System

Abstract

A system is provided.SOLUTION: A system comprising: means for pressing a "More" button to indicate that a user already has possession of a displayed advertisement; means for sending information that the "More" button has been pressed to a server; means for updating a user's profile based on information received by the server; means for selecting a relevant new advertisement based on the user's profile using a generative AI model; and means for delivering the selected new advertisement to the user on subsequent occasions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Current advertising delivery systems often display ads for products that consumers already own, which reduces the effectiveness of the ads. Furthermore, advertisers waste resources displaying unnecessary ads, resulting in a lower ROI (return on investment). There is a need for a system that can solve these issues and maximize advertising effectiveness by displaying ads that consumers truly need, thereby improving advertisers' ROI. [Means for solving the problem]

[0005] To solve the above problem, the present invention provides the following means. The present invention is a system including: a means for a user to press an "Already Have It" button in response to a displayed advertisement to indicate that the user already owns the product; a means for transmitting information that the "Already Have It" button was pressed to a server; a means for updating the user's profile based on the information received by the server; a means for selecting new relevant advertisements based on the user's profile using a generative AI model; and a means for delivering the selected new advertisements to the user from the next time onward. This makes it possible to avoid displaying advertisements for products that consumers already own and effectively display highly relevant advertisements, thereby maximizing advertising effectiveness and improving advertiser ROI.

[0006] The "I already have it" button is a button that users press to indicate that they already own the product displayed in the ad.

[0007] "Server" refers to a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements.

[0008] A "user profile" is a collection of personal information, including a user's purchasing history and behavioral data, and is data used to optimize advertising display.

[0009] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and select and generate optimal advertisements.

[0010] "Advertising delivery" refers to a series of processes in which a server sends selected advertisements to a user's device so that the user can view them.

[0011] "User database" refers to a database for storing and managing user profiles and behavioral data. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

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

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

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

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0029] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] preface

[0034] The present invention relates to an advertising recommendation system for maximizing advertising effectiveness. In particular, the system has the function of displaying advertisements related to products that a user already owns. The system is implemented using a server, a terminal, and a generative AI model.

[0035] System Configuration

[0036] The main components of the system are:

[0037] server

[0038] User Device

[0039] User Database

[0040] Generative AI Models

[0041] Advertising Database

[0042] server

[0043] The server is a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[0044] User Device

[0045] The user device is the device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent from the server and has the ability to display the "I Already Have It" button.

[0046] User Database

[0047] A user database is a database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[0048] Generative AI Models

[0049] The generative AI model is an algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavior data and generates relevant product ads to optimize the next ad display.

[0050] Advertising Database

[0051] The advertisement database is a database that stores advertisement information provided by advertisers, including the advertisement content, target products, related products, etc.

[0052] Program processing

[0053] Advertisement display

[0054] The server selects the most suitable advertisement based on the user profile and sends the advertisement data to the user's device.

[0055] The user's device will display the received advertisement along with the "Already Have It" button.

[0056] Pressing the "I already have it" button

[0057] When a user presses the "I already have it" button for a displayed advertisement, the user's device sends this information to the server.

[0058] Analyzing information and updating your profile

[0059] The server updates the user's profile based on the information it receives and already has.

[0060] The generative AI model analyzes the updated profile and selects relevant ads to display next time.

[0061] Selection and delivery of relevant advertisements

[0062] The selected relevant advertisements are sent from the server to the user's device and displayed the next time the user accesses the site.

[0063] The user's device displays relevant ads and records the user's clicks and interactions.

[0064] Specific examples

[0065] Example 1: Purchasing a bicycle

[0066] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button.

[0067] The user terminal transmits this information to the server.

[0068] The server analyzes this information using a generative AI model and updates the profile so that the next time an ad is displayed, it will serve ads for bicycle-related accessories (helmets, lights, custom parts, etc.).

[0069] The next time the server sends an advertisement to the terminal, it may be for bicycle accessories, which may pique the user's interest.

[0070] Example 2: Cookware

[0071] A user clicks on an advertisement for cooking equipment (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button.

[0072] The user device sends the information it already has to the server.

[0073] The server uses a generative AI model to analyze this information and update the profile to show ads for other products in the cookware series (pots, tongs, spatulas, etc.) in future visits.

[0074] The next time the server sends an advertisement to the terminal, it will be about another product of cooking equipment, which may be of interest to the user.

[0075] As described above, the present invention can maximize the effectiveness of advertising and improve advertisers' ROI by displaying only advertisements that users truly need.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] When the ad recommendation system starts, the server loads the ad database and the user database, obtains the latest user information and ad information from the database, and initializes the system.

[0079] Step 2:

[0080] The server retrieves target user profiles and behavioral data from a user database, including user purchase history, click history, and viewing history.

[0081] Step 3:

[0082] The server uses a generative AI model to analyze the user's profile and behavioral data and select the most appropriate ad for that user. The AI ​​model then scores the best ad based on past data.

[0083] Step 4:

[0084] The server sends the selected advertisement and the advertisement data, including the "I Already Have It Button," to the user's device. This advertisement data includes the content of the advertisement to be displayed and a script for recording the user's interaction.

[0085] Step 5:

[0086] The device analyzes the advertising data received from the server and displays the advertisement and the "Already Have It Button" on the user's screen. The advertisement is optimized for the user's viewing environment and displayed in a way that is easy for the user to see.

[0087] Step 6:

[0088] The user checks the displayed advertisement and presses the "I already have it" button if they already own it. This action records the user's interaction.

[0089] Step 7:

[0090] The device sends information to the server that the user pressed the "Already Have It Button," including the date and time of the press, the associated advertising ID, and the user ID.

[0091] Step 8:

[0092] The server updates the user profile in the user database based on the received "already owned" information, specifically by marking the items that the user already owns.

[0093] Step 9:

[0094] The server then uses the generative AI model to analyze the updated user profile and select the next ad to display, taking into account the user's "already owned" information and selecting ads for related accessories and complementary products.

[0095] Step 10:

[0096] The server prepares a list of newly selected relevant ads and sends it to the device for subsequent ad delivery. This list will be used the next time the user accesses the site.

[0097] Step 11:

[0098] The next time the user views an ad, the device will display relevant ads based on the new ad list sent from the server, which are likely to increase click-through rates and purchase rates because they are selected based on the user's interests and needs.

[0099] Step 12:

[0100] How the user responds to the new ad is recorded and this information is fed back to the server, which further influences future ad presentations.

[0101] By following these steps, it is possible to display ads that best fit the user's needs, maximizing advertising effectiveness and improving advertisers' ROI.

[0102] Example 1

[0103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] Conventional advertising systems often displayed ads for products that users already owned, which meant they were unable to respond to users' interests and needs. This resulted in lower ad click-through rates and conversion rates, and lower advertiser ROI. In addition, there was a lack of a way to reflect product information that users already owned in the ad display selection process, making it difficult to deliver effective ads.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0106] In this invention, the server includes a means for acquiring a user profile and behavioral data from a user database, a means for inputting a prompt sentence into a generative AI model based on the acquired data to generate an optimal advertisement, and a means for transmitting the advertisement data obtained from the generative AI model to a user terminal, thereby making it possible to exclude products already owned by the user and display advertisements that focus on related products.

[0107] The "server" is a device that retrieves user profiles and behavioral data from a user database and uses that data to generate optimal advertisements for the generative AI model.

[0108] A "user terminal" is a device that receives and displays advertising data sent from the server and provides users with interactions such as the "I Already Have It Button."

[0109] "User database" refers to data storage that stores and manages user behavioral data and profile information, such as purchase history, click history, and browsing history.

[0110] The "generative AI model" is an algorithm that selects and generates optimal advertisements based on acquired user data, and generates relevant advertisement information by inputting a prompt text.

[0111] A "prompt" is text data that is input into a generative AI model, and is an instruction to suggest optimal advertisements based on the user's profile and behavioral data.

[0112] The "Already Owned Button" is a user interface element that notifies the server that the user already owns the product in response to a displayed advertisement.

[0113] This invention relates to an advertising recommendation system that maximizes advertising effectiveness by displaying advertisements related to products that users already own. In particular, the system uses a generative AI model based on user behavior data and purchase history to generate optimal product advertisements and provide them to users.

[0114] System Configuration

[0115] The system mainly consists of the following components:

[0116] server

[0117] User Device

[0118] User Database

[0119] Generative AI Models

[0120] Advertising Database

[0121] server

[0122] The server has the following features:

[0123] 1. Retrieve user profile and behavioral data from your user database.

[0124] 2. Based on the acquired data, a prompt sentence is input into the generative AI model to generate the optimal ad.

[0125] 3. The advertising data obtained from the generative AI model is sent to the user's device.

[0126] User Device

[0127] The user terminal has the following features:

[0128] 1. Receives advertising data sent from the server and displays it on the screen.

[0129] 2. Display the "I Already Have It" button and record the user's interaction.

[0130] 3. When the user presses the "I already have it" button, the information is sent to the server.

[0131] User Database

[0132] The user database stores and manages user behavioral data and profile information, such as purchase history, click history, and browsing history, and provides data for selecting the most suitable advertisements for users.

[0133] Generative AI Models

[0134] The generative AI model uses machine learning algorithms to analyze user profile data and generate optimal ads. By inputting prompt text, the generative AI model generates ads that are likely to attract the user's attention.

[0135] Advertising Database

[0136] The advertisement database stores advertisement information provided by advertisers, including the content of the advertisement, target products, related products, etc.

[0137] Specific examples

[0138] Example 1: Purchasing a bicycle

[0139] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button. The user's device sends this information to the server. The server analyzes this information using a generative AI model and updates the user's profile so that the next time ads are displayed, they will be for bicycle accessories (e.g., helmets, lights, custom parts). The next time the server sends an ad to the user's device, it will be for bicycle accessories, which are more likely to interest the user.

[0140] Example 2: Cookware

[0141] A user clicks on an ad for cookware (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button. The user's device sends the "I already have it" information to the server. The server analyzes this information using a generative AI model and updates the profile so that ads for other products in the cookware series (for example, pots, tongs, and spatulas) will be displayed in the future. The next ad the server sends to the user's device will be for a different cookware product and is more likely to interest the user.

[0142] This invention reflects user profiles and behavioral data in real time, allowing for the display of highly relevant ads and improving advertisers' ROI. Using a generative AI model, it is possible to accurately capture user interests and reflect them in subsequent ad displays. This allows for less intrusive ad displays for users.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] The server retrieves user profile and behavioral data from a user database. The user ID is used as input. Specifically, it executes an SQL query to retrieve data from the database, which is then output. Based on the retrieved data, the server prepares the basis for determining which ad is most suitable.

[0146] Step 2:

[0147] The server inputs a prompt sentence into the generative AI model based on the acquired data, generating the optimal ad. User profile and behavioral data are used as input. Specifically, the server generates and inputs a prompt sentence such as "Please suggest ads that this user is likely to be interested in" into the generative AI model. The generative AI model then analyzes the prompt sentence and outputs the optimal ad.

[0148] Step 3:

[0149] The server sends the advertising data obtained from the generative AI model to the user device. The optimal advertising data output by the generative AI model is used as input. Specifically, the advertising data is sent using an HTTP request. The advertising data is sent to the user device, and this becomes the output.

[0150] Step 4:

[0151] The user's device displays the received advertising data along with the "I already have it button." The advertising data sent from the server is used as input. Specifically, the process of displaying the advertisement and button is carried out using front-end technologies (HTML, CSS, JavaScript). The output is the advertisement and button displayed on the screen.

[0152] Step 5:

[0153] The user presses the "I already have it" button for an ad. The user's click action is used as input. Specifically, when the user clicks the button, a click event occurs, which becomes the output.

[0154] Step 6:

[0155] The user device catches the button click event and sends that information to the server. The button click event is used as input. Specifically, the button click event is detected using JavaScript and the information is sent to the server using an AJAX request. The information sent to the server becomes the output.

[0156] Step 7:

[0157] When the server receives the "already have" information, it updates the corresponding user profile in the user database. The "already have" information is used as input. Specifically, an SQL query is used to update the user profile data. The update to the user database is the output.

[0158] Step 8:

[0159] The generative AI model analyzes the newly updated user data and selects the relevant ad to display next. The updated user data is used as input. Specifically, it generates and inputs a prompt such as "Please suggest ads for related products" based on the new profile data. The generated relevant ad is the output.

[0160] Step 9:

[0161] The server sends the selected relevant advertising data to the user device. The relevant advertising data selected by the generative AI model is used as input. Specifically, the relevant advertising data is sent using an HTTP request. The data is sent to the user device as output.

[0162] Step 10:

[0163] The user's device displays relevant ads the next time they access the site and records the user's clicks and interactions. The input is the relevant ad data sent from the server. Specifically, front-end technology is used to display ads, and if the user clicks on an ad, that information is sent back to the server. The output is a record of the user's interactions.

[0164] (Application example 1)

[0165] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0166] Conventional advertising systems have not provided sufficient methods for effectively displaying advertisements related to products that users already own. As a result, irrelevant or overlapping advertisements are displayed to users, reducing advertising effectiveness. Furthermore, there is a need for a method to increase the relevance of advertisements by dynamically displaying advertisements related to products that users already own, especially in new shopping experiences using virtual stores and smart devices.

[0167] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0168] In this invention, the server includes: means for a user to press an "Already Have It" button to indicate that the user already owns the displayed advertisement; means for transmitting information that the "Already Have It" button has been pressed to the server; means for updating the user's profile based on the information received by the server; means for selecting a relevant new advertisement based on the user's profile using a generative AI model; means for delivering the selected new advertisement to the user from the next time onward; means for dynamically displaying advertisements related to products owned by the user in a virtual store; and means for recognizing the user through the smart glasses and requesting advertisements based on the user's profile data. This makes it possible to dynamically display advertisements that are highly relevant to the user, thereby maximizing advertising effectiveness.

[0169] 1. "An 'I Already Have It' button that allows users to indicate that they already own the product when presented with an ad" is an interface element that allows users to indicate that they already own the product when presented with an ad.

[0170] 2. "Information indicating that the 'I already have it' button was pressed" refers to data generated when a user presses the 'I already have it' button, and is information indicating that the user already owns the product.

[0171] 3. "Means for transmitting to the server" means a communication system for transmitting data from the user terminal to the server.

[0172] 4. "Means for updating a user's profile based on information received by the server" refers to a function that analyzes the information received by the server and keeps the profile information up to date based on the user's purchasing history and behavioral data.

[0173] 5. "Generative AI model" is an artificial intelligence algorithm that analyzes user profile data and behavioral data to generate and select optimal advertisements.

[0174] 6. "Means for selecting new relevant ads" means a function that uses a generative AI model to select new ads that are highly relevant to a user based on the user's profile.

[0175] 7. "Means for delivering the selected new advertisement to the user from the next time onwards" means a system in which the server sends the selected advertisement to the user's terminal so that it will be displayed the next time the user accesses the site.

[0176] 8. "Virtual store" means an online shopping platform where users can browse and purchase products via the Internet.

