obtain promotional materials for stores in as little as a few minutes and immediately use them in promotional activities.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The user opens a dedicated application or web interface using their device. The user enters the design element data necessary to create the purchase location display into a form. This includes text messages, images, color schemes, fonts, and layout settings.

[0622] Step 2:

[0623] The terminal receives user input data and converts it into an appropriate data structure (e.g., JSON format). It performs error checking as needed to verify that the input data is correctly formatted.

[0624] Step 3:

[0625] The terminal uses a secure communication protocol (e.g., HTTPS) to send the converted data to the server. The data is encrypted to prevent unauthorized external access.

[0626] Step 4:

[0627] The server analyzes the data received from the terminal and extracts the necessary parameters based on the design elements. Based on this data, it invokes image generation technology to automatically generate the design of the purchase location display.

[0628] Step 5:

[0629] The server performs a creative check on the generated designs. This process verifies that the generated promotional materials comply with established design guidelines and advertising regulations. If problems are found, it either automatically corrects them or generates a message prompting the user to make corrections.

[0630] Step 6:

[0631] The server then sends the completed design data back to the terminal. The data sent includes the generated final design and, if necessary, suggested revisions.

[0632] Step 7:

[0633] The terminal displays the design received from the server to the user. The user reviews the design and downloads it if necessary. If the user is not satisfied with the design, they can return to step 1, modify the design elements, and repeat the process.

[0634] (Example 1)

[0635] 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".

[0636] Currently, creating promotional materials, including purchase location indicators, requires design skills and time, consuming significant resources. In particular, the process of verifying and revising designs to comply with company guidelines is time-consuming and hinders efficient promotional activities. There is a need to streamline these manual processes and generate high-quality designs quickly.

[0637] 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.

[0638] This invention includes means for a server to analyze design element data entered by a user, convert it into a data structure, and then transmit it to the server using a secure communication protocol; means for the server to automatically generate the design element data received from a terminal device using image generation technology; means for the server to check whether the generated design data conforms to pre-set guidelines, correct any non-conforming parts, or provide feedback to the user; and means for constructing a design based on prompt text using a generation AI model. This enables users to quickly and efficiently generate high-quality purchase point displays that conform to the guidelines and immediately utilize them in promotional activities.

[0639] "User" refers to a person who uses the system to input design element data and to review and use the generated promotional materials.

[0640] A "terminal device" refers to an input device used by users to input design element data and to equipment that communicates data with the server.

[0641] "Data structure" refers to a formatted form of information used to organize design element data and facilitate communication with the server.

[0642] A "secure communication protocol" refers to a means of communication used to transmit user data to a server while maintaining security.

[0643] A "server" refers to a central processing unit that processes data received from terminal devices and designs promotional materials using image generation technology.

[0644] "Image generation technology" refers to the technology that generates new images or designs based on set prompt text or input data.

[0645] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to automatically generate images and designs.

[0646] A "prompt statement" refers to an instruction given to a generative AI model, used to specify the content and style of the generated design.

[0647] "Complying with guidelines" means ensuring that the generated design adheres to pre-defined standards and requirements.

[0648] "Purchase location displays" refer to design materials used for product promotion and advertising at stores and sales locations.

[0649] This invention describes an embodiment of the automatic generation system for purchase location indicators.

[0650] First, the user accesses a dedicated application or web interface using their device and inputs design element data. Specifically, they configure the product's promotional message, images, color scheme, fonts, and layout. For example, the user might enter text such as "Design to announce the launch of a new product" and select product photos using the image upload function.

[0651] The terminal converts this data into a data structure. The data is organized into a standard format such as JSON. The terminal sends this organized data to the server using a secure communication protocol (e.g., HTTPS).

[0652] The server processes the received data and automatically generates design data using image generation technology. Specifically, the server uses a generation AI model (e.g., Stable Diffusion or DALL-E) to generate designs based on prompts. For example, by instructing the server to "Generate a promotional design using high-resolution images of the new product, including a message about a special price," the AI ​​model will generate a professional design in accordance with the instructions.

[0653] The server uses a creative check function to verify that the generated design conforms to the company's guidelines. If there are any parts that do not conform to the guidelines, it will either automatically correct them or send feedback to the user about the necessary revisions.

