system

Abstract

We provide the system. [Solution] A means of providing a terminal with a user interface for entering advertising data for store operators, A method for automatically generating advertising content from input advertising data using a generative model, A means for automatically selecting advertising distribution media and target audiences based on past advertising effectiveness data and market data using analytical methods, A means of communication for delivering advertisements to automatically selected distribution media, A means of providing an interface for measuring the effectiveness of delivered advertisements and reporting the analysis results, A system that includes this.

Description

Technical Field

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[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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, while the use of digital advertising has been rapidly progressing in store-operating enterprises, there is a problem that it is difficult for them to independently perform appropriate customer targeting and media selection because of the lack of specialized knowledge and experience for effectively using it. As a result, it has been pointed out that the efficiency of customer acquisition decreases because the effect of the advertisement cannot be maximized. An object of the present invention is to solve such problems caused by the lack of knowledge and experience in digital advertising and to provide a means for efficiently distributing advertisements.

Means for Solving the Problems

[0005] This invention provides an automated digital advertising generation and distribution system for use by retail businesses. Specifically, it includes means for inputting advertising data through a user interface and means for automatically generating advertising content from the input data using a generation model. It also includes means for automatically selecting the optimal distribution medium and target based on past advertising effectiveness data and market data using analytical means. Furthermore, it includes communication means for distributing advertisements to the selected medium, and provides an interface for measuring the effectiveness of the distributed advertisements and reporting the results to the user, thereby supporting the development of appropriate advertising strategies. As a result, users can operate effective digital advertising without performing complex operations.

[0006] "Advertising data" refers to a collection of information necessary for advertising, including product information, service overview, customer targeting, and location information.

[0007] A "user interface" is the means by which a user inputs data and performs operations on a system.

[0008] A "generative model" is an algorithm and method that uses artificial intelligence technology to automatically generate advertising copy and design.

[0009] "Advertising content" is a collection of visual and text-based elements, including advertising copy, images, and layouts, created using a generative model.

[0010] "Analysis tools" refer to functions that analyze data based on past advertising effectiveness data and market data to derive the optimal advertising delivery strategy.

[0011] "Distribution medium" is a term that refers to the platform or channel selected for delivering advertisements.

[0012] A "target" is a consumer group or market segment that is intentionally directed towards an advertisement, identified in order to maximize the effectiveness of the advertisement.

[0013] "Communication methods" refer to the technologies and techniques used to deliver advertising content to selected distribution media.

[0014] An "interface" refers to a display screen and operating means that reports the results of advertising effectiveness measurement to the user and enables them to perform necessary actions. [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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

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

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

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

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

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[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] This invention is a system designed to enable store operators to easily utilize digital advertising, and it automates the generation and distribution of advertisements using AI technology. Specific embodiments are shown below.

[0037] Users access the system via their devices and input the data required for advertising campaigns. This information includes the name and characteristics of the advertised product, the profile of the desired target customer, store location information, and associated image files. This data is transmitted from the device to the server and stored in a database.

[0038] The server automatically generates advertising content using a generative model based on the information it receives. The generative model combines natural language processing and image generation technologies to generate advertising copy and design. The generated advertising content is provided to the device through an interface, allowing the user to review and edit it as needed.

[0039] Next, the server uses analytical tools to evaluate past advertising effectiveness data and market trend data to select the optimal distribution channels and target audiences. This selection is made with the aim of maximizing the effectiveness of the advertisements.

[0040] Subsequently, the server uses communication methods to deliver the advertisement to the selected distribution media. At this time, a tracking code is embedded in the advertisement, and the behavioral data of users who view the advertisement is collected.

[0041] After the ad delivery ends, the server analyzes the collected data to measure the effectiveness of the ad campaign. The results of the effectiveness measurement are reported to the user's device through the interface, allowing the user to adjust their advertising strategy for future campaigns based on this information.

[0042] As a concrete example, when a cafe owner wants to advertise a new seasonal menu, they would first input information about the cafe's new menu and attach relevant images. Next, they would review the generated advertising content, and if satisfied, leave the delivery process to the server. Finally, the advertising performance would be reported, which could be used as foundational data to make the next campaign more effective. In this way, the present invention automates the entire advertising process, providing users with efficient and effective advertising activities.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user operates the terminal and logs into the system. Here, they enter their account information, undergo authentication, and are granted access to the system.

[0046] Step 2:

[0047] Users enter advertising data through the device interface. This includes product details, target customer attributes, desired advertising period, and image file uploads.

[0048] Step 3:

[0049] The device sends the collected advertising data to the server. The server receives this data, stores it in a database, and prepares for the next processing step.

[0050] Step 4:

[0051] The server automatically generates advertising content using a generative model. The generative model generates ad copy from text data and creates appropriate visual designs using an image generation algorithm.

[0052] Step 5:

[0053] The server sends the generated ad content to the device and prompts the user for confirmation. The user uses an editing interface on their device to check the content and make corrections as needed.

[0054] Step 6:

[0055] The server analyzes past advertising effectiveness data and market data to automatically select the optimal distribution channels and target customers. This selection result is then used in the next distribution step.

[0056] Step 7:

[0057] The server automatically delivers advertisements to selected distribution channels using communication methods. The timing and frequency of delivery are also managed by the server.

[0058] Step 8:

[0059] After delivery is complete, the server analyzes the ad performance data collected by the tracking code. This analysis includes ad views, clicks, and conversion rates.

[0060] Step 9:

[0061] The server compiles the analysis results and reports them to the user on their terminal via an interface. Based on this, the user can then develop their next advertising strategy.

[0062] (Example 1)

[0063] 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."

[0064] For businesses operating retail stores, advertising is essential for acquiring customers and increasing sales. However, creating and distributing advertisements efficiently is time-consuming and requires considerable effort. This is especially true for small and medium-sized enterprises (SMEs) and companies that cannot allocate sufficient resources to advertising; without specialized knowledge, implementing effective advertising campaigns is difficult. This leads to problems such as wasted advertising costs and failure to accurately reach the target audience. Therefore, there is a need for a system that can solve these problems and enable efficient and highly effective advertising activities.

[0065] 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.

[0066] In this invention, the server includes means for providing a user screen for users to input information to an information processing device, means for automatically generating information display content from the input information using a generation AI model, and means for automatically selecting the destination and recipient of information based on past information effectiveness data and market information using an evaluation means. This enables efficient automation from ad creation to distribution and effectiveness measurement, making it possible to obtain maximum advertising effectiveness with minimal resources.

[0067] A "user interface" refers to the interface used by users to input information in an information processing device.

[0068] A "generative AI model" is a generative model that utilizes artificial intelligence technology to automatically generate information display content based on input information.

[0069] "Evaluation methods" refer to algorithms and processes for selecting information recipients and recipients by analyzing past information effectiveness data and market information.

[0070] "Communication means" refers to the hardware and software used to transmit information to automatically selected destinations.

[0071] A "tracking code" is a code embedded in the displayed information, and its role is to collect data necessary to evaluate the effectiveness of the information after it has been transmitted.

[0072] The "editing screen" refers to a user interface that allows users to review the generated information display and make corrections as needed.

[0073] This invention is a system for efficiently automating advertising activities, and in particular aims to enable store operators to easily create, distribute, and evaluate digital advertisements. The core technologies of this system are a user-friendly interface that is easy for users to operate, automatic content generation using a generation AI model, and automatic selection of distribution media and target audiences.

[0074] Users access the information processing device using a terminal and input the necessary data for advertising through a dedicated user screen. This data includes the name and features of the advertised product or service, the profile of the target customer, the store's location information, and related images. The entered data is transmitted to a server via the internet and stored securely.

[0075] The server processes information received from users using a generative AI model. This generative AI model combines natural language processing and image generation technologies and has the ability to automatically generate advertising text and designs. This process utilizes high-performance computer systems and AI software.

[0076] The generated advertising content is delivered back to the user's device via the server, where the user can review the content through the editing screen and make corrections as needed. Once the advertising content is finalized, the server analyzes past advertising effectiveness data and market information to select the optimal distribution channels and target audience. This allows for the development of a distribution plan that maximizes the effectiveness of the advertisement.

[0077] The server uses advanced communication technology to deliver advertisements to selected media outlets, and each advertisement has a tracking code embedded in it. This tracking code is used to collect user behavior data, such as ad views and clicks.

[0078] Ultimately, the server analyzes the collected data to measure the effectiveness of the advertising campaign. These results are reported to the user through a user interface, providing foundational data for improving future advertising strategies.

[0079] As a concrete example, imagine a cafe owner who wants to advertise a new seasonal menu. The user enters a prompt message using a terminal, such as "New seasonal cafe menu, target audience: local women in their 20s, attach high-quality images." Based on this information, the system automatically generates advertising content and enables its delivery to the most suitable platform. Such a system allows advertising activities to be conducted efficiently and effectively.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uses a device to enter information for the advertising campaign. This information includes the product name, features, target customer profile, store location information, and associated image files. At this stage, the information entered by the user is structured on the device and sent to the server for subsequent processing.

[0083] Step 2:

[0084] The terminal transmits the collected information to the server in digital format. The transmitted data is received by the server and stored in a database. At this stage, the data format is normalized and prepared so that it can be efficiently processed by the generative AI model.

[0085] Step 3:

[0086] The server automatically generates advertising content using an AI model based on stored data. This generation process uses natural language processing to create text content and image generation technology to produce visual content. Using the stored data as input, the advertising text and design are output.

[0087] Step 4:

[0088] The generated advertising content is delivered to the user's device via the server. The user reviews the content on the editing screen and makes corrections as needed. Here, the user fine-tunes the ad text and images and finalizes the ad.

[0089] Step 5:

[0090] The server evaluates historical advertising data and market information to automatically select the optimal distribution channels and target audiences. This involves using statistical algorithms as evaluation tools to determine the best plan to maximize advertising effectiveness. The selection results are then used as input data for the next distribution step.

[0091] Step 6:

[0092] The server delivers advertisements to selected distribution media using communication methods. Tracking codes are embedded in the advertisements to collect user behavior data, such as ad impressions and clicks. At this stage, the actual delivery process takes place, and the delivery status is monitored in real time.

[0093] Step 7:

[0094] After ad delivery, the server analyzes the collected behavioral data to measure the effectiveness of the advertising campaign. This analysis is performed based on the received data, and the results of the effectiveness measurement are generated. These results are provided to the user's device through the interface and used to improve future advertising strategies.

[0095] (Application Example 1)

[0096] 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."

[0097] Traditional advertising delivery systems have a problem in that they cannot easily present the most relevant advertisements based on the user's real-time location information. Furthermore, there are challenges in effectively presenting advertisements using wearable devices. As a result, the targeting accuracy of advertisements decreases, and the maximization of advertising effectiveness is not achieved.

[0098] 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.

[0099] In this invention, the server includes means for providing an information processing device with a user interface for inputting advertising data for store operators, means for automatically generating advertising information from the input advertising data using a generative model, and means for operating on a wearable device in state S and presenting advertising information based on the user's location information and target information. This makes it possible to present optimal advertisements according to the user's real-time situation.

[0100] "Store operator" refers to any corporation or individual that operates a store in order to provide goods or services to customers.

[0101] "Advertising data" refers to the information used to generate advertisements, and includes product names, features, target customer profiles, store location information, and related image files.

[0102] A "user interface" is an operation screen that allows users to input data into a system via an information processing device and to check the generated information.

[0103] An "information processing device" is a general term for computers and devices that have the ability to process digital data and interact with users and other systems.

[0104] A "generative model" is a program that uses AI technology to automatically generate advertising information from input data.

[0105] "Advertising information" refers to generated content such as advertising copy and images, intended to encourage a specific customer segment to purchase products or services.

[0106] "Wearable devices in S state" refer to devices that can be worn by a user and are in a certain operating state or usage condition, such as smart glasses and smartwatches.

[0107] "User location information" refers to data that indicates the geographical location where the user is currently located, using methods such as GPS or wireless communication.

[0108] "Target information" refers to profile information related to a specific user or user group, including data such as interests, preferences, and consumption history.

[0109] The system for carrying out the present invention mainly includes a server, an information processing device, and a wearable device in S state. The user inputs advertising data via the information processing device. The input advertising data includes the product name, features, target customer profile, store location information, and associated image files. This data is transmitted from the information processing device to the server and stored in a database. The server analyzes the stored data using a generative model and automatically generates advertising information. This generative model utilizes AI that combines natural language processing and image generation technology. The generated advertising information uses technology that enables the user to instantly display advertising content tailored to their current situation and location using a wearable device.

[0110] The server analyzes collected historical advertising effectiveness data and market information to select the optimal advertising distribution channels and target audience. This selection involves comparing existing advertising effectiveness data with trend information. The selected advertisements are transmitted to the designated distribution channels via communication means, and a tracking code is embedded at that time. After the advertising effectiveness is measured, the analysis results are reported to the user through the information processing device.

[0111] As a concrete example, imagine a scenario where a traveler is strolling through a city and an advertisement for a discount coupon for a nearby tourist attraction appears on their smart glasses. In this case, the traveler can use the coupon on the spot and receive a discount by visiting the tourist attraction. Examples of prompts include the following:

[0112] Example prompt:

[0113] "User's current location: Near Tokyo Station"

[0114] User's areas of interest: Culture, Art

[0115] Ad content generation prompt: 'Generate a Street View ad that introduces users to the latest art exhibitions near Tokyo Station.'

