system

Abstract

We provide the system. [Solution] A means for receiving electronic links containing commercial transaction information, A means for automatically acquiring visual and linguistic information about a product from the aforementioned electronic link, A means for automatically generating the format of an advertising medium using acquired visual and linguistic information, A means for generating optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media, A means for measuring the effectiveness of the aforementioned electronic advertisement and presenting an optimized electronic advertisement, A system that includes this.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In e-commerce, in order for small and medium-sized enterprises and individual merchants to generate effective advertisements and effectively place and optimize them across multiple communication platforms, advanced technical knowledge and resources are required. For this reason, there is a problem that it takes time and cost and many merchants cannot fully utilize them. The object of this invention is to provide means for automating the process of advertisement generation and creating and optimizing effective advertisements with little technical knowledge.

Means for Solving the Problems

[0005] This invention provides a function to receive electronic links containing information related to commercial transactions, and to automatically acquire visual and linguistic information about products from these electronic links. It then automatically generates advertising media formats using the acquired information. Furthermore, it provides a function to generate optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media. In addition, by having a function to measure the effectiveness of electronic advertisements and suggest the optimal advertising format, it enables the easy implementation of effective advertising strategies.

[0006] "Commercial transaction information" refers to data about goods and services, including price, description, images, and other information necessary for conducting online transactions.

[0007] An "electronic link" refers to a hyperlink used to access a specific webpage or resource on the internet.

[0008] "Visual information" refers to digital information such as images and videos used to visually represent products and services.

[0009] "Linguistic information" refers to textual information that describes the details and features of a product, and is usually provided as a product description or copy.

[0010] "Advertising media format" refers to the design templates and formats that make up an advertisement, and means the standards for combining and arranging visual and linguistic information.

[0011] "Communication infrastructure" refers to the infrastructure used to deliver digital advertisements, such as the internet and mobile networks.

[0012] "Optimization" refers to the process of making adjustments and improvements to achieve maximum effectiveness under specific conditions, and in this context, it means optimizing the format and delivery method of advertisements.

[0013] "Electronic advertising" refers to advertisements placed on digital platforms and is a form of advertising delivered via the internet and social media.

[0014] "Measuring effectiveness" refers to the process of collecting and analyzing metrics and data used to evaluate the performance of an advertisement. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the 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 disk (e.g., hard disk), or magnetic tape, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] As an embodiment of this invention, a system for automatically generating advertisements using commercial transaction information will be described.

[0037] When a user promotes a product, they first enter an electronic link to the product they want to advertise through an interface installed on their device. This link is the URL of the webpage where the target product is listed. Upon receiving this request, the server accesses the linked page and automatically retrieves visual and linguistic information about the product using methods such as web scraping. This includes product images, product descriptions, and prices.

[0038] Next, the server uses a generative AI model to automatically generate advertising media formats based on the acquired information. This model leverages pre-trained data to generate various advertising patterns and copy. As a result, multiple advertising proposals that are visually appealing and have a clear message are presented.

[0039] As a concrete example, in the case of online shop products, when a user enters the product URL, the server extracts the product name, image, price, etc., and a generation AI model automatically generates an advertisement with eye-catching visual design along with copy such as "Limited-time sale! Offered at a special price." The generated advertisement is then applied to multiple design templates and presented to the user as a choice.

[0040] After the user selects their desired ad design, the server optimizes the selected design for each communication infrastructure. This process includes, for example, changing image sizes to suit different social media platforms and adjusting copy based on the delivery algorithm.

[0041] Ultimately, the server has the capability to track ad performance and measure its effectiveness. This allows users to see the actual ad performance and continuously update to the most effective ads.

[0042] Thus, the present invention enables effective marketing activities by automatically generating advertisements from commercial transaction information and applying them to various communication infrastructures in an optimized form.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user will use their own device to enter the electronic link of the product they want to advertise into a dedicated input field. After entering the information, the user will click the submit button to send the information to the server.

[0046] Step 2:

[0047] The server receives an electronic link sent by the user. The server parses this link and uses web scraping techniques to access the linked page in order to collect the necessary product information. Here, information such as product images, descriptions, and prices is automatically retrieved.

[0048] Step 3:

[0049] The server inputs the acquired product information into a generating AI model. Based on past data, the generating AI model automatically generates visually appealing advertising designs and creative taglines.

[0050] Step 4:

[0051] The server generates ad designs and copy, which are then applied to multiple ad templates. The server creates previews of these templates and presents them to the user as options.

[0052] Step 5:

[0053] The user selects the most suitable design from the ad templates provided by the server. The user can complete the selection with a simple click.

[0054] Step 6:

[0055] Based on user selections, the server optimizes the chosen ad design for different communication infrastructures. This optimization includes adjusting image sizes and simplifying language for each platform.

[0056] Step 7:

[0057] The server creates different ad variations and automatically conducts A / B testing. Each version of the ad is presented to different user groups, and the effectiveness of the ads is measured.

[0058] Step 8:

[0059] Users can view A / B test results updated in real time by the server from their devices. The server recommends the most effective ad design, and users can adjust their final ad strategy.

[0060] (Example 1)

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

[0062] In modern commerce, a wide variety of products are sold online, requiring the rapid and efficient generation of effective promotional materials for each product. However, traditional advertising methods face challenges in that acquiring visual and linguistic information about products and generating and optimizing advertisements requires considerable effort and time. Furthermore, optimizing and effectively delivering advertisements across different information and communication infrastructures is not easy.

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

[0064] In this invention, the server includes means for receiving electronic information containing commercial transaction information, means for automatically acquiring visual and linguistic data related to products from the electronic information, and means for inputting the acquired data as prompt text using a generation AI model to generate advertisements. This enables the efficient automatic generation of product advertisements, as well as the creation and distribution of optimal electronic advertisements compatible with different information and communication infrastructures.

[0065] "Commercial transaction information" refers to data related to the transaction of goods and services, primarily including information such as price, product description, and inventory status.

[0066] "Electronic information" refers to information expressed in digital format and data that can be transmitted and received via a network.

[0067] "Visual data" refers to data represented in a visually recognizable format, such as images or videos.

[0068] "Linguistic data" refers to data expressed as strings or text, including information such as product descriptions and taglines.

[0069] "Advertising information" refers to content created to promote products or services to consumers, and includes both visual and linguistic elements.

[0070] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate new content from data.

[0071] A "prompt statement" is a text-based instruction given to an AI model to provide specific generation instructions.

[0072] "Information and communication infrastructure" refers to the entire network and system that enables the transmission and reception of data, and includes communication hardware and software.

[0073] "Optimized digital advertising" refers to advertisements that have been adapted to the most suitable format and structure for different information and communication infrastructures and platforms.

[0074] This invention is implemented as a system for accurately and effectively utilizing commercial transaction information for advertising generation. The user enters an electronic link (URL) of the product they wish to promote via an interface on their terminal. This information is immediately transmitted to the server.

[0075] The server uses the received electronic link to access the corresponding webpage and automatically retrieves visual data (such as product images) and linguistic data (such as product descriptions and price information) related to the product using web scraping techniques. For example, the Python library BeautifulSoup can be used.

[0076] Based on this acquired data, the server automatically generates advertisements using a generative AI model. This AI model takes product information as prompt text. For example, prompt text such as "Product Name: Summer Sale Limited Sandals, Key Features: Lightweight, Comfortable, Waterproof, Sale Information: 30% Off" can be used. Various generative AI algorithms can be used for the generative AI model.

[0077] The generated advertisements are presented in visually appealing and diverse templates. Users can select from multiple generated ad designs according to their preferences via their devices. The selected ad is further optimized according to the information and communication infrastructure. For example, image sizes are adjusted and messages are fine-tuned for different social networking platforms. Image processing tools such as the Python Imaging Library (PIL) are used for this purpose.

[0078] Ultimately, the server has the capability to track and analyze advertising performance in real time. This allows users to continuously improve their advertising strategies. This system enables efficient and effective marketing activities that leverage commercial transaction information.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user enters the URL of the webpage for the product they wish to promote using the terminal's interface. The entered URL is sent to the information system. The input data is in URL format and is used to identify the product information page.

[0082] Step 2:

[0083] The server accesses the webpage based on the URL received from the user. It issues an HTTP request to retrieve the HTML data of the webpage. This input data is the content of the webpage, and the output is visual data (product images) and linguistic data (product descriptions, price information) related to the product. Web scraping is performed using analysis tools such as BeautifulSoup.

[0084] Step 3:

[0085] The server combines the acquired visual and linguistic data to create prompt statements for the generative AI model. These prompt statements are used as input data for the AI ​​model. Specifically, they are summarized in the format of "Product Name: Summer Sale Limited Sandals, Key Features: Lightweight, Comfortable, Waterproof, Sale Information: 30% Off".

[0086] Step 4:

[0087] The generative AI model receives prompt text as input and generates visually and linguistically engaging advertising content. This generation process creates diverse advertising patterns based on historical data learning. The output data is in a format applicable to multiple advertising templates.

[0088] Step 5:

[0089] The server applies the generated ads to different templates and presents them to the user via the terminal. The user can choose from multiple ad designs presented. The input data is the generated ad pattern, and the output data is the ad design selected by the user.

[0090] Step 6:

[0091] The server optimizes the user-selected ad design for various information and communication infrastructures. Specifically, it adjusts image sizes and fine-tunes messages. The input data is the user-selected ad design, and the optimized version becomes the output data. Image processing tools such as PIL are used in this process.

[0092] Step 7:

[0093] The server delivers optimized advertisements to each information and communication infrastructure and tracks their performance. After delivery, the effectiveness of the advertisements is measured based on data such as impressions and click-through rates. Users can then improve their advertising strategies based on this performance data.

[0094] (Application Example 1)

[0095] 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."

[0096] In today's world, there is a growing need to appeal to consumers more quickly and effectively with individual products and services. However, creating advertisements is time-consuming and labor-intensive, and the need for optimization tailored to each online platform presents challenges requiring specialized knowledge. Furthermore, there is a lack of systems to track advertisement performance in real time and continuously improve it in the most optimal way. Against this backdrop, there is a need for an environment that automates the ad generation and optimization process, allowing users to easily create highly effective advertisements.

[0097] 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.

[0098] In this invention, the server includes a device for receiving electronic links containing commercial transaction information, a device for automatically acquiring image and text information about a product from the electronic links, and a device for automatically generating the format of an electronic advertising medium using the acquired image and text information. This makes it possible for users to quickly and efficiently automatically generate optimized advertisements adapted to various communication platforms, even without specialized knowledge, and to easily implement a continuous advertising strategy that maximizes effectiveness.

[0099] "Commercial transaction information" refers to digital data used to provide details about products and services.

[0100] An "electronic link" is a URL that allows access to a specific webpage on the internet.

[0101] "Device" refers to a collection of hardware or software designed to process, acquire, generate, and display commercial transaction information.

[0102] "Image information" refers to digital image data that shows the visual characteristics of a product.

[0103] "Text information" refers to linguistic data used to describe a product, its features, benefits, etc.

[0104] "Electronic advertising media" refers to the digital format of advertising content delivered on digital platforms such as the internet.

[0105] A "Generating AI Module" is a system or program that uses artificial intelligence technology to automatically generate advertising copy.

[0106] A "design template" is a pre-configured design layout that can be easily used as a visual component of an advertisement.

[0107] A "communication platform" is a digital network or service on which electronic advertisements are delivered.

[0108] "Performance tracking" is the process of quantitatively measuring and evaluating the effectiveness and impact of advertising.

[0109] The system for implementing this invention automatically generates advertisements based on commercial transaction information and deploys optimized advertisements across multiple communication platforms. The main components of the system are a server, terminals, and a user interface.

[0110] The server receives electronic links entered by users and automatically retrieves image and text information about products from the linked web pages using web scraping techniques. The specific software used is Beautiful Soup in Python. The retrieved information is used to generate advertising copy via a generative AI model. Here, the AI ​​model used is Hugging Face's Transformers.

[0111] Users provide the system with electronic links to the products they wish to advertise via their devices. These links are URLs pointing to the web pages to be analyzed. The devices are equipped with a user interface for information input, allowing users to intuitively provide links.

[0112] Along with the generated ad copy, multiple design templates are automatically generated using the Figma API and presented as visually appealing ad layouts. The user selects the most suitable ad design from these options, and the server optimizes the selected design for different communication platforms and generates the electronic ad in a format appropriate for each.

[0113] Ultimately, the server tracks ad performance via the Google® Analytics API and provides the results to users, helping them continuously optimize their advertising strategies.

[0114] For example, if a user wants to create an advertisement for a "new smartphone," they would enter the product's URL through an input interface. The server would then retrieve product information from the URL and use a generation AI model to generate advertising copy such as, "Limited time offer! Get the latest smartphone with cutting-edge technology at a special price!"

[0115] Examples of prompts for a generative AI model are as follows:

[0116] "Generate catchy and memorable ad copy from the following data: Product Name: New Smartphone, Product Description: Equipped with the latest technology, Price: Special Price"

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

[0118] Step 1:

[0119] The user enters an electronic link for the product they want to advertise through the device's user interface. This input is sent to the server as a web page URL.

[0120] Step 2:

[0121] The server uses the received electronic link to web scrape the linked page using the Python Beautiful Soup library. This retrieves image and text information related to the product. The input is a URL, and the output is an image of the product and a descriptive text.

[0122] Step 3:

[0123] The server inputs the acquired image and text information, along with prompt text, into the Hugging Face Transformers, an AI model for generating content. Here, the AI ​​model automatically generates advertising copy. The input to this process is product information, and the output is a message-driven advertising copy.