[0654] The completed design is sent from the server to the terminal, where the user can review it. The terminal provides the user with an interface that allows for review and high-resolution download. This system enables users to quickly generate high-quality promotional materials and immediately use them in in-store promotional activities.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] The user activates their device and accesses a dedicated application or web interface. The user inputs design element data, specifically promotional messages, product images, color schemes, fonts, and layout settings, and then submits them. As input, detailed information tailored to the user's promotional objectives is received, and this information is composed of design element data.

[0658] Step 2:

[0659] The terminal converts the received design element data into a data structure. The system facilitates transmission to the server by converting the input information into a format such as JSON. Specifically, it organizes all data, including product image URLs and messages, into a unified format. The converted data is then sent to the server using a secure communication protocol.

[0660] Step 3:

[0661] The server receives data sent from the terminal and analyzes it. Here, each data element is extracted and classified into appropriate parameters. For example, messages are used as prompts to input to the generating AI model, while images are linked as reference files. This analyzed data is then prepared to be input to the generating AI model along with the prompts.

[0662] Step 4:

[0663] The server uses a generative AI model to generate promotional designs. It constructs appropriate prompts from the analyzed data and passes them to the model. For example, using a prompt such as "Generate promotional images for a new product," the model generates a design that meets the requirements. The output is a high-quality image design that conforms to specific specifications.

[0664] Step 5:

[0665] The server performs a guideline compliance check on the generated design. Here, it verifies it against pre-defined criteria and automatically corrects any non-compliance. If corrections are needed, it generates feedback indicating which parts have been corrected or what the user should manually correct. It then generates compliant design data and prepares it for transmission to the terminal.

[0666] Step 6:

[0667] The server sends the final design data to the terminal. The terminal receives it and displays it to the user. The user can review the design and save it as a high-resolution file or download it for printing. As output, the final version of the promotional material, tailored to the user's preferences, is provided and ready for immediate use in promotional activities.

[0668] (Application Example 1)

[0669] 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".

[0670] Traditional processes for creating product labels required specialized knowledge and design skills, making it difficult to keep up with rapidly changing market conditions. Furthermore, inefficient communication and verification processes hindered the rapid deployment of promotional materials. Therefore, there is a need for a method that allows users to easily and quickly generate professional, standard-compliant designs that can be immediately used in physical spaces such as stores.

[0671] 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.

[0672] In this invention, the server includes means for automatically generating design information for an item display using image generation technology, means for verifying whether the content of the public medium conforms to pre-set standards based on the design information, means for analyzing design element information received from an information processing device and extracting necessary variables for executing the image generation technology and verification, and means for analyzing visual information acquired from the information processing device to drive an image generation model and output the generated design to a physical medium. As a result, users can efficiently generate visually appealing item displays that meet the standards without relying on specialized technical knowledge, and immediately use them in a physical space.

[0673] "Image generation technology" is a technology that automatically generates new images based on input data or visual information.

[0674] "Item display materials" are physical media used in stores and product promotions to display information in a way that is visually appealing and attracts attention.

[0675] "Design information" refers to data that includes elements and attributes related to the visual appearance and structure of an item's display.

[0676] A "public medium" is a medium used to visually present information to the general public or users.

[0677] A "standard" is a predetermined set of quality and form criteria that the generated design must meet.

[0678] An "information processing device" is a device used for inputting, processing, transmitting, storing, and displaying data.

[0679] "Design element information" refers to information such as images, text, and layouts that users input when designing product displays.

[0680] "Information structure" refers to a format or framework in which design element information is organized in a way that allows for efficient processing.

[0681] An "image generation model" is a computer program that generates new images from input data using specific algorithms or neural networks.

[0682] "Physical media" refers to tangible media such as paper or digital signage used to actually display generated information or designs.

[0683] This system utilizes image generation technology to quickly generate product labels. The program processing required to implement this system is explained below in natural language.

[0684] The server analyzes design element information received from the user via an information processing device. This information includes elements such as text, images, and layout of the product display. The server uses this input information to extract necessary parameters and drive an image generation model. Specifically, it utilizes generative AI models (e.g., GANs and DALL-E) running in a Python environment to generate new designs.

[0685] The terminal receives the generated design from the server via a secure communication protocol and presents it to the user. After the user confirms the design, they can immediately begin promotional activities in physical stores by printing it out on physical media (e.g., printed materials or digital signage). The hardware used includes computers and smart devices with internet connectivity.