[0116] In this way, the system can provide advertising information tailored to the user's needs in real time.

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

[0118] Step 1:

[0119] The device receives advertising data entered by the user. This advertising data includes the product name, features, target customer profile, store location information, and associated image files. The device sends this data to the server and stores it in a database.

[0120] Step 2:

[0121] The server retrieves the stored advertising data and automatically generates advertising information using a generative AI model. Based on the input advertising data, the AI ​​model combines natural language processing and image generation technology to generate advertising copy and design, and compiles the results as advertising information.

[0122] Step 3:

[0123] The server analyzes past advertising effectiveness data and market information based on the generated advertising information. This allows it to automatically select the optimal advertising distribution channels and target audience. Past advertising effectiveness data and market trend data are used as input, and the selected distribution methods and targets are output.

[0124] Step 4:

[0125] The server delivers advertisements to selected transmission media using communication methods. Tracking codes are embedded in the advertisement information, enabling data collection to track user responses and behavior after delivery.

[0126] Step 5:

[0127] The server measures the effectiveness of delivered ads and analyzes the collected data. This analysis is based on data from the tracking code. The analysis results are reported to the device, allowing users to review the results and adjust their strategy for the next advertising campaign.

[0128] Step 6:

[0129] The server presents real-time advertising information based on location and profile information to users of wearable devices in the S state. The user's current location and profile information are used as input, and the server outputs the most suitable advertisements based on this information.

[0130] 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.

[0131] This invention provides a system for store operators to efficiently create and distribute digital advertisements, and by incorporating an emotion engine, it enables consideration of the emotional state of users and targets when inputting and distributing advertising data. The system comprises a user interface, a generative model, analytical means, communication means, an interface, an editing interface, a tracking code, and an emotion engine.

[0132] Users log in to the system via their device and input the information and image data required for the advertising campaign. The emotion engine analyzes the user's voice tone, facial expressions, input patterns, etc., to recognize the user's emotional state. This allows the interface to be dynamically adjusted based on the user's stress and excitement levels, improving usability.

[0133] Next, the server utilizes a generative model to automatically generate content based on the input advertising data. The generation process uses emotional information obtained by the emotion engine to adjust the content to have a more emotional impact.

[0134] The generated advertising content is presented to the user on their device and can be modified through an editing interface. Based on feedback from the sentiment engine, users can evaluate whether the content conveys the appropriate emotions and make adjustments as needed.

[0135] Subsequently, the server selects the distribution medium and target audience for ad delivery based on past advertising effectiveness and market data. At this stage, it considers the emotional state of the target users and delivers ads at the appropriate time and with appropriate content to ensure effective communication.

[0136] After delivery is complete, the data collected using the tracking code is analyzed on the server to evaluate the ad's performance and the emotional response of recipients. The analysis results are reported to the user through the interface, and the user can use the emotional data of the target audience to plan future advertising strategies.

[0137] For example, when advertising a new menu item at a cafe, if a user shows high interest when accessing the system and entering data, the generated advertisement will be enhanced with emotional language and visual elements. Furthermore, the advertisement can be adjusted to air during times when the target audience is relaxed. This invention enables users to experience a more personalized advertising experience than ever before, leading to improved marketing effectiveness.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user logs into the system using their device. Upon successful login, the user interface for creating advertising campaigns is displayed.

[0141] Step 2:

[0142] Users input product details, target customer profiles, and promotional messages and image files through the device's interface. During this process, the emotion engine recognizes the user's emotional state through their voice and facial expressions, dynamically adjusting the interface accordingly.

[0143] Step 3:

[0144] The terminal sends the data entered by the user to the server. The server receives this data and stores it in its database.

[0145] Step 4:

[0146] The server uses a generative model to automatically generate advertising content based on input data and collected sentiment information. The generated content includes wording and visual elements that correspond to the user's emotions.

[0147] Step 5:

[0148] The server sends the generated advertising content to the device. The user can view this content on their device and make any necessary modifications using the editing interface.

[0149] Step 6:

[0150] The server selects the optimal delivery medium and timing based on past advertising effectiveness data, market data, and target sentiment data obtained from the sentiment engine. This enables delivery tailored to the emotions of the target users.

[0151] Step 7:

[0152] The server delivers advertisements to selected distribution channels via communication methods. Tracking codes are embedded in the advertisements, allowing for tracking of target audience behavior and responses.

[0153] Step 8:

[0154] After the ad is delivered, the server analyzes the collected data to evaluate the ad's effectiveness and the target users' emotional response.

[0155] Step 9:

[0156] The server compiles the analysis results and reports them to the user's device via an interface. Based on this information, the user can plan and optimize their strategy for the next advertising campaign.

[0157] (Example 2)

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

[0159] Traditional systems for creating and distributing digital advertisements were unable to adjust content to consider the user's emotional state, making personalized and emotionally-driven effective communication difficult. As a result, ad acceptance rates were low, and it was difficult to collect sufficient data to improve marketing efficiency.

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

[0161] In this invention, the server includes means for providing a human-machine interface to the terminal, means for automatically generating advertising content using a generative artificial intelligence model, and means for analyzing the user's emotional state using an emotion evaluation mechanism and dynamically adjusting the interface. This enables the generation of personalized advertisements that take into account the user's emotional state and the optimization of the delivery process.

[0162] A "human-machine interface" is a means by which a user accesses a system through a terminal and inputs and manipulates information.

[0163] A "generative artificial intelligence model" is an artificial intelligence technology used to automatically generate advertising content based on input data.

[0164] An "emotion evaluation mechanism" is a mechanism that analyzes the user's voice tone, facial expressions, and input patterns to recognize their emotional state and adjust the interface accordingly.

[0165] "Advertising content" refers to content such as text, images, and videos that are generated according to the objectives of an advertising campaign.

[0166] "Transmission path" refers to the medium or channel through which generated advertising content is sent to the recipient.

[0167] A "tracking identifier" is a code embedded in advertising content that is used to measure the effectiveness of the ad after delivery and to analyze recipient interactions.

[0168] This invention begins with the user inputting advertising campaign information into the system using a terminal. The terminal is equipped with a human-machine interface, allowing the user to easily input advertising objectives, target audience, image data, taglines, and other information. This interface employs an intuitive and user-friendly design.

[0169] Upon receiving input data, the server automatically generates advertisement content using a generative artificial intelligence model. This model utilizes multiple neural networks to effectively learn the components of advertisements from a large amount of training data. As a result, advertisements optimized to match the user's preferences are generated. The server also incorporates an emotion evaluation mechanism, recognizing the user's emotional state by analyzing voice tone, facial expressions, and input patterns. This information is used to dynamically adjust the interface, improving the user experience.

[0170] Once an ad is generated, it is presented to the user through an editing platform on their device. Here, they can review the text and images included in the ad and re-edit them as needed. Sentiment-based feedback is also displayed, which users can use to fine-tune the content. This process ensures that the ad content more effectively evokes the emotions of the target audience.

[0171] For example, when a cafe advertises a new menu item, the generated ad will emphasize emotional language and visual elements when users show high interest. Furthermore, the ad will be timed to be delivered to the target audience during relaxed hours.

[0172] An example of a prompt message is: "Create an advertisement promoting the cafe's new menu. Generate content that resonates with the user's emotional state in an appropriate way." In this way, the system can efficiently and effectively create and deliver personalized digital advertisements.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] Users log in to the system using their terminal and input data for their advertising campaigns. This data includes the campaign objectives, target audience, and image and text information to be used. Specifically, users use a keyboard and mouse to input this information on a dedicated interface and save it to the system. The entered data is then sent directly to the server.

[0176] Step 2:

[0177] The server passes the received advertising data to an artificial intelligence model that generates advertising content automatically. In this process, the algorithm analyzes the input information, performs text generation and image processing, and generates the optimal advertising content. Specifically, the model determines the message and design to be conveyed based on a vast amount of training data, and combines them to output a single advertisement.

[0178] Step 3:

[0179] The server sends the generated ad content to the device and presents it to the user. The device then displays the ad and provides an interface that allows the user to review and edit it. The user reviews the ad text and visual elements and makes changes as needed. Specifically, the user can select a text box with the mouse, edit the content using the keyboard, or change images using drag and drop.

[0180] Step 4:

[0181] An emotion evaluation mechanism operates in the background, analyzing the user's voice tone, facial expressions, and input patterns. The system uses this data to determine whether the user is experiencing stress or satisfaction, and adjusts the interface accordingly. Furthermore, generated advertisements can be fine-tuned based on the user's emotional information. Specifically, the interface's color scheme and button placement change depending on the user's emotional state.

[0182] Step 5:

[0183] Once the user has finished editing the ad, the server selects the optimal delivery route and timing for sending the ad. This involves database queries to analyze historical ad data and market trends. Specifically, the server calculates the most effective broadcast time and channels and stores the results.

[0184] Step 6:

[0185] The server delivers ads using a predetermined transmission path and embeds tracking identifiers to measure recipient responses. After the ads are sent, their effectiveness is monitored in real time, and the collected data is analyzed and provided as feedback to the user. Specifically, the server records click counts and viewing time and generates analytical reports.

[0186] Step 7:

[0187] The analysis results are reported to the user via their device, providing valuable information for future advertising strategies. This process completes the entire workflow from ad generation to delivery, further enhancing the user's advertising experience. Specifically, users review data presented in graphs and numerical formats, and consider improvements to the next prompt and campaign.

[0188] (Application Example 2)

[0189] 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".

[0190] In today's advertising market, there is a demand to maximize consumer interest by dynamically adjusting advertising content based on the user's emotional state. However, conventional ad creation and delivery systems cannot adequately consider user emotions, making it difficult to provide a personalized advertising experience. Therefore, in order to maximize the effectiveness of advertising, it is necessary to realize technology that recognizes the user's emotional state, dynamically adjusts content based on the analysis results, and delivers it at the appropriate time.

[0191] 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.

[0192] In this invention, the server includes means for providing a terminal with a user interface for inputting advertising information for store management organizations, means for automatically generating advertising content from the input advertising information using a generative model, and means for recognizing the user's emotional state using an emotion engine and dynamically adjusting the advertising content generated by the generative model based on the analysis results. This enables the generation and delivery of personalized advertising content based on the user's emotions.

[0193] A "store operating organization" is a general term for commercial organizations or companies that operate and manage stores.

[0194] "Advertising information" refers to data and content used for the purpose of promoting specific products or services.

[0195] A "user interface" is a mechanism in an information system that includes screens and operating methods for exchanging information between the user and the system.

[0196] A "generative model" is an algorithm or program that automatically generates new content or information based on input data.

[0197] "Promotional content" refers to text, images, audio, and other materials created to widely publicize a specific project or product.

[0198] An "analytical device" is a system of hardware and software used to analyze collected information and data.

[0199] An "emotion engine" is a technology or program that analyzes an individual's emotional state using voice, facial expressions, and input data, and outputs the results.

[0200] "Smart devices" is a general term for intelligent electronic devices equipped with sensors and network connectivity.

[0201] A "communication device" is a configuration of hardware and software used to send and receive data between multiple computer systems.

[0202] A "tracking code" is a notation or script embedded in a web page or advertising content to measure user behavior and effectiveness after delivery.

[0203] This invention provides a system for store management organizations to create and deliver effective and emotionally resonant advertisements. The system includes a server and user terminals, each component performing a specific function.

[0204] The server receives advertising information from the user and automatically generates advertising content using a generative AI model based on that information. During this process, an emotion engine analyzes the user's voice and facial expressions, dynamically adjusting the generated advertising content according to their emotional state. It is recommended to use Python®-based libraries (e.g., OpenCV, librosa) for the emotion engine.

[0205] The user's device functions as a smart device, providing a user interface for inputting advertising information and, as needed, acquiring user sentiment data using the camera and microphone. Furthermore, it allows for adjustment of the generated advertising content through an editing interface.

[0206] The generated advertising content is then analyzed using an analytical device to select the optimal distribution channels and target audience based on past data, and delivered to the selected channels via a communication device. A tracking code is embedded during this process, allowing for the measurement of user responses and effectiveness after delivery.

[0207] For example, when promoting a new cafe menu, if a user accesses the system in an excited state, the emotion engine will recommend more vivid visuals and lively text for the advertisement generated by the generative AI model. A prompt such as, "Generate content for the new cafe menu advertisement. If the user is very excited, present it with friendly, relaxing visuals and vibrant text," can be used.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] Users log in to the system via their device and enter advertising information. This information includes text, image data, and the purpose of the advertisement. This input data is sent to the server.

[0211] Step 2:

[0212] The server processes the received advertising information and automatically generates advertising content using a generative AI model. During this process, the server uses prompt statements to instruct the generative model on the direction of the content, and the resulting output is the generated advertising content. These prompt statements are adjusted based on the advertising's purpose and target emotions.

[0213] Step 3:

[0214] The server activates the emotion engine and analyzes audio and facial expression data obtained from the user's device as input. Based on this analysis, it evaluates whether the generated advertising content is appropriate for the user's emotional state and dynamically adjusts the text and visual elements as needed.

[0215] Step 4:

[0216] Users review the generated promotional content and make necessary changes through the editing interface on their device. During editing, users can optimize the content based on feedback from the sentiment engine. The user's adjustments are then sent back to the server.