[0177] 9. "Means for dynamically displaying advertisements related to products owned by a user" means a system or function for displaying new advertisements related to products already owned by a user in real time.

[0178] 10. "Smart glasses" are wearable devices that have augmented reality or virtual reality capabilities and display information to the user.

[0179] 11. "Means for recognizing users and requesting advertisements based on their profile data" means the functionality of devices such as smart glasses to identify users and retrieve appropriate advertisements based on their user profile.

[0180] This invention is an advertisement recommendation system that updates a user's profile and displays new related advertisements when the user presses an "I already have it" button to indicate that they already own the product displayed in a virtual store. Specifically, it is composed of the following elements:

[0181] server

[0182] The server acts as the central management system and performs the following main functions:

[0183] 1. Receiving information and updating user profile: The server receives information from the user device when the "I already have it" button is pressed. Based on the received information, the server updates the user database and clarifies the product information the user owns.

[0184] 2. Use of generative AI models: The server uses generative AI models to analyze user profile data and behavioral data and select new ads that are highly relevant to the user.

[0185] 3. Advertisement delivery: The selected new advertisement is delivered to the user from the next time onwards. The server displays the advertisement at the appropriate time when the user accesses the website.

[0186] User Device

[0187] The user device is the device on which the user sees and interacts with the advertisement, typically a pair of smart glasses or a smartphone.

[0188] 1. Displaying Ads: While users are browsing the virtual store, relevant ads sent from the server are displayed, including an "Already Have It" button.

[0189] 2. Information transmission: When the user presses the "I already have it" button, the information is transmitted to the server.

[0190] 3. User Recognition: Recognizing users and obtaining their profiles through devices such as smart glasses using facial recognition technology and other identification technologies.

[0191] Generative AI Models

[0192] A generative AI model is a system that uses machine learning algorithms to analyze user profile and behavioral data, dynamically generating and selecting the most relevant ads.

[0193] 1. Profile Analysis: Analyzes updated user profile data to identify user interests and purchasing patterns.

[0194] 2. Ad generation: Generate relevant ads based on user profiles.

[0195] Advertising Database

[0196] It is a database that works with a server and stores advertisements for use by generative AI models, including the content of the advertisement, target audience, related products, etc.

[0197] Specific Examples

[0198] 1. A user is browsing the bicycle accessories section in a "virtual store":

[0199] User recognition: The smart glasses recognize User A and identify his User ID as 'd741f2'.

[0200] Get profile data: The server gets the profile data of User A from the user database and finds out that the user owns an expensive bicycle.

[0201] Ad request and display: The server uses the generative AI model to select an ad for an accessory (e.g., bike light) that is highly relevant to User A and displays it on the smart glasses.

[0202] Pressing the "I already have it" button: User A sees the advertisement and presses the "I already have it" button because he already owns the light. This information is sent to the server and reflected in User A's profile.

[0203] Prompt Sentence Examples

[0204] "User d741f2 has clicked 'already_have' on ad_id 12345. Update their profile to show related bicycle accessories next time."

[0205] This system will dynamically display ads that are highly relevant to users, maximizing advertising effectiveness.

[0206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0207] Step 1:

[0208] User Awareness

[0209] The smart glasses recognize the user. As input, they use facial recognition data from the smart glasses' camera. They use a facial recognition API (e.g., Face++, Azure Face API, etc.) to identify the user ID. The output is the identified user ID (e.g., d741f2).

[0210] Step 2:

[0211] Profile Data Acquisition

[0212] The server retrieves profile data corresponding to the identified user ID from a user database. It executes a database query using the user ID as input. The output is profile data including the user's purchasing history and behavioral data.

[0213] Step 3:

[0214] Ad request

[0215] The server sends the data necessary to generate advertisements to the generative AI model based on the user's profile data. The generative AI model analyzes the input profile data and selects the advertisements most relevant to the user. The output is the selected advertisement data.

[0216] Step 4:

[0217] Advertisement display

[0218] The user device (smart glasses) displays the advertising data received from the server, which includes the advertising content and an "I already have it" button. The input is the advertising data, and the output is the advertisement displayed to the user.

[0219] Step 5:

[0220] Pressing the "I already have it" button

[0221] The user presses the "I already have it" button for the displayed ad. The input is the user interaction, which triggers a button press event. The output is the transmission of this information from the device to the server.

[0222] Step 6:

[0223] Information transmission

[0224] The user terminal sends information that the "I already have it" button has been pressed to the server. The button press event data is used as input, and the information is transmitted to the server via a data transmission protocol. The output is the user's "I already have it" information received by the server.

[0225] Step 7:

[0226] Profile Update

[0227] The server updates the user database based on the "already-held" information it receives. The input is the "already-held" information, and updates the user's profile data accordingly. The output is the updated user profile.

[0228] Step 8:

[0229] Next ad selection

[0230] The server requests the generative AI model to select future advertisements based on the updated user profile. The generative AI model analyzes the latest profile data and selects future advertisements to display. The input is the updated profile data, and the output is future advertisement data.

[0231] Step 9:

[0232] Next ad delivery

[0233] The server delivers the selected new advertisement the next time the user visits the store. The input is the new advertisement data, and the output is the advertisement to be displayed the next time the user visits the store. This process is triggered the next time the user visits the virtual store.

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] preface

[0236] This invention relates to an advertising recommendation system for maximizing advertising effectiveness. In particular, this system has the function of displaying advertisements related to products that a user already owns, and also combines an emotion engine that recognizes the user's emotions. This system is implemented using a server, a terminal, a generative AI model, and an emotion engine.

[0237] System Configuration

[0238] The main components of the system are:

[0239] server

[0240] User Device

[0241] User Database

[0242] Generative AI Models

[0243] Emotion Engine

[0244] Advertising Database

[0245] server

[0246] The server is a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[0247] User Device

[0248] The user device is the device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent from the server and has the ability to display the "I Already Have It" button.

[0249] User Database

[0250] A user database is a database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[0251] Generative AI Models

[0252] The generative AI model is an algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavioral and emotional data to generate relevant product ads to optimize the next ad display.

[0253] Emotion Engine

[0254] The emotion engine is a system for recognizing user emotions and including emotional data in user profiles. The emotion engine analyzes emotional data from users' voices, facial expressions, text inputs, etc., and provides this information to generative AI models.

[0255] Advertising Database

[0256] The advertisement database is a database that stores advertisement information provided by advertisers, including the advertisement content, target products, related products, etc.

[0257] Program processing

[0258] Advertisement display

[0259] The server selects the most suitable advertisement based on the user profile and sends the advertisement data to the user's device.

[0260] The user's device will display the received advertisement along with the "Already Have It" button.

[0261] Pressing the "I already have it" button

[0262] When a user presses the "I already have it" button for a displayed advertisement, the user's device sends this information to the server.

[0263] Analyzing information and updating your profile

[0264] The server updates the user's profile based on the information it receives and already has.

[0265] The generative AI model analyzes the updated profile and selects relevant ads to display next time.

[0266] Acquiring emotion data

[0267] The emotion engine analyzes the user's voice, facial expressions, and text input to obtain emotional data about the ad they are viewing, which indicates whether the user is interested in the ad or dislikes it.

[0268] Use of Emotional Data

[0269] The emotion engine transmits the acquired emotion data to the server, which then updates the corresponding user profile in the user database.

[0270] The generative AI model analyzes the updated profile and sentiment data to further optimize the next ad shown.

[0271] Selection and delivery of relevant advertisements

[0272] The selected relevant advertisements are sent from the server to the user's device and displayed the next time the user accesses the site.

[0273] The user's device displays relevant ads and records the user's clicks and interactions.

[0274] Specific examples

[0275] Example 1: Bicycle purchase and sentiment data

[0276] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button.

[0277] The user terminal transmits this information to the server.

[0278] The server analyzes this information using a generative AI model and updates the profile so that the next time an ad is displayed, it will serve ads for bicycle-related accessories (helmets, lights, custom parts, etc.).

[0279] The emotion engine analyzes the user's emotions regarding the ad being displayed, and if it detects a positive emotion, it sets the display of related ads from the next time onwards. The next ad sent by the server to the device may be for bicycle accessories, which may pique the user's interest.

[0280] Example 2: Cookware and Emotional Data

[0281] A user clicks on an advertisement for cooking equipment (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button.

[0282] The user device sends the information it already has to the server.

[0283] The server uses a generative AI model to analyze this information and update the profile to show ads for other products in the cookware series (pots, tongs, spatulas, etc.) in future visits.

[0284] If the emotion engine detects a happy emotion from the user's voice or facial expression while viewing an ad, it will prioritize displaying related ads the next time. The next ad sent by the server to the device may be about another product in the same series of cookware, which may pique the user's interest.

[0285] As described above, the present invention can maximize the effectiveness of advertising and improve advertisers' ROI by displaying advertisements based on the user's needs and emotions.

[0286] The processing flow will be explained below.

[0287] Step 1:

[0288] When the ad recommendation system starts, the server loads the ad database and the user database, obtains the latest user information and ad information from the database, and initializes the system.

[0289] Step 2:

[0290] The server retrieves target user profiles and behavioral data from a user database, including user purchase history, click history, and viewing history.

[0291] Step 3:

[0292] The server uses a generative AI model to analyze the user's profile and behavioral data and select the most appropriate ad for that user. The AI ​​model then scores the best ad based on past data.

[0293] Step 4:

[0294] The server sends the selected advertisement and the advertisement data, including the "I Already Have It Button," to the user's device. This advertisement data includes the content of the advertisement to be displayed and a script for recording the user's interaction.

[0295] Step 5:

[0296] The device analyzes the advertising data received from the server and displays the advertisement and the "Already Have It Button" on the user's screen. The advertisement is optimized for the user's viewing environment and displayed in a way that is easy for the user to see.

[0297] Step 6:

[0298] The user checks the displayed advertisement and presses the "I already have it" button if they already own it. This action records the user's interaction.

[0299] Step 7:

[0300] The device sends information to the server that the user pressed the "Already Have It Button," including the date and time of the press, the associated advertising ID, and the user ID.

[0301] Step 8:

[0302] The server updates the user profile in the user database based on the received "already owned" information, specifically by marking the items that the user already owns.

[0303] Step 9:

[0304] The server then uses the generative AI model to analyze the updated user profile and select the next relevant ad to display, taking into account the user's "already owned" information and selecting ads for related accessories and complementary products.

[0305] Step 10:

[0306] The server prepares a list of newly selected relevant ads and sends it to the device for subsequent ad delivery. This list will be used the next time the user accesses the site.

[0307] Step 11:

[0308] The next time the user views an ad, the device will display relevant ads based on the new ad list sent from the server, which are likely to increase click-through rates and purchase rates because they are selected based on the user's interests and needs.

[0309] Step 12:

[0310] How the user responds to the new ad is recorded and this information is fed back to the server, which further influences future ad presentations.

[0311] Step 13:

[0312] The emotion engine analyzes the user's voice, facial expressions, and text input while viewing the advertisement to obtain the user's emotion data, which indicates the user's emotional state, such as joy, interest, or disgust.

[0313] Step 14:

[0314] The emotion engine sends the acquired emotion data to the server, which details the user's emotional response to the advertisement.

[0315] Step 15:

[0316] The server stores the emotion data in a user database and provides it to a generative AI model, which uses this information to select more relevant ads the next time the user sees them.

[0317] Step 16:

[0318] The generative AI model analyzes user profile and sentiment data to further optimize the ads that will be shown next, prioritizing ads that users responded positively to and ads for related products based on sentiment data.

[0319] Step 17:

[0320] The server then sends the selected new ad to the device for subsequent ad delivery. Through this process, it becomes possible to provide ads that best fit the user's needs and emotions.

[0321] Specifically, if the emotion engine detects the emotion of joy while a user is viewing a food advertisement, the server will select and deliver the same brand of food or recipe video for the next advertisement. In this way, it becomes possible to deliver advertisements that reflect the user's emotional state, maximizing the effectiveness of the advertisement.

[0322] Example 2

[0323] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0324] Conventional ad recommendation systems often display unnecessary ads for products that users already own, reducing the effectiveness of advertising. Additionally, there is a lack of systems that optimize ads by taking into account user emotional data, making it difficult to accurately grasp users' interests.

[0325] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0326] In this invention, the server includes means for allowing a user to press an "Already Have It" button to indicate that the user already owns the displayed advertisement, means for transmitting information that the "Already Have It" button has been pressed to the server, means for updating the user's profile based on the information received by the server, means for selecting a relevant new advertisement based on the user's profile using a generative AI model, means for delivering the selected new advertisement to the user from the next time onwards, means for acquiring user emotion data using an emotion engine, and means for optimizing the advertisement to be displayed next by the generative AI model using the emotion data. This enables optimal advertisement delivery that reflects the user's ownership status and emotions.

[0327] The "I Already Have It Button" is an interface button that users can use to indicate that they already own the displayed advertisement.

[0328] "Server" refers to a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements.

[0329] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and select and generate optimal advertisements.

[0330] An "emotion engine" is a system that analyzes emotional data from a user's voice, facial expressions, text input, etc., and includes this information in the user profile.

[0331] A "profile" is an individual data set that includes a user's purchasing history, browsing history, click history, emotional data, etc.

[0332] An "advertising database" is a database that stores advertising information provided by advertisers.

[0333] "User database" refers to a database for storing and managing user profiles and behavioral data.

[0334] "Means for transmitting information" refers to a communication means for transmitting data from a user terminal to a server.

[0335] The "means for receiving information" refers to a communication means by which the server receives data sent from the user terminal.

[0336] A "profile update method" is a process for updating a user's profile in a user database with new information.

[0337] "Means for delivering advertisements" refers to the process by which the server sends the advertisement data generated by the server to the user terminal so that it can be displayed.

[0338] "Means for selecting new advertisements" means a process for using a generative AI model to select relevant new advertisements based on a user's profile.

[0339] MODE FOR CARRYING OUT THE INVENTION

[0340] This invention relates to an advertising recommendation system for maximizing advertising effectiveness. This system displays advertisements related to products that a user already owns and has the function of recognizing the user's emotions to optimize the advertisements. Specifically, this system is implemented using a server, a terminal, a generative AI model, and an emotion engine.

[0341] System Configuration

[0342] The main components of this system are:

[0343] Server: A computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[0344] User Device: The device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent by the server and has the ability to display the "I Already Have It" button.

[0345] User database: A database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[0346] Generative AI model: An algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavioral and emotional data to generate relevant product ads to optimize the next ad display.

[0347] Emotion engine: A system for recognizing user emotions and including emotional data in user profiles. The emotion engine analyzes emotional data from users' voices, facial expressions, text inputs, etc., and provides this information to generative AI models.

[0348] Advertising database: A database that stores advertising information provided by advertisers, including the content of the advertisement, target products, related products, etc.

[0349] Specific examples

[0350] Example 1: Bicycle purchase and sentiment data

[0351] A user clicks on a bicycle ad and, because they already own the product, presses the "I already have it" button. The user's device sends this information to the server. The server analyzes this information using a generative AI model and updates the profile so that the next time ads are displayed, ads for bicycle-related accessories (helmets, lights, custom parts, etc.) are delivered. The emotion engine analyzes the user's emotions regarding the currently displayed ad, and if a positive emotion is detected, it sets the device to display related ads in the future. The next ad the server sends to the device will be for bicycle accessories, which may pique the user's interest.