[0686] For example, when rapidly deploying promotional activities for a new product, the user inputs a prompt into the system to generate a promotional design for the "newly released fruit tea." An example of a prompt is as follows:

[0687] "Generate a promotional design for a newly released fruit tea. Message: 'Summer Special Price!' Image: High-resolution photo of the fruit tea. Color scheme: Refreshing summer colors. Font: Easy-to-read modern style."

[0688] This allows users to efficiently generate professional designs without specialized design knowledge and use them immediately in the physical space.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The user accesses a dedicated application or web interface using an information processing device. The user inputs prompts, images, text, and layout details. These entries are converted into a data structure as design element information. The converted data is sent to the server via a secure communication protocol (HTTPS).

[0692] Step 2:

[0693] The server analyzes the received design element information. This analysis extracts the parameters necessary for image generation. Based on the extracted parameters, it prepares the generation AI model. For example, data calculations are performed regarding specific colors, layout styles, font types, etc.

[0694] Step 3:

[0695] The server drives an image generation model using the extracted parameters. Using a generative AI model (e.g., GAN or DALL-E), it generates the design desired by the user. During this process, new visual information is synthesized based on the input data. The generated design is saved on the server as a temporary file.

[0696] Step 4:

[0697] The generated design is checked on the server to ensure it conforms to pre-defined criteria. This step verifies color consistency, font size, and layout balance. If any parts do not meet the criteria, automatic corrections are made, and the user is provided with feedback.

[0698] Step 5:

[0699] Designs that meet the standards are transmitted to the terminal via a secure communication protocol. The user reviews the design and provides feedback as needed. If the user is satisfied, the design data can be output in high resolution to physical media or digital signage.

[0700] Step 6:

[0701] Users print or display the final design in a format suitable for in-store promotional activities. This allows for a quick and efficient transition of promotional materials into practical use.

[0702] 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.

[0703] This invention is an automated system for generating purchase location indicators that combines image generation technology with an emotion engine that recognizes user emotions. Specific embodiments are described below.

[0704] First, the user inputs design element data for the purchase location display using a dedicated application or web interface on their device. This data includes product promotional messages and images, recommended color schemes, fonts, etc. The device converts this data into a data structure and supplies it to the emotion engine.

[0705] The emotion engine analyzes user input data and infers the user's emotional state based on text analysis and user interaction history on the interface. Based on this emotional state, suggestions are generated to adjust the design of purchase location displays. For example, if the user is showing positive emotions, brighter colors and more positive copy may be recommended.

[0706] The server analyzes the data received from the terminal and, combined with the emotion recognition results, uses image generation technology to automatically generate design data for the purchase location display. Furthermore, it performs a creative check to ensure that the generated design data conforms to pre-set guidelines and regulations. If a violation is detected, the server either automatically corrects it or sends a suggestion for correction to the user.

[0707] The completed design data is sent to the terminal, and the user can view the results through the interface. If the user is not satisfied with the displayed design, the system can use its emotion engine again to propose a newly adjusted design, providing a final design that is closer to the user's wishes. This allows users to efficiently and quickly create professional promotional materials and use them in actual promotional activities.

[0708] For example, when a user preparing a promotional campaign for a new product uses the emotion engine, the system senses the user's positive motivation and recommends a visually impactful design. The server generates a design tailored to this emotional state and sends it to the terminal, allowing the user to obtain a market-responsive point-of-purchase indicator that responds immediately.

[0709] The following describes the processing flow.

[0710] Step 1:

[0711] Users use their devices to open a dedicated application or web interface and input design element data for the purchase location display. This input includes product promotional messages, related images, desired color schemes, and font information.

[0712] Step 2:

[0713] The terminal validates the input design element data and converts it into a standard data structure such as JSON. The terminal then prepares this data for transfer to the emotion engine.

[0714] Step 3:

[0715] The device invokes an emotion engine to analyze user input data. The emotion engine utilizes natural language processing technology to identify the user's emotions from their input text and past interaction history. Based on this identified emotion data, it generates appropriate design arrangement instructions.

[0716] Step 4:

[0717] The device sends design element data, including emotional data, to the server using an encrypted communication protocol. This protocol uses a reliable method such as HTTPS.

[0718] Step 5:

[0719] The server analyzes the data received from the terminal and applies image generation technology to generate design data that reflects specific design instructions tailored to the user's emotional state. During this process, the server performs a creative check to ensure the quality of the generated design.

[0720] Step 6:

[0721] The server generates and sends the completed design data to the terminal, which then displays the design to the user. The data from the server also includes customization information based on the user's emotions.