[0124] Step 4:

[0125] The server uses the Figma API to automatically create multiple ad design templates, including the generated ad copy. The input for this step is the ad copy, and the output is multiple design options. The server then presents these to the user.

[0126] Step 5:

[0127] The user selects the most suitable design from the presented design templates. This selection is sent to the server via the device.

[0128] Step 6:

[0129] The server optimizes the selected ad design for various communication platforms. For example, it converts images to the appropriate size and format for each social networking platform. The input is the selected design proposal, and the output is the ad design optimized for each platform.

[0130] Step 7:

[0131] Optimized ads are delivered to each communication platform. The server tracks ad performance in real time via the Google Analytics API and provides feedback to the user. The input is the ad design, and the output is performance data.

[0132] 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.

[0133] As an embodiment of this invention, a system is described that automatically generates advertisements using information related to commercial transactions and improves the effectiveness of the advertisements by utilizing user emotions.

[0134] When a user promotes a product using their device, they first enter an electronic link to the product they want to advertise into a dedicated input interface. After receiving this link, the server accesses the linked page and collects the necessary product information. The server uses web scraping technology to automatically retrieve product images, descriptions, price information, and other relevant data.

[0135] Next, the server uses a generative AI model to generate advertisement designs based on the acquired information. The generative AI model creates advertisement styles and structures suitable for the product characteristics and presents them to the user as multiple options.

[0136] Furthermore, the system is equipped with an emotion engine that analyzes the user's emotions based on data obtained from the user's device. The emotion engine utilizes video data from the camera, audio data, and other sources to determine the user's emotional state.

[0137] As a concrete example, suppose a user is browsing a product page on an online store, and the system determines, based on facial expression data obtained through the camera, that the user's emotions at that time are positive. In this case, the server will prioritize displaying bright and attractive ad designs that reflect the user's positive emotions.

[0138] The server also leverages this sentiment information to dynamically adjust the language of the advertisement, displaying the message that best resonates with the user's current mood. For example, if the user is relaxed, the copy will be written in a calm tone.

[0139] This system personalizes the user experience and improves ad engagement by generating and adjusting ads based on sentiment analysis. Furthermore, selected designs are optimized for each platform to ensure the most effective ads are displayed across various communication infrastructures.

[0140] Thus, the present invention enhances the user experience and maximizes the effectiveness of advertising in commercial transactions by combining an emotion engine with the generation and optimization of advertisements.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The user will use their device to enter an electronic link for the product they want to advertise. This operation is performed through a dedicated web interface, and the electronic link is sent to the server.

[0144] Step 2:

[0145] The server receives an electronic link sent by the user. The server accesses the specified webpage and automatically retrieves visual and linguistic information related to the product using web scraping techniques. This includes product images, product descriptions, and prices.

[0146] Step 3:

[0147] The server inputs the acquired product information into a generation AI model. This model automatically generates advertising media formats and copy based on visual and linguistic information. The generated advertising proposals are presented to the user as multiple templates.

[0148] Step 4:

[0149] The emotion engine built into the user's device activates. This engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. Camera footage and microphone audio are used for this purpose.

[0150] Step 5:

[0151] The server receives the results analyzed by the emotion engine and optimizes the design and copy of the advertisements according to the user's emotional state. If positive emotions are detected, it prioritizes bright, engaging designs and compelling messages.

[0152] Step 6:

[0153] The server delivers optimized advertisements in a format compatible with multiple communication infrastructures. Optimized ad templates are created for each platform, with adjustments made, for example, for social media and search engines.

[0154] Step 7:

[0155] Users can monitor the effectiveness of their ads in real time through a dashboard provided on their device. The server aggregates performance data from ad campaigns and provides users with feedback on the most effective strategies.

[0156] Step 8:

[0157] Based on the data provided by the server, users adjust their next advertising strategy based on the results of their ads. This enables continuous ad optimization.

[0158] (Example 2)

[0159] 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".

[0160] In commercial transactions, it is necessary to improve ad engagement and effectiveness by generating and dynamically adjusting ads based on the user's emotional state. However, conventional ad generation systems struggle to provide a personalized ad experience that takes user emotions into account, which can result in missed business opportunities. Therefore, there is a need for personalized ad generation systems that utilize emotional information.

[0161] 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.

[0162] In this invention, the server includes means for receiving electronic links containing commercial transaction information, means for automatically acquiring visual and linguistic information about a product from the electronic links, means for automatically generating advertising media using a generative model with the acquired visual and linguistic information, means for determining the emotional state based on video or audio data received from the user, means for dynamically adjusting the generated advertising media according to the emotional state, means for presenting the user with multiple advertising media and allowing them to select the optimal advertisement, means for optimizing the generated advertising media for multiple communication infrastructures, and means for measuring the effectiveness of the electronic advertisement and presenting the optimized advertisement. This enables the generation and optimization of personalized advertisements that reflect the user's real-time emotional state.

[0163] "Commercial transactions" is a concept that refers to acts and information related to the buying and selling of goods and services.

[0164] "Electronic link" refers to URLs and URIs, which are identifying information that points to a specific web page or resource on the internet.

[0165] "Visual information" refers to information related to product images and visual design, and is acquired as video data.

[0166] "Linguistic information" refers to text data that describes a product, its features, price, and other relevant information.

[0167] A "generative model" is a software algorithm that uses machine learning or artificial intelligence to generate specific deliverables (in this case, advertising designs).

[0168] An "advertising medium" is a visual and text format created to convey information about products or services to users.

[0169] "Communication infrastructure" refers to the infrastructure for delivering and displaying electronic advertisements, and includes web platforms and applications.

[0170] "Emotional state" refers to the user's psychological or emotional condition, and is analyzed from video and audio data.

[0171] "Dynamically adjusting" means changing or updating advertising content in real time according to the user's situation and emotions.

[0172] "Measuring effectiveness" refers to quantitatively or qualitatively evaluating the extent to which the generated advertisements are achieving their objectives.

[0173] This invention is a system that optimizes advertisements by leveraging user emotions based on information related to commercial transactions. The system consists mainly of a server and terminals and operates as follows.

[0174] The server first receives an electronic link from the user. This is the URL to the webpage where the product to be advertised is listed. Next, the server accesses this page using web scraping techniques and automatically collects visual and linguistic information such as product images, descriptions, and pricing information. Libraries such as Beautiful Soup and Scrapy are used for this collection.

[0175] Subsequently, the server uses a generative AI model to create an advertisement design based on the collected information. The generative AI model used utilizes generative modeling technology to automatically generate an advertisement style and structure suitable for the product's characteristics. Commands such as "Generate an attractive advertisement design based on this product" are given as prompts to the generative AI model.

[0176] The device collects user emotion data using the user's camera, microphone, and other devices. This data is sent to the server in the form of a video stream or audio recording. The server uses this emotion data to analyze the user's emotional state using an emotion engine. For this analysis, emotion recognition software such as Azure® Emotion API and Face++ are utilized.

[0177] The server then dynamically adjusts the generated advertisements based on the user's emotional state, which is the result of the analysis. If a positive emotion is detected, the advertisement is adjusted to be brighter and more appealing. In this way, the advertisements are optimized for the user's real-time emotions, and high engagement is expected.

[0178] As a concrete example of this behavior, if a user is browsing a new smartphone page and their emotional state is analyzed to be relaxed and positive, the server may generate an ad design that includes calming and appealing visuals.

[0179] This system aims to enable personalized advertising experiences for users and enhance the effectiveness of advertising in commercial transactions.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The user enters the URL of the webpage for the product they want to advertise into the input interface on their device. The device then sends this URL to the server. Input: Product webpage URL. Output: URL sent to the server.

[0183] Step 2:

[0184] The server accesses the webpage based on the received URL. Specifically, it issues an HTTP request and retrieves page data. The retrieved data includes product images, descriptions, and price information. The server then analyzes this information using web scraping techniques and extracts it as visual and linguistic information. Input: Product webpage data. Output: Visual and linguistic information about the product.

[0185] Step 3:

[0186] The server uses the acquired visual and linguistic information to generate an ad design by inputting prompt text into a generative AI model. This prompt text includes specific instructions such as "Generate an attractive ad design based on this product." The generative AI model automatically generates the visual format of the ad from this information. Input: Visual and linguistic information, prompt text. Output: Generated ad design.

[0187] Step 4:

[0188] The device uses the user's camera and microphone to collect emotional data such as facial expressions and voice. The device then sends this data to the server. Input: User's video and audio data. Output: Emotional data sent to the server.

[0189] Step 5:

[0190] The server analyzes the received emotional data using an emotion engine to determine the user's emotional state. Specifically, it analyzes video and audio data and assigns emotional labels such as positive or relaxed. Input: User's emotional data. Output: User's emotional state.

[0191] Step 6:

[0192] The server dynamically adjusts the generated ad design based on the user's emotional state. If the emotion is positive, it adjusts brightness, color tone, and message to create an appealing ad. This includes adjusting colors and changing the tone of the copy. Input: Generated ad design, user's emotional state. Output: Optimized ad design.

[0193] Step 7:

[0194] The user views multiple optimized ad designs on their device and selects the one they deem best. Input: Options for optimized ad designs. Output: The selected ad design.

[0195] In this way, there is a clear input and output flow at each step, and the system is designed to optimize advertisements based on the user's emotional state.

[0196] (Application Example 2)

[0197] 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".

[0198] Conventional ad generation systems have faced challenges in personalizing ads based on user emotions, resulting in limited user engagement. This invention aims to dynamically optimize ads based on user emotions, thereby improving ad effectiveness and user experience.

[0199] 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.

[0200] In this invention, the server includes means for receiving electronic links containing commercial transaction information, means for automatically acquiring visual and linguistic information about products from the electronic links, means for automatically generating the format of advertising media using the acquired visual and linguistic information, means for analyzing the user's emotions via the user's terminal and dynamically adjusting the advertising design based on that emotional information, means for generating optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media, and means for measuring the effectiveness of electronic advertisements and presenting optimized electronic advertisements. This makes it possible to personalize advertisements according to the user's emotions and improve user engagement.

[0201] "Commercial transaction information" refers to digital information related to the purchase or sale of goods, and is expressed as electronic links.

[0202] An "electronic link" is a hyperlink that allows access to specific information or pages on the internet.

[0203] "Visual information" is a general term for image data and digital visual content related to a product.

[0204] "Linguistic information" refers to text data and descriptions related to a product.

[0205] "Advertising media style" refers to the style of design and format that makes up an advertisement.

[0206] "User emotions" refer to the user's psychological state and sensitivities, and are analyzed using an emotion engine.

[0207] "Communication infrastructure" refers to the platform and network infrastructure used to send and receive digital data.

[0208] "Optimized digital advertising" refers to digital advertising content that has been tailored to the user's characteristics and where it will be displayed.

[0209] "Engagement" refers to the degree of interest and response that users show to advertising content.

[0210] The system for realizing this invention is a process that utilizes information from commercial transactions to optimize advertisements according to the user's emotions. Basically, a server receives electronic links containing commercial transaction information. This automatically acquires visual and linguistic information about the product using web scraping technology. The software used includes "Beautiful Soup" for web scraping data and "OpenAI® API" for the generative AI model. Based on this information, the generative AI model generates the format of the advertising medium and presents it to the user.

[0211] Next, the user's device analyzes their emotions in real time using its camera and microphone. This emotion analysis utilizes the "Google Cloud Vision API," which leverages facial recognition and voice analysis technologies. The analyzed emotion information is sent to a server, and the ad design is dynamically adjusted based on that emotion.

[0212] The generated advertisements are optimized and delivered across multiple communication platforms. This optimization includes parameter adjustments based on user characteristics and the media to which the advertisements are displayed. As a result, users see advertisements that are relevant to them, leading to improved engagement.

[0213] As a concrete example, when a user is relaxing in their living room, the server presents advertisements with colors and tones that reflect that state. The generative AI model generates advertisements using prompts such as: "Generate an advertisement for a home projector with a tone that is appropriate for when the user is relaxed." This process is expected to ensure that the advertisements consistently fit the user's emotions and enhance commercial success.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The server receives electronic links containing commercial transaction information. The input is an electronic link provided by the user, and the server uses this link to access the product page. The output is the HTML data of the target product's webpage. During this process, the server checks the reliability of the link and verifies that it is accessible.

[0217] Step 2:

[0218] The server uses web scraping techniques to obtain visual and linguistic information about products. It uses the previously obtained HTML data as input and processes the data. The output consists of visual and linguistic information such as product images, descriptions, and price information. Specifically, it uses "Beautiful Soup" to parse the HTML and extract the necessary data.

[0219] Step 3:

[0220] The server uses a generative AI model to generate advertising media formats based on acquired product information. Input data includes product information, price, and description, while output consists of multiple advertising design proposals. During this process, the generative AI model is prompted with prompts to perform data calculations to generate appropriate advertising styles.

[0221] Step 4:

[0222] The user's device uses its camera and microphone to analyze their emotions. The input is the user's captured video and audio data. The output is data indicating the user's emotional state. This process utilizes the Google Cloud Vision API to analyze emotions in real time.

[0223] Step 5:

[0224] The server dynamically adjusts the ad design based on the analyzed sentiment information. The input includes user sentiment data, and the output is an ad design tailored to the user. Specifically, the generative AI model optimizes the ad style by adjusting the prompt text based on the obtained sentiment information.

[0225] Step 6:

[0226] The server delivers optimized advertisements to multiple communication infrastructures. The input is the optimized advertisement design, and the output is the display of the advertisement under conditions suitable for each infrastructure. In this process, data calculations are performed to optimize the format according to the medium.