[0352] Example 2: Cookware and Emotional Data

[0353] A user clicks on an ad for cooking utensils (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button. The user's device sends the "I already have it" information to the server. The server analyzes this information using a generative AI model and updates the profile so that ads for other products in the cooking utensil series (pots, tongs, spatulas, etc.) will be displayed from the next time onwards. If the emotion engine detects an emotion of joy from the user's voice or facial expression regarding the currently displayed ad, it will prioritize displaying related ads next time. Next time, the server will send the generated advertisement for cooking utensils to the user's device.

[0354] Prompt Sentence Examples

[0355] Examples of prompts to be input to a generative AI model include:

[0356] "If a user clicks on an ad for a bicycle and hits the 'I already have it' button because they already own one, generate an ad for a related bicycle accessory."

[0357] "When a user clicks on an ad for a frying pan and presses the 'I already have it' button, generate an ad for other cookware in the same series."

[0358] "Next time, show me the ad where the sentiment engine detects a positive sentiment in the user."

[0359] With the above configuration, the present invention can maximize advertising effectiveness and improve advertisers' ROI by displaying optimal advertisements that reflect the user's possession status and emotions.

[0360] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0361] Program processing flow

[0362] Step 1: Prepare user profiles and advertising data

[0363] The server retrieves data from a user database and an advertising database. The user profile includes purchase history, browsing history, click history, etc., while the advertising database stores information on advertising content and related products.

[0364] Specific behavior:

[0365] Input: User database, Ad database

[0366] Data processing: The server uses SQL queries to retrieve user profile information and advertising information.

[0367] Output: User profile data, advertising data

[0368] Step 2: Ad selection using generative AI models

[0369] Based on the acquired data, the server sends prompt text to the generative AI model, causing it to generate the optimal advertisement.

[0370] Specific behavior:

[0371] Input: User profile data, advertising data

[0372] Data processing: The server sends a prompt to the generative AI model saying, "Please generate the most appropriate ad based on the user profile." The generative AI model analyzes this and selects the most relevant ad.

[0373] Output: Selected advertising data

[0374] Step 3: Displaying ads on user devices

[0375] The server sends the advertising data received from the generative AI model to the user's device, which displays the received advertisement along with the "Already Have It" button.

[0376] Specific behavior:

[0377] Input: Selected advertising data

[0378] Data processing: The server sends the generated advertising data to the user's device as an HTTP response.

[0379] Output: Advertisement display screen

[0380] Step 4: Press the "I already have it" button

[0381] If a user sees an advertisement for a product that they already own, they press the "I already own it" button, and the user's device sends this information to the server.

[0382] Specific behavior:

[0383] Input: "I already have it" button press data

[0384] Data processing: The user device sends a POST request to the server by pressing a button.

[0385] Output: Send "I already have it" information

[0386] Step 5: Analyze information and update your profile

[0387] The server analyzes the information it already has and updates the user's profile.

[0388] Specific behavior:

[0389] Input: Information you already have

[0390] Data processing: The server updates the user database profile based on the received data.

[0391] Output: Updated profile data

[0392] Step 6: Next ad selection by generative AI model

[0393] The generative AI model analyzes the new profile and selects relevant product ads for the next ad display.

[0394] Specific behavior:

[0395] Input: Updated profile data

[0396] Data processing: The server sends a prompt to the generative AI model saying, "Please generate an ad based on a new profile." The generative AI model analyzes this and selects the next ad.

[0397] Output: Ad data to be displayed next time

[0398] Step 7: Acquire and analyze emotion data

[0399] The emotion engine analyzes the user's voice, facial expressions, and text input while the ad is being displayed to obtain emotional data.

[0400] Specific behavior:

[0401] Input: User voice, facial expressions, and text data

[0402] Data processing: The emotion engine analyzes these input data and generates emotion data.

[0403] Output: Emotion data

[0404] Step 8: Use emotion data and update your profile

[0405] The emotion engine transmits the acquired emotion data to the server, which then updates the corresponding user profile in the user database.

[0406] Specific behavior:

[0407] Input: Emotion data

[0408] Data processing: The server stores the emotion data in the user database and updates the user profile.

[0409] Output: Updated profile data

[0410] Step 9: Select and deliver relevant ads

[0411] The server selects an optimized advertisement for the next display based on the data obtained by the generative AI model and sends it to the user's device.

[0412] Specific behavior:

[0413] Input: Updated profile data and emotion data

[0414] Data processing: The server sends the prompt text to the generative AI model again to generate the optimal ad. The ad data is saved to be displayed the next time the user accesses the site.

[0415] Output: Optimized advertising data

[0416] Step 10: Optimized Ad Display

[0417] The selected advertisement is sent from the server to the user's device, which displays the relevant advertisement on the user's next visit, and records the user's clicks and interactions.

[0418] Specific behavior:

[0419] Input: Optimized advertising data

[0420] Data processing: The server sends the advertising data to the user's device, and the user's device displays the received advertisement.

[0421] Output: Advertisement display screen

[0422] Through these steps, the system can deliver optimal advertisements that reflect the user's ownership status and emotions.

[0423] (Application example 2)

[0424] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0425] Conventional advertising systems deliver advertisements based on a user's purchasing behavior and browsing history, but they are inadequate in responding to user emotions or when the user already owns a product. As a result, advertisements that do not interest the user or advertisements related to products the user already owns are often displayed, reducing the effectiveness of the advertisements. The objective of this invention is to maximize the effectiveness of advertisements by utilizing user emotional data to display more personalized advertisements.

[0426] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for pressing an "Already Have It" button to indicate that the user already owns a displayed advertisement, means for transmitting information that the "Already Have It" button has been pressed to the server, means for updating the user's profile based on the information received by the server, means for selecting a relevant new advertisement based on the user's profile using a generative AI model, means for using an emotion engine that acquires and analyzes user emotion data, means for the generative AI model to optimize advertisement display based on the emotion data analyzed by the emotion engine, and means for delivering the selected new advertisement to the user from the next time onwards. This makes it possible to optimize advertisement display based on user emotion data.

[0427] The "I Already Have It" button is an interface that users can press to indicate that they already own a product.

[0428] A "server" is a computer system that manages user behavioral and emotional data and delivers advertisements.

[0429] A "user profile" is a collection of information about an individual user, including the user's purchasing history, behavioral data, emotional data, etc.

[0430] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and generate and select optimal advertisements.

[0431] An "emotion engine" is a system that analyzes a user's voice, facial expressions, and text input to obtain emotional data.

[0432] An "advertising database" is a database that stores advertising information provided by advertisers.

[0433] "Ad display optimization" is the process of selecting the most appropriate ads to display based on user profile and emotional data.

[0434] "Relevant new ads" are ads that are predicted to be of interest to the user based on the user's profile.

[0435] This invention is a system for displaying advertisements related to products that a user already owns and optimizing the advertisements based on emotion data. The system's main components include a server, a user terminal, a user database, a generative AI model, an emotion engine, and an advertisement database.

[0436] System configuration

[0437] The system is configured as follows:

[0438] 1. Server:

[0439] The server is a computer system that manages user behavior data and purchase history and delivers advertisements.

[0440] The server maintains a user database and stores the received "already held" information and emotional data.

[0441] 2. User Device:

[0442] The user device is a device such as a smartphone or tablet that displays the advertisement and displays the "I Already Have It Button."

[0443] The user device displays advertisements, records user interactions, and can also collect voice and facial expression data.

[0444] 3. User Database:

[0445] The user database is a database that stores and manages user profiles, emotional data, purchase history, and browsing history.

[0446] 4. Generative AI Models:

[0447] A generative AI model is an algorithm that uses machine learning to analyze user profile data and generate and select optimal ads.

[0448] 5. Emotion Engine:

[0449] The emotion engine is a system that analyzes the user's voice, facial expressions, and text input to obtain emotional data. This part can use external services such as the Emotion API.

[0450] 6. Advertising Database:

[0451] The advertisement database is a database that stores advertisement information provided by advertisers.

[0452] What the program does

[0453] 1. Data Collection:

[0454] The user device collects information on when the "I already have it" button is pressed, voice data, facial expression data, and purchase history, and sends the collected data to the server.

[0455] 2. Data transmission:

[0456] The information collected on the user's device is sent in real time to a server, which analyzes and stores it.

[0457] 3. Emotion analysis:

[0458] Once the voice and facial expression data arrives at the server, it is analyzed by an emotion engine, which uses Microsoft Azure's Emotion API and other emotion analysis tools.

[0459] 4. Ad generation using generative AI models:

[0460] Based on the sentiment data and user profile data, a generative AI model, such as GPT-4 or BERT, selects the optimal ad.

[0461] 5. Advertising:

[0462] The selected advertisement is delivered from the server to the user's device and displayed the next time the user uses the device. The advertisement also includes an "Already Have It" button.

[0463] 6. Use of Feedback:

[0464] After the ad is displayed, the user's emotional data is collected and analyzed again, and this information is used to select the next ad.

[0465] Specific examples

[0466] As a concrete example, we will show the implementation of a function related to bicycle-related products.

[0467] Example 1: When a user clicks on an ad for a bicycle and presses the "I Already Have It" button, the server updates the user's profile. From then on, ads for bicycle helmets and lights will be displayed based on the user's emotional data, increasing the likelihood of the user being interested.

[0468] Prompt Sentence Examples

[0469] Below are some example prompts to use with generative AI models (such as GPT-4):

[0470] “Imagine a user already owns a bike. Generate ads for relevant bike accessories (e.g., helmets, lights, custom parts, etc.) for this user. Make the ads interesting by taking into account the user’s sentiment data.”

[0471] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0472] Step 1:

[0473] The user's device displays an advertisement. When the user presses the "I Already Have It" button in response to the displayed advertisement, that information is entered into the user's device. The user's device then sends this input information to the server. The specific operations performed on the device side are capturing the button press event and generating the corresponding data packet. The output is the "I Already Have It" button press information.

[0474] Step 2:

[0475] The server receives the "I already have it" information sent from the user's device. The server analyzes this information and processes the data to update the user's profile. Specifically, it adds a new field to the corresponding user profile in the user database and updates the product status. The input is the "I already have it" information, and the output is the updated user profile.

[0476] Step 3:

[0477] The server uses an emotion engine to collect the user's voice data and facial expression data in response to the currently displayed advertisement. The user's device sends this data to the server. The specific operation of emotion data is to capture the user's tone of voice and facial expression using the device's microphone and camera. The input is voice data and facial expression data, and the output is emotion data.

[0478] Step 4:

[0479] The server analyzes the received emotional data using an emotion engine. This analysis uses commercially available emotion analysis software. Specifically, the server identifies the user's emotion (e.g., joy, disgust) from the tone of voice and converts it into numerical data. The input is the collected emotional data, and the output is the analyzed emotional result data.

[0480] Step 5:

[0481] The server inputs the updated user profile and emotional result data into the generative AI model. The generative AI model analyzes this data and selects the relevant ad to display next. At this stage, the machine learning algorithm generates new ad copy using prompt text based on the user's purchase history and emotional data. The input is the updated user profile and emotional result data, and the output is the generated ad copy.

[0482] Step 6:

[0483] The server sends the generated ad copy to the user's device. The next time the user opens the app, the new ad will be displayed. The specific operation performed on the server side is to generate and send packets of ad data. The output is the ad data to be displayed next time.

[0484] Step 7:

[0485] The user's device displays a new ad and records the user's clicks and interactions, providing useful feedback data for future ad displays. The specific actions performed by the device are displaying the ad and capturing the user's interactions. The output is user interaction data.

[0486] The above are the specific processing steps for carrying out the present invention. The operation methods of the hardware and software used in each step and the data flow will become clear, which will help in putting the present invention into practical use.

[0487] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0488] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0489] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0490] [Second embodiment]

[0491] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0492] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0493] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0494] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0495] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0496] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0497] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0498] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0499] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0500] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0501] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0502] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0503] preface

[0504] The present invention relates to an advertising recommendation system for maximizing advertising effectiveness. In particular, the system has the function of displaying advertisements related to products that a user already owns. The system is implemented using a server, a terminal, and a generative AI model.

[0505] System Configuration

[0506] The main components of the system are:

[0507] server

[0508] User Device

[0509] User Database

[0510] Generative AI Models

[0511] Advertising Database

[0512] server

[0513] The server is a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[0514] User Device

[0515] The user device is the device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent from the server and has the ability to display the "I Already Have It" button.

[0516] User Database

[0517] A user database is a database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[0518] Generative AI Models

[0519] The generative AI model is an algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavior data and generates relevant product ads to optimize the next ad display.

[0520] Advertising Database

[0521] The advertisement database is a database that stores advertisement information provided by advertisers, including the advertisement content, target products, related products, etc.

[0522] Program processing

[0523] Advertisement display

[0524] The server selects the most suitable advertisement based on the user profile and sends the advertisement data to the user's device.

[0525] The user's device will display the received advertisement along with the "Already Have It" button.

[0526] Pressing the "I already have it" button

[0527] When a user presses the "I already have it" button for a displayed advertisement, the user's device sends this information to the server.

[0528] Analyzing information and updating your profile

[0529] The server updates the user's profile based on the information it receives and already has.

[0530] The generative AI model analyzes the updated profile and selects relevant ads to display next time.

[0531] Selection and delivery of relevant advertisements

[0532] The selected relevant advertisements are sent from the server to the user's device and displayed the next time the user accesses the site.

[0533] The user's device displays relevant ads and records the user's clicks and interactions.

[0534] Specific examples

[0535] Example 1: Purchasing a bicycle

[0536] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button.

[0537] The user terminal transmits this information to the server.

[0538] The server analyzes this information using a generative AI model and updates the profile so that the next time an ad is displayed, it will serve ads for bicycle-related accessories (helmets, lights, custom parts, etc.).

[0539] The next time the server sends an advertisement to the terminal, it may be for bicycle accessories, which may pique the user's interest.

[0540] Example 2: Cookware

[0541] A user clicks on an advertisement for cooking equipment (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button.

[0542] The user device sends the information it already has to the server.

[0543] The server uses a generative AI model to analyze this information and update the profile to show ads for other products in the cookware series (pots, tongs, spatulas, etc.) in future visits.

[0544] The next time the server sends an advertisement to the terminal, it will be about another product of cooking equipment, which may be of interest to the user.

[0545] As described above, the present invention can maximize the effectiveness of advertising and improve advertisers' ROI by displaying only advertisements that users truly need.

[0546] The processing flow will be explained below.

[0547] Step 1:

[0548] When the ad recommendation system starts, the server loads the ad database and the user database, obtains the latest user information and ad information from the database, and initializes the system.

[0549] Step 2:

[0550] The server retrieves target user profiles and behavioral data from a user database, including user purchase history, click history, and viewing history.