[0722] Step 7:

[0723] The user reviews the design of the presented purchase location display. If the design is deemed to match their intentions, the user downloads a high-resolution version for use in promotional activities. If further customization is required, the process is restarted from the beginning and continued until the result meets the user's expectations.

[0724] (Example 2)

[0725] 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".

[0726] In today's highly competitive market environment, users are under pressure to create effective promotional materials quickly, efficiently, and effectively. However, conventional systems struggle to provide flexible design suggestions that incorporate user emotions, and require manual correction of designs that violate guidelines. Therefore, there is a need for a system that can automatically and quickly generate designs that reflect user emotions and can correct guideline violations.

[0727] 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.

[0728] In this invention, the server includes means for automatically generating design data for purchase location displays using image generation technology, means for inferring the user's emotional state and generating design adjustment suggestions based on the analyzed emotional state, and means for automatically correcting the generated design or suggesting corrections to the user if the generated design violates guidelines. This makes it possible to quickly generate professional designs that reflect the user's emotions and provide results that comply with the regulations.

[0729] "Image generation technology" refers to the technology used to automatically generate images using computer algorithms.

[0730] "Purchase location indicators" refer to promotional materials placed at the point of sale of a product, used to increase consumer purchasing intent.

[0731] "Design data" refers to information used to design the purchase location signage, and includes elements such as graphics, text, color schemes, and fonts.

[0732] An "emotion engine" is a software component that infers a user's emotional state based on text analysis and user interaction patterns.

[0733] A "guideline violation" refers to a situation where the generated design does not conform to the standards or rules set by the company or industry.

[0734] "Automatic correction" refers to the process by which the system detects guideline violations and makes corrections to the design to conform to the regulations without user intervention.

[0735] This invention provides an automated design generation system equipped with emotion recognition capabilities, and aims to dynamically generate designs based on user input.

[0736] The user first inputs the design element data necessary to create the purchase location display through a dedicated application or web interface installed on their device. This includes product promotional messages, image data, recommended color schemes and fonts, etc. The device converts this data into a data structure and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0737] The server analyzes the received data and uses an emotion engine to infer the user's emotional state. The emotion engine utilizes text analysis technology to evaluate the user's input data and interface operation history. Based on the emotional state, design adjustment suggestions are generated, and design data for purchase location displays is automatically generated using image generation technology. The image generation technology used here is based on a generation AI model and determines various design elements using prompt text and other means.

[0738] The generated designs undergo further creative checks to ensure they comply with pre-defined guidelines and regulations. The server has the capability to automatically correct guideline violations or, if necessary, send correction suggestions to the user.

[0739] The final design data is sent to the device, and the user checks the results through the interface. If the user is not satisfied with the design, the system receives feedback and can readjust the design using the emotion engine. This allows users to efficiently create effective point-of-purchase displays that reflect their own emotions and thoughts.

[0740] As a concrete example, when generating a design for a new product promotion, if the user enters the prompt, "Generate a design for the new product promotion that includes a bright and positive message. The user has motivated and positive emotions," the emotion engine will recognize the user's positive emotions and generate a design that suggests a bright color scheme and a positive message.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The user opens a dedicated application or web interface on their device. Here, they input design element data for the purchase location display. This input data includes promotional messages in text format, image files, color schemes, font styles, etc. This action allows the device to receive the raw input data and prepare it for conversion into a data structure.

[0744] Step 2:

[0745] The terminal converts the design element data entered by the user into a consistent data structure and formats it into a format such as JSON. This data is sent to the server via a secure communication protocol. Specifically, the terminal validates the input and sends only valid data to the server.

[0746] Step 3:

[0747] The server receives data sent from the terminal. Based on the received data, it performs text analysis to prepare it for transmission to the emotion engine. Using text analysis techniques (NLP), it extracts features from the input promotional messages, etc., to infer the user's emotional state. This result is then provided to the emotion engine.

[0748] Step 4:

[0749] The emotion engine running on the server infers the user's emotional state based on the analyzed data. It analyzes extracted features and past user interaction history to determine whether the emotion is positive or negative. This information is used to suggest adjustments during the design generation process.

[0750] Step 5:

[0751] The server utilizes the results of the emotion engine and executes the image generation process using a generative AI model. It passes design suggestions based on emotional states as prompts to the AI ​​model, generating a variety of design options. The generated design data is output as a diagram to be used as a purchase location display.