[0217] Step 5:

[0218] The server uses analytical equipment to refer to past advertising data and market information to automatically select the optimal distribution channel and target audience. At this time, the timing of delivery is also optimized based on sentiment data.

[0219] Step 6:

[0220] The server uses communication equipment to deliver advertisements to selected distribution media. A tracking code is embedded during delivery, allowing for the collection of user behavior data after delivery.

[0221] Step 7:

[0222] After delivery, the server analyzes the data collected based on the tracking code to measure the effectiveness of the advertisement. Users can view these analysis results through their devices and use them to inform their next advertising strategy.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is a system designed to enable store operators to easily utilize digital advertising, and it automates the generation and distribution of advertisements using AI technology. Specific embodiments are shown below.

[0240] Users access the system via their devices and input the data required for advertising campaigns. This information includes the name and characteristics of the advertised product, the profile of the desired target customer, store location information, and associated image files. This data is transmitted from the device to the server and stored in a database.

[0241] The server automatically generates advertising content using a generative model based on the information it receives. The generative model combines natural language processing and image generation technologies to generate advertising copy and design. The generated advertising content is provided to the device through an interface, allowing the user to review and edit it as needed.

[0242] Next, the server uses analytical tools to evaluate past advertising effectiveness data and market trend data to select the optimal distribution channels and target audiences. This selection is made with the aim of maximizing the effectiveness of the advertisements.

[0243] Subsequently, the server uses communication methods to deliver the advertisement to the selected distribution media. At this time, a tracking code is embedded in the advertisement, and the behavioral data of users who view the advertisement is collected.

[0244] After the ad delivery ends, the server analyzes the collected data to measure the effectiveness of the ad campaign. The results of the effectiveness measurement are reported to the user's device through the interface, allowing the user to adjust their advertising strategy for future campaigns based on this information.

[0245] As a concrete example, when a cafe owner wants to advertise a new seasonal menu, they would first input information about the cafe's new menu and attach relevant images. Next, they would review the generated advertising content, and if satisfied, leave the delivery process to the server. Finally, the advertising performance would be reported, which could be used as foundational data to make the next campaign more effective. In this way, the present invention automates the entire advertising process, providing users with efficient and effective advertising activities.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The user operates the terminal and logs into the system. Here, they enter their account information, undergo authentication, and are granted access to the system.

[0249] Step 2:

[0250] Users enter advertising data through the device interface. This includes product details, target customer attributes, desired advertising period, and image file uploads.

[0251] Step 3:

[0252] The device sends the collected advertising data to the server. The server receives this data, stores it in a database, and prepares for the next processing step.

[0253] Step 4:

[0254] The server automatically generates advertising content using a generative model. The generative model generates ad copy from text data and creates appropriate visual designs using an image generation algorithm.

[0255] Step 5:

[0256] The server sends the generated ad content to the device and prompts the user for confirmation. The user uses an editing interface on their device to check the content and make corrections as needed.

[0257] Step 6:

[0258] The server analyzes past advertising effectiveness data and market data to automatically select the optimal distribution channels and target customers. This selection result is then used in the next distribution step.

[0259] Step 7:

[0260] The server automatically delivers advertisements to selected distribution channels using communication methods. The timing and frequency of delivery are also managed by the server.

[0261] Step 8:

[0262] After delivery is complete, the server analyzes the ad performance data collected by the tracking code. This analysis includes ad views, clicks, and conversion rates.

[0263] Step 9:

[0264] The server compiles the analysis results and reports them to the user on their terminal via an interface. Based on this, the user can then develop their next advertising strategy.

[0265] (Example 1)

[0266] 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."

[0267] For businesses operating retail stores, advertising is essential for acquiring customers and increasing sales. However, creating and distributing advertisements efficiently is time-consuming and requires considerable effort. This is especially true for small and medium-sized enterprises (SMEs) and companies that cannot allocate sufficient resources to advertising; without specialized knowledge, implementing effective advertising campaigns is difficult. This leads to problems such as wasted advertising costs and failure to accurately reach the target audience. Therefore, there is a need for a system that can solve these problems and enable efficient and highly effective advertising activities.

[0268] 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.

[0269] In this invention, the server includes means for providing a user screen for users to input information to an information processing device, means for automatically generating information display content from the input information using a generation AI model, and means for automatically selecting the destination and recipient of information based on past information effectiveness data and market information using an evaluation means. This enables efficient automation from ad creation to distribution and effectiveness measurement, making it possible to obtain maximum advertising effectiveness with minimal resources.

[0270] A "user interface" refers to the interface used by users to input information in an information processing device.

[0271] A "generative AI model" is a generative model that utilizes artificial intelligence technology to automatically generate information display content based on input information.

[0272] "Evaluation methods" refer to algorithms and processes for selecting information recipients and recipients by analyzing past information effectiveness data and market information.

[0273] "Communication means" refers to the hardware and software used to transmit information to automatically selected destinations.

[0274] A "tracking code" is a code embedded in the displayed information, and its role is to collect data necessary to evaluate the effectiveness of the information after it has been transmitted.

[0275] The "editing screen" refers to a user interface that allows users to review the generated information display and make corrections as needed.

[0276] This invention is a system for efficiently automating advertising activities, and in particular aims to enable store operators to easily create, distribute, and evaluate digital advertisements. The core technologies of this system are a user-friendly interface that is easy for users to operate, automatic content generation using a generation AI model, and automatic selection of distribution media and target audiences.

[0277] Users access the information processing device using a terminal and input the necessary data for advertising through a dedicated user screen. This data includes the name and features of the advertised product or service, the profile of the target customer, the store's location information, and related images. The entered data is transmitted to a server via the internet and stored securely.

[0278] The server processes information received from users using a generative AI model. This generative AI model combines natural language processing and image generation technologies and has the ability to automatically generate advertising text and designs. This process utilizes high-performance computer systems and AI software.

[0279] The generated advertising content is delivered back to the user's device via the server, where the user can review the content through the editing screen and make corrections as needed. Once the advertising content is finalized, the server analyzes past advertising effectiveness data and market information to select the optimal distribution channels and target audience. This allows for the development of a distribution plan that maximizes the effectiveness of the advertisement.

[0280] The server uses advanced communication technology to deliver advertisements to selected media outlets, and each advertisement has a tracking code embedded in it. This tracking code is used to collect user behavior data, such as ad views and clicks.

[0281] Ultimately, the server analyzes the collected data to measure the effectiveness of the advertising campaign. These results are reported to the user through a user interface, providing foundational data for improving future advertising strategies.

[0282] As a specific example, there is a café operator who wants to promote the menu of the new season. The user inputs a prompt sentence such as "Café menu for the new season, targeting local 20-something women, with high-quality images attached" using a terminal. Based on this information, the system automatically generates advertising content and enables distribution to the optimal platform. With such a system, advertising activities can be carried out efficiently and effectively.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user uses a terminal to input information for an advertising campaign. The input content includes the name of the product, features, profile of the target customers, location information of the store, and related image files. At this stage, the information input by the user is structured on the terminal and sent to the server for subsequent processing.

[0286] Step 2:

[0287] The terminal sends the collected information to the server in digital form. The transmitted data is received by the server and stored in the database. At this stage, the data format is normalized and prepared so that the generated AI model can process it efficiently.

[0288] Step 3:

[0289] The server automatically generates advertising content using the generated AI model from the stored data. In this generation process, natural language processing technology is used to create text content, and image generation technology is used to generate visual content. Using the data stored as input, the text and design of the advertisement are output.

[0290] Step 4:

[0291] The generated advertising content is delivered to the user's device via the server. The user reviews the content on the editing screen and makes corrections as needed. Here, the user fine-tunes the ad text and images and finalizes the ad.

[0292] Step 5:

[0293] The server evaluates historical advertising data and market information to automatically select the optimal distribution channels and target audiences. This involves using statistical algorithms as evaluation tools to determine the best plan to maximize advertising effectiveness. The selection results are then used as input data for the next distribution step.

[0294] Step 6:

[0295] The server delivers advertisements to selected distribution media using communication methods. Tracking codes are embedded in the advertisements to collect user behavior data, such as ad impressions and clicks. At this stage, the actual delivery process takes place, and the delivery status is monitored in real time.

[0296] Step 7:

[0297] After ad delivery, the server analyzes the collected behavioral data to measure the effectiveness of the advertising campaign. This analysis is performed based on the received data, and the results of the effectiveness measurement are generated. These results are provided to the user's device through the interface and used to improve future advertising strategies.

[0298] (Application Example 1)

[0299] 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."

[0300] In conventional advertising distribution systems, there is a problem that it is difficult to present optimal advertisements based on real-time location information of users. There is also an issue that it is difficult to present effective advertisements utilizing wearable devices. As a result, the target accuracy of advertisements decreases, and the maximization of advertising effects has not been achieved.

[0301] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0302] In this invention, the server includes means for providing an information processing apparatus with a user interface for inputting advertisement data for the store operation entity, means for automatically generating advertisement information from the input advertisement data using a generation model, and means for operating in a wearable device in the S state and presenting advertisement information based on the location information and target information of the user. Thereby, it becomes possible to present optimal advertisements according to the real-time situation of the user.

[0303] The "store operation entity" is a general term for legal persons or individuals who operate stores to provide goods and services to customers.

[0304] "Advertisement data" is the information that serves as the basis for generating advertisements and includes the name of the product, features, profile of the target customer, location information of the store, related image files, and the like.

[0305] The "user interface" is an operation screen for a user to input data to the system or check the generated information via the information processing apparatus.

[0306] The "information processing apparatus" is a general term for computers and devices that process digital data and have the ability to interact with users and other systems.

[0307] The "generation model" is a program for automatically creating advertisement information from the input data using AI technology.

[0308] "Advertising information" refers to generated content such as advertising copy and images, intended to encourage a specific customer segment to purchase products or services.

[0309] "Wearable devices in S state" refer to devices that can be worn by a user and are in a certain operating state or usage condition, such as smart glasses and smartwatches.

[0310] "User location information" refers to data that indicates the geographical location where the user is currently located, using methods such as GPS or wireless communication.

[0311] "Target information" refers to profile information related to a specific user or user group, including data such as interests, preferences, and consumption history.

[0312] The system for carrying out the present invention mainly includes a server, an information processing device, and a wearable device in S state. The user inputs advertising data via the information processing device. The input advertising data includes the product name, features, target customer profile, store location information, and associated image files. This data is transmitted from the information processing device to the server and stored in a database. The server analyzes the stored data using a generative model and automatically generates advertising information. This generative model utilizes AI that combines natural language processing and image generation technology. The generated advertising information uses technology that enables the user to instantly display advertising content tailored to their current situation and location using a wearable device.

[0313] The server analyzes collected historical advertising effectiveness data and market information to select the optimal advertising distribution channels and target audience. This selection involves comparing existing advertising effectiveness data with trend information. The selected advertisements are transmitted to the designated distribution channels via communication means, and a tracking code is embedded at that time. After the advertising effectiveness is measured, the analysis results are reported to the user through the information processing device.

[0314] As a concrete example, imagine a scenario where a traveler is strolling through a city and an advertisement for a discount coupon for a nearby tourist attraction appears on their smart glasses. In this case, the traveler can use the coupon on the spot and receive a discount by visiting the tourist attraction. Examples of prompts include the following:

[0315] Example prompt:

[0316] "User's current location: Near Tokyo Station"

[0317] User's areas of interest: Culture, Art

[0318] Ad content generation prompt: 'Generate a Street View ad that introduces users to the latest art exhibitions near Tokyo Station.'

[0319] In this way, the system can provide advertising information tailored to the user's needs in real time.

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] The device receives advertising data entered by the user. This advertising data includes the product name, features, target customer profile, store location information, and associated image files. The device sends this data to the server and stores it in a database.

[0323] Step 2:

[0324] The server retrieves the stored advertising data and automatically generates advertising information using a generative AI model. Based on the input advertising data, the AI ​​model combines natural language processing and image generation technology to generate advertising copy and design, and compiles the results as advertising information.

[0325] Step 3:

[0326] The server analyzes past advertising effectiveness data and market information based on the generated advertising information. This allows it to automatically select the optimal advertising distribution channels and target audience. Past advertising effectiveness data and market trend data are used as input, and the selected distribution methods and targets are output.

[0327] Step 4:

[0328] The server delivers advertisements to selected transmission media using communication methods. Tracking codes are embedded in the advertisement information, enabling data collection to track user responses and behavior after delivery.

[0329] Step 5:

[0330] The server measures the effectiveness of delivered ads and analyzes the collected data. This analysis is based on data from the tracking code. The analysis results are reported to the device, allowing users to review the results and adjust their strategy for the next advertising campaign.

[0331] Step 6:

[0332] The server presents real-time advertising information based on location and profile information to users of wearable devices in the S state. The user's current location and profile information are used as input, and the server outputs the most suitable advertisements based on this information.

[0333] 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.

[0334] This invention provides a system for store operators to efficiently create and distribute digital advertisements, and by incorporating an emotion engine, it enables consideration of the emotional state of users and targets when inputting and distributing advertising data. The system comprises a user interface, a generative model, analytical means, communication means, an interface, an editing interface, a tracking code, and an emotion engine.

[0335] Users log in to the system via their device and input the information and image data required for the advertising campaign. The emotion engine analyzes the user's voice tone, facial expressions, input patterns, etc., to recognize the user's emotional state. This allows the interface to be dynamically adjusted based on the user's stress and excitement levels, improving usability.