[0551] Step 3:

[0552] The server uses a generative AI model to analyze the user's profile and behavioral data and select the most appropriate ad for that user. The AI ​​model then scores the best ad based on past data.

[0553] Step 4:

[0554] The server sends the selected advertisement and the advertisement data, including the "I Already Have It Button," to the user's device. This advertisement data includes the content of the advertisement to be displayed and a script for recording the user's interaction.

[0555] Step 5:

[0556] The device analyzes the advertising data received from the server and displays the advertisement and the "Already Have It Button" on the user's screen. The advertisement is optimized for the user's viewing environment and displayed in a way that is easy for the user to see.

[0557] Step 6:

[0558] The user checks the displayed advertisement and presses the "I already have it" button if they already own it. This action records the user's interaction.

[0559] Step 7:

[0560] The device sends information to the server that the user pressed the "Already Have It Button," including the date and time of the press, the associated advertising ID, and the user ID.

[0561] Step 8:

[0562] The server updates the user profile in the user database based on the received "already owned" information, specifically by marking the items that the user already owns.

[0563] Step 9:

[0564] The server then uses the generative AI model to analyze the updated user profile and select the next ad to display, taking into account the user's "already owned" information and selecting ads for related accessories and complementary products.

[0565] Step 10:

[0566] The server prepares a list of newly selected relevant ads and sends it to the device for subsequent ad delivery. This list will be used the next time the user accesses the site.

[0567] Step 11:

[0568] The next time the user views an ad, the device will display relevant ads based on the new ad list sent from the server, which are likely to increase click-through rates and purchase rates because they are selected based on the user's interests and needs.

[0569] Step 12:

[0570] How the user responds to the new ad is recorded and this information is fed back to the server, which further influences future ad presentations.

[0571] By following these steps, it is possible to display ads that best fit the user's needs, maximizing advertising effectiveness and improving advertisers' ROI.

[0572] Example 1

[0573] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0574] Conventional advertising systems often displayed ads for products that users already owned, which meant they were unable to respond to users' interests and needs. This resulted in lower ad click-through rates and conversion rates, and lower advertiser ROI. In addition, there was a lack of a way to reflect product information that users already owned in the ad display selection process, making it difficult to deliver effective ads.

[0575] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0576] In this invention, the server includes a means for acquiring a user profile and behavioral data from a user database, a means for inputting a prompt sentence into a generative AI model based on the acquired data to generate an optimal advertisement, and a means for transmitting the advertisement data obtained from the generative AI model to a user terminal, thereby making it possible to exclude products already owned by the user and display advertisements that focus on related products.

[0577] The "server" is a device that retrieves user profiles and behavioral data from a user database and uses that data to generate optimal advertisements for the generative AI model.

[0578] A "user terminal" is a device that receives and displays advertising data sent from the server and provides users with interactions such as the "I Already Have It Button."

[0579] "User database" refers to data storage that stores and manages user behavioral data and profile information, such as purchase history, click history, and browsing history.

[0580] The "generative AI model" is an algorithm that selects and generates optimal advertisements based on acquired user data, and generates relevant advertisement information by inputting a prompt text.

[0581] A "prompt" is text data that is input into a generative AI model, and is an instruction to suggest optimal advertisements based on the user's profile and behavioral data.

[0582] The "Already Owned Button" is a user interface element that notifies the server that the user already owns the product in response to a displayed advertisement.

[0583] This invention relates to an advertising recommendation system that maximizes advertising effectiveness by displaying advertisements related to products that users already own. In particular, the system uses a generative AI model based on user behavior data and purchase history to generate optimal product advertisements and provide them to users.

[0584] System Configuration

[0585] The system mainly consists of the following components:

[0586] server

[0587] User Device

[0588] User Database

[0589] Generative AI Models

[0590] Advertising Database

[0591] server

[0592] The server has the following features:

[0593] 1. Retrieve user profile and behavioral data from your user database.

[0594] 2. Based on the acquired data, a prompt sentence is input into the generative AI model to generate the optimal ad.

[0595] 3. The advertising data obtained from the generative AI model is sent to the user's device.

[0596] User Device

[0597] The user terminal has the following features:

[0598] 1. Receives advertising data sent from the server and displays it on the screen.

[0599] 2. Display the "I Already Have It" button and record the user's interaction.

[0600] 3. When the user presses the "I already have it" button, the information is sent to the server.

[0601] User Database

[0602] The user database stores and manages user behavioral data and profile information, such as purchase history, click history, and browsing history, and provides data for selecting the most suitable advertisements for users.

[0603] Generative AI Models

[0604] The generative AI model uses machine learning algorithms to analyze user profile data and generate optimal ads. By inputting prompt text, the generative AI model generates ads that are likely to attract the user's attention.

[0605] Advertising Database

[0606] The advertisement database stores advertisement information provided by advertisers, including the content of the advertisement, target products, related products, etc.

[0607] Specific examples

[0608] Example 1: Purchasing a bicycle

[0609] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button. The user's device sends this information to the server. The server analyzes this information using a generative AI model and updates the user's profile so that the next time ads are displayed, they will be for bicycle accessories (e.g., helmets, lights, custom parts). The next time the server sends an ad to the user's device, it will be for bicycle accessories, which are more likely to interest the user.

[0610] Example 2: Cookware

[0611] A user clicks on an ad for cookware (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button. The user's device sends the "I already have it" information to the server. The server analyzes this information using a generative AI model and updates the profile so that ads for other products in the cookware series (for example, pots, tongs, and spatulas) will be displayed in the future. The next ad the server sends to the user's device will be for a different cookware product and is more likely to interest the user.

[0612] This invention reflects user profiles and behavioral data in real time, allowing for the display of highly relevant ads and improving advertisers' ROI. Using a generative AI model, it is possible to accurately capture user interests and reflect them in subsequent ad displays. This allows for less intrusive ad displays for users.

[0613] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0614] Step 1:

[0615] The server retrieves user profile and behavioral data from a user database. The user ID is used as input. Specifically, it executes an SQL query to retrieve data from the database, which is then output. Based on the retrieved data, the server prepares the basis for determining which ad is most suitable.

[0616] Step 2:

[0617] The server inputs a prompt sentence into the generative AI model based on the acquired data, generating the optimal ad. User profile and behavioral data are used as input. Specifically, the server generates and inputs a prompt sentence such as "Please suggest ads that this user is likely to be interested in" into the generative AI model. The generative AI model then analyzes the prompt sentence and outputs the optimal ad.

[0618] Step 3:

[0619] The server sends the advertising data obtained from the generative AI model to the user device. The optimal advertising data output by the generative AI model is used as input. Specifically, the advertising data is sent using an HTTP request. The advertising data is sent to the user device, and this becomes the output.

[0620] Step 4:

[0621] The user's device displays the received advertising data along with the "I already have it button." The advertising data sent from the server is used as input. Specifically, the process of displaying the advertisement and button is carried out using front-end technologies (HTML, CSS, JavaScript). The output is the advertisement and button displayed on the screen.

[0622] Step 5:

[0623] The user presses the "I already have it" button for an ad. The user's click action is used as input. Specifically, when the user clicks the button, a click event occurs, which becomes the output.

[0624] Step 6:

[0625] The user device catches the button click event and sends that information to the server. The button click event is used as input. Specifically, the button click event is detected using JavaScript and the information is sent to the server using an AJAX request. The information sent to the server becomes the output.

[0626] Step 7:

[0627] When the server receives the "already have" information, it updates the corresponding user profile in the user database. The "already have" information is used as input. Specifically, an SQL query is used to update the user profile data. The update to the user database is the output.

[0628] Step 8:

[0629] The generative AI model analyzes the newly updated user data and selects the relevant ad to display next. The updated user data is used as input. Specifically, it generates and inputs a prompt such as "Please suggest ads for related products" based on the new profile data. The generated relevant ad is the output.

[0630] Step 9:

[0631] The server sends the selected relevant advertising data to the user device. The relevant advertising data selected by the generative AI model is used as input. Specifically, the relevant advertising data is sent using an HTTP request. The data is sent to the user device as output.

[0632] Step 10:

[0633] The user's device displays relevant ads the next time they access the site and records the user's clicks and interactions. The input is the relevant ad data sent from the server. Specifically, front-end technology is used to display ads, and if the user clicks on an ad, that information is sent back to the server. The output is a record of the user's interactions.

[0634] (Application example 1)

[0635] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0636] Conventional advertising systems have not provided sufficient methods for effectively displaying advertisements related to products that users already own. As a result, irrelevant or overlapping advertisements are displayed to users, reducing advertising effectiveness. Furthermore, there is a need for a method to increase the relevance of advertisements by dynamically displaying advertisements related to products that users already own, especially in new shopping experiences using virtual stores and smart devices.

[0637] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0638] In this invention, the server includes: means for a user to press an "Already Have It" button to indicate that the user already owns the displayed advertisement; means for transmitting information that the "Already Have It" button has been pressed to the server; means for updating the user's profile based on the information received by the server; means for selecting a relevant new advertisement based on the user's profile using a generative AI model; means for delivering the selected new advertisement to the user from the next time onward; means for dynamically displaying advertisements related to products owned by the user in a virtual store; and means for recognizing the user through the smart glasses and requesting advertisements based on the user's profile data. This makes it possible to dynamically display advertisements that are highly relevant to the user, thereby maximizing advertising effectiveness.

[0639] 1. "An 'I Already Have It' button that allows users to indicate that they already own the product when presented with an ad" is an interface element that allows users to indicate that they already own the product when presented with an ad.

[0640] 2. "Information indicating that the 'I already have it' button was pressed" refers to data generated when a user presses the 'I already have it' button, and is information indicating that the user already owns the product.

[0641] 3. "Means for transmitting to the server" means a communication system for transmitting data from the user terminal to the server.

[0642] 4. "Means for updating a user's profile based on information received by the server" refers to a function that analyzes the information received by the server and keeps the profile information up to date based on the user's purchasing history and behavioral data.

[0643] 5. "Generative AI model" is an artificial intelligence algorithm that analyzes user profile data and behavioral data to generate and select optimal advertisements.

[0644] 6. "Means for selecting new relevant ads" means a function that uses a generative AI model to select new ads that are highly relevant to a user based on the user's profile.

[0645] 7. "Means for delivering the selected new advertisement to the user from the next time onwards" means a system in which the server sends the selected advertisement to the user's terminal so that it will be displayed the next time the user accesses the site.

[0646] 8. "Virtual store" means an online shopping platform where users can browse and purchase products via the Internet.

[0647] 9. "Means for dynamically displaying advertisements related to products owned by a user" means a system or function for displaying new advertisements related to products already owned by a user in real time.

[0648] 10. "Smart glasses" are wearable devices that have augmented reality or virtual reality capabilities and display information to the user.

[0649] 11. "Means for recognizing users and requesting advertisements based on their profile data" means the functionality of devices such as smart glasses to identify users and retrieve appropriate advertisements based on their user profile.

[0650] This invention is an advertisement recommendation system that updates a user's profile and displays new related advertisements when the user presses an "I already have it" button to indicate that they already own the product displayed in a virtual store. Specifically, it is composed of the following elements:

[0651] server

[0652] The server acts as the central management system and performs the following main functions:

[0653] 1. Receiving information and updating user profile: The server receives information from the user device when the "I already have it" button is pressed. Based on the received information, the server updates the user database and clarifies the product information the user owns.

[0654] 2. Use of generative AI models: The server uses generative AI models to analyze user profile data and behavioral data and select new ads that are highly relevant to the user.

[0655] 3. Advertisement delivery: The selected new advertisement is delivered to the user from the next time onwards. The server displays the advertisement at the appropriate time when the user accesses the website.

[0656] User Device

[0657] The user device is the device on which the user sees and interacts with the advertisement, typically a pair of smart glasses or a smartphone.

[0658] 1. Displaying Ads: While users are browsing the virtual store, relevant ads sent from the server are displayed, including an "Already Have It" button.

[0659] 2. Information transmission: When the user presses the "I already have it" button, the information is transmitted to the server.

[0660] 3. User Recognition: Recognizing users and obtaining their profiles through devices such as smart glasses using facial recognition technology and other identification technologies.

[0661] Generative AI Models

[0662] A generative AI model is a system that uses machine learning algorithms to analyze user profile and behavioral data, dynamically generating and selecting the most relevant ads.

[0663] 1. Profile Analysis: Analyzes updated user profile data to identify user interests and purchasing patterns.

[0664] 2. Ad generation: Generate relevant ads based on user profiles.

[0665] Advertising Database

[0666] It is a database that works with a server and stores advertisements for use by generative AI models, including the content of the advertisement, target audience, related products, etc.

[0667] Specific Examples

[0668] 1. A user is browsing the bicycle accessories section in a "virtual store":

[0669] User recognition: The smart glasses recognize User A and identify his User ID as 'd741f2'.

[0670] Get profile data: The server gets the profile data of User A from the user database and finds out that the user owns an expensive bicycle.

[0671] Ad request and display: The server uses the generative AI model to select an ad for an accessory (e.g., bike light) that is highly relevant to User A and displays it on the smart glasses.

[0672] Pressing the "I already have it" button: User A sees the advertisement and presses the "I already have it" button because he already owns the light. This information is sent to the server and reflected in User A's profile.

[0673] Prompt Sentence Examples

[0674] "User d741f2 has clicked 'already_have' on ad_id 12345. Update their profile to show related bicycle accessories next time."

[0675] This system will dynamically display ads that are highly relevant to users, maximizing advertising effectiveness.

[0676] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0677] Step 1:

[0678] User Awareness

[0679] The smart glasses recognize the user. As input, they use facial recognition data from the smart glasses' camera. They use a facial recognition API (e.g., Face++, Azure Face API, etc.) to identify the user ID. The output is the identified user ID (e.g., d741f2).

[0680] Step 2:

[0681] Profile Data Acquisition

[0682] The server retrieves profile data corresponding to the identified user ID from a user database. It executes a database query using the user ID as input. The output is profile data including the user's purchasing history and behavioral data.

[0683] Step 3:

[0684] Ad request

[0685] The server sends the data necessary to generate advertisements to the generative AI model based on the user's profile data. The generative AI model analyzes the input profile data and selects the advertisements most relevant to the user. The output is the selected advertisement data.

[0686] Step 4:

[0687] Advertisement display

[0688] The user device (smart glasses) displays the advertising data received from the server, which includes the advertising content and an "I already have it" button. The input is the advertising data, and the output is the advertisement displayed to the user.

[0689] Step 5:

[0690] Pressing the "I already have it" button

[0691] The user presses the "I already have it" button for the displayed ad. The input is the user interaction, which triggers a button press event. The output is the transmission of this information from the device to the server.

[0692] Step 6:

[0693] Information transmission

[0694] The user terminal sends information that the "I already have it" button has been pressed to the server. The button press event data is used as input, and the information is transmitted to the server via a data transmission protocol. The output is the user's "I already have it" information received by the server.

[0695] Step 7:

[0696] Profile Update

[0697] The server updates the user database based on the "already-held" information it receives. The input is the "already-held" information, and updates the user's profile data accordingly. The output is the updated user profile.