[0227] Step 7:

[0228] Users view optimized advertisements through their devices. Personalized content is displayed based on the user's mood, promoting engagement. When a user is relaxed, advertisements with a calming tone are displayed. This process allows for the measurement of ad effectiveness and provides data feedback to the server.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] As an embodiment of this invention, a system for automatically generating advertisements using commercial transaction information will be described.

[0246] When a user promotes a product, they first enter an electronic link to the product they want to advertise through an interface installed on their device. This link is the URL of the webpage where the target product is listed. Upon receiving this request, the server accesses the linked page and automatically retrieves visual and linguistic information about the product using methods such as web scraping. This includes product images, product descriptions, and prices.

[0247] Next, the server uses a generative AI model to automatically generate advertising media formats based on the acquired information. This model leverages pre-trained data to generate various advertising patterns and copy. As a result, multiple advertising proposals that are visually appealing and have a clear message are presented.

[0248] As a concrete example, in the case of online shop products, when a user enters the product URL, the server extracts the product name, image, price, etc., and a generation AI model automatically generates an advertisement with eye-catching visual design along with copy such as "Limited-time sale! Offered at a special price." The generated advertisement is then applied to multiple design templates and presented to the user as a choice.

[0249] After the user selects their desired ad design, the server optimizes the selected design for each communication infrastructure. This process includes, for example, changing image sizes to suit different social media platforms and adjusting copy based on the delivery algorithm.

[0250] Ultimately, the server has the capability to track ad performance and measure its effectiveness. This allows users to see the actual ad performance and continuously update to the most effective ads.

[0251] Thus, the present invention enables effective marketing activities by automatically generating advertisements from commercial transaction information and applying them to various communication infrastructures in an optimized form.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The user will use their own device to enter the electronic link of the product they want to advertise into a dedicated input field. After entering the information, the user will click the submit button to send the information to the server.

[0255] Step 2:

[0256] The server receives an electronic link sent by the user. The server parses this link and uses web scraping techniques to access the linked page in order to collect the necessary product information. Here, information such as product images, descriptions, and prices is automatically retrieved.

[0257] Step 3:

[0258] The server inputs the acquired product information into a generating AI model. Based on past data, the generating AI model automatically generates visually appealing advertising designs and creative taglines.

[0259] Step 4:

[0260] The server generates ad designs and copy, which are then applied to multiple ad templates. The server creates previews of these templates and presents them to the user as options.

[0261] Step 5:

[0262] The user selects the most suitable design from the ad templates provided by the server. The user can complete the selection with a simple click.

[0263] Step 6:

[0264] Based on user selections, the server optimizes the chosen ad design for different communication infrastructures. This optimization includes adjusting image sizes and simplifying language for each platform.

[0265] Step 7:

[0266] The server creates different ad variations and automatically conducts A / B testing. Each version of the ad is presented to different user groups, and the effectiveness of the ads is measured.

[0267] Step 8:

[0268] Users can view A / B test results updated in real time by the server from their devices. The server recommends the most effective ad design, and users can adjust their final ad strategy.

[0269] (Example 1)

[0270] 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."

[0271] In modern commerce, a wide variety of products are sold online, requiring the rapid and efficient generation of effective promotional materials for each product. However, traditional advertising methods face challenges in that acquiring visual and linguistic information about products and generating and optimizing advertisements requires considerable effort and time. Furthermore, optimizing and effectively delivering advertisements across different information and communication infrastructures is not easy.

[0272] 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.

[0273] In this invention, the server includes means for receiving electronic information containing commercial transaction information, means for automatically acquiring visual and linguistic data related to products from the electronic information, and means for inputting the acquired data as prompt text using a generation AI model to generate advertisements. This enables the efficient automatic generation of product advertisements, as well as the creation and distribution of optimal electronic advertisements compatible with different information and communication infrastructures.

[0274] "Commercial transaction information" refers to data related to the transaction of goods and services, primarily including information such as price, product description, and inventory status.

[0275] "Electronic information" refers to information expressed in digital format and data that can be transmitted and received via a network.

[0276] "Visual data" refers to data represented in a visually recognizable format, such as images or videos.

[0277] "Linguistic data" refers to data expressed as strings or text, including information such as product descriptions and taglines.

[0278] "Advertising information" refers to content created to promote products or services to consumers, and includes both visual and linguistic elements.

[0279] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate new content from data.

[0280] A "prompt statement" is a text-based instruction given to an AI model to provide specific generation instructions.

[0281] "Information and communication infrastructure" refers to the entire network and system that enables the transmission and reception of data, and includes communication hardware and software.

[0282] "Optimized digital advertising" refers to advertisements that have been adapted to the most suitable format and structure for different information and communication infrastructures and platforms.

[0283] This invention is implemented as a system for accurately and effectively utilizing commercial transaction information for advertising generation. The user enters an electronic link (URL) of the product they wish to promote via an interface on their terminal. This information is immediately transmitted to the server.

[0284] The server accesses the corresponding web page using the received electronic link and automatically obtains visual data (such as product images) and language data (such as product descriptions, price information, etc.) regarding the product using web scraping technology. As an example, the Python library BeautifulSoup can be used.

[0285] Based on these acquired data, the server automatically generates advertisements using a generative AI model. Product information is input into this AI model as a prompt sentence. A prompt sentence such as "Product name: Summer Sale Limited Sandals, Main features: Lightweight, comfortable, waterproof, Sale information: 30% off" can be used. Various generative AI algorithms can be utilized as the generative AI model.

[0286] The generated advertisements are presented in visually appealing and diverse templates. The user can select according to their preference from the multiple generated advertisement designs through the terminal. The selected advertisement is further optimized according to the information and communication infrastructure. For example, the image size is adjusted and the message is finely tuned for different SNS platforms. Image processing tools such as the Python Imaging Library (PIL) are used for this.

[0287] Finally, the server has the function of tracking the performance of the advertisement in real time and analyzing its effectiveness. As a result, the user can continuously improve an effective advertising strategy. Through this system, an efficient and effective marketing activity leveraging commercial transaction information is realized.

[0288] The flow of the specific process in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The user enters the URL of the webpage for the product they wish to promote using the terminal's interface. The entered URL is sent to the information system. The input data is in URL format and is used to identify the product information page.

[0291] Step 2:

[0292] The server accesses the webpage based on the URL received from the user. It issues an HTTP request to retrieve the HTML data of the webpage. This input data is the content of the webpage, and the output is visual data (product images) and linguistic data (product descriptions, price information) related to the product. Web scraping is performed using analysis tools such as BeautifulSoup.

[0293] Step 3:

[0294] The server combines the acquired visual and linguistic data to create prompt statements for the generative AI model. These prompt statements are used as input data for the AI ​​model. Specifically, they are summarized in the format of "Product Name: Summer Sale Limited Sandals, Key Features: Lightweight, Comfortable, Waterproof, Sale Information: 30% Off".

[0295] Step 4:

[0296] The generative AI model receives prompt text as input and generates visually and linguistically engaging advertising content. This generation process creates diverse advertising patterns based on historical data learning. The output data is in a format applicable to multiple advertising templates.

[0297] Step 5:

[0298] The server applies the generated ads to different templates and presents them to the user via the terminal. The user can choose from multiple ad designs presented. The input data is the generated ad pattern, and the output data is the ad design selected by the user.

[0299] Step 6:

[0300] The server optimizes the user-selected ad design for various information and communication infrastructures. Specifically, it adjusts image sizes and fine-tunes messages. The input data is the user-selected ad design, and the optimized version becomes the output data. Image processing tools such as PIL are used in this process.

[0301] Step 7:

[0302] The server delivers optimized advertisements to each information and communication infrastructure and tracks their performance. After delivery, the effectiveness of the advertisements is measured based on data such as impressions and click-through rates. Users can then improve their advertising strategies based on this performance data.

[0303] (Application Example 1)

[0304] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0305] In today's world, there is a growing need to appeal to consumers more quickly and effectively with individual products and services. However, creating advertisements is time-consuming and labor-intensive, and the need for optimization tailored to each online platform presents challenges requiring specialized knowledge. Furthermore, there is a lack of systems to track advertisement performance in real time and continuously improve it in the most optimal way. Against this backdrop, there is a need for an environment that automates the ad generation and optimization process, allowing users to easily create highly effective advertisements.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0307] In this invention, the server includes a device that receives an electronic link containing commercial transaction information, a device that automatically acquires image information and text information regarding a product from the electronic link, and a device that automatically generates the format of an electronic advertising medium using the acquired image information and text information. As a result, even without specialized knowledge, users can automatically generate optimized advertisements adapted to various communication platforms quickly and efficiently, and can easily realize a continuous advertising strategy that maximizes the effect.

[0308] "Commercial transaction information" is digital data used to provide details of products and services.

[0309] "Electronic link" is a URL that enables access to a specific web page on the Internet.

[0310] "Device" is an aggregate of hardware or software designed to process, acquire, generate, and display commercial transaction information.

[0311] "Image information" is digital image data indicating the visual characteristics of a product.

[0312] "Text information" is language data used to explain a product and its features, advantages, etc.

[0313] "Electronic advertising medium" is the digital format of advertising content distributed on a digital platform such as the Internet.

[0314] "Generation AI module" is a system or program that automatically generates advertising copy using artificial intelligence technology.

[0315] A "design template" is a pre-configured design layout that can be easily used as a visual component of an advertisement.

[0316] A "communication platform" is a digital network or service on which electronic advertisements are delivered.

[0317] "Performance tracking" is the process of quantitatively measuring and evaluating the effectiveness and impact of advertising.

[0318] The system for implementing this invention automatically generates advertisements based on commercial transaction information and deploys optimized advertisements across multiple communication platforms. The main components of the system are a server, terminals, and a user interface.

[0319] The server receives electronic links entered by users and automatically retrieves image and text information about products from the linked web pages using web scraping techniques. The specific software used is Beautiful Soup in Python. The retrieved information is used to generate advertising copy via a generative AI model. Here, the AI ​​model used is Hugging Face's Transformers.

[0320] Users provide the system with electronic links to the products they wish to advertise via their devices. These links are URLs pointing to the web pages to be analyzed. The devices are equipped with a user interface for information input, allowing users to intuitively provide links.

[0321] Along with the generated ad copy, multiple design templates are automatically generated using the Figma API and presented as visually appealing ad layouts. The user selects the most suitable ad design from these options, and the server optimizes the selected design for different communication platforms and generates the electronic ad in a format appropriate for each.

[0322] Ultimately, the server tracks ad performance via the Google Analytics API and provides the results to users, helping them continuously optimize their advertising strategies.

[0323] For example, if a user wants to create an advertisement for a "new smartphone," they would enter the product's URL through an input interface. The server would then retrieve product information from the URL and use a generation AI model to generate advertising copy such as, "Limited time offer! Get the latest smartphone with cutting-edge technology at a special price!"

[0324] Examples of prompts for a generative AI model are as follows:

[0325] "Generate catchy and memorable ad copy from the following data: Product Name: New Smartphone, Product Description: Equipped with the latest technology, Price: Special Price"

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] The user enters an electronic link for the product they want to advertise through the device's user interface. This input is sent to the server as a web page URL.

[0329] Step 2:

[0330] The server uses the received electronic link to web scrape the linked page using the Python Beautiful Soup library. This retrieves image and text information related to the product. The input is a URL, and the output is an image of the product and a descriptive text.

[0331] Step 3:

[0332] The server inputs the acquired image and text information, along with prompt text, into the Hugging Face Transformers, an AI model for generating content. Here, the AI ​​model automatically generates advertising copy. The input to this process is product information, and the output is a message-driven advertising copy.

[0333] Step 4:

[0334] The server uses the Figma API to automatically create multiple ad design templates, including the generated ad copy. The input for this step is the ad copy, and the output is multiple design options. The server then presents these to the user.

[0335] Step 5:

[0336] The user selects the most suitable design from the presented design templates. This selection is sent to the server via the device.

[0337] Step 6:

[0338] The server optimizes the selected ad design for various communication platforms. For example, it converts images to the appropriate size and format for each social networking platform. The input is the selected design proposal, and the output is the ad design optimized for each platform.

[0339] Step 7:

[0340] Optimized ads are delivered to each communication platform. The server tracks ad performance in real time via the Google Analytics API and provides feedback to the user. The input is the ad design, and the output is performance data.

[0341] 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.

[0342] As an embodiment of this invention, a system is described that automatically generates advertisements using information related to commercial transactions and improves the effectiveness of the advertisements by utilizing user emotions.

[0343] When a user promotes a product using their device, they first enter an electronic link to the product they want to advertise into a dedicated input interface. After receiving this link, the server accesses the linked page and collects the necessary product information. The server uses web scraping technology to automatically retrieve product images, descriptions, price information, and other relevant data.

[0344] Next, the server uses a generative AI model to generate advertisement designs based on the acquired information. The generative AI model creates advertisement styles and structures suitable for the product characteristics and presents them to the user as multiple options.

[0345] Furthermore, the system is equipped with an emotion engine that analyzes the user's emotions based on data obtained from the user's device. The emotion engine utilizes video data from the camera, audio data, and other sources to determine the user's emotional state.

[0346] As a concrete example, suppose a user is browsing a product page on an online store, and the system determines, based on facial expression data obtained through the camera, that the user's emotions at that time are positive. In this case, the server will prioritize displaying bright and attractive ad designs that reflect the user's positive emotions.

[0347] The server also leverages this sentiment information to dynamically adjust the language of the advertisement, displaying the message that best resonates with the user's current mood. For example, if the user is relaxed, the copy will be written in a calm tone.