[0336] Next, the server utilizes a generative model to automatically generate content based on the input advertising data. The generation process uses emotional information obtained by the emotion engine to adjust the content to have a more emotional impact.

[0337] The generated advertising content is presented to the user on their device and can be modified through an editing interface. Based on feedback from the sentiment engine, users can evaluate whether the content conveys the appropriate emotions and make adjustments as needed.

[0338] Subsequently, the server selects the distribution medium and target audience for ad delivery based on past advertising effectiveness and market data. At this stage, it considers the emotional state of the target users and delivers ads at the appropriate time and with appropriate content to ensure effective communication.

[0339] After delivery is complete, the data collected using the tracking code is analyzed on the server to evaluate the ad's performance and the emotional response of recipients. The analysis results are reported to the user through the interface, and the user can use the emotional data of the target audience to plan future advertising strategies.

[0340] For example, when advertising a new menu item at a cafe, if a user shows high interest when accessing the system and entering data, the generated advertisement will be enhanced with emotional language and visual elements. Furthermore, the advertisement can be adjusted to air during times when the target audience is relaxed. This invention enables users to experience a more personalized advertising experience than ever before, leading to improved marketing effectiveness.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The user logs into the system using their device. Upon successful login, the user interface for creating advertising campaigns is displayed.

[0344] Step 2:

[0345] Users input product details, target customer profiles, and promotional messages and image files through the device's interface. During this process, the emotion engine recognizes the user's emotional state through their voice and facial expressions, dynamically adjusting the interface accordingly.

[0346] Step 3:

[0347] The terminal sends the data entered by the user to the server. The server receives this data and stores it in its database.

[0348] Step 4:

[0349] The server uses a generative model to automatically generate advertising content based on input data and collected sentiment information. The generated content includes wording and visual elements that correspond to the user's emotions.

[0350] Step 5:

[0351] The server sends the generated advertising content to the device. The user can view this content on their device and make any necessary modifications using the editing interface.

[0352] Step 6:

[0353] The server selects the optimal delivery medium and timing based on past advertising effectiveness data, market data, and target sentiment data obtained from the sentiment engine. This enables delivery tailored to the emotions of the target users.

[0354] Step 7:

[0355] The server delivers advertisements to selected distribution channels via communication methods. Tracking codes are embedded in the advertisements, allowing for tracking of target audience behavior and responses.

[0356] Step 8:

[0357] After the ad is delivered, the server analyzes the collected data to evaluate the ad's effectiveness and the target users' emotional response.

[0358] Step 9:

[0359] The server compiles the analysis results and reports them to the user's device via an interface. Based on this information, the user can plan and optimize their strategy for the next advertising campaign.

[0360] (Example 2)

[0361] 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".

[0362] Traditional systems for creating and distributing digital advertisements were unable to adjust content to consider the user's emotional state, making personalized and emotionally-driven effective communication difficult. As a result, ad acceptance rates were low, and it was difficult to collect sufficient data to improve marketing efficiency.

[0363] 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.

[0364] In this invention, the server includes means for providing a human-machine interface to the terminal, means for automatically generating advertising content using a generative artificial intelligence model, and means for analyzing the user's emotional state using an emotion evaluation mechanism and dynamically adjusting the interface. This enables the generation of personalized advertisements that take into account the user's emotional state and the optimization of the delivery process.

[0365] A "human-machine interface" is a means by which a user accesses a system through a terminal and inputs and manipulates information.

[0366] A "generative artificial intelligence model" is an artificial intelligence technology used to automatically generate advertising content based on input data.

[0367] An "emotion evaluation mechanism" is a mechanism that analyzes the user's voice tone, facial expressions, and input patterns to recognize their emotional state and adjust the interface accordingly.

[0368] "Advertising content" refers to content such as text, images, and videos that are generated according to the objectives of an advertising campaign.

[0369] "Transmission path" refers to the medium or channel through which generated advertising content is sent to the recipient.

[0370] A "tracking identifier" is a code embedded in advertising content that is used to measure the effectiveness of the ad after delivery and to analyze recipient interactions.

[0371] This invention begins with the user inputting advertising campaign information into the system using a terminal. The terminal is equipped with a human-machine interface, allowing the user to easily input advertising objectives, target audience, image data, taglines, and other information. This interface employs an intuitive and user-friendly design.

[0372] Upon receiving input data, the server automatically generates advertisement content using a generative artificial intelligence model. This model utilizes multiple neural networks to effectively learn the components of advertisements from a large amount of training data. As a result, advertisements optimized to match the user's preferences are generated. The server also incorporates an emotion evaluation mechanism, recognizing the user's emotional state by analyzing voice tone, facial expressions, and input patterns. This information is used to dynamically adjust the interface, improving the user experience.

[0373] Once an ad is generated, it is presented to the user through an editing platform on their device. Here, they can review the text and images included in the ad and re-edit them as needed. Sentiment-based feedback is also displayed, which users can use to fine-tune the content. This process ensures that the ad content more effectively evokes the emotions of the target audience.

[0374] For example, when a cafe advertises a new menu item, the generated ad will emphasize emotional language and visual elements when users show high interest. Furthermore, the ad will be timed to be delivered to the target audience during relaxed hours.

[0375] An example of a prompt message is: "Create an advertisement promoting the cafe's new menu. Generate content that resonates with the user's emotional state in an appropriate way." In this way, the system can efficiently and effectively create and deliver personalized digital advertisements.

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1:

[0378] Users log in to the system using their terminal and input data for their advertising campaigns. This data includes the campaign objectives, target audience, and image and text information to be used. Specifically, users use a keyboard and mouse to input this information on a dedicated interface and save it to the system. The entered data is then sent directly to the server.

[0379] Step 2:

[0380] The server passes the received advertising data to an artificial intelligence model that generates advertising content automatically. In this process, the algorithm analyzes the input information, performs text generation and image processing, and generates the optimal advertising content. Specifically, the model determines the message and design to be conveyed based on a vast amount of training data, and combines them to output a single advertisement.

[0381] Step 3:

[0382] The server sends the generated ad content to the device and presents it to the user. The device then displays the ad and provides an interface that allows the user to review and edit it. The user reviews the ad text and visual elements and makes changes as needed. Specifically, the user can select a text box with the mouse, edit the content using the keyboard, or change images using drag and drop.

[0383] Step 4:

[0384] An emotion evaluation mechanism operates in the background, analyzing the user's voice tone, facial expressions, and input patterns. The system uses this data to determine whether the user is experiencing stress or satisfaction, and adjusts the interface accordingly. Furthermore, generated advertisements can be fine-tuned based on the user's emotional information. Specifically, the interface's color scheme and button placement change depending on the user's emotional state.

[0385] Step 5:

[0386] Once the user has finished editing the ad, the server selects the optimal delivery route and timing for sending the ad. This involves database queries to analyze historical ad data and market trends. Specifically, the server calculates the most effective broadcast time and channels and stores the results.

[0387] Step 6:

[0388] The server delivers ads using a predetermined transmission path and embeds tracking identifiers to measure recipient responses. After the ads are sent, their effectiveness is monitored in real time, and the collected data is analyzed and provided as feedback to the user. Specifically, the server records click counts and viewing time and generates analytical reports.

[0389] Step 7:

[0390] The analysis results are reported to the user via their device, providing valuable information for future advertising strategies. This process completes the entire workflow from ad generation to delivery, further enhancing the user's advertising experience. Specifically, users review data presented in graphs and numerical formats, and consider improvements to the next prompt and campaign.

[0391] (Application Example 2)

[0392] 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."

[0393] In today's advertising market, there is a demand to maximize consumer interest by dynamically adjusting advertising content based on the user's emotional state. However, conventional ad creation and delivery systems cannot adequately consider user emotions, making it difficult to provide a personalized advertising experience. Therefore, in order to maximize the effectiveness of advertising, it is necessary to realize technology that recognizes the user's emotional state, dynamically adjusts content based on the analysis results, and delivers it at the appropriate time.

[0394] 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.

[0395] In this invention, the server includes means for providing a terminal with a user interface for inputting advertising information for store management organizations, means for automatically generating advertising content from the input advertising information using a generative model, and means for recognizing the user's emotional state using an emotion engine and dynamically adjusting the advertising content generated by the generative model based on the analysis results. This enables the generation and delivery of personalized advertising content based on the user's emotions.

[0396] A "store operating organization" is a general term for commercial organizations or companies that operate and manage stores.

[0397] "Advertising information" refers to data and content used for the purpose of promoting specific products or services.

[0398] A "user interface" is a mechanism in an information system that includes screens and operating methods for exchanging information between the user and the system.

[0399] A "generative model" is an algorithm or program that automatically generates new content or information based on input data.

[0400] "Promotional content" refers to text, images, audio, and other materials created to widely publicize a specific project or product.

[0401] An "analytical device" is a system of hardware and software used to analyze collected information and data.

[0402] An "emotion engine" is a technology or program that analyzes an individual's emotional state using voice, facial expressions, and input data, and outputs the results.

[0403] "Smart devices" is a general term for intelligent electronic devices equipped with sensors and network connectivity.

[0404] A "communication device" is a configuration of hardware and software used to send and receive data between multiple computer systems.

[0405] A "tracking code" is a notation or script embedded in a web page or advertising content to measure user behavior and effectiveness after delivery.

[0406] This invention provides a system for store management organizations to create and deliver effective and emotionally resonant advertisements. The system includes a server and user terminals, each component performing a specific function.

[0407] The server receives advertising information from the user and automatically generates advertising content using a generative AI model based on that information. During this process, an emotion engine analyzes the user's voice and facial expressions, dynamically adjusting the generated advertising content according to their emotional state. It is recommended to use Python-based libraries (e.g., OpenCV, librosa) for the emotion engine.

[0408] The user's device functions as a smart device, providing a user interface for inputting advertising information and, as needed, acquiring user sentiment data using the camera and microphone. Furthermore, it allows for adjustment of the generated advertising content through an editing interface.

[0409] The generated advertising content is then analyzed using an analytical device to select the optimal distribution channels and target audience based on past data, and delivered to the selected channels via a communication device. A tracking code is embedded during this process, allowing for the measurement of user responses and effectiveness after delivery.

[0410] For example, when promoting a new cafe menu, if a user accesses the system in an excited state, the emotion engine will recommend more vivid visuals and lively text for the advertisement generated by the generative AI model. A prompt such as, "Generate content for the new cafe menu advertisement. If the user is very excited, present it with friendly, relaxing visuals and vibrant text," can be used.

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] Users log in to the system via their device and enter advertising information. This information includes text, image data, and the purpose of the advertisement. This input data is sent to the server.

[0414] Step 2:

[0415] The server processes the received advertising information and automatically generates advertising content using a generative AI model. During this process, the server uses prompt statements to instruct the generative model on the direction of the content, and the resulting output is the generated advertising content. These prompt statements are adjusted based on the advertising's purpose and target emotions.

[0416] Step 3:

[0417] The server activates the emotion engine and analyzes audio and facial expression data obtained from the user's device as input. Based on this analysis, it evaluates whether the generated advertising content is appropriate for the user's emotional state and dynamically adjusts the text and visual elements as needed.

[0418] Step 4:

[0419] Users review the generated promotional content and make necessary changes through the editing interface on their device. During editing, users can optimize the content based on feedback from the sentiment engine. The user's adjustments are then sent back to the server.

[0420] Step 5:

[0421] The server uses analytical equipment to refer to past advertising data and market information to automatically select the optimal distribution channel and target audience. At this time, the timing of delivery is also optimized based on sentiment data.

[0422] Step 6:

[0423] The server uses communication equipment to deliver advertisements to selected distribution media. A tracking code is embedded during delivery, allowing for the collection of user behavior data after delivery.

[0424] Step 7:

[0425] After delivery, the server analyzes the data collected based on the tracking code to measure the effectiveness of the advertisement. Users can view these analysis results through their devices and use them to inform their next advertising strategy.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] [Third Embodiment]

[0430] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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".

[0442] This invention is a system designed to enable store operators to easily utilize digital advertising, and it automates the generation and distribution of advertisements using AI technology. Specific embodiments are shown below.

[0443] Users access the system via their devices and input the data required for advertising campaigns. This information includes the name and characteristics of the advertised product, the profile of the desired target customer, store location information, and associated image files. This data is transmitted from the device to the server and stored in a database.

[0444] The server automatically generates advertising content using a generative model based on the information it receives. The generative model combines natural language processing and image generation technologies to generate advertising copy and design. The generated advertising content is provided to the device through an interface, allowing the user to review and edit it as needed.

[0445] Next, the server uses analytical tools to evaluate past advertising effectiveness data and market trend data to select the optimal distribution channels and target audiences. This selection is made with the aim of maximizing the effectiveness of the advertisements.

[0446] Subsequently, the server uses communication methods to deliver the advertisement to the selected distribution media. At this time, a tracking code is embedded in the advertisement, and the behavioral data of users who view the advertisement is collected.

[0447] After the ad delivery ends, the server analyzes the collected data to measure the effectiveness of the ad campaign. The results of the effectiveness measurement are reported to the user's device through the interface, allowing the user to adjust their advertising strategy for future campaigns based on this information.