[0698] Step 8:

[0699] Next ad selection

[0700] The server requests the generative AI model to select future advertisements based on the updated user profile. The generative AI model analyzes the latest profile data and selects future advertisements to display. The input is the updated profile data, and the output is future advertisement data.

[0701] Step 9:

[0702] Next ad delivery

[0703] The server delivers the selected new advertisement the next time the user visits the store. The input is the new advertisement data, and the output is the advertisement to be displayed the next time the user visits the store. This process is triggered the next time the user visits the virtual store.

[0704] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0705] preface

[0706] This invention relates to an advertising recommendation system for maximizing advertising effectiveness. In particular, this system has the function of displaying advertisements related to products that a user already owns, and also combines an emotion engine that recognizes the user's emotions. This system is implemented using a server, a terminal, a generative AI model, and an emotion engine.

[0707] System Configuration

[0708] The main components of the system are:

[0709] server

[0710] User Device

[0711] User Database

[0712] Generative AI Models

[0713] Emotion Engine

[0714] Advertising Database

[0715] server

[0716] The server is a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[0717] User Device

[0718] The user device is the device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent from the server and has the ability to display the "I Already Have It" button.

[0719] User Database

[0720] A user database is a database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[0721] Generative AI Models

[0722] The generative AI model is an algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavioral and emotional data to generate relevant product ads to optimize the next ad display.

[0723] Emotion Engine

[0724] The emotion engine is a system for recognizing user emotions and including emotional data in user profiles. The emotion engine analyzes emotional data from users' voices, facial expressions, text inputs, etc., and provides this information to generative AI models.

[0725] Advertising Database

[0726] The advertisement database is a database that stores advertisement information provided by advertisers, including the advertisement content, target products, related products, etc.

[0727] Program processing

[0728] Advertisement display

[0729] The server selects the most suitable advertisement based on the user profile and sends the advertisement data to the user's device.

[0730] The user's device will display the received advertisement along with the "Already Have It" button.

[0731] Pressing the "I already have it" button

[0732] When a user presses the "I already have it" button for a displayed advertisement, the user's device sends this information to the server.

[0733] Analyzing information and updating your profile

[0734] The server updates the user's profile based on the information it receives and already has.

[0735] The generative AI model analyzes the updated profile and selects relevant ads to display next time.

[0736] Acquiring emotion data

[0737] The emotion engine analyzes the user's voice, facial expressions, and text input to obtain emotional data about the ad they are viewing, which indicates whether the user is interested in the ad or dislikes it.

[0738] Use of Emotional Data

[0739] The emotion engine transmits the acquired emotion data to the server, which then updates the corresponding user profile in the user database.

[0740] The generative AI model analyzes the updated profile and sentiment data to further optimize the next ad shown.

[0741] Selection and delivery of relevant advertisements

[0742] The selected relevant advertisements are sent from the server to the user's device and displayed the next time the user accesses the site.

[0743] The user's device displays relevant ads and records the user's clicks and interactions.

[0744] Specific examples

[0745] Example 1: Bicycle purchase and sentiment data

[0746] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button.

[0747] The user terminal transmits this information to the server.

[0748] The server analyzes this information using a generative AI model and updates the profile so that the next time an ad is displayed, it will serve ads for bicycle-related accessories (helmets, lights, custom parts, etc.).

[0749] The emotion engine analyzes the user's emotions regarding the ad being displayed, and if it detects a positive emotion, it sets the display of related ads from the next time onwards. The next ad sent by the server to the device may be for bicycle accessories, which may pique the user's interest.

[0750] Example 2: Cookware and Emotional Data

[0751] A user clicks on an advertisement for cooking equipment (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button.

[0752] The user device sends the information it already has to the server.

[0753] The server uses a generative AI model to analyze this information and update the profile to show ads for other products in the cookware series (pots, tongs, spatulas, etc.) in future visits.

[0754] If the emotion engine detects a happy emotion from the user's voice or facial expression while viewing an ad, it will prioritize displaying related ads the next time. The next ad sent by the server to the device may be about another product in the same series of cookware, which may pique the user's interest.

[0755] As described above, the present invention can maximize the effectiveness of advertising and improve advertisers' ROI by displaying advertisements based on the user's needs and emotions.

[0756] The processing flow will be explained below.

[0757] Step 1:

[0758] When the ad recommendation system starts, the server loads the ad database and the user database, obtains the latest user information and ad information from the database, and initializes the system.

[0759] Step 2:

[0760] The server retrieves target user profiles and behavioral data from a user database, including user purchase history, click history, and viewing history.

[0761] Step 3:

[0762] The server uses a generative AI model to analyze the user's profile and behavioral data and select the most appropriate ad for that user. The AI ​​model then scores the best ad based on past data.

[0763] Step 4:

[0764] The server sends the selected advertisement and the advertisement data, including the "I Already Have It Button," to the user's device. This advertisement data includes the content of the advertisement to be displayed and a script for recording the user's interaction.

[0765] Step 5:

[0766] The device analyzes the advertising data received from the server and displays the advertisement and the "Already Have It Button" on the user's screen. The advertisement is optimized for the user's viewing environment and displayed in a way that is easy for the user to see.

[0767] Step 6:

[0768] The user checks the displayed advertisement and presses the "I already have it" button if they already own it. This action records the user's interaction.

[0769] Step 7:

[0770] The device sends information to the server that the user pressed the "Already Have It Button," including the date and time of the press, the associated advertising ID, and the user ID.

[0771] Step 8:

[0772] The server updates the user profile in the user database based on the received "already owned" information, specifically by marking the items that the user already owns.

[0773] Step 9:

[0774] The server then uses the generative AI model to analyze the updated user profile and select the next relevant ad to display, taking into account the user's "already owned" information and selecting ads for related accessories and complementary products.

[0775] Step 10:

[0776] The server prepares a list of newly selected relevant ads and sends it to the device for subsequent ad delivery. This list will be used the next time the user accesses the site.

[0777] Step 11:

[0778] The next time the user views an ad, the device will display relevant ads based on the new ad list sent from the server, which are likely to increase click-through rates and purchase rates because they are selected based on the user's interests and needs.

[0779] Step 12:

[0780] How the user responds to the new ad is recorded and this information is fed back to the server, which further influences future ad presentations.

[0781] Step 13:

[0782] The emotion engine analyzes the user's voice, facial expressions, and text input while viewing the advertisement to obtain the user's emotion data, which indicates the user's emotional state, such as joy, interest, or disgust.

[0783] Step 14:

[0784] The emotion engine sends the acquired emotion data to the server, which details the user's emotional response to the advertisement.

[0785] Step 15:

[0786] The server stores the emotion data in a user database and provides it to a generative AI model, which uses this information to select more relevant ads the next time the user sees them.

[0787] Step 16:

[0788] The generative AI model analyzes user profile and sentiment data to further optimize the ads that will be shown next, prioritizing ads that users responded positively to and ads for related products based on sentiment data.

[0789] Step 17:

[0790] The server then sends the selected new ad to the device for subsequent ad delivery. Through this process, it becomes possible to provide ads that best fit the user's needs and emotions.

[0791] Specifically, if the emotion engine detects the emotion of joy while a user is viewing a food advertisement, the server will select and deliver the same brand of food or recipe video for the next advertisement. In this way, it becomes possible to deliver advertisements that reflect the user's emotional state, maximizing the effectiveness of the advertisement.

[0792] Example 2

[0793] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0794] Conventional ad recommendation systems often display unnecessary ads for products that users already own, reducing the effectiveness of advertising. Additionally, there is a lack of systems that optimize ads by taking into account user emotional data, making it difficult to accurately grasp users' interests.

[0795] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0796] In this invention, the server includes means for allowing a user to press an "Already Have It" button to indicate that the user already owns the displayed advertisement, means for transmitting information that the "Already Have It" button has been pressed to the server, means for updating the user's profile based on the information received by the server, means for selecting a relevant new advertisement based on the user's profile using a generative AI model, means for delivering the selected new advertisement to the user from the next time onwards, means for acquiring user emotion data using an emotion engine, and means for optimizing the advertisement to be displayed next by the generative AI model using the emotion data. This enables optimal advertisement delivery that reflects the user's ownership status and emotions.

[0797] The "I Already Have It Button" is an interface button that users can use to indicate that they already own the displayed advertisement.

[0798] "Server" refers to a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements.

[0799] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and select and generate optimal advertisements.

[0800] An "emotion engine" is a system that analyzes emotional data from a user's voice, facial expressions, text input, etc., and includes this information in the user profile.

[0801] A "profile" is an individual data set that includes a user's purchasing history, browsing history, click history, emotional data, etc.

[0802] An "advertising database" is a database that stores advertising information provided by advertisers.

[0803] "User database" refers to a database for storing and managing user profiles and behavioral data.

[0804] "Means for transmitting information" refers to a communication means for transmitting data from a user terminal to a server.

[0805] The "means for receiving information" refers to a communication means by which the server receives data sent from the user terminal.

[0806] A "profile update method" is a process for updating a user's profile in a user database with new information.

[0807] "Means for delivering advertisements" refers to the process by which the server sends the advertisement data generated by the server to the user terminal so that it can be displayed.

[0808] "Means for selecting new advertisements" means a process for using a generative AI model to select relevant new advertisements based on a user's profile.

[0809] MODE FOR CARRYING OUT THE INVENTION

[0810] This invention relates to an advertising recommendation system for maximizing advertising effectiveness. This system displays advertisements related to products that a user already owns and has the function of recognizing the user's emotions to optimize the advertisements. Specifically, this system is implemented using a server, a terminal, a generative AI model, and an emotion engine.

[0811] System Configuration

[0812] The main components of this system are:

[0813] Server: A computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[0814] User Device: The device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent by the server and has the ability to display the "I Already Have It" button.

[0815] User database: A database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[0816] Generative AI model: An algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavioral and emotional data to generate relevant product ads to optimize the next ad display.

[0817] Emotion engine: A system for recognizing user emotions and including emotional data in user profiles. The emotion engine analyzes emotional data from users' voices, facial expressions, text inputs, etc., and provides this information to generative AI models.

[0818] Advertising database: A database that stores advertising information provided by advertisers, including the content of the advertisement, target products, related products, etc.

[0819] Specific examples

[0820] Example 1: Bicycle purchase and sentiment data

[0821] A user clicks on a bicycle ad and, because they already own the product, presses the "I already have it" button. The user's device sends this information to the server. The server analyzes this information using a generative AI model and updates the profile so that the next time ads are displayed, ads for bicycle-related accessories (helmets, lights, custom parts, etc.) are delivered. The emotion engine analyzes the user's emotions regarding the currently displayed ad, and if a positive emotion is detected, it sets the device to display related ads in the future. The next ad the server sends to the device will be for bicycle accessories, which may pique the user's interest.

[0822] Example 2: Cookware and Emotional Data

[0823] A user clicks on an ad for cooking utensils (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button. The user's device sends the "I already have it" information to the server. The server analyzes this information using a generative AI model and updates the profile so that ads for other products in the cooking utensil series (pots, tongs, spatulas, etc.) will be displayed from the next time onwards. If the emotion engine detects an emotion of joy from the user's voice or facial expression regarding the currently displayed ad, it will prioritize displaying related ads next time. Next time, the server will send the generated advertisement for cooking utensils to the user's device.

[0824] Prompt Sentence Examples

[0825] Examples of prompts to be input to a generative AI model include:

[0826] "If a user clicks on an ad for a bicycle and hits the 'I already have it' button because they already own one, generate an ad for a related bicycle accessory."

[0827] "When a user clicks on an ad for a frying pan and presses the 'I already have it' button, generate an ad for other cookware in the same series."

[0828] "Next time, show me the ad where the sentiment engine detects a positive sentiment in the user."

[0829] With the above configuration, the present invention can maximize advertising effectiveness and improve advertisers' ROI by displaying optimal advertisements that reflect the user's possession status and emotions.

[0830] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0831] Program processing flow

[0832] Step 1: Prepare user profiles and advertising data

[0833] The server retrieves data from a user database and an advertising database. The user profile includes purchase history, browsing history, click history, etc., while the advertising database stores information on advertising content and related products.

[0834] Specific behavior:

[0835] Input: User database, Ad database

[0836] Data processing: The server uses SQL queries to retrieve user profile information and advertising information.

[0837] Output: User profile data, advertising data

[0838] Step 2: Ad selection using generative AI models

[0839] Based on the acquired data, the server sends prompt text to the generative AI model, causing it to generate the optimal advertisement.

[0840] Specific behavior:

[0841] Input: User profile data, advertising data

[0842] Data processing: The server sends a prompt to the generative AI model saying, "Please generate the most appropriate ad based on the user profile." The generative AI model analyzes this and selects the most relevant ad.

[0843] Output: Selected advertising data

[0844] Step 3: Displaying ads on user devices

[0845] The server sends the advertising data received from the generative AI model to the user's device, which displays the received advertisement along with the "Already Have It" button.

[0846] Specific behavior:

[0847] Input: Selected advertising data

[0848] Data processing: The server sends the generated advertising data to the user's device as an HTTP response.

[0849] Output: Advertisement display screen

[0850] Step 4: Press the "I already have it" button

[0851] If a user sees an advertisement for a product that they already own, they press the "I already own it" button, and the user's device sends this information to the server.

[0852] Specific behavior:

[0853] Input: "I already have it" button press data

[0854] Data processing: The user device sends a POST request to the server by pressing a button.

[0855] Output: Send "I already have it" information

[0856] Step 5: Analyze information and update your profile

[0857] The server analyzes the information it already has and updates the user's profile.

[0858] Specific behavior:

[0859] Input: Information you already have

[0860] Data processing: The server updates the user database profile based on the received data.

[0861] Output: Updated profile data

[0862] Step 6: Next ad selection by generative AI model

[0863] The generative AI model analyzes the new profile and selects relevant product ads for the next ad display.

[0864] Specific behavior:

[0865] Input: Updated profile data

[0866] Data processing: The server sends a prompt to the generative AI model saying, "Please generate an ad based on a new profile." The generative AI model analyzes this and selects the next ad.

[0867] Output: Ad data to be displayed next time

[0868] Step 7: Acquire and analyze emotion data

[0869] The emotion engine analyzes the user's voice, facial expressions, and text input while the ad is being displayed to obtain emotional data.

[0870] Specific behavior:

[0871] Input: User voice, facial expressions, and text data

[0872] Data processing: The emotion engine analyzes these input data and generates emotion data.

[0873] Output: Emotion data

[0874] Step 8: Use emotion data and update your profile

[0875] The emotion engine transmits the acquired emotion data to the server, which then updates the corresponding user profile in the user database.

[0876] Specific behavior:

[0877] Input: Emotion data

[0878] Data processing: The server stores the emotion data in the user database and updates the user profile.

[0879] Output: Updated profile data

[0880] Step 9: Select and deliver relevant ads

[0881] The server selects an optimized advertisement for the next display based on the data obtained by the generative AI model and sends it to the user's device.