[0348] This system personalizes the user experience and improves ad engagement by generating and adjusting ads based on sentiment analysis. Furthermore, selected designs are optimized for each platform to ensure the most effective ads are displayed across various communication infrastructures.

[0349] Thus, the present invention enhances the user experience and maximizes the effectiveness of advertising in commercial transactions by combining an emotion engine with the generation and optimization of advertisements.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] The user will use their device to enter an electronic link for the product they want to advertise. This operation is performed through a dedicated web interface, and the electronic link is sent to the server.

[0353] Step 2:

[0354] The server receives an electronic link sent by the user. The server accesses the specified webpage and automatically retrieves visual and linguistic information related to the product using web scraping techniques. This includes product images, product descriptions, and prices.

[0355] Step 3:

[0356] The server inputs the acquired product information into a generation AI model. This model automatically generates advertising media formats and copy based on visual and linguistic information. The generated advertising proposals are presented to the user as multiple templates.

[0357] Step 4:

[0358] The emotion engine built into the user's device activates. This engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. Camera footage and microphone audio are used for this purpose.

[0359] Step 5:

[0360] The server receives the results analyzed by the emotion engine and optimizes the design and copy of the advertisements according to the user's emotional state. If positive emotions are detected, it prioritizes bright, engaging designs and compelling messages.

[0361] Step 6:

[0362] The server delivers optimized advertisements in a format compatible with multiple communication infrastructures. Optimized ad templates are created for each platform, with adjustments made, for example, for social media and search engines.

[0363] Step 7:

[0364] Users can monitor the effectiveness of their ads in real time through a dashboard provided on their device. The server aggregates performance data from ad campaigns and provides users with feedback on the most effective strategies.

[0365] Step 8:

[0366] Based on the data provided by the server, users adjust their next advertising strategy based on the results of their ads. This enables continuous ad optimization.

[0367] (Example 2)

[0368] 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".

[0369] In commercial transactions, it is necessary to improve ad engagement and effectiveness by generating and dynamically adjusting ads based on the user's emotional state. However, conventional ad generation systems struggle to provide a personalized ad experience that takes user emotions into account, which can result in missed business opportunities. Therefore, there is a need for personalized ad generation systems that utilize emotional information.

[0370] 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.

[0371] In this invention, the server includes means for receiving electronic links containing commercial transaction information, means for automatically acquiring visual and linguistic information about a product from the electronic links, means for automatically generating advertising media using a generative model with the acquired visual and linguistic information, means for determining the emotional state based on video or audio data received from the user, means for dynamically adjusting the generated advertising media according to the emotional state, means for presenting the user with multiple advertising media and allowing them to select the optimal advertisement, means for optimizing the generated advertising media for multiple communication infrastructures, and means for measuring the effectiveness of the electronic advertisement and presenting the optimized advertisement. This enables the generation and optimization of personalized advertisements that reflect the user's real-time emotional state.

[0372] "Commercial transactions" is a concept that refers to acts and information related to the buying and selling of goods and services.

[0373] "Electronic link" refers to URLs and URIs, which are identifying information that points to a specific web page or resource on the internet.

[0374] "Visual information" refers to information related to product images and visual design, and is acquired as video data.

[0375] "Linguistic information" refers to text data that describes a product, its features, price, and other relevant information.

[0376] A "generative model" is a software algorithm that uses machine learning or artificial intelligence to generate specific deliverables (in this case, advertising designs).

[0377] An "advertising medium" is a visual and text format created to convey information about products or services to users.

[0378] "Communication infrastructure" refers to the infrastructure for delivering and displaying electronic advertisements, and includes web platforms and applications.

[0379] "Emotional state" refers to the user's psychological or emotional condition, and is analyzed from video and audio data.

[0380] "Dynamically adjusting" means changing or updating advertising content in real time according to the user's situation and emotions.

[0381] "Measuring effectiveness" refers to quantitatively or qualitatively evaluating the extent to which the generated advertisements are achieving their objectives.

[0382] This invention is a system that optimizes advertisements by leveraging user emotions based on information related to commercial transactions. The system consists mainly of a server and terminals and operates as follows.

[0383] The server first receives an electronic link from the user. This is the URL to the webpage where the product to be advertised is listed. Next, the server accesses this page using web scraping techniques and automatically collects visual and linguistic information such as product images, descriptions, and pricing information. Libraries such as Beautiful Soup and Scrapy are used for this collection.

[0384] Subsequently, the server uses a generative AI model to create an advertisement design based on the collected information. The generative AI model used utilizes generative modeling technology to automatically generate an advertisement style and structure suitable for the product's characteristics. Commands such as "Generate an attractive advertisement design based on this product" are given as prompts to the generative AI model.

[0385] The device collects user emotion data using the user's camera, microphone, and other devices. This data is sent to the server in the form of a video stream or audio recording. The server uses this emotion data to analyze the user's emotional state using an emotion engine. For this analysis, emotion recognition software such as Azure Emotion API and Face++ are utilized.

[0386] The server then dynamically adjusts the generated advertisements based on the user's emotional state, which is the result of the analysis. If a positive emotion is detected, the advertisement is adjusted to be brighter and more appealing. In this way, the advertisements are optimized for the user's real-time emotions, and high engagement is expected.

[0387] As a concrete example of this behavior, if a user is browsing a new smartphone page and their emotional state is analyzed to be relaxed and positive, the server may generate an ad design that includes calming and appealing visuals.

[0388] This system aims to enable personalized advertising experiences for users and enhance the effectiveness of advertising in commercial transactions.

[0389] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0390] Step 1:

[0391] The user enters the URL of the webpage for the product they want to advertise into the input interface on their device. The device then sends this URL to the server. Input: Product webpage URL. Output: URL sent to the server.

[0392] Step 2:

[0393] The server accesses the webpage based on the received URL. Specifically, it issues an HTTP request and retrieves page data. The retrieved data includes product images, descriptions, and price information. The server then analyzes this information using web scraping techniques and extracts it as visual and linguistic information. Input: Product webpage data. Output: Visual and linguistic information about the product.

[0394] Step 3:

[0395] The server uses the acquired visual and linguistic information to generate an ad design by inputting prompt text into a generative AI model. This prompt text includes specific instructions such as "Generate an attractive ad design based on this product." The generative AI model automatically generates the visual format of the ad from this information. Input: Visual and linguistic information, prompt text. Output: Generated ad design.

[0396] Step 4:

[0397] The device uses the user's camera and microphone to collect emotional data such as facial expressions and voice. The device then sends this data to the server. Input: User's video and audio data. Output: Emotional data sent to the server.

[0398] Step 5:

[0399] The server analyzes the received emotional data using an emotion engine to determine the user's emotional state. Specifically, it analyzes video and audio data and assigns emotional labels such as positive or relaxed. Input: User's emotional data. Output: User's emotional state.

[0400] Step 6:

[0401] The server dynamically adjusts the generated ad design based on the user's emotional state. If the emotion is positive, it adjusts brightness, color tone, and message to create an appealing ad. This includes adjusting colors and changing the tone of the copy. Input: Generated ad design, user's emotional state. Output: Optimized ad design.

[0402] Step 7:

[0403] The user views multiple optimized ad designs on their device and selects the one they deem best. Input: Options for optimized ad designs. Output: The selected ad design.

[0404] In this way, there is a clear input and output flow at each step, and the system is designed to optimize advertisements based on the user's emotional state.

[0405] (Application Example 2)

[0406] 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."

[0407] Conventional ad generation systems have faced challenges in personalizing ads based on user emotions, resulting in limited user engagement. This invention aims to dynamically optimize ads based on user emotions, thereby improving ad effectiveness and user experience.

[0408] 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.

[0409] In this invention, the server includes means for receiving electronic links containing commercial transaction information, means for automatically acquiring visual and linguistic information about products from the electronic links, means for automatically generating the format of advertising media using the acquired visual and linguistic information, means for analyzing the user's emotions via the user's terminal and dynamically adjusting the advertising design based on that emotional information, means for generating optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media, and means for measuring the effectiveness of electronic advertisements and presenting optimized electronic advertisements. This makes it possible to personalize advertisements according to the user's emotions and improve user engagement.

[0410] "Commercial transaction information" refers to digital information related to the purchase or sale of goods, and is expressed as electronic links.

[0411] An "electronic link" is a hyperlink that allows access to specific information or pages on the internet.

[0412] "Visual information" is a general term for image data and digital visual content related to a product.

[0413] "Linguistic information" refers to text data and descriptions related to a product.

[0414] "Advertising media style" refers to the style of design and format that makes up an advertisement.

[0415] "User emotions" refer to the user's psychological state and sensitivities, and are analyzed using an emotion engine.

[0416] "Communication infrastructure" refers to the platform and network infrastructure used to send and receive digital data.

[0417] "Optimized digital advertising" refers to digital advertising content that has been tailored to the user's characteristics and where it will be displayed.

[0418] "Engagement" refers to the degree of interest and response that users show to advertising content.

[0419] The system for realizing this invention is a process that utilizes information from commercial transactions to optimize advertisements according to the user's emotions. Basically, a server receives an electronic link containing commercial transaction information. This automatically acquires visual and linguistic information about the product using web scraping technology. The software used includes "Beautiful Soup" for web scraping data and "OpenAI API" for the generative AI model. Based on this information, the generative AI model generates the format of the advertising medium and presents it to the user.

[0420] Next, the user's device analyzes their emotions in real time using its camera and microphone. This emotion analysis utilizes the "Google Cloud Vision API," which leverages facial recognition and voice analysis technologies. The analyzed emotion information is sent to a server, and the ad design is dynamically adjusted based on that emotion.

[0421] The generated advertisements are optimized and delivered across multiple communication platforms. This optimization includes parameter adjustments based on user characteristics and the media to which the advertisements are displayed. As a result, users see advertisements that are relevant to them, leading to improved engagement.

[0422] As a concrete example, when a user is relaxing in their living room, the server presents advertisements with colors and tones that reflect that state. The generative AI model generates advertisements using prompts such as: "Generate an advertisement for a home projector with a tone that is appropriate for when the user is relaxed." This process is expected to ensure that the advertisements consistently fit the user's emotions and enhance commercial success.

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] The server receives electronic links containing commercial transaction information. The input is an electronic link provided by the user, and the server uses this link to access the product page. The output is the HTML data of the target product's webpage. During this process, the server checks the reliability of the link and verifies that it is accessible.

[0426] Step 2:

[0427] The server uses web scraping techniques to obtain visual and linguistic information about products. It uses the previously obtained HTML data as input and processes the data. The output consists of visual and linguistic information such as product images, descriptions, and price information. Specifically, it uses "Beautiful Soup" to parse the HTML and extract the necessary data.

[0428] Step 3:

[0429] The server uses a generative AI model to generate advertising media formats based on acquired product information. Input data includes product information, price, and description, while output consists of multiple advertising design proposals. During this process, the generative AI model is prompted with prompts to perform data calculations to generate appropriate advertising styles.

[0430] Step 4:

[0431] The user's device uses its camera and microphone to analyze their emotions. The input is the user's captured video and audio data. The output is data indicating the user's emotional state. This process utilizes the Google Cloud Vision API to analyze emotions in real time.

[0432] Step 5:

[0433] The server dynamically adjusts the ad design based on the analyzed sentiment information. The input includes user sentiment data, and the output is an ad design tailored to the user. Specifically, the generative AI model optimizes the ad style by adjusting the prompt text based on the obtained sentiment information.

[0434] Step 6:

[0435] The server delivers optimized advertisements to multiple communication infrastructures. The input is the optimized advertisement design, and the output is the display of the advertisement under conditions suitable for each infrastructure. In this process, data calculations are performed to optimize the format according to the medium.

[0436] Step 7:

[0437] Users view optimized advertisements through their devices. Personalized content is displayed based on the user's mood, promoting engagement. When a user is relaxed, advertisements with a calming tone are displayed. This process allows for the measurement of ad effectiveness and provides data feedback to the server.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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".

[0454] As an embodiment of this invention, a system for automatically generating advertisements using commercial transaction information will be described.

[0455] When a user promotes a product, they first enter an electronic link to the product they want to advertise through an interface installed on their device. This link is the URL of the webpage where the target product is listed. Upon receiving this request, the server accesses the linked page and automatically retrieves visual and linguistic information about the product using methods such as web scraping. This includes product images, product descriptions, and prices.

[0456] Next, the server uses a generative AI model to automatically generate advertising media formats based on the acquired information. This model leverages pre-trained data to generate various advertising patterns and copy. As a result, multiple advertising proposals that are visually appealing and have a clear message are presented.

[0457] As a concrete example, in the case of online shop products, when a user enters the product URL, the server extracts the product name, image, price, etc., and a generation AI model automatically generates an advertisement with eye-catching visual design along with copy such as "Limited-time sale! Offered at a special price." The generated advertisement is then applied to multiple design templates and presented to the user as a choice.

[0458] After the user selects their desired ad design, the server optimizes the selected design for each communication infrastructure. This process includes, for example, changing image sizes to suit different social media platforms and adjusting copy based on the delivery algorithm.

[0459] Ultimately, the server has the capability to track ad performance and measure its effectiveness. This allows users to see the actual ad performance and continuously update to the most effective ads.

[0460] Thus, the present invention enables effective marketing activities by automatically generating advertisements from commercial transaction information and applying them to various communication infrastructures in an optimized form.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The user will use their own device to enter the electronic link of the product they want to advertise into a dedicated input field. After entering the information, the user will click the submit button to send the information to the server.

[0464] Step 2:

[0465] The server receives an electronic link sent by the user. The server parses this link and uses web scraping techniques to access the linked page in order to collect the necessary product information. Here, information such as product images, descriptions, and prices is automatically retrieved.