[0752] Step 6:

[0753] The server performs a creative check to ensure the generated design conforms to the company's guidelines and regulations. If any violations are detected during this process, it attempts to make automatic corrections. The design after the creative check is output as final verification data.

[0754] Step 7:

[0755] The server sends the final design, which has passed the creative check, to the terminal. The terminal displays the received design in the user interface. The user can review the displayed design and, if necessary, save it or request further adjustments.

[0756] In this way, users can quickly and efficiently create purchase location indicators.

[0757] (Application Example 2)

[0758] 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".

[0759] In advertising design, it is difficult to quickly generate effective and appropriate visuals based on user emotions. Traditional methods require manual adjustments and trial and error, which are time-consuming and costly. Furthermore, the process of verifying that the generated designs conform to pre-defined guidelines is also time-consuming, hindering efficient promotional activities.

[0760] 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.

[0761] In this invention, the server includes means for dynamically adjusting and automatically generating design information for purchase location displays based on the user's emotional state using image generation technology and emotion recognition technology; means for verifying whether the content of advertising materials matches pre-set rules based on the design information; and means for analyzing design element information received from a terminal device and extracting necessary variables for executing the image generation technology, emotion recognition technology, and verification. This enables the generation of effective and rapid advertising designs.

[0762] "Image generation technology" is a technology that uses computer programs to automatically create visual content.

[0763] "Emotion recognition technology" is a technology that analyzes a user's text input and operation history to infer the user's emotional state.

[0764] "Purchase location indicators" refer to visual materials and advertising materials designed to promote products and services.

[0765] "Design information" refers to information containing elemental data necessary for generating purchase location signs, including color, font, and message.

[0766] "Advertising materials" refer to visual or text-based means of communication intended to promote products or services.

[0767] "Guidelines" are pre-established standards or rules that the generated design must follow.

[0768] "Terminal device" refers to an electronic device used by a user to input design information, and includes computers, smartphones, and the like.

[0769] The system for implementing this invention integrates emotion recognition technology and image generation technology to automatically generate effective advertising materials based on design elements input by the user.

[0770] The terminal is responsible for acquiring design element data from the user and converting it into an appropriate data structure. Specifically, a smartphone or computer is used, and this data is transmitted to the server using a secure communication protocol. The design elements entered by the user include product images, promotional messages, color schemes, font information, and so on.

[0771] The server analyzes the user's emotional state using the Google Cloud Natural Language API based on the received data. This allows it to infer emotions from input messages and user interface interaction history, and extract the parameters necessary for design generation. Next, it uses the OpenAI DALL-E API to generate an appropriate image based on the inferred emotions. This process also verifies that the generated image conforms to pre-defined guidelines.

[0772] The generated final design is sent to the device, where the user can review and save the results via the interface. If the user is not satisfied with the design, they can perform sentiment recognition again and propose a new design.

[0773] For example, if a user who wants to create an advertisement for a new product, the "Eco-Friendly Bottle," enters "This bottle is kind to the planet and makes everyday life more sustainable" into the input field, the system will recognize this positive environmental sentiment and generate a design that emphasizes the beauty of nature.

[0774] Examples of prompts for a generative AI model:

[0775] "Generate an advertisement image that represents 'eco-friendly' and 'sustainability'. The design should be bright and hopeful, targeting environmentally conscious consumers."

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] Users use a terminal to input design element data such as product images, promotional messages, color schemes, and font information. The input data is converted into a data structure such as JSON format. This prepares the data in a format that can be efficiently processed by the system.

[0779] Step 2:

[0780] The terminal sends the converted data to the server using the secure HTTP protocol. Here, measures are taken to ensure data integrity while preventing unauthorized external access.

[0781] Step 3:

[0782] The server analyzes the received design element data and uses the Google Cloud Natural Language API to infer the user's emotional state. This process takes text information as input and outputs an emotional score and other relevant psychological indicators.

[0783] Step 4:

[0784] The server uses the emotion analysis results to call OpenAI's DALL-E API and generate images that match the emotional state. It provides emotion scores and metrics as input data and receives visually appropriate advertising designs as output. Generative AI models and prompt messages are utilized in this process.

[0785] Step 5:

[0786] The server verifies whether the generated image design matches the pre-defined guidelines. This involves using algorithms to check if the image's color scheme and message content conform to the specified requirements. The output provides feedback on the conformance.