[0448] As a concrete example, when a cafe owner wants to advertise a new seasonal menu, they would first input information about the cafe's new menu and attach relevant images. Next, they would review the generated advertising content, and if satisfied, leave the delivery process to the server. Finally, the advertising performance would be reported, which could be used as foundational data to make the next campaign more effective. In this way, the present invention automates the entire advertising process, providing users with efficient and effective advertising activities.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The user operates the terminal and logs into the system. Here, they enter their account information, undergo authentication, and are granted access to the system.

[0452] Step 2:

[0453] Users enter advertising data through the device interface. This includes product details, target customer attributes, desired advertising period, and image file uploads.

[0454] Step 3:

[0455] The device sends the collected advertising data to the server. The server receives this data, stores it in a database, and prepares for the next processing step.

[0456] Step 4:

[0457] The server automatically generates advertising content using a generative model. The generative model generates ad copy from text data and creates appropriate visual designs using an image generation algorithm.

[0458] Step 5:

[0459] The server sends the generated ad content to the device and prompts the user for confirmation. The user uses an editing interface on their device to check the content and make corrections as needed.

[0460] Step 6:

[0461] The server analyzes past advertising effectiveness data and market data to automatically select the optimal distribution channels and target customers. This selection result is then used in the next distribution step.

[0462] Step 7:

[0463] The server automatically delivers advertisements to selected distribution channels using communication methods. The timing and frequency of delivery are also managed by the server.

[0464] Step 8:

[0465] After delivery is complete, the server analyzes the ad performance data collected by the tracking code. This analysis includes ad views, clicks, and conversion rates.

[0466] Step 9:

[0467] The server compiles the analysis results and reports them to the user on their terminal via an interface. Based on this, the user can then develop their next advertising strategy.

[0468] (Example 1)

[0469] 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."

[0470] For businesses operating retail stores, advertising is essential for acquiring customers and increasing sales. However, creating and distributing advertisements efficiently is time-consuming and requires considerable effort. This is especially true for small and medium-sized enterprises (SMEs) and companies that cannot allocate sufficient resources to advertising; without specialized knowledge, implementing effective advertising campaigns is difficult. This leads to problems such as wasted advertising costs and failure to accurately reach the target audience. Therefore, there is a need for a system that can solve these problems and enable efficient and highly effective advertising activities.

[0471] 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.

[0472] In this invention, the server includes means for providing a user screen for users to input information to an information processing device, means for automatically generating information display content from the input information using a generation AI model, and means for automatically selecting the destination and recipient of information based on past information effectiveness data and market information using an evaluation means. This enables efficient automation from ad creation to distribution and effectiveness measurement, making it possible to obtain maximum advertising effectiveness with minimal resources.

[0473] A "user interface" refers to the interface used by users to input information in an information processing device.

[0474] A "generative AI model" is a generative model that utilizes artificial intelligence technology to automatically generate information display content based on input information.

[0475] "Evaluation methods" refer to algorithms and processes for selecting information recipients and recipients by analyzing past information effectiveness data and market information.

[0476] "Communication means" refers to the hardware and software used to transmit information to automatically selected destinations.

[0477] A "tracking code" is a code embedded in the displayed information, and its role is to collect data necessary to evaluate the effectiveness of the information after it has been transmitted.

[0478] The "editing screen" refers to a user interface that allows users to review the generated information display and make corrections as needed.

[0479] This invention is a system for efficiently automating advertising activities, and in particular aims to enable store operators to easily create, distribute, and evaluate digital advertisements. The core technologies of this system are a user-friendly interface that is easy for users to operate, automatic content generation using a generation AI model, and automatic selection of distribution media and target audiences.

[0480] Users access the information processing device using a terminal and input the necessary data for advertising through a dedicated user screen. This data includes the name and features of the advertised product or service, the profile of the target customer, the store's location information, and related images. The entered data is transmitted to a server via the internet and stored securely.

[0481] The server processes information received from users using a generative AI model. This generative AI model combines natural language processing and image generation technologies and has the ability to automatically generate advertising text and designs. This process utilizes high-performance computer systems and AI software.

[0482] The generated advertising content is delivered back to the user's device via the server, where the user can review the content through the editing screen and make corrections as needed. Once the advertising content is finalized, the server analyzes past advertising effectiveness data and market information to select the optimal distribution channels and target audience. This allows for the development of a distribution plan that maximizes the effectiveness of the advertisement.

[0483] The server uses advanced communication technology to deliver advertisements to selected media outlets, and each advertisement has a tracking code embedded in it. This tracking code is used to collect user behavior data, such as ad views and clicks.

[0484] Ultimately, the server analyzes the collected data to measure the effectiveness of the advertising campaign. These results are reported to the user through a user interface, providing foundational data for improving future advertising strategies.

[0485] As a concrete example, imagine a cafe owner who wants to advertise a new seasonal menu. The user enters a prompt message using a terminal, such as "New seasonal cafe menu, target audience: local women in their 20s, attach high-quality images." Based on this information, the system automatically generates advertising content and enables its delivery to the most suitable platform. Such a system allows advertising activities to be conducted efficiently and effectively.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user uses a device to enter information for the advertising campaign. This information includes the product name, features, target customer profile, store location information, and associated image files. At this stage, the information entered by the user is structured on the device and sent to the server for subsequent processing.

[0489] Step 2:

[0490] The terminal transmits the collected information to the server in digital format. The transmitted data is received by the server and stored in a database. At this stage, the data format is normalized and prepared so that it can be efficiently processed by the generative AI model.

[0491] Step 3:

[0492] The server automatically generates advertising content using an AI model based on stored data. This generation process uses natural language processing to create text content and image generation technology to produce visual content. Using the stored data as input, the advertising text and design are output.

[0493] Step 4:

[0494] The generated advertising content is delivered to the user's device via the server. The user reviews the content on the editing screen and makes corrections as needed. Here, the user fine-tunes the ad text and images and finalizes the ad.

[0495] Step 5:

[0496] The server evaluates historical advertising data and market information to automatically select the optimal distribution channels and target audiences. This involves using statistical algorithms as evaluation tools to determine the best plan to maximize advertising effectiveness. The selection results are then used as input data for the next distribution step.

[0497] Step 6:

[0498] The server delivers advertisements to selected distribution media using communication methods. Tracking codes are embedded in the advertisements to collect user behavior data, such as ad impressions and clicks. At this stage, the actual delivery process takes place, and the delivery status is monitored in real time.

[0499] Step 7:

[0500] After ad delivery, the server analyzes the collected behavioral data to measure the effectiveness of the advertising campaign. This analysis is performed based on the received data, and the results of the effectiveness measurement are generated. These results are provided to the user's device through the interface and used to improve future advertising strategies.

[0501] (Application Example 1)

[0502] 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."

[0503] Traditional advertising delivery systems have a problem in that they cannot easily present the most relevant advertisements based on the user's real-time location information. Furthermore, there are challenges in effectively presenting advertisements using wearable devices. As a result, the targeting accuracy of advertisements decreases, and the maximization of advertising effectiveness is not achieved.

[0504] 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.

[0505] In this invention, the server includes means for providing an information processing device with a user interface for inputting advertising data for store operators, means for automatically generating advertising information from the input advertising data using a generative model, and means for operating on a wearable device in state S and presenting advertising information based on the user's location information and target information. This makes it possible to present optimal advertisements according to the user's real-time situation.

[0506] "Store operator" refers to any corporation or individual that operates a store in order to provide goods or services to customers.

[0507] "Advertising data" refers to the information used to generate advertisements, and includes product names, features, target customer profiles, store location information, and related image files.

[0508] A "user interface" is an operation screen that allows users to input data into a system via an information processing device and to check the generated information.

[0509] An "information processing device" is a general term for computers and devices that have the ability to process digital data and interact with users and other systems.

[0510] A "generative model" is a program that uses AI technology to automatically generate advertising information from input data.

[0511] "Advertising information" refers to generated content such as advertising copy and images, intended to encourage a specific customer segment to purchase products or services.

[0512] "Wearable devices in S state" refer to devices that can be worn by a user and are in a certain operating state or usage condition, such as smart glasses and smartwatches.

[0513] "User location information" refers to data that indicates the geographical location where the user is currently located, using methods such as GPS or wireless communication.

[0514] "Target information" refers to profile information related to a specific user or user group, including data such as interests, preferences, and consumption history.

[0515] The system for carrying out the present invention mainly includes a server, an information processing device, and a wearable device in S state. The user inputs advertising data via the information processing device. The input advertising data includes the product name, features, target customer profile, store location information, and associated image files. This data is transmitted from the information processing device to the server and stored in a database. The server analyzes the stored data using a generative model and automatically generates advertising information. This generative model utilizes AI that combines natural language processing and image generation technology. The generated advertising information uses technology that enables the user to instantly display advertising content tailored to their current situation and location using a wearable device.

[0516] The server analyzes collected historical advertising effectiveness data and market information to select the optimal advertising distribution channels and target audience. This selection involves comparing existing advertising effectiveness data with trend information. The selected advertisements are transmitted to the designated distribution channels via communication means, and a tracking code is embedded at that time. After the advertising effectiveness is measured, the analysis results are reported to the user through the information processing device.

[0517] As a concrete example, imagine a scenario where a traveler is strolling through a city and an advertisement for a discount coupon for a nearby tourist attraction appears on their smart glasses. In this case, the traveler can use the coupon on the spot and receive a discount by visiting the tourist attraction. Examples of prompts include the following:

[0518] Example prompt:

[0519] "User's current location: Near Tokyo Station"

[0520] User's areas of interest: Culture, Art

[0521] Ad content generation prompt: 'Generate a Street View ad that introduces users to the latest art exhibitions near Tokyo Station.'

[0522] In this way, the system can provide advertising information tailored to the user's needs in real time.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The device receives advertising data entered by the user. This advertising data includes the product name, features, target customer profile, store location information, and associated image files. The device sends this data to the server and stores it in a database.

[0526] Step 2:

[0527] The server retrieves the stored advertising data and automatically generates advertising information using a generative AI model. Based on the input advertising data, the AI ​​model combines natural language processing and image generation technology to generate advertising copy and design, and compiles the results as advertising information.

[0528] Step 3:

[0529] The server analyzes past advertising effectiveness data and market information based on the generated advertising information. This allows it to automatically select the optimal advertising distribution channels and target audience. Past advertising effectiveness data and market trend data are used as input, and the selected distribution methods and targets are output.

[0530] Step 4:

[0531] The server delivers advertisements to selected transmission media using communication methods. Tracking codes are embedded in the advertisement information, enabling data collection to track user responses and behavior after delivery.

[0532] Step 5:

[0533] The server measures the effectiveness of delivered ads and analyzes the collected data. This analysis is based on data from the tracking code. The analysis results are reported to the device, allowing users to review the results and adjust their strategy for the next advertising campaign.

[0534] Step 6:

[0535] The server presents real-time advertising information based on location and profile information to users of wearable devices in the S state. The user's current location and profile information are used as input, and the server outputs the most suitable advertisements based on this information.

[0536] 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.

[0537] This invention provides a system for store operators to efficiently create and distribute digital advertisements, and by incorporating an emotion engine, it enables consideration of the emotional state of users and targets when inputting and distributing advertising data. The system comprises a user interface, a generative model, analytical means, communication means, an interface, an editing interface, a tracking code, and an emotion engine.

[0538] Users log in to the system via their device and input the information and image data required for the advertising campaign. The emotion engine analyzes the user's voice tone, facial expressions, input patterns, etc., to recognize the user's emotional state. This allows the interface to be dynamically adjusted based on the user's stress and excitement levels, improving usability.

[0539] Next, the server utilizes a generative model to automatically generate content based on the input advertising data. The generation process uses emotional information obtained by the emotion engine to adjust the content to have a more emotional impact.

[0540] The generated advertising content is presented to the user on their device and can be modified through an editing interface. Based on feedback from the sentiment engine, users can evaluate whether the content conveys the appropriate emotions and make adjustments as needed.

[0541] Subsequently, the server selects the distribution medium and target audience for ad delivery based on past advertising effectiveness and market data. At this stage, it considers the emotional state of the target users and delivers ads at the appropriate time and with appropriate content to ensure effective communication.

[0542] After delivery is complete, the data collected using the tracking code is analyzed on the server to evaluate the ad's performance and the emotional response of recipients. The analysis results are reported to the user through the interface, and the user can use the emotional data of the target audience to plan future advertising strategies.

[0543] For example, when advertising a new menu item at a cafe, if a user shows high interest when accessing the system and entering data, the generated advertisement will be enhanced with emotional language and visual elements. Furthermore, the advertisement can be adjusted to air during times when the target audience is relaxed. This invention enables users to experience a more personalized advertising experience than ever before, leading to improved marketing effectiveness.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The user logs into the system using their device. Upon successful login, the user interface for creating advertising campaigns is displayed.

[0547] Step 2:

[0548] Users input product details, target customer profiles, and promotional messages and image files through the device's interface. During this process, the emotion engine recognizes the user's emotional state through their voice and facial expressions, dynamically adjusting the interface accordingly.

[0549] Step 3:

[0550] The terminal sends the data entered by the user to the server. The server receives this data and stores it in its database.

[0551] Step 4:

[0552] The server uses a generative model to automatically generate advertising content based on input data and collected sentiment information. The generated content includes wording and visual elements that correspond to the user's emotions.