[0466] Step 3:

[0467] The server inputs the acquired product information into a generating AI model. Based on past data, the generating AI model automatically generates visually appealing advertising designs and creative taglines.

[0468] Step 4:

[0469] The server generates ad designs and copy, which are then applied to multiple ad templates. The server creates previews of these templates and presents them to the user as options.

[0470] Step 5:

[0471] The user selects the most suitable design from the ad templates provided by the server. The user can complete the selection with a simple click.

[0472] Step 6:

[0473] Based on user selections, the server optimizes the chosen ad design for different communication infrastructures. This optimization includes adjusting image sizes and simplifying language for each platform.

[0474] Step 7:

[0475] The server creates different ad variations and automatically conducts A / B testing. Each version of the ad is presented to different user groups, and the effectiveness of the ads is measured.

[0476] Step 8:

[0477] Users can view A / B test results updated in real time by the server from their devices. The server recommends the most effective ad design, and users can adjust their final ad strategy.

[0478] (Example 1)

[0479] 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."

[0480] In modern commerce, a wide variety of products are sold online, requiring the rapid and efficient generation of effective promotional materials for each product. However, traditional advertising methods face challenges in that acquiring visual and linguistic information about products and generating and optimizing advertisements requires considerable effort and time. Furthermore, optimizing and effectively delivering advertisements across different information and communication infrastructures is not easy.

[0481] 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.

[0482] In this invention, the server includes means for receiving electronic information containing commercial transaction information, means for automatically acquiring visual and linguistic data related to products from the electronic information, and means for inputting the acquired data as prompt text using a generation AI model to generate advertisements. This enables the efficient automatic generation of product advertisements, as well as the creation and distribution of optimal electronic advertisements compatible with different information and communication infrastructures.

[0483] "Commercial transaction information" refers to data related to the transaction of goods and services, primarily including information such as price, product description, and inventory status.

[0484] "Electronic information" refers to information expressed in digital format and data that can be transmitted and received via a network.

[0485] "Visual data" refers to data represented in a visually recognizable format, such as images or videos.

[0486] "Linguistic data" refers to data expressed as strings or text, including information such as product descriptions and taglines.

[0487] "Advertising information" refers to content created to promote products or services to consumers, and includes both visual and linguistic elements.

[0488] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate new content from data.

[0489] A "prompt statement" is a text-based instruction given to an AI model to provide specific generation instructions.

[0490] "Information and communication infrastructure" refers to the entire network and system that enables the transmission and reception of data, and includes communication hardware and software.

[0491] "Optimized digital advertising" refers to advertisements that have been adapted to the most suitable format and structure for different information and communication infrastructures and platforms.

[0492] This invention is implemented as a system for accurately and effectively utilizing commercial transaction information for advertising generation. The user enters an electronic link (URL) of the product they wish to promote via an interface on their terminal. This information is immediately transmitted to the server.

[0493] The server uses the received electronic link to access the corresponding webpage and automatically retrieves visual data (such as product images) and linguistic data (such as product descriptions and price information) related to the product using web scraping techniques. For example, the Python library BeautifulSoup can be used.

[0494] Based on this acquired data, the server automatically generates advertisements using a generative AI model. This AI model takes product information as prompt text. For example, prompt text such as "Product Name: Summer Sale Limited Sandals, Key Features: Lightweight, Comfortable, Waterproof, Sale Information: 30% Off" can be used. Various generative AI algorithms can be used for the generative AI model.

[0495] The generated advertisements are presented in visually appealing and diverse templates. Users can select from multiple generated ad designs according to their preferences via their devices. The selected ad is further optimized according to the information and communication infrastructure. For example, image sizes are adjusted and messages are fine-tuned for different social networking platforms. Image processing tools such as the Python Imaging Library (PIL) are used for this purpose.

[0496] Ultimately, the server has the capability to track and analyze advertising performance in real time. This allows users to continuously improve their advertising strategies. This system enables efficient and effective marketing activities that leverage commercial transaction information.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] The user enters the URL of the webpage for the product they wish to promote using the terminal's interface. The entered URL is sent to the information system. The input data is in URL format and is used to identify the product information page.

[0500] Step 2:

[0501] The server accesses the webpage based on the URL received from the user. It issues an HTTP request to retrieve the HTML data of the webpage. This input data is the content of the webpage, and the output is visual data (product images) and linguistic data (product descriptions, price information) related to the product. Web scraping is performed using analysis tools such as BeautifulSoup.

[0502] Step 3:

[0503] The server combines the acquired visual and linguistic data to create prompt statements for the generative AI model. These prompt statements are used as input data for the AI ​​model. Specifically, they are summarized in the format of "Product Name: Summer Sale Limited Sandals, Key Features: Lightweight, Comfortable, Waterproof, Sale Information: 30% Off".

[0504] Step 4:

[0505] The generative AI model receives prompt text as input and generates visually and linguistically engaging advertising content. This generation process creates diverse advertising patterns based on historical data learning. The output data is in a format applicable to multiple advertising templates.

[0506] Step 5:

[0507] The server applies the generated ads to different templates and presents them to the user via the terminal. The user can choose from multiple ad designs presented. The input data is the generated ad pattern, and the output data is the ad design selected by the user.

[0508] Step 6:

[0509] The server optimizes the user-selected ad design for various information and communication infrastructures. Specifically, it adjusts image sizes and fine-tunes messages. The input data is the user-selected ad design, and the optimized version becomes the output data. Image processing tools such as PIL are used in this process.

[0510] Step 7:

[0511] The server delivers optimized advertisements to each information and communication infrastructure and tracks their performance. After delivery, the effectiveness of the advertisements is measured based on data such as impressions and click-through rates. Users can then improve their advertising strategies based on this performance data.

[0512] (Application Example 1)

[0513] 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."

[0514] In today's world, there is a growing need to appeal to consumers more quickly and effectively with individual products and services. However, creating advertisements is time-consuming and labor-intensive, and the need for optimization tailored to each online platform presents challenges requiring specialized knowledge. Furthermore, there is a lack of systems to track advertisement performance in real time and continuously improve it in the most optimal way. Against this backdrop, there is a need for an environment that automates the ad generation and optimization process, allowing users to easily create highly effective advertisements.

[0515] 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.

[0516] In this invention, the server includes a device for receiving electronic links containing commercial transaction information, a device for automatically acquiring image and text information about a product from the electronic links, and a device for automatically generating the format of an electronic advertising medium using the acquired image and text information. This makes it possible for users to quickly and efficiently automatically generate optimized advertisements adapted to various communication platforms, even without specialized knowledge, and to easily implement a continuous advertising strategy that maximizes effectiveness.

[0517] "Commercial transaction information" refers to digital data used to provide details about products and services.

[0518] An "electronic link" is a URL that allows access to a specific webpage on the internet.

[0519] "Device" refers to a collection of hardware or software designed to process, acquire, generate, and display commercial transaction information.

[0520] "Image information" refers to digital image data that shows the visual characteristics of a product.

[0521] "Text information" refers to linguistic data used to describe a product, its features, benefits, etc.

[0522] "Electronic advertising media" refers to the digital format of advertising content delivered on digital platforms such as the internet.

[0523] A "Generating AI Module" is a system or program that uses artificial intelligence technology to automatically generate advertising copy.

[0524] A "design template" is a pre-configured design layout that can be easily used as a visual component of an advertisement.

[0525] A "communication platform" is a digital network or service on which electronic advertisements are delivered.

[0526] "Performance tracking" is the process of quantitatively measuring and evaluating the effectiveness and impact of advertising.

[0527] The system for implementing this invention automatically generates advertisements based on commercial transaction information and deploys optimized advertisements across multiple communication platforms. The main components of the system are a server, terminals, and a user interface.

[0528] The server receives electronic links entered by users and automatically retrieves image and text information about products from the linked web pages using web scraping techniques. The specific software used is Beautiful Soup in Python. The retrieved information is used to generate advertising copy via a generative AI model. Here, the AI ​​model used is Hugging Face's Transformers.

[0529] Users provide the system with electronic links to the products they wish to advertise via their devices. These links are URLs pointing to the web pages to be analyzed. The devices are equipped with a user interface for information input, allowing users to intuitively provide links.

[0530] Along with the generated ad copy, multiple design templates are automatically generated using the Figma API and presented as visually appealing ad layouts. The user selects the most suitable ad design from these options, and the server optimizes the selected design for different communication platforms and generates the electronic ad in a format appropriate for each.

[0531] Ultimately, the server tracks ad performance via the Google Analytics API and provides the results to users, helping them continuously optimize their advertising strategies.

[0532] For example, if a user wants to create an advertisement for a "new smartphone," they would enter the product's URL through an input interface. The server would then retrieve product information from the URL and use a generation AI model to generate advertising copy such as, "Limited time offer! Get the latest smartphone with cutting-edge technology at a special price!"

[0533] Examples of prompts for a generative AI model are as follows:

[0534] "Generate catchy and memorable ad copy from the following data: Product Name: New Smartphone, Product Description: Equipped with the latest technology, Price: Special Price"

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] The user enters an electronic link for the product they want to advertise through the device's user interface. This input is sent to the server as a web page URL.

[0538] Step 2:

[0539] The server uses the received electronic link to web scrape the linked page using the Python Beautiful Soup library. This retrieves image and text information related to the product. The input is a URL, and the output is an image of the product and a descriptive text.

[0540] Step 3:

[0541] The server inputs the acquired image and text information, along with prompt text, into the Hugging Face Transformers, an AI model for generating content. Here, the AI ​​model automatically generates advertising copy. The input to this process is product information, and the output is a message-driven advertising copy.

[0542] Step 4:

[0543] The server uses the Figma API to automatically create multiple ad design templates, including the generated ad copy. The input for this step is the ad copy, and the output is multiple design options. The server then presents these to the user.

[0544] Step 5:

[0545] The user selects the most suitable design from the presented design templates. This selection is sent to the server via the device.

[0546] Step 6:

[0547] The server optimizes the selected ad design for various communication platforms. For example, it converts images to the appropriate size and format for each social networking platform. The input is the selected design proposal, and the output is the ad design optimized for each platform.

[0548] Step 7:

[0549] Optimized ads are delivered to each communication platform. The server tracks ad performance in real time via the Google Analytics API and provides feedback to the user. The input is the ad design, and the output is performance data.

[0550] 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.

[0551] As an embodiment of this invention, a system is described that automatically generates advertisements using information related to commercial transactions and improves the effectiveness of the advertisements by utilizing user emotions.

[0552] When a user promotes a product using their device, they first enter an electronic link to the product they want to advertise into a dedicated input interface. After receiving this link, the server accesses the linked page and collects the necessary product information. The server uses web scraping technology to automatically retrieve product images, descriptions, price information, and other relevant data.

[0553] Next, the server uses a generative AI model to generate advertisement designs based on the acquired information. The generative AI model creates advertisement styles and structures suitable for the product characteristics and presents them to the user as multiple options.

[0554] Furthermore, the system is equipped with an emotion engine that analyzes the user's emotions based on data obtained from the user's device. The emotion engine utilizes video data from the camera, audio data, and other sources to determine the user's emotional state.

[0555] As a concrete example, suppose a user is browsing a product page on an online store, and the system determines, based on facial expression data obtained through the camera, that the user's emotions at that time are positive. In this case, the server will prioritize displaying bright and attractive ad designs that reflect the user's positive emotions.

[0556] The server also leverages this sentiment information to dynamically adjust the language of the advertisement, displaying the message that best resonates with the user's current mood. For example, if the user is relaxed, the copy will be written in a calm tone.

[0557] This system personalizes the user experience and improves ad engagement by generating and adjusting ads based on sentiment analysis. Furthermore, selected designs are optimized for each platform to ensure the most effective ads are displayed across various communication infrastructures.

[0558] Thus, the present invention enhances the user experience and maximizes the effectiveness of advertising in commercial transactions by combining an emotion engine with the generation and optimization of advertisements.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] The user will use their device to enter an electronic link for the product they want to advertise. This operation is performed through a dedicated web interface, and the electronic link is sent to the server.

[0562] Step 2:

[0563] The server receives an electronic link sent by the user. The server accesses the specified webpage and automatically retrieves visual and linguistic information related to the product using web scraping techniques. This includes product images, product descriptions, and prices.

[0564] Step 3:

[0565] The server inputs the acquired product information into a generation AI model. This model automatically generates advertising media formats and copy based on visual and linguistic information. The generated advertising proposals are presented to the user as multiple templates.

[0566] Step 4:

[0567] The emotion engine built into the user's device activates. This engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. Camera footage and microphone audio are used for this purpose.

[0568] Step 5:

[0569] The server receives the results analyzed by the emotion engine and optimizes the design and copy of the advertisements according to the user's emotional state. If positive emotions are detected, it prioritizes bright, engaging designs and compelling messages.

[0570] Step 6:

[0571] The server delivers optimized advertisements in a format compatible with multiple communication infrastructures. Optimized ad templates are created for each platform, with adjustments made, for example, for social media and search engines.

[0572] Step 7:

[0573] Users can monitor the effectiveness of their ads in real time through a dashboard provided on their device. The server aggregates performance data from ad campaigns and provides users with feedback on the most effective strategies.

[0574] Step 8:

[0575] Based on the data provided by the server, users adjust their next advertising strategy based on the results of their ads. This enables continuous ad optimization.

[0576] (Example 2)

[0577] 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."

[0578] In commercial transactions, it is necessary to improve ad engagement and effectiveness by generating and dynamically adjusting ads based on the user's emotional state. However, conventional ad generation systems struggle to provide a personalized ad experience that takes user emotions into account, which can result in missed business opportunities. Therefore, there is a need for personalized ad generation systems that utilize emotional information.