[0882] Specific behavior:

[0883] Input: Updated profile data and emotion data

[0884] Data processing: The server sends the prompt text to the generative AI model again to generate the optimal ad. The ad data is saved to be displayed the next time the user accesses the site.

[0885] Output: Optimized advertising data

[0886] Step 10: Optimized Ad Display

[0887] The selected advertisement is sent from the server to the user's device, which displays the relevant advertisement on the user's next visit, and records the user's clicks and interactions.

[0888] Specific behavior:

[0889] Input: Optimized advertising data

[0890] Data processing: The server sends the advertising data to the user's device, and the user's device displays the received advertisement.

[0891] Output: Advertisement display screen

[0892] Through these steps, the system can deliver optimal advertisements that reflect the user's ownership status and emotions.

[0893] (Application example 2)

[0894] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0895] Conventional advertising systems deliver advertisements based on a user's purchasing behavior and browsing history, but they are inadequate in responding to user emotions or when the user already owns a product. As a result, advertisements that do not interest the user or advertisements related to products the user already owns are often displayed, reducing the effectiveness of the advertisements. The objective of this invention is to maximize the effectiveness of advertisements by utilizing user emotional data to display more personalized advertisements.

[0896] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for pressing an "Already Have It" button to indicate that the user already owns a displayed advertisement, means for transmitting information that the "Already Have It" button has been pressed to the server, means for updating the user's profile based on the information received by the server, means for selecting a relevant new advertisement based on the user's profile using a generative AI model, means for using an emotion engine that acquires and analyzes user emotion data, means for the generative AI model to optimize advertisement display based on the emotion data analyzed by the emotion engine, and means for delivering the selected new advertisement to the user from the next time onwards. This makes it possible to optimize advertisement display based on user emotion data.

[0897] The "I Already Have It" button is an interface that users can press to indicate that they already own a product.

[0898] A "server" is a computer system that manages user behavioral and emotional data and delivers advertisements.

[0899] A "user profile" is a collection of information about an individual user, including the user's purchasing history, behavioral data, emotional data, etc.

[0900] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and generate and select optimal advertisements.

[0901] An "emotion engine" is a system that analyzes a user's voice, facial expressions, and text input to obtain emotional data.

[0902] An "advertising database" is a database that stores advertising information provided by advertisers.

[0903] "Ad display optimization" is the process of selecting the most appropriate ads to display based on user profile and emotional data.

[0904] "Relevant new ads" are ads that are predicted to be of interest to the user based on the user's profile.

[0905] This invention is a system for displaying advertisements related to products that a user already owns and optimizing the advertisements based on emotion data. The system's main components include a server, a user terminal, a user database, a generative AI model, an emotion engine, and an advertisement database.

[0906] System configuration

[0907] The system is configured as follows:

[0908] 1. Server:

[0909] The server is a computer system that manages user behavior data and purchase history and delivers advertisements.

[0910] The server maintains a user database and stores the received "already held" information and emotional data.

[0911] 2. User Device:

[0912] The user device is a device such as a smartphone or tablet that displays the advertisement and displays the "I Already Have It Button."

[0913] The user device displays advertisements, records user interactions, and can also collect voice and facial expression data.

[0914] 3. User Database:

[0915] The user database is a database that stores and manages user profiles, emotional data, purchase history, and browsing history.

[0916] 4. Generative AI Models:

[0917] A generative AI model is an algorithm that uses machine learning to analyze user profile data and generate and select optimal ads.

[0918] 5. Emotion Engine:

[0919] The emotion engine is a system that analyzes the user's voice, facial expressions, and text input to obtain emotional data. This part can use external services such as the Emotion API.

[0920] 6. Advertising Database:

[0921] The advertisement database is a database that stores advertisement information provided by advertisers.

[0922] What the program does

[0923] 1. Data Collection:

[0924] The user device collects information on when the "I already have it" button is pressed, voice data, facial expression data, and purchase history, and sends the collected data to the server.

[0925] 2. Data transmission:

[0926] The information collected on the user's device is sent in real time to a server, which analyzes and stores it.

[0927] 3. Emotion analysis:

[0928] Once the voice and facial expression data arrives at the server, it is analyzed by an emotion engine, which uses Microsoft Azure's Emotion API and other emotion analysis tools.

[0929] 4. Ad generation using generative AI models:

[0930] Based on the sentiment data and user profile data, a generative AI model, such as GPT-4 or BERT, selects the optimal ad.

[0931] 5. Advertising:

[0932] The selected advertisement is delivered from the server to the user's device and displayed the next time the user uses the device. The advertisement also includes an "Already Have It" button.

[0933] 6. Use of Feedback:

[0934] After the ad is displayed, the user's emotional data is collected and analyzed again, and this information is used to select the next ad.

[0935] Specific examples

[0936] As a concrete example, we will show the implementation of a function related to bicycle-related products.

[0937] Example 1: When a user clicks on an ad for a bicycle and presses the "I Already Have It" button, the server updates the user's profile. From then on, ads for bicycle helmets and lights will be displayed based on the user's emotional data, increasing the likelihood of the user being interested.

[0938] Prompt Sentence Examples

[0939] Below are some example prompts to use with generative AI models (such as GPT-4):

[0940] “Imagine a user already owns a bike. Generate ads for relevant bike accessories (e.g., helmets, lights, custom parts, etc.) for this user. Make the ads interesting by taking into account the user’s sentiment data.”

[0941] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0942] Step 1:

[0943] The user's device displays an advertisement. When the user presses the "I Already Have It" button in response to the displayed advertisement, that information is entered into the user's device. The user's device then sends this input information to the server. The specific operations performed on the device side are capturing the button press event and generating the corresponding data packet. The output is the "I Already Have It" button press information.

[0944] Step 2:

[0945] The server receives the "I already have it" information sent from the user's device. The server analyzes this information and processes the data to update the user's profile. Specifically, it adds a new field to the corresponding user profile in the user database and updates the product status. The input is the "I already have it" information, and the output is the updated user profile.

[0946] Step 3:

[0947] The server uses an emotion engine to collect the user's voice data and facial expression data in response to the currently displayed advertisement. The user's device sends this data to the server. The specific operation of emotion data is to capture the user's tone of voice and facial expression using the device's microphone and camera. The input is voice data and facial expression data, and the output is emotion data.

[0948] Step 4:

[0949] The server analyzes the received emotional data using an emotion engine. This analysis uses commercially available emotion analysis software. Specifically, the server identifies the user's emotion (e.g., joy, disgust) from the tone of voice and converts it into numerical data. The input is the collected emotional data, and the output is the analyzed emotional result data.

[0950] Step 5:

[0951] The server inputs the updated user profile and emotional result data into the generative AI model. The generative AI model analyzes this data and selects the relevant ad to display next. At this stage, the machine learning algorithm generates new ad copy using prompt text based on the user's purchase history and emotional data. The input is the updated user profile and emotional result data, and the output is the generated ad copy.

[0952] Step 6:

[0953] The server sends the generated ad copy to the user's device. The next time the user opens the app, the new ad will be displayed. The specific operation performed on the server side is to generate and send packets of ad data. The output is the ad data to be displayed next time.

[0954] Step 7:

[0955] The user's device displays a new ad and records the user's clicks and interactions, providing useful feedback data for future ad displays. The specific actions performed by the device are displaying the ad and capturing the user's interactions. The output is user interaction data.

[0956] The above are the specific processing steps for carrying out the present invention. The operation methods of the hardware and software used in each step and the data flow will become clear, which will help in putting the present invention into practical use.

[0957] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0958] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0959] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0960] [Third embodiment]

[0961] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0962] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0963] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0964] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0965] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0966] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0967] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0968] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0969] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0970] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0971] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0972] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0973] preface

[0974] The present invention relates to an advertising recommendation system for maximizing advertising effectiveness. In particular, the system has the function of displaying advertisements related to products that a user already owns. The system is implemented using a server, a terminal, and a generative AI model.

[0975] System Configuration

[0976] The main components of the system are:

[0977] server

[0978] User Device

[0979] User Database

[0980] Generative AI Models

[0981] Advertising Database

[0982] server

[0983] The server is a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[0984] User Device

[0985] The user device is the device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent from the server and has the ability to display the "I Already Have It" button.

[0986] User Database

[0987] A user database is a database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[0988] Generative AI Models

[0989] The generative AI model is an algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavior data and generates relevant product ads to optimize the next ad display.

[0990] Advertising Database

[0991] The advertisement database is a database that stores advertisement information provided by advertisers, including the advertisement content, target products, related products, etc.

[0992] Program processing

[0993] Advertisement display

[0994] The server selects the most suitable advertisement based on the user profile and sends the advertisement data to the user's device.

[0995] The user's device will display the received advertisement along with the "Already Have It" button.

[0996] Pressing the "I already have it" button

[0997] When a user presses the "I already have it" button for a displayed advertisement, the user's device sends this information to the server.

[0998] Analyzing information and updating your profile

[0999] The server updates the user's profile based on the information it receives and already has.

[1000] The generative AI model analyzes the updated profile and selects relevant ads to display next time.

[1001] Selection and delivery of relevant advertisements

[1002] The selected relevant advertisements are sent from the server to the user's device and displayed the next time the user accesses the site.

[1003] The user's device displays relevant ads and records the user's clicks and interactions.

[1004] Specific examples

[1005] Example 1: Purchasing a bicycle

[1006] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button.

[1007] The user terminal transmits this information to the server.

[1008] The server analyzes this information using a generative AI model and updates the profile so that the next time an ad is displayed, it will serve ads for bicycle-related accessories (helmets, lights, custom parts, etc.).

[1009] The next time the server sends an advertisement to the terminal, it may be for bicycle accessories, which may pique the user's interest.

[1010] Example 2: Cookware

[1011] A user clicks on an advertisement for cooking equipment (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button.

[1012] The user device sends the information it already has to the server.

[1013] The server uses a generative AI model to analyze this information and update the profile to show ads for other products in the cookware series (pots, tongs, spatulas, etc.) in future visits.

[1014] The next time the server sends an advertisement to the terminal, it will be about another product of cooking equipment, which may be of interest to the user.

[1015] As described above, the present invention can maximize the effectiveness of advertising and improve advertisers' ROI by displaying only advertisements that users truly need.

[1016] The processing flow will be explained below.

[1017] Step 1:

[1018] When the ad recommendation system starts, the server loads the ad database and the user database, obtains the latest user information and ad information from the database, and initializes the system.

[1019] Step 2:

[1020] The server retrieves target user profiles and behavioral data from a user database, including user purchase history, click history, and viewing history.

[1021] Step 3:

[1022] The server uses a generative AI model to analyze the user's profile and behavioral data and select the most appropriate ad for that user. The AI ​​model then scores the best ad based on past data.

[1023] Step 4:

[1024] The server sends the selected advertisement and the advertisement data, including the "I Already Have It Button," to the user's device. This advertisement data includes the content of the advertisement to be displayed and a script for recording the user's interaction.

[1025] Step 5:

[1026] The device analyzes the advertising data received from the server and displays the advertisement and the "Already Have It Button" on the user's screen. The advertisement is optimized for the user's viewing environment and displayed in a way that is easy for the user to see.

[1027] Step 6:

[1028] The user checks the displayed advertisement and presses the "I already have it" button if they already own it. This action records the user's interaction.

[1029] Step 7:

[1030] The device sends information to the server that the user pressed the "Already Have It Button," including the date and time of the press, the associated advertising ID, and the user ID.

[1031] Step 8:

[1032] The server updates the user profile in the user database based on the received "already owned" information, specifically by marking the items that the user already owns.

[1033] Step 9:

[1034] The server then uses the generative AI model to analyze the updated user profile and select the next ad to display, taking into account the user's "already owned" information and selecting ads for related accessories and complementary products.

[1035] Step 10:

[1036] The server prepares a list of newly selected relevant ads and sends it to the device for subsequent ad delivery. This list will be used the next time the user accesses the site.

[1037] Step 11:

[1038] The next time the user views an ad, the device will display relevant ads based on the new ad list sent from the server, which are likely to increase click-through rates and purchase rates because they are selected based on the user's interests and needs.

[1039] Step 12:

[1040] How the user responds to the new ad is recorded and this information is fed back to the server, which further influences future ad presentations.

[1041] By following these steps, it is possible to display ads that best fit the user's needs, maximizing advertising effectiveness and improving advertisers' ROI.

[1042] Example 1

[1043] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1044] Conventional advertising systems often displayed ads for products that users already owned, which meant they were unable to respond to users' interests and needs. This resulted in lower ad click-through rates and conversion rates, and lower advertiser ROI. In addition, there was a lack of a way to reflect product information that users already owned in the ad display selection process, making it difficult to deliver effective ads.

[1045] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1046] In this invention, the server includes a means for acquiring a user profile and behavioral data from a user database, a means for inputting a prompt sentence into a generative AI model based on the acquired data to generate an optimal advertisement, and a means for transmitting the advertisement data obtained from the generative AI model to a user terminal, thereby making it possible to exclude products already owned by the user and display advertisements that focus on related products.

[1047] The "server" is a device that retrieves user profiles and behavioral data from a user database and uses that data to generate optimal advertisements for the generative AI model.

[1048] A "user terminal" is a device that receives and displays advertising data sent from the server and provides users with interactions such as the "I Already Have It Button."

[1049] "User database" refers to data storage that stores and manages user behavioral data and profile information, such as purchase history, click history, and browsing history.

[1050] The "generative AI model" is an algorithm that selects and generates optimal advertisements based on acquired user data, and generates relevant advertisement information by inputting a prompt text.

[1051] A "prompt" is text data that is input into a generative AI model, and is an instruction to suggest optimal advertisements based on the user's profile and behavioral data.

[1052] The "Already Owned Button" is a user interface element that notifies the server that the user already owns the product in response to a displayed advertisement.

[1053] This invention relates to an advertising recommendation system that maximizes advertising effectiveness by displaying advertisements related to products that users already own. In particular, the system uses a generative AI model based on user behavior data and purchase history to generate optimal product advertisements and provide them to users.

[1054] System Configuration

[1055] The system mainly consists of the following components:

[1056] server

[1057] User Device

[1058] User Database

[1059] Generative AI Models

[1060] Advertising Database

[1061] server

[1062] The server has the following features:

[1063] 1. Retrieve user profile and behavioral data from your user database.

[1064] 2. Based on the acquired data, a prompt sentence is input into the generative AI model to generate the optimal ad.

[1065] 3. The advertising data obtained from the generative AI model is sent to the user's device.

[1066] User Device

[1067] The user terminal has the following features:

[1068] 1. Receives advertising data sent from the server and displays it on the screen.

[1069] 2. Display the "I Already Have It" button and record the user's interaction.

[1070] 3. When the user presses the "I already have it" button, the information is sent to the server.

[1071] User Database

[1072] The user database stores and manages user behavioral data and profile information, such as purchase history, click history, and browsing history, and provides data for selecting the most suitable advertisements for users.

[1073] Generative AI Models

[1074] The generative AI model uses machine learning algorithms to analyze user profile data and generate optimal ads. By inputting prompt text, the generative AI model generates ads that are likely to attract the user's attention.