[0553] Step 5:

[0554] The server sends the generated advertising content to the device. The user can view this content on their device and make any necessary modifications using the editing interface.

[0555] Step 6:

[0556] The server selects the optimal delivery medium and timing based on past advertising effectiveness data, market data, and target sentiment data obtained from the sentiment engine. This enables delivery tailored to the emotions of the target users.

[0557] Step 7:

[0558] The server delivers advertisements to selected distribution channels via communication methods. Tracking codes are embedded in the advertisements, allowing for tracking of target audience behavior and responses.

[0559] Step 8:

[0560] After the ad is delivered, the server analyzes the collected data to evaluate the ad's effectiveness and the target users' emotional response.

[0561] Step 9:

[0562] The server compiles the analysis results and reports them to the user's device via an interface. Based on this information, the user can plan and optimize their strategy for the next advertising campaign.

[0563] (Example 2)

[0564] 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."

[0565] Traditional systems for creating and distributing digital advertisements were unable to adjust content to consider the user's emotional state, making personalized and emotionally-driven effective communication difficult. As a result, ad acceptance rates were low, and it was difficult to collect sufficient data to improve marketing efficiency.

[0566] 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.

[0567] In this invention, the server includes means for providing a human-machine interface to the terminal, means for automatically generating advertising content using a generative artificial intelligence model, and means for analyzing the user's emotional state using an emotion evaluation mechanism and dynamically adjusting the interface. This enables the generation of personalized advertisements that take into account the user's emotional state and the optimization of the delivery process.

[0568] A "human-machine interface" is a means by which a user accesses a system through a terminal and inputs and manipulates information.

[0569] A "generative artificial intelligence model" is an artificial intelligence technology used to automatically generate advertising content based on input data.

[0570] An "emotion evaluation mechanism" is a mechanism that analyzes the user's voice tone, facial expressions, and input patterns to recognize their emotional state and adjust the interface accordingly.

[0571] "Advertising content" refers to content such as text, images, and videos that are generated according to the objectives of an advertising campaign.

[0572] "Transmission path" refers to the medium or channel through which generated advertising content is sent to the recipient.

[0573] A "tracking identifier" is a code embedded in advertising content that is used to measure the effectiveness of the ad after delivery and to analyze recipient interactions.

[0574] This invention begins with the user inputting advertising campaign information into the system using a terminal. The terminal is equipped with a human-machine interface, allowing the user to easily input advertising objectives, target audience, image data, taglines, and other information. This interface employs an intuitive and user-friendly design.

[0575] Upon receiving input data, the server automatically generates advertisement content using a generative artificial intelligence model. This model utilizes multiple neural networks to effectively learn the components of advertisements from a large amount of training data. As a result, advertisements optimized to match the user's preferences are generated. The server also incorporates an emotion evaluation mechanism, recognizing the user's emotional state by analyzing voice tone, facial expressions, and input patterns. This information is used to dynamically adjust the interface, improving the user experience.

[0576] Once an ad is generated, it is presented to the user through an editing platform on their device. Here, they can review the text and images included in the ad and re-edit them as needed. Sentiment-based feedback is also displayed, which users can use to fine-tune the content. This process ensures that the ad content more effectively evokes the emotions of the target audience.

[0577] For example, when a cafe advertises a new menu item, the generated ad will emphasize emotional language and visual elements when users show high interest. Furthermore, the ad will be timed to be delivered to the target audience during relaxed hours.

[0578] An example of a prompt message is: "Create an advertisement promoting the cafe's new menu. Generate content that resonates with the user's emotional state in an appropriate way." In this way, the system can efficiently and effectively create and deliver personalized digital advertisements.

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

[0580] Step 1:

[0581] Users log in to the system using their terminal and input data for their advertising campaigns. This data includes the campaign objectives, target audience, and image and text information to be used. Specifically, users use a keyboard and mouse to input this information on a dedicated interface and save it to the system. The entered data is then sent directly to the server.

[0582] Step 2:

[0583] The server passes the received advertising data to an artificial intelligence model that generates advertising content automatically. In this process, the algorithm analyzes the input information, performs text generation and image processing, and generates the optimal advertising content. Specifically, the model determines the message and design to be conveyed based on a vast amount of training data, and combines them to output a single advertisement.

[0584] Step 3:

[0585] The server sends the generated ad content to the device and presents it to the user. The device then displays the ad and provides an interface that allows the user to review and edit it. The user reviews the ad text and visual elements and makes changes as needed. Specifically, the user can select a text box with the mouse, edit the content using the keyboard, or change images using drag and drop.

[0586] Step 4:

[0587] An emotion evaluation mechanism operates in the background, analyzing the user's voice tone, facial expressions, and input patterns. The system uses this data to determine whether the user is experiencing stress or satisfaction, and adjusts the interface accordingly. Furthermore, generated advertisements can be fine-tuned based on the user's emotional information. Specifically, the interface's color scheme and button placement change depending on the user's emotional state.

[0588] Step 5:

[0589] Once the user has finished editing the ad, the server selects the optimal delivery route and timing for sending the ad. This involves database queries to analyze historical ad data and market trends. Specifically, the server calculates the most effective broadcast time and channels and stores the results.

[0590] Step 6:

[0591] The server delivers ads using a predetermined transmission path and embeds tracking identifiers to measure recipient responses. After the ads are sent, their effectiveness is monitored in real time, and the collected data is analyzed and provided as feedback to the user. Specifically, the server records click counts and viewing time and generates analytical reports.

[0592] Step 7:

[0593] The analysis results are reported to the user via their device, providing valuable information for future advertising strategies. This process completes the entire workflow from ad generation to delivery, further enhancing the user's advertising experience. Specifically, users review data presented in graphs and numerical formats, and consider improvements to the next prompt and campaign.

[0594] (Application Example 2)

[0595] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0596] In today's advertising market, there is a demand to maximize consumer interest by dynamically adjusting advertising content based on the user's emotional state. However, conventional ad creation and delivery systems cannot adequately consider user emotions, making it difficult to provide a personalized advertising experience. Therefore, in order to maximize the effectiveness of advertising, it is necessary to realize technology that recognizes the user's emotional state, dynamically adjusts content based on the analysis results, and delivers it at the appropriate time.

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

[0598] In this invention, the server includes means for providing a terminal with a user interface for inputting advertising information for store management organizations, means for automatically generating advertising content from the input advertising information using a generative model, and means for recognizing the user's emotional state using an emotion engine and dynamically adjusting the advertising content generated by the generative model based on the analysis results. This enables the generation and delivery of personalized advertising content based on the user's emotions.

[0599] A "store operating organization" is a general term for commercial organizations or companies that operate and manage stores.

[0600] "Advertising information" refers to data and content used for the purpose of promoting specific products or services.

[0601] A "user interface" is a mechanism in an information system that includes screens and operating methods for exchanging information between the user and the system.

[0602] A "generative model" is an algorithm or program that automatically generates new content or information based on input data.

[0603] "Promotional content" refers to text, images, audio, and other materials created to widely publicize a specific project or product.

[0604] An "analytical device" is a system of hardware and software used to analyze collected information and data.

[0605] An "emotion engine" is a technology or program that analyzes an individual's emotional state using voice, facial expressions, and input data, and outputs the results.

[0606] "Smart devices" is a general term for intelligent electronic devices equipped with sensors and network connectivity.

[0607] A "communication device" is a configuration of hardware and software used to send and receive data between multiple computer systems.

[0608] A "tracking code" is a notation or script embedded in a web page or advertising content to measure user behavior and effectiveness after delivery.

[0609] This invention provides a system for store management organizations to create and deliver effective and emotionally resonant advertisements. The system includes a server and user terminals, each component performing a specific function.

[0610] The server receives advertising information from the user and automatically generates advertising content using a generative AI model based on that information. During this process, an emotion engine analyzes the user's voice and facial expressions, dynamically adjusting the generated advertising content according to their emotional state. It is recommended to use Python-based libraries (e.g., OpenCV, librosa) for the emotion engine.

[0611] The user's device functions as a smart device, providing a user interface for inputting advertising information and, as needed, acquiring user sentiment data using the camera and microphone. Furthermore, it allows for adjustment of the generated advertising content through an editing interface.

[0612] The generated advertising content is then analyzed using an analytical device to select the optimal distribution channels and target audience based on past data, and delivered to the selected channels via a communication device. A tracking code is embedded during this process, allowing for the measurement of user responses and effectiveness after delivery.

[0613] For example, when promoting a new cafe menu, if a user accesses the system in an excited state, the emotion engine will recommend more vivid visuals and lively text for the advertisement generated by the generative AI model. A prompt such as, "Generate content for the new cafe menu advertisement. If the user is very excited, present it with friendly, relaxing visuals and vibrant text," can be used.

[0614] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0615] Step 1:

[0616] Users log in to the system via their device and enter advertising information. This information includes text, image data, and the purpose of the advertisement. This input data is sent to the server.

[0617] Step 2:

[0618] The server processes the received advertising information and automatically generates advertising content using a generative AI model. During this process, the server uses prompt statements to instruct the generative model on the direction of the content, and the resulting output is the generated advertising content. These prompt statements are adjusted based on the advertising's purpose and target emotions.

[0619] Step 3:

[0620] The server activates the emotion engine and analyzes audio and facial expression data obtained from the user's device as input. Based on this analysis, it evaluates whether the generated advertising content is appropriate for the user's emotional state and dynamically adjusts the text and visual elements as needed.

[0621] Step 4:

[0622] Users review the generated promotional content and make necessary changes through the editing interface on their device. During editing, users can optimize the content based on feedback from the sentiment engine. The user's adjustments are then sent back to the server.

[0623] Step 5:

[0624] The server uses analytical equipment to refer to past advertising data and market information to automatically select the optimal distribution channel and target audience. At this time, the timing of delivery is also optimized based on sentiment data.

[0625] Step 6:

[0626] The server uses communication equipment to deliver advertisements to selected distribution media. A tracking code is embedded during delivery, allowing for the collection of user behavior data after delivery.

[0627] Step 7:

[0628] After delivery, the server analyzes the data collected based on the tracking code to measure the effectiveness of the advertisement. Users can view these analysis results through their devices and use them to inform their next advertising strategy.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] [Fourth Embodiment]

[0633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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".

[0646] This invention is a system designed to enable store operators to easily utilize digital advertising, and it automates the generation and distribution of advertisements using AI technology. Specific embodiments are shown below.

[0647] Users access the system via their devices and input the data required for advertising campaigns. This information includes the name and characteristics of the advertised product, the profile of the desired target customer, store location information, and associated image files. This data is transmitted from the device to the server and stored in a database.

[0648] The server automatically generates advertising content using a generative model based on the information it receives. The generative model combines natural language processing and image generation technologies to generate advertising copy and design. The generated advertising content is provided to the device through an interface, allowing the user to review and edit it as needed.

[0649] Next, the server uses analytical tools to evaluate past advertising effectiveness data and market trend data to select the optimal distribution channels and target audiences. This selection is made with the aim of maximizing the effectiveness of the advertisements.

[0650] Subsequently, the server uses communication methods to deliver the advertisement to the selected distribution media. At this time, a tracking code is embedded in the advertisement, and the behavioral data of users who view the advertisement is collected.

[0651] After the ad delivery ends, the server analyzes the collected data to measure the effectiveness of the ad campaign. The results of the effectiveness measurement are reported to the user's device through the interface, allowing the user to adjust their advertising strategy for future campaigns based on this information.

[0652] As a concrete example, when a cafe owner wants to advertise a new seasonal menu, they would first input information about the cafe's new menu and attach relevant images. Next, they would review the generated advertising content, and if satisfied, leave the delivery process to the server. Finally, the advertising performance would be reported, which could be used as foundational data to make the next campaign more effective. In this way, the present invention automates the entire advertising process, providing users with efficient and effective advertising activities.

[0653] The following describes the processing flow.

[0654] Step 1:

[0655] The user operates the terminal and logs into the system. Here, they enter their account information, undergo authentication, and are granted access to the system.

[0656] Step 2:

[0657] Users enter advertising data through the device interface. This includes product details, target customer attributes, desired advertising period, and image file uploads.

[0658] Step 3:

[0659] The device sends the collected advertising data to the server. The server receives this data, stores it in a database, and prepares for the next processing step.

[0660] Step 4:

[0661] The server automatically generates advertising content using a generative model. The generative model generates ad copy from text data and creates appropriate visual designs using an image generation algorithm.

[0662] Step 5:

[0663] The server sends the generated ad content to the device and prompts the user for confirmation. The user uses an editing interface on their device to check the content and make corrections as needed.

[0664] Step 6:

[0665] The server analyzes past advertising effectiveness data and market data to automatically select the optimal distribution channels and target customers. This selection result is then used in the next distribution step.

[0666] Step 7:

[0667] The server automatically delivers advertisements to selected distribution channels using communication methods. The timing and frequency of delivery are also managed by the server.

[0668] Step 8:

[0669] After delivery is complete, the server analyzes the ad performance data collected by the tracking code. This analysis includes ad views, clicks, and conversion rates.

[0670] Step 9:

[0671] The server compiles the analysis results and reports them to the user on their terminal via an interface. Based on this, the user can then develop their next advertising strategy.

[0672] (Example 1)

[0673] 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".