[0579] 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.

[0580] In this invention, the server includes means for receiving electronic links containing commercial transaction information, means for automatically acquiring visual and linguistic information about a product from the electronic links, means for automatically generating advertising media using a generative model with the acquired visual and linguistic information, means for determining the emotional state based on video or audio data received from the user, means for dynamically adjusting the generated advertising media according to the emotional state, means for presenting the user with multiple advertising media and allowing them to select the optimal advertisement, means for optimizing the generated advertising media for multiple communication infrastructures, and means for measuring the effectiveness of the electronic advertisement and presenting the optimized advertisement. This enables the generation and optimization of personalized advertisements that reflect the user's real-time emotional state.

[0581] "Commercial transactions" is a concept that refers to acts and information related to the buying and selling of goods and services.

[0582] "Electronic link" refers to URLs and URIs, which are identifying information that points to a specific web page or resource on the internet.

[0583] "Visual information" refers to information related to product images and visual design, and is acquired as video data.

[0584] "Linguistic information" refers to text data that describes a product, its features, price, and other relevant information.

[0585] A "generative model" is a software algorithm that uses machine learning or artificial intelligence to generate specific deliverables (in this case, advertising designs).

[0586] An "advertising medium" is a visual and text format created to convey information about products or services to users.

[0587] "Communication infrastructure" refers to the infrastructure for delivering and displaying electronic advertisements, and includes web platforms and applications.

[0588] "Emotional state" refers to the user's psychological or emotional condition, and is analyzed from video and audio data.

[0589] "Dynamically adjusting" means changing or updating advertising content in real time according to the user's situation and emotions.

[0590] "Measuring effectiveness" refers to quantitatively or qualitatively evaluating the extent to which the generated advertisements are achieving their objectives.

[0591] This invention is a system that optimizes advertisements by leveraging user emotions based on information related to commercial transactions. The system consists mainly of a server and terminals and operates as follows.

[0592] The server first receives an electronic link from the user. This is the URL to the webpage where the product to be advertised is listed. Next, the server accesses this page using web scraping techniques and automatically collects visual and linguistic information such as product images, descriptions, and pricing information. Libraries such as Beautiful Soup and Scrapy are used for this collection.

[0593] Subsequently, the server uses a generative AI model to create an advertisement design based on the collected information. The generative AI model used utilizes generative modeling technology to automatically generate an advertisement style and structure suitable for the product's characteristics. Commands such as "Generate an attractive advertisement design based on this product" are given as prompts to the generative AI model.

[0594] The device collects user emotion data using the user's camera, microphone, and other devices. This data is sent to the server in the form of a video stream or audio recording. The server uses this emotion data to analyze the user's emotional state using an emotion engine. For this analysis, emotion recognition software such as Azure Emotion API and Face++ are utilized.

[0595] The server then dynamically adjusts the generated advertisements based on the user's emotional state, which is the result of the analysis. If a positive emotion is detected, the advertisement is adjusted to be brighter and more appealing. In this way, the advertisements are optimized for the user's real-time emotions, and high engagement is expected.

[0596] As a concrete example of this behavior, if a user is browsing a new smartphone page and their emotional state is analyzed to be relaxed and positive, the server may generate an ad design that includes calming and appealing visuals.

[0597] This system aims to enable personalized advertising experiences for users and enhance the effectiveness of advertising in commercial transactions.

[0598] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0599] Step 1:

[0600] The user enters the URL of the webpage for the product they want to advertise into the input interface on their device. The device then sends this URL to the server. Input: Product webpage URL. Output: URL sent to the server.

[0601] Step 2:

[0602] The server accesses the webpage based on the received URL. Specifically, it issues an HTTP request and retrieves page data. The retrieved data includes product images, descriptions, and price information. The server then analyzes this information using web scraping techniques and extracts it as visual and linguistic information. Input: Product webpage data. Output: Visual and linguistic information about the product.

[0603] Step 3:

[0604] The server uses the acquired visual and linguistic information to generate an ad design by inputting prompt text into a generative AI model. This prompt text includes specific instructions such as "Generate an attractive ad design based on this product." The generative AI model automatically generates the visual format of the ad from this information. Input: Visual and linguistic information, prompt text. Output: Generated ad design.

[0605] Step 4:

[0606] The device uses the user's camera and microphone to collect emotional data such as facial expressions and voice. The device then sends this data to the server. Input: User's video and audio data. Output: Emotional data sent to the server.

[0607] Step 5:

[0608] The server analyzes the received emotional data using an emotion engine to determine the user's emotional state. Specifically, it analyzes video and audio data and assigns emotional labels such as positive or relaxed. Input: User's emotional data. Output: User's emotional state.

[0609] Step 6:

[0610] The server dynamically adjusts the generated ad design based on the user's emotional state. If the emotion is positive, it adjusts brightness, color tone, and message to create an appealing ad. This includes adjusting colors and changing the tone of the copy. Input: Generated ad design, user's emotional state. Output: Optimized ad design.

[0611] Step 7:

[0612] The user views multiple optimized ad designs on their device and selects the one they deem best. Input: Options for optimized ad designs. Output: The selected ad design.

[0613] In this way, there is a clear input and output flow at each step, and the system is designed to optimize advertisements based on the user's emotional state.

[0614] (Application Example 2)

[0615] 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."

[0616] Conventional ad generation systems have faced challenges in personalizing ads based on user emotions, resulting in limited user engagement. This invention aims to dynamically optimize ads based on user emotions, thereby improving ad effectiveness and user experience.

[0617] 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.

[0618] In this invention, the server includes means for receiving electronic links containing commercial transaction information, means for automatically acquiring visual and linguistic information about products from the electronic links, means for automatically generating the format of advertising media using the acquired visual and linguistic information, means for analyzing the user's emotions via the user's terminal and dynamically adjusting the advertising design based on that emotional information, means for generating optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media, and means for measuring the effectiveness of electronic advertisements and presenting optimized electronic advertisements. This makes it possible to personalize advertisements according to the user's emotions and improve user engagement.

[0619] "Commercial transaction information" refers to digital information related to the purchase or sale of goods, and is expressed as electronic links.

[0620] An "electronic link" is a hyperlink that allows access to specific information or pages on the internet.

[0621] "Visual information" is a general term for image data and digital visual content related to a product.

[0622] "Linguistic information" refers to text data and descriptions related to a product.

[0623] "Advertising media style" refers to the style of design and format that makes up an advertisement.

[0624] "User emotions" refer to the user's psychological state and sensitivities, and are analyzed using an emotion engine.

[0625] "Communication infrastructure" refers to the platform and network infrastructure used to send and receive digital data.

[0626] "Optimized digital advertising" refers to digital advertising content that has been tailored to the user's characteristics and where it will be displayed.

[0627] "Engagement" refers to the degree of interest and response that users show to advertising content.

[0628] The system for realizing this invention is a process that utilizes information from commercial transactions to optimize advertisements according to the user's emotions. Basically, a server receives an electronic link containing commercial transaction information. This automatically acquires visual and linguistic information about the product using web scraping technology. The software used includes "Beautiful Soup" for web scraping data and "OpenAI API" for the generative AI model. Based on this information, the generative AI model generates the format of the advertising medium and presents it to the user.

[0629] Next, the user's device analyzes their emotions in real time using its camera and microphone. This emotion analysis utilizes the "Google Cloud Vision API," which leverages facial recognition and voice analysis technologies. The analyzed emotion information is sent to a server, and the ad design is dynamically adjusted based on that emotion.

[0630] The generated advertisements are optimized and delivered across multiple communication platforms. This optimization includes parameter adjustments based on user characteristics and the media to which the advertisements are displayed. As a result, users see advertisements that are relevant to them, leading to improved engagement.

[0631] As a concrete example, when a user is relaxing in their living room, the server presents advertisements with colors and tones that reflect that state. The generative AI model generates advertisements using prompts such as: "Generate an advertisement for a home projector with a tone that is appropriate for when the user is relaxed." This process is expected to ensure that the advertisements consistently fit the user's emotions and enhance commercial success.

[0632] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0633] Step 1:

[0634] The server receives electronic links containing commercial transaction information. The input is an electronic link provided by the user, and the server uses this link to access the product page. The output is the HTML data of the target product's webpage. During this process, the server checks the reliability of the link and verifies that it is accessible.

[0635] Step 2:

[0636] The server uses web scraping techniques to obtain visual and linguistic information about products. It uses the previously obtained HTML data as input and processes the data. The output consists of visual and linguistic information such as product images, descriptions, and price information. Specifically, it uses "Beautiful Soup" to parse the HTML and extract the necessary data.

[0637] Step 3:

[0638] The server uses a generative AI model to generate advertising media formats based on acquired product information. Input data includes product information, price, and description, while output consists of multiple advertising design proposals. During this process, the generative AI model is prompted with prompts to perform data calculations to generate appropriate advertising styles.

[0639] Step 4:

[0640] The user's device uses its camera and microphone to analyze their emotions. The input is the user's captured video and audio data. The output is data indicating the user's emotional state. This process utilizes the Google Cloud Vision API to analyze emotions in real time.

[0641] Step 5:

[0642] The server dynamically adjusts the ad design based on the analyzed sentiment information. The input includes user sentiment data, and the output is an ad design tailored to the user. Specifically, the generative AI model optimizes the ad style by adjusting the prompt text based on the obtained sentiment information.

[0643] Step 6:

[0644] The server delivers optimized advertisements to multiple communication infrastructures. The input is the optimized advertisement design, and the output is the display of the advertisement under conditions suitable for each infrastructure. In this process, data calculations are performed to optimize the format according to the medium.

[0645] Step 7:

[0646] Users view optimized advertisements through their devices. Personalized content is displayed based on the user's mood, promoting engagement. When a user is relaxed, advertisements with a calming tone are displayed. This process allows for the measurement of ad effectiveness and provides data feedback to the server.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] [Fourth Embodiment]

[0651] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0652] 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.

[0653] 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).

[0654] 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.

[0655] 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.

[0656] 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).

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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".

[0664] As an embodiment of this invention, a system for automatically generating advertisements using commercial transaction information will be described.

[0665] When a user promotes a product, they first enter an electronic link to the product they want to advertise through an interface installed on their device. This link is the URL of the webpage where the target product is listed. Upon receiving this request, the server accesses the linked page and automatically retrieves visual and linguistic information about the product using methods such as web scraping. This includes product images, product descriptions, and prices.

[0666] Next, the server uses a generative AI model to automatically generate advertising media formats based on the acquired information. This model leverages pre-trained data to generate various advertising patterns and copy. As a result, multiple advertising proposals that are visually appealing and have a clear message are presented.

[0667] As a concrete example, in the case of online shop products, when a user enters the product URL, the server extracts the product name, image, price, etc., and a generation AI model automatically generates an advertisement with eye-catching visual design along with copy such as "Limited-time sale! Offered at a special price." The generated advertisement is then applied to multiple design templates and presented to the user as a choice.

[0668] After the user selects their desired ad design, the server optimizes the selected design for each communication infrastructure. This process includes, for example, changing image sizes to suit different social media platforms and adjusting copy based on the delivery algorithm.

[0669] Ultimately, the server has the capability to track ad performance and measure its effectiveness. This allows users to see the actual ad performance and continuously update to the most effective ads.

[0670] Thus, the present invention enables effective marketing activities by automatically generating advertisements from commercial transaction information and applying them to various communication infrastructures in an optimized form.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The user will use their own device to enter the electronic link of the product they want to advertise into a dedicated input field. After entering the information, the user will click the submit button to send the information to the server.

[0674] Step 2:

[0675] The server receives an electronic link sent by the user. The server parses this link and uses web scraping techniques to access the linked page in order to collect the necessary product information. Here, information such as product images, descriptions, and prices is automatically retrieved.

[0676] Step 3:

[0677] The server inputs the acquired product information into a generating AI model. Based on past data, the generating AI model automatically generates visually appealing advertising designs and creative taglines.

[0678] Step 4:

[0679] The server generates ad designs and copy, which are then applied to multiple ad templates. The server creates previews of these templates and presents them to the user as options.

[0680] Step 5:

[0681] The user selects the most suitable design from the ad templates provided by the server. The user can complete the selection with a simple click.

[0682] Step 6:

[0683] Based on user selections, the server optimizes the chosen ad design for different communication infrastructures. This optimization includes adjusting image sizes and simplifying language for each platform.

[0684] Step 7:

[0685] The server creates different ad variations and automatically conducts A / B testing. Each version of the ad is presented to different user groups, and the effectiveness of the ads is measured.

[0686] Step 8:

[0687] Users can view A / B test results updated in real time by the server from their devices. The server recommends the most effective ad design, and users can adjust their final ad strategy.

[0688] (Example 1)

[0689] 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".

[0690] In modern commerce, a wide variety of products are sold online, requiring the rapid and efficient generation of effective promotional materials for each product. However, traditional advertising methods face challenges in that acquiring visual and linguistic information about products and generating and optimizing advertisements requires considerable effort and time. Furthermore, optimizing and effectively delivering advertisements across different information and communication infrastructures is not easy.

[0691] 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.

[0692] In this invention, the server includes means for receiving electronic information containing commercial transaction information, means for automatically acquiring visual and linguistic data related to products from the electronic information, and means for inputting the acquired data as prompt text using a generation AI model to generate advertisements. This enables the efficient automatic generation of product advertisements, as well as the creation and distribution of optimal electronic advertisements compatible with different information and communication infrastructures.

[0693] "Commercial transaction information" refers to data related to the transaction of goods and services, primarily including information such as price, product description, and inventory status.