[0787] Step 6:

[0788] Once the image design is confirmed to be suitable, it is sent from the server to the terminal. The user reviews the final design on the interface and saves it to local storage if necessary. The output here is the completed advertisement design.

[0789] Step 7:

[0790] If a user is not satisfied with the design, they can provide feedback again, and the sentiment recognition process will be repeated to propose a new design. By reviewing the readjusted design on their device, it becomes possible to obtain the most suitable advertising materials.

[0791] 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.

[0792] 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.

[0793] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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."

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] The following is further disclosed regarding the embodiments described above.

[0813] (Claim 1)

[0814] A means for automatically generating design data for purchase location signs using image generation technology,

[0815] A means to confirm whether the content of the promotional materials conforms to pre-established regulations based on the aforementioned design data,

[0816] A means for analyzing design element data received from a terminal device and extracting the necessary parameters for performing the image generation technology and verification,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising a means for converting user-inputted design element data into a data structure and transmitting it using a secure communication protocol in a terminal device.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising means for transmitting the results of promotional materials generated based on design element data to a terminal device, and providing an interface for the user to check and save the results.

[0822] "Example 1"

[0823] (Claim 1)

[0824] A means of analyzing user-inputted design element data, converting it into a data structure, and then transmitting it to a server using a secure communication protocol,

[0825] A server provides a means for automatically generating design element data received from a terminal device using image generation technology,

[0826] The generated design data includes a means for verifying whether it conforms to pre-set guidelines, and for correcting or providing feedback to the user on any non-conforming parts.

[0827] A means of constructing a design based on prompt text using a generative AI model,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, which transmits the generated promotional material design to a terminal device and provides an interface for the user to review and save the results.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising means for streamlining the process for quickly and efficiently generating purchase location indicators.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A means for automatically generating design information for product labels using image generation technology,

[0836] A means for verifying whether the content of the public media conforms to pre-set standards based on the aforementioned design information,

[0837] A means for analyzing design element information received from an information processing device and extracting necessary variables for performing the image generation technology and verification,

[0838] A means for analyzing visual information acquired from an information processing device to drive an image generation model and outputting the generated design to a physical medium,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, further comprising means for converting design element information input by a user into an information structure and transmitting it using a secure communication protocol in an information processing device.

[0842] (Claim 3)

[0843] The system according to claim 1, further comprising means for transmitting the results of a public medium generated based on design element information to an information processing device, and providing an interface for a user to confirm and save the results.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] A means for automatically generating design data for purchase location signs using image generation technology,

[0847] A means to confirm whether the content of the promotional materials conforms to pre-established regulations based on the aforementioned design data,

[0848] A means for analyzing design element data received from a terminal device and extracting the necessary parameters for performing the image generation technology and verification,

[0849] A means for inferring the user's emotional state and generating design adjustment suggestions based on the analyzed emotional state,

[0850] A means of automatically correcting or suggesting corrections to the user if the generated design violates guidelines,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising a means for converting user-inputted design element data into a data structure and transmitting it using a secure communication protocol in a terminal device.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising means for transmitting the results of promotional materials generated based on design element data to a terminal device, and providing an interface for the user to check and save the results.

[0856] "Application example 2 when combining with an emotional engine"

[0857] (Claim 1)

[0858] A means for dynamically adjusting and automatically generating design information for purchase location displays based on the user's emotional state, utilizing image generation technology and emotion recognition technology.

[0859] A means for verifying whether the content of the advertising materials conforms to pre-set rules based on the aforementioned design information,

[0860] A means for analyzing design element information received from a terminal device and extracting necessary variables for performing the image generation technology, emotion recognition technology, and verification,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising a terminal device for converting user-inputted design element information into a data structure and transmitting it using secure communication means.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising means for transmitting the results of advertising materials generated based on design element information to a terminal device, and providing a screen for the user to confirm and save the results. [Explanation of symbols]

[0866] 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 automatically generating design data for purchase location signs using image generation technology, A means to confirm whether the content of the promotional materials conforms to pre-established regulations based on the aforementioned design data, A means for analyzing design element data received from a terminal device and extracting the necessary parameters for performing the image generation technology and verification, A system that includes this.

2. The system according to claim 1, further comprising a means for converting user-inputted design element data into a data structure and transmitting it using a secure communication protocol in a terminal device.

3. The system according to claim 1, further comprising means for transmitting the results of promotional materials generated based on design element data to a terminal device, and providing an interface for the user to check and save the results.

Images