[0674] For businesses operating retail stores, advertising is essential for acquiring customers and increasing sales. However, creating and distributing advertisements efficiently is time-consuming and requires considerable effort. This is especially true for small and medium-sized enterprises (SMEs) and companies that cannot allocate sufficient resources to advertising; without specialized knowledge, implementing effective advertising campaigns is difficult. This leads to problems such as wasted advertising costs and failure to accurately reach the target audience. Therefore, there is a need for a system that can solve these problems and enable efficient and highly effective advertising activities.

[0675] 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.

[0676] In this invention, the server includes means for providing a user screen for users to input information to an information processing device, means for automatically generating information display content from the input information using a generation AI model, and means for automatically selecting the destination and recipient of information based on past information effectiveness data and market information using an evaluation means. This enables efficient automation from ad creation to distribution and effectiveness measurement, making it possible to obtain maximum advertising effectiveness with minimal resources.

[0677] A "user interface" refers to the interface used by users to input information in an information processing device.

[0678] A "generative AI model" is a generative model that utilizes artificial intelligence technology to automatically generate information display content based on input information.

[0679] "Evaluation methods" refer to algorithms and processes for selecting information recipients and recipients by analyzing past information effectiveness data and market information.

[0680] "Communication means" refers to the hardware and software used to transmit information to automatically selected destinations.

[0681] A "tracking code" is a code embedded in the displayed information, and its role is to collect data necessary to evaluate the effectiveness of the information after it has been transmitted.

[0682] The "editing screen" refers to a user interface that allows users to review the generated information display and make corrections as needed.

[0683] This invention is a system for efficiently automating advertising activities, and in particular aims to enable store operators to easily create, distribute, and evaluate digital advertisements. The core technologies of this system are a user-friendly interface that is easy for users to operate, automatic content generation using a generation AI model, and automatic selection of distribution media and target audiences.

[0684] Users access the information processing device using a terminal and input the necessary data for advertising through a dedicated user screen. This data includes the name and features of the advertised product or service, the profile of the target customer, the store's location information, and related images. The entered data is transmitted to a server via the internet and stored securely.

[0685] The server processes information received from users using a generative AI model. This generative AI model combines natural language processing and image generation technologies and has the ability to automatically generate advertising text and designs. This process utilizes high-performance computer systems and AI software.

[0686] The generated advertising content is delivered back to the user's device via the server, where the user can review the content through the editing screen and make corrections as needed. Once the advertising content is finalized, the server analyzes past advertising effectiveness data and market information to select the optimal distribution channels and target audience. This allows for the development of a distribution plan that maximizes the effectiveness of the advertisement.

[0687] The server uses advanced communication technology to deliver advertisements to selected media outlets, and each advertisement has a tracking code embedded in it. This tracking code is used to collect user behavior data, such as ad views and clicks.

[0688] Ultimately, the server analyzes the collected data to measure the effectiveness of the advertising campaign. These results are reported to the user through a user interface, providing foundational data for improving future advertising strategies.

[0689] As a concrete example, imagine a cafe owner who wants to advertise a new seasonal menu. The user enters a prompt message using a terminal, such as "New seasonal cafe menu, target audience: local women in their 20s, attach high-quality images." Based on this information, the system automatically generates advertising content and enables its delivery to the most suitable platform. Such a system allows advertising activities to be conducted efficiently and effectively.

[0690] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0691] Step 1:

[0692] The user uses a device to enter information for the advertising campaign. This information includes the product name, features, target customer profile, store location information, and associated image files. At this stage, the information entered by the user is structured on the device and sent to the server for subsequent processing.

[0693] Step 2:

[0694] The terminal transmits the collected information to the server in digital format. The transmitted data is received by the server and stored in a database. At this stage, the data format is normalized and prepared so that it can be efficiently processed by the generative AI model.

[0695] Step 3:

[0696] The server automatically generates advertising content using an AI model based on stored data. This generation process uses natural language processing to create text content and image generation technology to produce visual content. Using the stored data as input, the advertising text and design are output.

[0697] Step 4:

[0698] The generated advertising content is delivered to the user's device via the server. The user reviews the content on the editing screen and makes corrections as needed. Here, the user fine-tunes the ad text and images and finalizes the ad.

[0699] Step 5:

[0700] The server evaluates historical advertising data and market information to automatically select the optimal distribution channels and target audiences. This involves using statistical algorithms as evaluation tools to determine the best plan to maximize advertising effectiveness. The selection results are then used as input data for the next distribution step.

[0701] Step 6:

[0702] The server delivers advertisements to selected distribution media using communication methods. Tracking codes are embedded in the advertisements to collect user behavior data, such as ad impressions and clicks. At this stage, the actual delivery process takes place, and the delivery status is monitored in real time.

[0703] Step 7:

[0704] After ad delivery, the server analyzes the collected behavioral data to measure the effectiveness of the advertising campaign. This analysis is performed based on the received data, and the results of the effectiveness measurement are generated. These results are provided to the user's device through the interface and used to improve future advertising strategies.

[0705] (Application Example 1)

[0706] 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".

[0707] Traditional advertising delivery systems have a problem in that they cannot easily present the most relevant advertisements based on the user's real-time location information. Furthermore, there are challenges in effectively presenting advertisements using wearable devices. As a result, the targeting accuracy of advertisements decreases, and the maximization of advertising effectiveness is not achieved.

[0708] 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.

[0709] In this invention, the server includes means for providing an information processing device with a user interface for inputting advertising data for store operators, means for automatically generating advertising information from the input advertising data using a generative model, and means for operating on a wearable device in state S and presenting advertising information based on the user's location information and target information. This makes it possible to present optimal advertisements according to the user's real-time situation.

[0710] "Store operator" refers to any corporation or individual that operates a store in order to provide goods or services to customers.

[0711] "Advertising data" refers to the information used to generate advertisements, and includes product names, features, target customer profiles, store location information, and related image files.

[0712] A "user interface" is an operation screen that allows users to input data into a system via an information processing device and to check the generated information.

[0713] An "information processing device" is a general term for computers and devices that have the ability to process digital data and interact with users and other systems.

[0714] A "generative model" is a program that uses AI technology to automatically generate advertising information from input data.

[0715] "Advertising information" refers to generated content such as advertising copy and images, intended to encourage a specific customer segment to purchase products or services.

[0716] "Wearable devices in S state" refer to devices that can be worn by a user and are in a certain operating state or usage condition, such as smart glasses and smartwatches.

[0717] "User location information" refers to data that indicates the geographical location where the user is currently located, using methods such as GPS or wireless communication.

[0718] "Target information" refers to profile information related to a specific user or user group, including data such as interests, preferences, and consumption history.

[0719] The system for carrying out the present invention mainly includes a server, an information processing device, and a wearable device in S state. The user inputs advertising data via the information processing device. The input advertising data includes the product name, features, target customer profile, store location information, and associated image files. This data is transmitted from the information processing device to the server and stored in a database. The server analyzes the stored data using a generative model and automatically generates advertising information. This generative model utilizes AI that combines natural language processing and image generation technology. The generated advertising information uses technology that enables the user to instantly display advertising content tailored to their current situation and location using a wearable device.

[0720] The server analyzes collected historical advertising effectiveness data and market information to select the optimal advertising distribution channels and target audience. This selection involves comparing existing advertising effectiveness data with trend information. The selected advertisements are transmitted to the designated distribution channels via communication means, and a tracking code is embedded at that time. After the advertising effectiveness is measured, the analysis results are reported to the user through the information processing device.

[0721] As a concrete example, imagine a scenario where a traveler is strolling through a city and an advertisement for a discount coupon for a nearby tourist attraction appears on their smart glasses. In this case, the traveler can use the coupon on the spot and receive a discount by visiting the tourist attraction. Examples of prompts include the following:

[0722] Example prompt:

[0723] "User's current location: Near Tokyo Station"

[0724] User's areas of interest: Culture, Art

[0725] Ad content generation prompt: 'Generate a Street View ad that introduces users to the latest art exhibitions near Tokyo Station.'

[0726] In this way, the system can provide advertising information tailored to the user's needs in real time.

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] The device receives advertising data entered by the user. This advertising data includes the product name, features, target customer profile, store location information, and associated image files. The device sends this data to the server and stores it in a database.

[0730] Step 2:

[0731] The server retrieves the stored advertising data and automatically generates advertising information using a generative AI model. Based on the input advertising data, the AI ​​model combines natural language processing and image generation technology to generate advertising copy and design, and compiles the results as advertising information.

[0732] Step 3:

[0733] The server analyzes past advertising effectiveness data and market information based on the generated advertising information. This allows it to automatically select the optimal advertising distribution channels and target audience. Past advertising effectiveness data and market trend data are used as input, and the selected distribution methods and targets are output.

[0734] Step 4:

[0735] The server delivers advertisements to selected transmission media using communication methods. Tracking codes are embedded in the advertisement information, enabling data collection to track user responses and behavior after delivery.

[0736] Step 5:

[0737] The server measures the effectiveness of delivered ads and analyzes the collected data. This analysis is based on data from the tracking code. The analysis results are reported to the device, allowing users to review the results and adjust their strategy for the next advertising campaign.

[0738] Step 6:

[0739] The server presents real-time advertising information based on location and profile information to users of wearable devices in the S state. The user's current location and profile information are used as input, and the server outputs the most suitable advertisements based on this information.

[0740] 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.

[0741] This invention provides a system for store operators to efficiently create and distribute digital advertisements, and by incorporating an emotion engine, it enables consideration of the emotional state of users and targets when inputting and distributing advertising data. The system comprises a user interface, a generative model, analytical means, communication means, an interface, an editing interface, a tracking code, and an emotion engine.

[0742] Users log in to the system via their device and input the information and image data required for the advertising campaign. The emotion engine analyzes the user's voice tone, facial expressions, input patterns, etc., to recognize the user's emotional state. This allows the interface to be dynamically adjusted based on the user's stress and excitement levels, improving usability.

[0743] Next, the server utilizes a generative model to automatically generate content based on the input advertising data. The generation process uses emotional information obtained by the emotion engine to adjust the content to have a more emotional impact.

[0744] The generated advertising content is presented to the user on their device and can be modified through an editing interface. Based on feedback from the sentiment engine, users can evaluate whether the content conveys the appropriate emotions and make adjustments as needed.

[0745] Subsequently, the server selects the distribution medium and target audience for ad delivery based on past advertising effectiveness and market data. At this stage, it considers the emotional state of the target users and delivers ads at the appropriate time and with appropriate content to ensure effective communication.

[0746] After delivery is complete, the data collected using the tracking code is analyzed on the server to evaluate the ad's performance and the emotional response of recipients. The analysis results are reported to the user through the interface, and the user can use the emotional data of the target audience to plan future advertising strategies.

[0747] For example, when advertising a new menu item at a cafe, if a user shows high interest when accessing the system and entering data, the generated advertisement will be enhanced with emotional language and visual elements. Furthermore, the advertisement can be adjusted to air during times when the target audience is relaxed. This invention enables users to experience a more personalized advertising experience than ever before, leading to improved marketing effectiveness.

[0748] The following describes the processing flow.

[0749] Step 1:

[0750] The user logs into the system using their device. Upon successful login, the user interface for creating advertising campaigns is displayed.

[0751] Step 2:

[0752] Users input product details, target customer profiles, and promotional messages and image files through the device's interface. During this process, the emotion engine recognizes the user's emotional state through their voice and facial expressions, dynamically adjusting the interface accordingly.

[0753] Step 3:

[0754] The terminal sends the data entered by the user to the server. The server receives this data and stores it in its database.

[0755] Step 4:

[0756] The server uses a generative model to automatically generate advertising content based on input data and collected sentiment information. The generated content includes wording and visual elements that correspond to the user's emotions.

[0757] Step 5:

[0758] The server sends the generated advertising content to the device. The user can view this content on their device and make any necessary modifications using the editing interface.

[0759] Step 6:

[0760] The server selects the optimal delivery medium and timing based on past advertising effectiveness data, market data, and target sentiment data obtained from the sentiment engine. This enables delivery tailored to the emotions of the target users.

[0761] Step 7:

[0762] The server delivers advertisements to selected distribution channels via communication methods. Tracking codes are embedded in the advertisements, allowing for tracking of target audience behavior and responses.

[0763] Step 8:

[0764] After the ad is delivered, the server analyzes the collected data to evaluate the ad's effectiveness and the target users' emotional response.

[0765] Step 9:

[0766] The server compiles the analysis results and reports them to the user's device via an interface. Based on this information, the user can plan and optimize their strategy for the next advertising campaign.

[0767] (Example 2)

[0768] 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".

[0769] Traditional systems for creating and distributing digital advertisements were unable to adjust content to consider the user's emotional state, making personalized and emotionally-driven effective communication difficult. As a result, ad acceptance rates were low, and it was difficult to collect sufficient data to improve marketing efficiency.

[0770] 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.

[0771] In this invention, the server includes means for providing a human-machine interface to the terminal, means for automatically generating advertising content using a generative artificial intelligence model, and means for analyzing the user's emotional state using an emotion evaluation mechanism and dynamically adjusting the interface. This enables the generation of personalized advertisements that take into account the user's emotional state and the optimization of the delivery process.

[0772] A "human-machine interface" is a means by which a user accesses a system through a terminal and inputs and manipulates information.

[0773] A "generative artificial intelligence model" is an artificial intelligence technology used to automatically generate advertising content based on input data.