[0694] "Electronic information" refers to information expressed in digital format and data that can be transmitted and received via a network.

[0695] "Visual data" refers to data represented in a visually recognizable format, such as images or videos.

[0696] "Linguistic data" refers to data expressed as strings or text, including information such as product descriptions and taglines.

[0697] "Advertising information" refers to content created to promote products or services to consumers, and includes both visual and linguistic elements.

[0698] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate new content from data.

[0699] A "prompt statement" is a text-based instruction given to an AI model to provide specific generation instructions.

[0700] "Information and communication infrastructure" refers to the entire network and system that enables the transmission and reception of data, and includes communication hardware and software.

[0701] "Optimized digital advertising" refers to advertisements that have been adapted to the most suitable format and structure for different information and communication infrastructures and platforms.

[0702] This invention is implemented as a system for accurately and effectively utilizing commercial transaction information for advertising generation. The user enters an electronic link (URL) of the product they wish to promote via an interface on their terminal. This information is immediately transmitted to the server.

[0703] The server uses the received electronic link to access the corresponding webpage and automatically retrieves visual data (such as product images) and linguistic data (such as product descriptions and price information) related to the product using web scraping techniques. For example, the Python library BeautifulSoup can be used.

[0704] Based on this acquired data, the server automatically generates advertisements using a generative AI model. This AI model takes product information as prompt text. For example, prompt text such as "Product Name: Summer Sale Limited Sandals, Key Features: Lightweight, Comfortable, Waterproof, Sale Information: 30% Off" can be used. Various generative AI algorithms can be used for the generative AI model.

[0705] The generated advertisements are presented in visually appealing and diverse templates. Users can select from multiple generated ad designs according to their preferences via their devices. The selected ad is further optimized according to the information and communication infrastructure. For example, image sizes are adjusted and messages are fine-tuned for different social networking platforms. Image processing tools such as the Python Imaging Library (PIL) are used for this purpose.

[0706] Ultimately, the server has the capability to track and analyze advertising performance in real time. This allows users to continuously improve their advertising strategies. This system enables efficient and effective marketing activities that leverage commercial transaction information.

[0707] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0708] Step 1:

[0709] The user enters the URL of the webpage for the product they wish to promote using the terminal's interface. The entered URL is sent to the information system. The input data is in URL format and is used to identify the product information page.

[0710] Step 2:

[0711] The server accesses the webpage based on the URL received from the user. It issues an HTTP request to retrieve the HTML data of the webpage. This input data is the content of the webpage, and the output is visual data (product images) and linguistic data (product descriptions, price information) related to the product. Web scraping is performed using analysis tools such as BeautifulSoup.

[0712] Step 3:

[0713] The server combines the acquired visual and linguistic data to create prompt statements for the generative AI model. These prompt statements are used as input data for the AI ​​model. Specifically, they are summarized in the format of "Product Name: Summer Sale Limited Sandals, Key Features: Lightweight, Comfortable, Waterproof, Sale Information: 30% Off".

[0714] Step 4:

[0715] The generative AI model receives prompt text as input and generates visually and linguistically engaging advertising content. This generation process creates diverse advertising patterns based on historical data learning. The output data is in a format applicable to multiple advertising templates.

[0716] Step 5:

[0717] The server applies the generated ads to different templates and presents them to the user via the terminal. The user can choose from multiple ad designs presented. The input data is the generated ad pattern, and the output data is the ad design selected by the user.

[0718] Step 6:

[0719] The server optimizes the user-selected ad design for various information and communication infrastructures. Specifically, it adjusts image sizes and fine-tunes messages. The input data is the user-selected ad design, and the optimized version becomes the output data. Image processing tools such as PIL are used in this process.

[0720] Step 7:

[0721] The server delivers optimized advertisements to each information and communication infrastructure and tracks their performance. After delivery, the effectiveness of the advertisements is measured based on data such as impressions and click-through rates. Users can then improve their advertising strategies based on this performance data.

[0722] (Application Example 1)

[0723] 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".

[0724] In today's world, there is a growing need to appeal to consumers more quickly and effectively with individual products and services. However, creating advertisements is time-consuming and labor-intensive, and the need for optimization tailored to each online platform presents challenges requiring specialized knowledge. Furthermore, there is a lack of systems to track advertisement performance in real time and continuously improve it in the most optimal way. Against this backdrop, there is a need for an environment that automates the ad generation and optimization process, allowing users to easily create highly effective advertisements.

[0725] 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.

[0726] In this invention, the server includes a device for receiving electronic links containing commercial transaction information, a device for automatically acquiring image and text information about a product from the electronic links, and a device for automatically generating the format of an electronic advertising medium using the acquired image and text information. This makes it possible for users to quickly and efficiently automatically generate optimized advertisements adapted to various communication platforms, even without specialized knowledge, and to easily implement a continuous advertising strategy that maximizes effectiveness.

[0727] "Commercial transaction information" refers to digital data used to provide details about products and services.

[0728] An "electronic link" is a URL that allows access to a specific webpage on the internet.

[0729] "Device" refers to a collection of hardware or software designed to process, acquire, generate, and display commercial transaction information.

[0730] "Image information" refers to digital image data that shows the visual characteristics of a product.

[0731] "Text information" refers to linguistic data used to describe a product, its features, benefits, etc.

[0732] "Electronic advertising media" refers to the digital format of advertising content delivered on digital platforms such as the internet.

[0733] A "Generating AI Module" is a system or program that uses artificial intelligence technology to automatically generate advertising copy.

[0734] A "design template" is a pre-configured design layout that can be easily used as a visual component of an advertisement.

[0735] A "communication platform" is a digital network or service on which electronic advertisements are delivered.

[0736] "Performance tracking" is the process of quantitatively measuring and evaluating the effectiveness and impact of advertising.

[0737] The system for implementing this invention automatically generates advertisements based on commercial transaction information and deploys optimized advertisements across multiple communication platforms. The main components of the system are a server, terminals, and a user interface.

[0738] The server receives electronic links entered by users and automatically retrieves image and text information about products from the linked web pages using web scraping techniques. The specific software used is Beautiful Soup in Python. The retrieved information is used to generate advertising copy via a generative AI model. Here, the AI ​​model used is Hugging Face's Transformers.

[0739] Users provide the system with electronic links to the products they wish to advertise via their devices. These links are URLs pointing to the web pages to be analyzed. The devices are equipped with a user interface for information input, allowing users to intuitively provide links.

[0740] Along with the generated ad copy, multiple design templates are automatically generated using the Figma API and presented as visually appealing ad layouts. The user selects the most suitable ad design from these options, and the server optimizes the selected design for different communication platforms and generates the electronic ad in a format appropriate for each.

[0741] Ultimately, the server tracks ad performance via the Google Analytics API and provides the results to users, helping them continuously optimize their advertising strategies.

[0742] For example, if a user wants to create an advertisement for a "new smartphone," they would enter the product's URL through an input interface. The server would then retrieve product information from the URL and use a generation AI model to generate advertising copy such as, "Limited time offer! Get the latest smartphone with cutting-edge technology at a special price!"

[0743] Examples of prompts for a generative AI model are as follows:

[0744] "Generate catchy and memorable ad copy from the following data: Product Name: New Smartphone, Product Description: Equipped with the latest technology, Price: Special Price"

[0745] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0746] Step 1:

[0747] The user enters an electronic link for the product they want to advertise through the device's user interface. This input is sent to the server as a web page URL.

[0748] Step 2:

[0749] The server uses the received electronic link to web scrape the linked page using the Python Beautiful Soup library. This retrieves image and text information related to the product. The input is a URL, and the output is an image of the product and a descriptive text.

[0750] Step 3:

[0751] The server inputs the acquired image and text information, along with prompt text, into the Hugging Face Transformers, an AI model for generating content. Here, the AI ​​model automatically generates advertising copy. The input to this process is product information, and the output is a message-driven advertising copy.

[0752] Step 4:

[0753] The server uses the Figma API to automatically create multiple ad design templates, including the generated ad copy. The input for this step is the ad copy, and the output is multiple design options. The server then presents these to the user.

[0754] Step 5:

[0755] The user selects the most suitable design from the presented design templates. This selection is sent to the server via the device.

[0756] Step 6:

[0757] The server optimizes the selected ad design for various communication platforms. For example, it converts images to the appropriate size and format for each social networking platform. The input is the selected design proposal, and the output is the ad design optimized for each platform.

[0758] Step 7:

[0759] Optimized ads are delivered to each communication platform. The server tracks ad performance in real time via the Google Analytics API and provides feedback to the user. The input is the ad design, and the output is performance data.

[0760] 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.

[0761] As an embodiment of this invention, a system is described that automatically generates advertisements using information related to commercial transactions and improves the effectiveness of the advertisements by utilizing user emotions.

[0762] When a user promotes a product using their device, they first enter an electronic link to the product they want to advertise into a dedicated input interface. After receiving this link, the server accesses the linked page and collects the necessary product information. The server uses web scraping technology to automatically retrieve product images, descriptions, price information, and other relevant data.

[0763] Next, the server uses a generative AI model to generate advertisement designs based on the acquired information. The generative AI model creates advertisement styles and structures suitable for the product characteristics and presents them to the user as multiple options.

[0764] Furthermore, the system is equipped with an emotion engine that analyzes the user's emotions based on data obtained from the user's device. The emotion engine utilizes video data from the camera, audio data, and other sources to determine the user's emotional state.

[0765] As a concrete example, suppose a user is browsing a product page on an online store, and the system determines, based on facial expression data obtained through the camera, that the user's emotions at that time are positive. In this case, the server will prioritize displaying bright and attractive ad designs that reflect the user's positive emotions.

[0766] The server also leverages this sentiment information to dynamically adjust the language of the advertisement, displaying the message that best resonates with the user's current mood. For example, if the user is relaxed, the copy will be written in a calm tone.

[0767] This system personalizes the user experience and improves ad engagement by generating and adjusting ads based on sentiment analysis. Furthermore, selected designs are optimized for each platform to ensure the most effective ads are displayed across various communication infrastructures.

[0768] Thus, the present invention enhances the user experience and maximizes the effectiveness of advertising in commercial transactions by combining an emotion engine with the generation and optimization of advertisements.

[0769] The following describes the processing flow.

[0770] Step 1:

[0771] The user will use their device to enter an electronic link for the product they want to advertise. This operation is performed through a dedicated web interface, and the electronic link is sent to the server.

[0772] Step 2:

[0773] The server receives an electronic link sent by the user. The server accesses the specified webpage and automatically retrieves visual and linguistic information related to the product using web scraping techniques. This includes product images, product descriptions, and prices.

[0774] Step 3:

[0775] The server inputs the acquired product information into a generation AI model. This model automatically generates advertising media formats and copy based on visual and linguistic information. The generated advertising proposals are presented to the user as multiple templates.

[0776] Step 4:

[0777] The emotion engine built into the user's device activates. This engine analyzes the user's facial expressions and tone of voice to determine their current emotional state. Camera footage and microphone audio are used for this purpose.

[0778] Step 5:

[0779] The server receives the results analyzed by the emotion engine and optimizes the design and copy of the advertisements according to the user's emotional state. If positive emotions are detected, it prioritizes bright, engaging designs and compelling messages.

[0780] Step 6:

[0781] The server delivers optimized advertisements in a format compatible with multiple communication infrastructures. Optimized ad templates are created for each platform, with adjustments made, for example, for social media and search engines.

[0782] Step 7:

[0783] Users can monitor the effectiveness of their ads in real time through a dashboard provided on their device. The server aggregates performance data from ad campaigns and provides users with feedback on the most effective strategies.

[0784] Step 8:

[0785] Based on the data provided by the server, users adjust their next advertising strategy based on the results of their ads. This enables continuous ad optimization.

[0786] (Example 2)

[0787] 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".

[0788] In commercial transactions, it is necessary to improve ad engagement and effectiveness by generating and dynamically adjusting ads based on the user's emotional state. However, conventional ad generation systems struggle to provide a personalized ad experience that takes user emotions into account, which can result in missed business opportunities. Therefore, there is a need for personalized ad generation systems that utilize emotional information.

[0789] 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.

[0790] In this invention, the server includes means for receiving electronic links containing commercial transaction information, means for automatically acquiring visual and linguistic information about a product from the electronic links, means for automatically generating advertising media using a generative model with the acquired visual and linguistic information, means for determining the emotional state based on video or audio data received from the user, means for dynamically adjusting the generated advertising media according to the emotional state, means for presenting the user with multiple advertising media and allowing them to select the optimal advertisement, means for optimizing the generated advertising media for multiple communication infrastructures, and means for measuring the effectiveness of the electronic advertisement and presenting the optimized advertisement. This enables the generation and optimization of personalized advertisements that reflect the user's real-time emotional state.

[0791] "Commercial transactions" is a concept that refers to acts and information related to the buying and selling of goods and services.

[0792] "Electronic link" refers to URLs and URIs, which are identifying information that points to a specific web page or resource on the internet.

[0793] "Visual information" refers to information related to product images and visual design, and is acquired as video data.

[0794] "Linguistic information" refers to text data that describes a product, its features, price, and other relevant information.

[0795] A "generative model" is a software algorithm that uses machine learning or artificial intelligence to generate specific deliverables (in this case, advertising designs).

[0796] An "advertising medium" is a visual and text format created to convey information about products or services to users.

[0797] "Communication infrastructure" refers to the infrastructure for delivering and displaying electronic advertisements, and includes web platforms and applications.

[0798] "Emotional state" refers to the user's psychological or emotional condition, and is analyzed from video and audio data.