[1075] Advertising Database

[1076] The advertisement database stores advertisement information provided by advertisers, including the content of the advertisement, target products, related products, etc.

[1077] Specific examples

[1078] Example 1: Purchasing a bicycle

[1079] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button. The user's device sends this information to the server. The server analyzes this information using a generative AI model and updates the user's profile so that the next time ads are displayed, they will be for bicycle accessories (e.g., helmets, lights, custom parts). The next time the server sends an ad to the user's device, it will be for bicycle accessories, which are more likely to interest the user.

[1080] Example 2: Cookware

[1081] A user clicks on an ad for cookware (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button. The user's device sends the "I already have it" information to the server. The server analyzes this information using a generative AI model and updates the profile so that ads for other products in the cookware series (for example, pots, tongs, and spatulas) will be displayed in the future. The next ad the server sends to the user's device will be for a different cookware product and is more likely to interest the user.

[1082] This invention reflects user profiles and behavioral data in real time, allowing for the display of highly relevant ads and improving advertisers' ROI. Using a generative AI model, it is possible to accurately capture user interests and reflect them in subsequent ad displays. This allows for less intrusive ad displays for users.

[1083] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1084] Step 1:

[1085] The server retrieves user profile and behavioral data from a user database. The user ID is used as input. Specifically, it executes an SQL query to retrieve data from the database, which is then output. Based on the retrieved data, the server prepares the basis for determining which ad is most suitable.

[1086] Step 2:

[1087] The server inputs a prompt sentence into the generative AI model based on the acquired data, generating the optimal ad. User profile and behavioral data are used as input. Specifically, the server generates and inputs a prompt sentence such as "Please suggest ads that this user is likely to be interested in" into the generative AI model. The generative AI model then analyzes the prompt sentence and outputs the optimal ad.

[1088] Step 3:

[1089] The server sends the advertising data obtained from the generative AI model to the user device. The optimal advertising data output by the generative AI model is used as input. Specifically, the advertising data is sent using an HTTP request. The advertising data is sent to the user device, and this becomes the output.

[1090] Step 4:

[1091] The user's device displays the received advertising data along with the "I already have it button." The advertising data sent from the server is used as input. Specifically, the process of displaying the advertisement and button is carried out using front-end technologies (HTML, CSS, JavaScript). The output is the advertisement and button displayed on the screen.

[1092] Step 5:

[1093] The user presses the "I already have it" button for an ad. The user's click action is used as input. Specifically, when the user clicks the button, a click event occurs, which becomes the output.

[1094] Step 6:

[1095] The user device catches the button click event and sends that information to the server. The button click event is used as input. Specifically, the button click event is detected using JavaScript and the information is sent to the server using an AJAX request. The information sent to the server becomes the output.

[1096] Step 7:

[1097] When the server receives the "already have" information, it updates the corresponding user profile in the user database. The "already have" information is used as input. Specifically, an SQL query is used to update the user profile data. The update to the user database is the output.

[1098] Step 8:

[1099] The generative AI model analyzes the newly updated user data and selects the relevant ad to display next. The updated user data is used as input. Specifically, it generates and inputs a prompt such as "Please suggest ads for related products" based on the new profile data. The generated relevant ad is the output.

[1100] Step 9:

[1101] The server sends the selected relevant advertising data to the user device. The relevant advertising data selected by the generative AI model is used as input. Specifically, the relevant advertising data is sent using an HTTP request. The data is sent to the user device as output.

[1102] Step 10:

[1103] The user's device displays relevant ads the next time they access the site and records the user's clicks and interactions. The input is the relevant ad data sent from the server. Specifically, front-end technology is used to display ads, and if the user clicks on an ad, that information is sent back to the server. The output is a record of the user's interactions.

[1104] (Application example 1)

[1105] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1106] Conventional advertising systems have not provided sufficient methods for effectively displaying advertisements related to products that users already own. As a result, irrelevant or overlapping advertisements are displayed to users, reducing advertising effectiveness. Furthermore, there is a need for a method to increase the relevance of advertisements by dynamically displaying advertisements related to products that users already own, especially in new shopping experiences using virtual stores and smart devices.

[1107] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1108] In this invention, the server includes: means for a user to press an "Already Have It" button to indicate that the user already owns the displayed advertisement; means for transmitting information that the "Already Have It" button has been pressed to the server; means for updating the user's profile based on the information received by the server; means for selecting a relevant new advertisement based on the user's profile using a generative AI model; means for delivering the selected new advertisement to the user from the next time onward; means for dynamically displaying advertisements related to products owned by the user in a virtual store; and means for recognizing the user through the smart glasses and requesting advertisements based on the user's profile data. This makes it possible to dynamically display advertisements that are highly relevant to the user, thereby maximizing advertising effectiveness.

[1109] 1. "An 'I Already Have It' button that allows users to indicate that they already own the product when presented with an ad" is an interface element that allows users to indicate that they already own the product when presented with an ad.

[1110] 2. "Information indicating that the 'I already have it' button was pressed" refers to data generated when a user presses the 'I already have it' button, and is information indicating that the user already owns the product.

[1111] 3. "Means for transmitting to the server" means a communication system for transmitting data from the user terminal to the server.

[1112] 4. "Means for updating a user's profile based on information received by the server" refers to a function that analyzes the information received by the server and keeps the profile information up to date based on the user's purchasing history and behavioral data.

[1113] 5. "Generative AI model" is an artificial intelligence algorithm that analyzes user profile data and behavioral data to generate and select optimal advertisements.

[1114] 6. "Means for selecting new relevant ads" means a function that uses a generative AI model to select new ads that are highly relevant to a user based on the user's profile.

[1115] 7. "Means for delivering the selected new advertisement to the user from the next time onwards" means a system in which the server sends the selected advertisement to the user's terminal so that it will be displayed the next time the user accesses the site.

[1116] 8. "Virtual store" means an online shopping platform where users can browse and purchase products via the Internet.

[1117] 9. "Means for dynamically displaying advertisements related to products owned by a user" means a system or function for displaying new advertisements related to products already owned by a user in real time.

[1118] 10. "Smart glasses" are wearable devices that have augmented reality or virtual reality capabilities and display information to the user.

[1119] 11. "Means for recognizing users and requesting advertisements based on their profile data" means the functionality of devices such as smart glasses to identify users and retrieve appropriate advertisements based on their user profile.

[1120] This invention is an advertisement recommendation system that updates a user's profile and displays new related advertisements when the user presses an "I already have it" button to indicate that they already own the product displayed in a virtual store. Specifically, it is composed of the following elements:

[1121] server

[1122] The server acts as the central management system and performs the following main functions:

[1123] 1. Receiving information and updating user profile: The server receives information from the user device when the "I already have it" button is pressed. Based on the received information, the server updates the user database and clarifies the product information the user owns.

[1124] 2. Use of generative AI models: The server uses generative AI models to analyze user profile data and behavioral data and select new ads that are highly relevant to the user.

[1125] 3. Advertisement delivery: The selected new advertisement is delivered to the user from the next time onwards. The server displays the advertisement at the appropriate time when the user accesses the website.

[1126] User Device

[1127] The user device is the device on which the user sees and interacts with the advertisement, typically a pair of smart glasses or a smartphone.

[1128] 1. Displaying Ads: While users are browsing the virtual store, relevant ads sent from the server are displayed, including an "Already Have It" button.

[1129] 2. Information transmission: When the user presses the "I already have it" button, the information is transmitted to the server.

[1130] 3. User Recognition: Recognizing users and obtaining their profiles through devices such as smart glasses using facial recognition technology and other identification technologies.

[1131] Generative AI Models

[1132] A generative AI model is a system that uses machine learning algorithms to analyze user profile and behavioral data, dynamically generating and selecting the most relevant ads.

[1133] 1. Profile Analysis: Analyzes updated user profile data to identify user interests and purchasing patterns.

[1134] 2. Ad generation: Generate relevant ads based on user profiles.

[1135] Advertising Database

[1136] It is a database that works with a server and stores advertisements for use by generative AI models, including the content of the advertisement, target audience, related products, etc.

[1137] Specific Examples

[1138] 1. A user is browsing the bicycle accessories section in a "virtual store":

[1139] User recognition: The smart glasses recognize User A and identify his User ID as 'd741f2'.

[1140] Get profile data: The server gets the profile data of User A from the user database and finds out that the user owns an expensive bicycle.

[1141] Ad request and display: The server uses the generative AI model to select an ad for an accessory (e.g., bike light) that is highly relevant to User A and displays it on the smart glasses.

[1142] Pressing the "I already have it" button: User A sees the advertisement and presses the "I already have it" button because he already owns the light. This information is sent to the server and reflected in User A's profile.

[1143] Prompt Sentence Examples

[1144] "User d741f2 has clicked 'already_have' on ad_id 12345. Update their profile to show related bicycle accessories next time."

[1145] This system will dynamically display ads that are highly relevant to users, maximizing advertising effectiveness.

[1146] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1147] Step 1:

[1148] User Awareness

[1149] The smart glasses recognize the user. As input, they use facial recognition data from the smart glasses' camera. They use a facial recognition API (e.g., Face++, Azure Face API, etc.) to identify the user ID. The output is the identified user ID (e.g., d741f2).

[1150] Step 2:

[1151] Profile Data Acquisition

[1152] The server retrieves profile data corresponding to the identified user ID from a user database. It executes a database query using the user ID as input. The output is profile data including the user's purchasing history and behavioral data.

[1153] Step 3:

[1154] Ad request

[1155] The server sends the data necessary to generate advertisements to the generative AI model based on the user's profile data. The generative AI model analyzes the input profile data and selects the advertisements most relevant to the user. The output is the selected advertisement data.

[1156] Step 4:

[1157] Advertisement display

[1158] The user device (smart glasses) displays the advertising data received from the server, which includes the advertising content and an "I already have it" button. The input is the advertising data, and the output is the advertisement displayed to the user.

[1159] Step 5:

[1160] Pressing the "I already have it" button

[1161] The user presses the "I already have it" button for the displayed ad. The input is the user interaction, which triggers a button press event. The output is the transmission of this information from the device to the server.

[1162] Step 6:

[1163] Information transmission

[1164] The user terminal sends information that the "I already have it" button has been pressed to the server. The button press event data is used as input, and the information is transmitted to the server via a data transmission protocol. The output is the user's "I already have it" information received by the server.

[1165] Step 7:

[1166] Profile Update

[1167] The server updates the user database based on the "already-held" information it receives. The input is the "already-held" information, and updates the user's profile data accordingly. The output is the updated user profile.

[1168] Step 8:

[1169] Next ad selection

[1170] The server requests the generative AI model to select future advertisements based on the updated user profile. The generative AI model analyzes the latest profile data and selects future advertisements to display. The input is the updated profile data, and the output is future advertisement data.

[1171] Step 9:

[1172] Next ad delivery

[1173] The server delivers the selected new advertisement the next time the user visits the store. The input is the new advertisement data, and the output is the advertisement to be displayed the next time the user visits the store. This process is triggered the next time the user visits the virtual store.

[1174] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1175] preface

[1176] This invention relates to an advertising recommendation system for maximizing advertising effectiveness. In particular, this system has the function of displaying advertisements related to products that a user already owns, and also combines an emotion engine that recognizes the user's emotions. This system is implemented using a server, a terminal, a generative AI model, and an emotion engine.

[1177] System Configuration

[1178] The main components of the system are:

[1179] server

[1180] User Device

[1181] User Database

[1182] Generative AI Models

[1183] Emotion Engine

[1184] Advertising Database

[1185] server

[1186] The server is a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[1187] User Device

[1188] The user device is the device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent from the server and has the ability to display the "I Already Have It" button.

[1189] User Database

[1190] A user database is a database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[1191] Generative AI Models

[1192] The generative AI model is an algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavioral and emotional data to generate relevant product ads to optimize the next ad display.

[1193] Emotion Engine

[1194] The emotion engine is a system for recognizing user emotions and including emotional data in user profiles. The emotion engine analyzes emotional data from users' voices, facial expressions, text inputs, etc., and provides this information to generative AI models.

[1195] Advertising Database

[1196] The advertisement database is a database that stores advertisement information provided by advertisers, including the advertisement content, target products, related products, etc.

[1197] Program processing

[1198] Advertisement display

[1199] The server selects the most suitable advertisement based on the user profile and sends the advertisement data to the user's device.

[1200] The user's device will display the received advertisement along with the "Already Have It" button.

[1201] Pressing the "I already have it" button

[1202] When a user presses the "I already have it" button for a displayed advertisement, the user's device sends this information to the server.

[1203] Analyzing information and updating your profile

[1204] The server updates the user's profile based on the information it receives and already has.

[1205] The generative AI model analyzes the updated profile and selects relevant ads to display next time.

[1206] Acquiring emotion data

[1207] The emotion engine analyzes the user's voice, facial expressions, and text input to obtain emotional data about the ad they are viewing, which indicates whether the user is interested in the ad or dislikes it.

[1208] Use of Emotional Data

[1209] The emotion engine transmits the acquired emotion data to the server, which then updates the corresponding user profile in the user database.

[1210] The generative AI model analyzes the updated profile and sentiment data to further optimize the next ad shown.

[1211] Selection and delivery of relevant advertisements

[1212] The selected relevant advertisements are sent from the server to the user's device and displayed the next time the user accesses the site.

[1213] The user's device displays relevant ads and records the user's clicks and interactions.

[1214] Specific examples

[1215] Example 1: Bicycle purchase and sentiment data

[1216] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button.

[1217] The user terminal transmits this information to the server.

[1218] The server analyzes this information using a generative AI model and updates the profile so that the next time an ad is displayed, it will serve ads for bicycle-related accessories (helmets, lights, custom parts, etc.).

[1219] The emotion engine analyzes the user's emotions regarding the ad being displayed, and if it detects a positive emotion, it sets the display of related ads from the next time onwards. The next ad sent by the server to the device may be for bicycle accessories, which may pique the user's interest.

[1220] Example 2: Cookware and Emotional Data

[1221] A user clicks on an advertisement for cooking equipment (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button.

[1222] The user device sends the information it already has to the server.

[1223] The server uses a generative AI model to analyze this information and update the profile to show ads for other products in the cookware series (pots, tongs, spatulas, etc.) in future visits.

[1224] If the emotion engine detects a happy emotion from the user's voice or facial expression while viewing an ad, it will prioritize displaying related ads the next time. The next ad sent by the server to the device may be about another product in the same series of cookware, which may pique the user's interest.

[1225] As described above, the present invention can maximize the effectiveness of advertising and improve advertisers' ROI by displaying advertisements based on the user's needs and emotions.

[1226] The processing flow will be explained below.

[1227] Step 1:

[1228] When the ad recommendation system starts, the server loads the ad database and the user database, obtains the latest user information and ad information from the database, and initializes the system.

[1229] Step 2:

[1230] The server retrieves target user profiles and behavioral data from a user database, including user purchase history, click history, and viewing history.

[1231] Step 3:

[1232] The server uses a generative AI model to analyze the user's profile and behavioral data and select the most appropriate ad for that user. The AI ​​model then scores the best ad based on past data.