[0774] An "emotion evaluation mechanism" is a mechanism that analyzes the user's voice tone, facial expressions, and input patterns to recognize their emotional state and adjust the interface accordingly.

[0775] "Advertising content" refers to content such as text, images, and videos that are generated according to the objectives of an advertising campaign.

[0776] "Transmission path" refers to the medium or channel through which generated advertising content is sent to the recipient.

[0777] A "tracking identifier" is a code embedded in advertising content that is used to measure the effectiveness of the ad after delivery and to analyze recipient interactions.

[0778] This invention begins with the user inputting advertising campaign information into the system using a terminal. The terminal is equipped with a human-machine interface, allowing the user to easily input advertising objectives, target audience, image data, taglines, and other information. This interface employs an intuitive and user-friendly design.

[0779] Upon receiving input data, the server automatically generates advertisement content using a generative artificial intelligence model. This model utilizes multiple neural networks to effectively learn the components of advertisements from a large amount of training data. As a result, advertisements optimized to match the user's preferences are generated. The server also incorporates an emotion evaluation mechanism, recognizing the user's emotional state by analyzing voice tone, facial expressions, and input patterns. This information is used to dynamically adjust the interface, improving the user experience.

[0780] Once an ad is generated, it is presented to the user through an editing platform on their device. Here, they can review the text and images included in the ad and re-edit them as needed. Sentiment-based feedback is also displayed, which users can use to fine-tune the content. This process ensures that the ad content more effectively evokes the emotions of the target audience.

[0781] For example, when a cafe advertises a new menu item, the generated ad will emphasize emotional language and visual elements when users show high interest. Furthermore, the ad will be timed to be delivered to the target audience during relaxed hours.

[0782] An example of a prompt message is: "Create an advertisement promoting the cafe's new menu. Generate content that resonates with the user's emotional state in an appropriate way." In this way, the system can efficiently and effectively create and deliver personalized digital advertisements.

[0783] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0784] Step 1:

[0785] Users log in to the system using their terminal and input data for their advertising campaigns. This data includes the campaign objectives, target audience, and image and text information to be used. Specifically, users use a keyboard and mouse to input this information on a dedicated interface and save it to the system. The entered data is then sent directly to the server.

[0786] Step 2:

[0787] The server passes the received advertising data to an artificial intelligence model that generates advertising content automatically. In this process, the algorithm analyzes the input information, performs text generation and image processing, and generates the optimal advertising content. Specifically, the model determines the message and design to be conveyed based on a vast amount of training data, and combines them to output a single advertisement.

[0788] Step 3:

[0789] The server sends the generated ad content to the device and presents it to the user. The device then displays the ad and provides an interface that allows the user to review and edit it. The user reviews the ad text and visual elements and makes changes as needed. Specifically, the user can select a text box with the mouse, edit the content using the keyboard, or change images using drag and drop.

[0790] Step 4:

[0791] An emotion evaluation mechanism operates in the background, analyzing the user's voice tone, facial expressions, and input patterns. The system uses this data to determine whether the user is experiencing stress or satisfaction, and adjusts the interface accordingly. Furthermore, generated advertisements can be fine-tuned based on the user's emotional information. Specifically, the interface's color scheme and button placement change depending on the user's emotional state.

[0792] Step 5:

[0793] Once the user has finished editing the ad, the server selects the optimal delivery route and timing for sending the ad. This involves database queries to analyze historical ad data and market trends. Specifically, the server calculates the most effective broadcast time and channels and stores the results.

[0794] Step 6:

[0795] The server delivers ads using a predetermined transmission path and embeds tracking identifiers to measure recipient responses. After the ads are sent, their effectiveness is monitored in real time, and the collected data is analyzed and provided as feedback to the user. Specifically, the server records click counts and viewing time and generates analytical reports.

[0796] Step 7:

[0797] The analysis results are reported to the user via their device, providing valuable information for future advertising strategies. This process completes the entire workflow from ad generation to delivery, further enhancing the user's advertising experience. Specifically, users review data presented in graphs and numerical formats, and consider improvements to the next prompt and campaign.

[0798] (Application Example 2)

[0799] 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".

[0800] In today's advertising market, there is a demand to maximize consumer interest by dynamically adjusting advertising content based on the user's emotional state. However, conventional ad creation and delivery systems cannot adequately consider user emotions, making it difficult to provide a personalized advertising experience. Therefore, in order to maximize the effectiveness of advertising, it is necessary to realize technology that recognizes the user's emotional state, dynamically adjusts content based on the analysis results, and delivers it at the appropriate time.

[0801] 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.

[0802] In this invention, the server includes means for providing a terminal with a user interface for inputting advertising information for store management organizations, means for automatically generating advertising content from the input advertising information using a generative model, and means for recognizing the user's emotional state using an emotion engine and dynamically adjusting the advertising content generated by the generative model based on the analysis results. This enables the generation and delivery of personalized advertising content based on the user's emotions.

[0803] A "store operating organization" is a general term for commercial organizations or companies that operate and manage stores.

[0804] "Advertising information" refers to data and content used for the purpose of promoting specific products or services.

[0805] A "user interface" is a mechanism in an information system that includes screens and operating methods for exchanging information between the user and the system.

[0806] A "generative model" is an algorithm or program that automatically generates new content or information based on input data.

[0807] "Promotional content" refers to text, images, audio, and other materials created to widely publicize a specific project or product.

[0808] An "analytical device" is a system of hardware and software used to analyze collected information and data.

[0809] An "emotion engine" is a technology or program that analyzes an individual's emotional state using voice, facial expressions, and input data, and outputs the results.

[0810] "Smart devices" is a general term for intelligent electronic devices equipped with sensors and network connectivity.

[0811] A "communication device" is a configuration of hardware and software used to send and receive data between multiple computer systems.

[0812] A "tracking code" is a notation or script embedded in a web page or advertising content to measure user behavior and effectiveness after delivery.

[0813] This invention provides a system for store management organizations to create and deliver effective and emotionally resonant advertisements. The system includes a server and user terminals, each component performing a specific function.

[0814] The server receives advertising information from the user and automatically generates advertising content using a generative AI model based on that information. During this process, an emotion engine analyzes the user's voice and facial expressions, dynamically adjusting the generated advertising content according to their emotional state. It is recommended to use Python-based libraries (e.g., OpenCV, librosa) for the emotion engine.

[0815] The user's device functions as a smart device, providing a user interface for inputting advertising information and, as needed, acquiring user sentiment data using the camera and microphone. Furthermore, it allows for adjustment of the generated advertising content through an editing interface.

[0816] The generated advertising content is then analyzed using an analytical device to select the optimal distribution channels and target audience based on past data, and delivered to the selected channels via a communication device. A tracking code is embedded during this process, allowing for the measurement of user responses and effectiveness after delivery.

[0817] For example, when promoting a new cafe menu, if a user accesses the system in an excited state, the emotion engine will recommend more vivid visuals and lively text for the advertisement generated by the generative AI model. A prompt such as, "Generate content for the new cafe menu advertisement. If the user is very excited, present it with friendly, relaxing visuals and vibrant text," can be used.

[0818] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0819] Step 1:

[0820] Users log in to the system via their device and enter advertising information. This information includes text, image data, and the purpose of the advertisement. This input data is sent to the server.

[0821] Step 2:

[0822] The server processes the received advertising information and automatically generates advertising content using a generative AI model. During this process, the server uses prompt statements to instruct the generative model on the direction of the content, and the resulting output is the generated advertising content. These prompt statements are adjusted based on the advertising's purpose and target emotions.

[0823] Step 3:

[0824] The server activates the emotion engine and analyzes audio and facial expression data obtained from the user's device as input. Based on this analysis, it evaluates whether the generated advertising content is appropriate for the user's emotional state and dynamically adjusts the text and visual elements as needed.

[0825] Step 4:

[0826] Users review the generated promotional content and make necessary changes through the editing interface on their device. During editing, users can optimize the content based on feedback from the sentiment engine. The user's adjustments are then sent back to the server.

[0827] Step 5:

[0828] The server uses analytical equipment to refer to past advertising data and market information to automatically select the optimal distribution channel and target audience. At this time, the timing of delivery is also optimized based on sentiment data.

[0829] Step 6:

[0830] The server uses communication equipment to deliver advertisements to selected distribution media. A tracking code is embedded during delivery, allowing for the collection of user behavior data after delivery.

[0831] Step 7:

[0832] After delivery, the server analyzes the data collected based on the tracking code to measure the effectiveness of the advertisement. Users can view these analysis results through their devices and use them to inform their next advertising strategy.

[0833] 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.

[0834] 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.

[0835] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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."

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0854] The following is further disclosed regarding the embodiments described above.

[0855] (Claim 1)

[0856] A means of providing a terminal with a user interface for entering advertising data for store operators,

[0857] A method for automatically generating advertising content from input advertising data using a generative model,

[0858] A means for automatically selecting advertising distribution media and target audiences based on past advertising effectiveness data and market data using analytical methods,

[0859] A means of communication for delivering advertisements to automatically selected distribution media,

[0860] A means of providing an interface for measuring the effectiveness of delivered advertisements and reporting the analysis results,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which provides an editing interface for enabling modification of generated advertising content.

[0864] (Claim 3)

[0865] The system according to claim 1, which embeds a tracking code into advertising content to enable measurement of effectiveness after delivery.

[0866] "Example 1"

[0867] (Claim 1)

[0868] A means for providing an information processing device with a user screen for users to input information,

[0869] A method for automatically generating information display content from input information using a generative AI model,

[0870] A means for automatically selecting information transmission destinations and recipients based on past information effectiveness data and market information using an evaluation means,

[0871] A communication means for sending information to an automatically selected recipient,

[0872] A means for evaluating the effectiveness of transmitted information and providing a screen for reporting the evaluation results,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, which provides an editing screen for modifying the generated information display content.

[0876] (Claim 3)

[0877] The system according to claim 1, which embeds a tracking code into the information display content and enables evaluation of the effect after transmission.

[0878] "Application Example 1"

[0879] (Claim 1)

[0880] A means for providing an information processing device with a user interface for inputting advertising data for store operators,

[0881] A method for automatically generating advertising information from input advertising data using a generative model,

[0882] A means for automatically selecting advertising transmission media and target audience based on past advertising effectiveness information and market information using analytical means,

[0883] A means of communication for sending advertisements to automatically selected transmission media,

[0884] A means of providing an interface for measuring the effectiveness of delivered advertisements and reporting the analysis results,

[0885] A means that operates in a wearable device in state S and presents advertising information based on the user's location information and target information,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, which provides an editing interface for enabling modification of generated advertising information.

[0889] (Claim 3)

[0890] The system according to claim 1, which embeds a tracking code in advertising information and enables measurement of effectiveness after transmission.

[0891] "Example 2 of combining an emotion engine"

[0892] (Claim 1)

[0893] A means of providing a terminal with a human-machine interface for inputting advertising information for store operations,

[0894] A means for automatically generating advertising content from input advertising information using a generative artificial intelligence model,

[0895] A means for automatically selecting advertising transmission channels and recipients based on past advertising effectiveness information and market information using analytical means,

[0896] A means of analyzing the user's emotional state using an emotion evaluation mechanism and dynamically adjusting the interface,

[0897] A means of providing an editorial platform that presents generated ad content and allows for content adjustment based on emotional information,

[0898] A communication means for delivering advertisements to automatically selected transmission routes,

[0899] A means of providing an interface for measuring the effectiveness of delivered advertisements and reporting the analysis results,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, which embeds a tracking identifier into the ad content and enables measurement of effectiveness after delivery.

[0903] (Claim 3)

[0904] The system according to claim 1, comprising an emotion evaluation mechanism that analyzes the user's voice tone, facial expressions, and input patterns to recognize the user's emotional state.

[0905] "Application example 2 when combining with an emotional engine"

[0906] (Claim 1)

[0907] A means of providing a terminal with a user interface for entering advertising information for store management organizations,

[0908] A method for automatically generating advertising content from input advertising information using a generative model,

[0909] A means for automatically selecting advertising distribution media and target audiences based on past advertising effectiveness information and market information using an analytical device,

[0910] A means of recognizing the user's emotional state using an emotion engine and dynamically adjusting the advertising content generated by a generative model based on the analysis results,

[0911] A means of acquiring user data using sensors in smart devices,

[0912] A communication device for distributing advertisements to automatically selected distribution media,

[0913] A means of providing an interface for measuring the effectiveness of distributed advertisements and providing analysis results,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, which provides an editing interface to enable modification of generated promotional content and supports users in adjusting content based on feedback from an emotion engine.

[0917] (Claim 3)

[0918] The system according to claim 1 for embedding tracking codes in advertising content, enabling post-delivery effectiveness measurement, and evaluating user emotional responses. [Explanation of Symbols]

[0919] 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 of providing a terminal with a user interface for entering advertising data for store operators, A method for automatically generating advertising content from input advertising data using a generative model, A means for automatically selecting advertising distribution media and target audiences based on past advertising effectiveness data and market data using analytical methods, A means of communication for delivering advertisements to automatically selected distribution media, A means of providing an interface for measuring the effectiveness of delivered advertisements and reporting the analysis results, A system that includes this.

2. The system according to claim 1, which provides an editing interface for enabling modification of generated advertising content.

3. The system according to claim 1, which embeds a tracking code into advertising content and enables measurement of effectiveness after delivery.

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