[0799] "Dynamically adjusting" means changing or updating advertising content in real time according to the user's situation and emotions.

[0800] "Measuring effectiveness" refers to quantitatively or qualitatively evaluating the extent to which the generated advertisements are achieving their objectives.

[0801] This invention is a system that optimizes advertisements by leveraging user emotions based on information related to commercial transactions. The system consists mainly of a server and terminals and operates as follows.

[0802] The server first receives an electronic link from the user. This is the URL to the webpage where the product to be advertised is listed. Next, the server accesses this page using web scraping techniques and automatically collects visual and linguistic information such as product images, descriptions, and pricing information. Libraries such as Beautiful Soup and Scrapy are used for this collection.

[0803] Subsequently, the server uses a generative AI model to create an advertisement design based on the collected information. The generative AI model used utilizes generative modeling technology to automatically generate an advertisement style and structure suitable for the product's characteristics. Commands such as "Generate an attractive advertisement design based on this product" are given as prompts to the generative AI model.

[0804] The device collects user emotion data using the user's camera, microphone, and other devices. This data is sent to the server in the form of a video stream or audio recording. The server uses this emotion data to analyze the user's emotional state using an emotion engine. For this analysis, emotion recognition software such as Azure Emotion API and Face++ are utilized.

[0805] The server then dynamically adjusts the generated advertisements based on the user's emotional state, which is the result of the analysis. If a positive emotion is detected, the advertisement is adjusted to be brighter and more appealing. In this way, the advertisements are optimized for the user's real-time emotions, and high engagement is expected.

[0806] As a concrete example of this behavior, if a user is browsing a new smartphone page and their emotional state is analyzed to be relaxed and positive, the server may generate an ad design that includes calming and appealing visuals.

[0807] This system aims to enable personalized advertising experiences for users and enhance the effectiveness of advertising in commercial transactions.

[0808] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0809] Step 1:

[0810] The user enters the URL of the webpage for the product they want to advertise into the input interface on their device. The device then sends this URL to the server. Input: Product webpage URL. Output: URL sent to the server.

[0811] Step 2:

[0812] The server accesses the webpage based on the received URL. Specifically, it issues an HTTP request and retrieves page data. The retrieved data includes product images, descriptions, and price information. The server then analyzes this information using web scraping techniques and extracts it as visual and linguistic information. Input: Product webpage data. Output: Visual and linguistic information about the product.

[0813] Step 3:

[0814] The server uses the acquired visual and linguistic information to generate an ad design by inputting prompt text into a generative AI model. This prompt text includes specific instructions such as "Generate an attractive ad design based on this product." The generative AI model automatically generates the visual format of the ad from this information. Input: Visual and linguistic information, prompt text. Output: Generated ad design.

[0815] Step 4:

[0816] The device uses the user's camera and microphone to collect emotional data such as facial expressions and voice. The device then sends this data to the server. Input: User's video and audio data. Output: Emotional data sent to the server.

[0817] Step 5:

[0818] The server analyzes the received emotional data using an emotion engine to determine the user's emotional state. Specifically, it analyzes video and audio data and assigns emotional labels such as positive or relaxed. Input: User's emotional data. Output: User's emotional state.

[0819] Step 6:

[0820] The server dynamically adjusts the generated ad design based on the user's emotional state. If the emotion is positive, it adjusts brightness, color tone, and message to create an appealing ad. This includes adjusting colors and changing the tone of the copy. Input: Generated ad design, user's emotional state. Output: Optimized ad design.

[0821] Step 7:

[0822] The user views multiple optimized ad designs on their device and selects the one they deem best. Input: Options for optimized ad designs. Output: The selected ad design.

[0823] In this way, there is a clear input and output flow at each step, and the system is designed to optimize advertisements based on the user's emotional state.

[0824] (Application Example 2)

[0825] 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".

[0826] Conventional ad generation systems have faced challenges in personalizing ads based on user emotions, resulting in limited user engagement. This invention aims to dynamically optimize ads based on user emotions, thereby improving ad effectiveness and user experience.

[0827] 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.

[0828] In this invention, the server includes means for receiving electronic links containing commercial transaction information, means for automatically acquiring visual and linguistic information about products from the electronic links, means for automatically generating the format of advertising media using the acquired visual and linguistic information, means for analyzing the user's emotions via the user's terminal and dynamically adjusting the advertising design based on that emotional information, means for generating optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media, and means for measuring the effectiveness of electronic advertisements and presenting optimized electronic advertisements. This makes it possible to personalize advertisements according to the user's emotions and improve user engagement.

[0829] "Commercial transaction information" refers to digital information related to the purchase or sale of goods, and is expressed as electronic links.

[0830] An "electronic link" is a hyperlink that allows access to specific information or pages on the internet.

[0831] "Visual information" is a general term for image data and digital visual content related to a product.

[0832] "Linguistic information" refers to text data and descriptions related to a product.

[0833] "Advertising media style" refers to the style of design and format that makes up an advertisement.

[0834] "User emotions" refer to the user's psychological state and sensitivities, and are analyzed using an emotion engine.

[0835] "Communication infrastructure" refers to the platform and network infrastructure used to send and receive digital data.

[0836] "Optimized digital advertising" refers to digital advertising content that has been tailored to the user's characteristics and where it will be displayed.

[0837] "Engagement" refers to the degree of interest and response that users show to advertising content.

[0838] The system for realizing this invention is a process that utilizes information from commercial transactions to optimize advertisements according to the user's emotions. Basically, a server receives an electronic link containing commercial transaction information. This automatically acquires visual and linguistic information about the product using web scraping technology. The software used includes "Beautiful Soup" for web scraping data and "OpenAI API" for the generative AI model. Based on this information, the generative AI model generates the format of the advertising medium and presents it to the user.

[0839] Next, the user's device analyzes their emotions in real time using its camera and microphone. This emotion analysis utilizes the "Google Cloud Vision API," which leverages facial recognition and voice analysis technologies. The analyzed emotion information is sent to a server, and the ad design is dynamically adjusted based on that emotion.

[0840] The generated advertisements are optimized and delivered across multiple communication platforms. This optimization includes parameter adjustments based on user characteristics and the media to which the advertisements are displayed. As a result, users see advertisements that are relevant to them, leading to improved engagement.

[0841] As a concrete example, when a user is relaxing in their living room, the server presents advertisements with colors and tones that reflect that state. The generative AI model generates advertisements using prompts such as: "Generate an advertisement for a home projector with a tone that is appropriate for when the user is relaxed." This process is expected to ensure that the advertisements consistently fit the user's emotions and enhance commercial success.

[0842] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0843] Step 1:

[0844] The server receives electronic links containing commercial transaction information. The input is an electronic link provided by the user, and the server uses this link to access the product page. The output is the HTML data of the target product's webpage. During this process, the server checks the reliability of the link and verifies that it is accessible.

[0845] Step 2:

[0846] The server uses web scraping techniques to obtain visual and linguistic information about products. It uses the previously obtained HTML data as input and processes the data. The output consists of visual and linguistic information such as product images, descriptions, and price information. Specifically, it uses "Beautiful Soup" to parse the HTML and extract the necessary data.

[0847] Step 3:

[0848] The server uses a generative AI model to generate advertising media formats based on acquired product information. Input data includes product information, price, and description, while output consists of multiple advertising design proposals. During this process, the generative AI model is prompted with prompts to perform data calculations to generate appropriate advertising styles.

[0849] Step 4:

[0850] The user's device uses its camera and microphone to analyze their emotions. The input is the user's captured video and audio data. The output is data indicating the user's emotional state. This process utilizes the Google Cloud Vision API to analyze emotions in real time.

[0851] Step 5:

[0852] The server dynamically adjusts the ad design based on the analyzed sentiment information. The input includes user sentiment data, and the output is an ad design tailored to the user. Specifically, the generative AI model optimizes the ad style by adjusting the prompt text based on the obtained sentiment information.

[0853] Step 6:

[0854] The server delivers optimized advertisements to multiple communication infrastructures. The input is the optimized advertisement design, and the output is the display of the advertisement under conditions suitable for each infrastructure. In this process, data calculations are performed to optimize the format according to the medium.

[0855] Step 7:

[0856] Users view optimized advertisements through their devices. Personalized content is displayed based on the user's mood, promoting engagement. When a user is relaxed, advertisements with a calming tone are displayed. This process allows for the measurement of ad effectiveness and provides data feedback to the server.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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."

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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 as being incorporated by reference.

[0878] The following is further disclosed regarding the embodiments described above.

[0879] (Claim 1)

[0880] A means for receiving electronic links containing commercial transaction information,

[0881] A means for automatically acquiring visual and linguistic information about a product from the aforementioned electronic link,

[0882] A means for automatically generating the format of an advertising medium using acquired visual and linguistic information,

[0883] A means for generating optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media,

[0884] A means for measuring the effectiveness of the aforementioned electronic advertisement and presenting an optimized electronic advertisement,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, further comprising means for presenting multiple options for the format of an advertising medium based on the information about the acquired product.

[0888] (Claim 3)

[0889] The system according to claim 1, further comprising means for comparing the effectiveness of electronic advertising across the multiple communication infrastructures and identifying the most effective format.

[0890] "Example 1"

[0891] (Claim 1)

[0892] A means for receiving electronic information including commercial transaction information,

[0893] A means for automatically acquiring visual data and linguistic data related to a product from the aforementioned electronic information,

[0894] A means for automatically generating advertising information using acquired visual and linguistic data,

[0895] A means of generating an advertisement by inputting data obtained using a generative AI model as a prompt,

[0896] A means for generating optimized electronic advertisements compatible with multiple information and communication infrastructures based on generated advertising information,

[0897] A means for analyzing the effectiveness of the aforementioned electronic advertisement and presenting an optimized electronic advertisement,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, further comprising means for presenting multiple options for the format of advertising information based on the acquired information about the product.

[0901] (Claim 3)

[0902] The system according to claim 1, further comprising means for comparing the effectiveness of electronic advertising in the aforementioned multiple information and communication infrastructures and identifying the most effective format.

[0903] "Application Example 1"

[0904] (Claim 1)

[0905] A device that receives electronic links containing commercial transaction information,

[0906] A device that automatically acquires image and text information about a product from the aforementioned electronic link,

[0907] A device that automatically generates the format of an electronic advertising medium using acquired image information and text information,

[0908] A device that generates optimized electronic advertisements adapted to multiple communication platforms based on the generated electronic advertising media,

[0909] A device for evaluating the effectiveness of the aforementioned electronic advertisement and displaying an optimized electronic advertisement,

[0910] A device for inputting the electronic link of a product via a user interface,

[0911] A device including an artificial intelligence module that automatically generates compelling advertising copy based on product information,

[0912] A device for designing visual ad layouts using multiple ad design templates,

[0913] A device that tracks advertising performance and provides results to users,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, further comprising a device that presents multiple options for the format of an electronic advertising medium based on the information about the acquired product.

[0917] (Claim 3)

[0918] The system according to claim 1, further comprising an apparatus for comparing the effectiveness of electronic advertising on the aforementioned multiple communication platforms, identifying the most effective format, and providing the results.

[0919] "Example 2 of combining an emotion engine"

[0920] (Claim 1)

[0921] A means for receiving electronic links containing commercial transaction information,

[0922] A means for automatically acquiring visual and linguistic information about a product from the aforementioned electronic link,

[0923] A means for automatically generating advertising media formats using a generative model based on acquired visual and linguistic information,

[0924] A means for determining an emotional state based on video or audio data received from a user,

[0925] A means for dynamically adjusting the generated advertising media according to the aforementioned emotional state,

[0926] A method of presenting users with multiple advertising media and allowing them to select the most suitable advertisement,

[0927] A means for generating advertising media optimized for multiple communication infrastructures,

[0928] A means for measuring the effectiveness of the aforementioned electronic advertisement and presenting an optimized advertisement,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, further comprising means for presenting multiple options for the format of an advertising medium based on the information about the acquired product.

[0932] (Claim 3)

[0933] The system according to claim 1, further comprising means for comparing the effectiveness of electronic advertising across the multiple communication infrastructures and identifying the most effective format.

[0934] "Application example 2 when combining with an emotional engine"

[0935] (Claim 1)

[0936] A means for receiving electronic links containing commercial transaction information,

[0937] A means for automatically acquiring visual and linguistic information about a product from the aforementioned electronic link,

[0938] A means for automatically generating the format of an advertising medium using acquired visual and linguistic information,

[0939] A means of analyzing the user's emotions through the user's device and dynamically adjusting the ad design based on that emotional information,

[0940] A means for generating optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media,

[0941] A means for measuring the effectiveness of the aforementioned electronic advertisement and presenting an optimized electronic advertisement,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, further comprising means for presenting multiple options for the format of an advertising medium based on the information about the acquired product.

[0945] (Claim 3)

[0946] The system according to claim 1, further comprising means for comparing the effectiveness of electronic advertising across the multiple communication infrastructures and identifying the most effective format. [Explanation of symbols]

[0947] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving electronic links containing commercial transaction information, A means for automatically acquiring visual and linguistic information about a product from the aforementioned electronic link, A means for automatically generating the format of an advertising medium using acquired visual and linguistic information, A means for generating optimized electronic advertisements compatible with multiple communication infrastructures based on the generated advertising media, A means for measuring the effectiveness of the aforementioned electronic advertisement and presenting an optimized electronic advertisement, A system that includes this.

2. The system according to claim 1, further comprising means for presenting multiple options for the format of an advertising medium based on the information about the acquired product.

3. The system according to claim 1, further comprising means for comparing the effectiveness of electronic advertising across the aforementioned multiple communication infrastructures and identifying the most effective format.

Images