[1233] Step 4:

[1234] The server sends the selected advertisement and the advertisement data, including the "I Already Have It Button," to the user's device. This advertisement data includes the content of the advertisement to be displayed and a script for recording the user's interaction.

[1235] Step 5:

[1236] The device analyzes the advertising data received from the server and displays the advertisement and the "Already Have It Button" on the user's screen. The advertisement is optimized for the user's viewing environment and displayed in a way that is easy for the user to see.

[1237] Step 6:

[1238] The user checks the displayed advertisement and presses the "I already have it" button if they already own it. This action records the user's interaction.

[1239] Step 7:

[1240] The device sends information to the server that the user pressed the "Already Have It Button," including the date and time of the press, the associated advertising ID, and the user ID.

[1241] Step 8:

[1242] The server updates the user profile in the user database based on the received "already owned" information, specifically by marking the items that the user already owns.

[1243] Step 9:

[1244] The server then uses the generative AI model to analyze the updated user profile and select the next relevant ad to display, taking into account the user's "already owned" information and selecting ads for related accessories and complementary products.

[1245] Step 10:

[1246] The server prepares a list of newly selected relevant ads and sends it to the device for subsequent ad delivery. This list will be used the next time the user accesses the site.

[1247] Step 11:

[1248] The next time the user views an ad, the device will display relevant ads based on the new ad list sent from the server, which are likely to increase click-through rates and purchase rates because they are selected based on the user's interests and needs.

[1249] Step 12:

[1250] How the user responds to the new ad is recorded and this information is fed back to the server, which further influences future ad presentations.

[1251] Step 13:

[1252] The emotion engine analyzes the user's voice, facial expressions, and text input while viewing the advertisement to obtain the user's emotion data, which indicates the user's emotional state, such as joy, interest, or disgust.

[1253] Step 14:

[1254] The emotion engine sends the acquired emotion data to the server, which details the user's emotional response to the advertisement.

[1255] Step 15:

[1256] The server stores the emotion data in a user database and provides it to a generative AI model, which uses this information to select more relevant ads the next time the user sees them.

[1257] Step 16:

[1258] The generative AI model analyzes user profile and sentiment data to further optimize the ads that will be shown next, prioritizing ads that users responded positively to and ads for related products based on sentiment data.

[1259] Step 17:

[1260] The server then sends the selected new ad to the device for subsequent ad delivery. Through this process, it becomes possible to provide ads that best fit the user's needs and emotions.

[1261] Specifically, if the emotion engine detects the emotion of joy while a user is viewing a food advertisement, the server will select and deliver the same brand of food or recipe video for the next advertisement. In this way, it becomes possible to deliver advertisements that reflect the user's emotional state, maximizing the effectiveness of the advertisement.

[1262] Example 2

[1263] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1264] Conventional ad recommendation systems often display unnecessary ads for products that users already own, reducing the effectiveness of advertising. Additionally, there is a lack of systems that optimize ads by taking into account user emotional data, making it difficult to accurately grasp users' interests.

[1265] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1266] In this invention, the server includes means for allowing a user to press an "Already Have It" button to indicate that the user already owns the displayed advertisement, means for transmitting information that the "Already Have It" button has been pressed to the server, means for updating the user's profile based on the information received by the server, means for selecting a relevant new advertisement based on the user's profile using a generative AI model, means for delivering the selected new advertisement to the user from the next time onwards, means for acquiring user emotion data using an emotion engine, and means for optimizing the advertisement to be displayed next by the generative AI model using the emotion data. This enables optimal advertisement delivery that reflects the user's ownership status and emotions.

[1267] The "I Already Have It Button" is an interface button that users can use to indicate that they already own the displayed advertisement.

[1268] "Server" refers to a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements.

[1269] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and select and generate optimal advertisements.

[1270] An "emotion engine" is a system that analyzes emotional data from a user's voice, facial expressions, text input, etc., and includes this information in the user profile.

[1271] A "profile" is an individual data set that includes a user's purchasing history, browsing history, click history, emotional data, etc.

[1272] An "advertising database" is a database that stores advertising information provided by advertisers.

[1273] "User database" refers to a database for storing and managing user profiles and behavioral data.

[1274] "Means for transmitting information" refers to a communication means for transmitting data from a user terminal to a server.

[1275] The "means for receiving information" refers to a communication means by which the server receives data sent from the user terminal.

[1276] A "profile update method" is a process for updating a user's profile in a user database with new information.

[1277] "Means for delivering advertisements" refers to the process by which the server sends the advertisement data generated by the server to the user terminal so that it can be displayed.

[1278] "Means for selecting new advertisements" means a process for using a generative AI model to select relevant new advertisements based on a user's profile.

[1279] MODE FOR CARRYING OUT THE INVENTION

[1280] This invention relates to an advertising recommendation system for maximizing advertising effectiveness. This system displays advertisements related to products that a user already owns and has the function of recognizing the user's emotions to optimize the advertisements. Specifically, this system is implemented using a server, a terminal, a generative AI model, and an emotion engine.

[1281] System Configuration

[1282] The main components of this system are:

[1283] Server: A computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[1284] User Device: The device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent by the server and has the ability to display the "I Already Have It" button.

[1285] User database: A database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[1286] Generative AI model: An algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavioral and emotional data to generate relevant product ads to optimize the next ad display.

[1287] Emotion engine: A system for recognizing user emotions and including emotional data in user profiles. The emotion engine analyzes emotional data from users' voices, facial expressions, text inputs, etc., and provides this information to generative AI models.

[1288] Advertising database: A database that stores advertising information provided by advertisers, including the content of the advertisement, target products, related products, etc.

[1289] Specific examples

[1290] Example 1: Bicycle purchase and sentiment data

[1291] A user clicks on a bicycle ad and, because they already own the product, presses the "I already have it" button. The user's device sends this information to the server. The server analyzes this information using a generative AI model and updates the profile so that the next time ads are displayed, ads for bicycle-related accessories (helmets, lights, custom parts, etc.) are delivered. The emotion engine analyzes the user's emotions regarding the currently displayed ad, and if a positive emotion is detected, it sets the device to display related ads in the future. The next ad the server sends to the device will be for bicycle accessories, which may pique the user's interest.

[1292] Example 2: Cookware and Emotional Data

[1293] A user clicks on an ad for cooking utensils (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button. The user's device sends the "I already have it" information to the server. The server analyzes this information using a generative AI model and updates the profile so that ads for other products in the cooking utensil series (pots, tongs, spatulas, etc.) will be displayed from the next time onwards. If the emotion engine detects an emotion of joy from the user's voice or facial expression regarding the currently displayed ad, it will prioritize displaying related ads next time. Next time, the server will send the generated advertisement for cooking utensils to the user's device.

[1294] Prompt Sentence Examples

[1295] Examples of prompts to be input to a generative AI model include:

[1296] "If a user clicks on an ad for a bicycle and hits the 'I already have it' button because they already own one, generate an ad for a related bicycle accessory."

[1297] "When a user clicks on an ad for a frying pan and presses the 'I already have it' button, generate an ad for other cookware in the same series."

[1298] "Next time, show me the ad where the sentiment engine detects a positive sentiment in the user."

[1299] With the above configuration, the present invention can maximize advertising effectiveness and improve advertisers' ROI by displaying optimal advertisements that reflect the user's possession status and emotions.

[1300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1301] Program processing flow

[1302] Step 1: Prepare user profiles and advertising data

[1303] The server retrieves data from a user database and an advertising database. The user profile includes purchase history, browsing history, click history, etc., while the advertising database stores information on advertising content and related products.

[1304] Specific behavior:

[1305] Input: User database, Ad database

[1306] Data processing: The server uses SQL queries to retrieve user profile information and advertising information.

[1307] Output: User profile data, advertising data

[1308] Step 2: Ad selection using generative AI models

[1309] Based on the acquired data, the server sends prompt text to the generative AI model, causing it to generate the optimal advertisement.

[1310] Specific behavior:

[1311] Input: User profile data, advertising data

[1312] Data processing: The server sends a prompt to the generative AI model saying, "Please generate the most appropriate ad based on the user profile." The generative AI model analyzes this and selects the most relevant ad.

[1313] Output: Selected advertising data

[1314] Step 3: Displaying ads on user devices

[1315] The server sends the advertising data received from the generative AI model to the user's device, which displays the received advertisement along with the "Already Have It" button.

[1316] Specific behavior:

[1317] Input: Selected advertising data

[1318] Data processing: The server sends the generated advertising data to the user's device as an HTTP response.

[1319] Output: Advertisement display screen

[1320] Step 4: Press the "I already have it" button

[1321] If a user sees an advertisement for a product that they already own, they press the "I already own it" button, and the user's device sends this information to the server.

[1322] Specific behavior:

[1323] Input: "I already have it" button press data

[1324] Data processing: The user device sends a POST request to the server by pressing a button.

[1325] Output: Send "I already have it" information

[1326] Step 5: Analyze information and update your profile

[1327] The server analyzes the information it already has and updates the user's profile.

[1328] Specific behavior:

[1329] Input: Information you already have

[1330] Data processing: The server updates the user database profile based on the received data.

[1331] Output: Updated profile data

[1332] Step 6: Next ad selection by generative AI model

[1333] The generative AI model analyzes the new profile and selects relevant product ads for the next ad display.

[1334] Specific behavior:

[1335] Input: Updated profile data

[1336] Data processing: The server sends a prompt to the generative AI model saying, "Please generate an ad based on a new profile." The generative AI model analyzes this and selects the next ad.

[1337] Output: Ad data to be displayed next time

[1338] Step 7: Acquire and analyze emotion data

[1339] The emotion engine analyzes the user's voice, facial expressions, and text input while the ad is being displayed to obtain emotional data.

[1340] Specific behavior:

[1341] Input: User voice, facial expressions, and text data

[1342] Data processing: The emotion engine analyzes these input data and generates emotion data.

[1343] Output: Emotion data

[1344] Step 8: Use emotion data and update your profile

[1345] The emotion engine transmits the acquired emotion data to the server, which then updates the corresponding user profile in the user database.

[1346] Specific behavior:

[1347] Input: Emotion data

[1348] Data processing: The server stores the emotion data in the user database and updates the user profile.

[1349] Output: Updated profile data

[1350] Step 9: Select and deliver relevant ads

[1351] The server selects an optimized advertisement for the next display based on the data obtained by the generative AI model and sends it to the user's device.

[1352] Specific behavior:

[1353] Input: Updated profile data and emotion data

[1354] Data processing: The server sends the prompt text to the generative AI model again to generate the optimal ad. The ad data is saved to be displayed the next time the user accesses the site.

[1355] Output: Optimized advertising data

[1356] Step 10: Optimized Ad Display

[1357] The selected advertisement is sent from the server to the user's device, which displays the relevant advertisement on the user's next visit, and records the user's clicks and interactions.

[1358] Specific behavior:

[1359] Input: Optimized advertising data

[1360] Data processing: The server sends the advertising data to the user's device, and the user's device displays the received advertisement.

[1361] Output: Advertisement display screen

[1362] Through these steps, the system can deliver optimal advertisements that reflect the user's ownership status and emotions.

[1363] (Application example 2)

[1364] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1365] Conventional advertising systems deliver advertisements based on a user's purchasing behavior and browsing history, but they are inadequate in responding to user emotions or when the user already owns a product. As a result, advertisements that do not interest the user or advertisements related to products the user already owns are often displayed, reducing the effectiveness of the advertisements. The objective of this invention is to maximize the effectiveness of advertisements by utilizing user emotional data to display more personalized advertisements.

[1366] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for pressing an "Already Have It" button to indicate that the user already owns a displayed advertisement, means for transmitting information that the "Already Have It" button has been pressed to the server, means for updating the user's profile based on the information received by the server, means for selecting a relevant new advertisement based on the user's profile using a generative AI model, means for using an emotion engine that acquires and analyzes user emotion data, means for the generative AI model to optimize advertisement display based on the emotion data analyzed by the emotion engine, and means for delivering the selected new advertisement to the user from the next time onwards. This makes it possible to optimize advertisement display based on user emotion data.

[1367] The "I Already Have It" button is an interface that users can press to indicate that they already own a product.

[1368] A "server" is a computer system that manages user behavioral and emotional data and delivers advertisements.

[1369] A "user profile" is a collection of information about an individual user, including the user's purchasing history, behavioral data, emotional data, etc.

[1370] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and generate and select optimal advertisements.

[1371] An "emotion engine" is a system that analyzes a user's voice, facial expressions, and text input to obtain emotional data.

[1372] An "advertising database" is a database that stores advertising information provided by advertisers.

[1373] "Ad display optimization" is the process of selecting the most appropriate ads to display based on user profile and emotional data.

[1374] "Relevant new ads" are ads that are predicted to be of interest to the user based on the user's profile.

[1375] This invention is a system for displaying advertisements related to products that a user already owns and optimizing the advertisements based on emotion data. The system's main components include a server, a user terminal, a user database, a generative AI model, an emotion engine, and an advertisement database.

[1376] System configuration

[1377] The system is configured as follows:

[1378] 1. Server:

[1379] The server is a computer system that manages user behavior data and purchase history and delivers advertisements.

[1380] The server maintains a user database and stores the received "already held" information and emotional data.

[1381] 2. User Device:

[1382] The user device is a device such as a smartphone or tablet that displays the advertisement and displays the "I Already Have It Button."

[1383] The user device displays advertisements, records user interactions, and can also collect voice and facial expression data.

[1384] 3. User Database:

[1385] The user database is a database that stores and manages user profiles, emotional data, purchase history, and browsing history.

[1386] 4. Generative AI Models:

[1387] A generative AI model is an algorithm that uses machine learning to analyze user profile data and generate and select optimal ads.

[1388] 5. Emotion Engine:

[1389] The emotion engine is a system that analyzes the user's voice, facial expressions, and text input to obtain emotional data. This part can use external services such as the Emotion API.

[1390] 6. Advertising Database:

[1391] The advertisement database is a database that stores advertisement information provided by advertisers.

[1392] What the program does

[1393] 1. Data Collection:

[1394] The user device collects information on when the "I already have it" button is pressed, voice data, facial expression data, and purchase history, and sends the collected data to the server.

[1395] 2. Data transmission:

[1396] The information collected on the user's device is sent in real time to a server, which analyzes and stores it.

[1397] 3. Emotion analysis:

[1398] Once the voice and facial expression data arrives at the server, it is analyzed by an emotion engine, which uses Microsoft Azure's Emotion API and other emotion analysis tools.

[1399] 4. Ad generation using generative AI models:

[1400] Based on the sentiment data and user profile data, a generative AI model, such as GPT-4 or BERT, selects the optimal ad.

[1401] 5. Advertising:

[1402] The selected advertisement is delivered from the server to the user's device and displayed the next time the user uses the device. The advertisement also includes an "Already Have It" button.

[1403] 6. Use of Feedback:

[1404] After the ad is displayed, the user's emotional data is collected and analyzed again, and this information is used to select the next ad.

[1405] Specific examples

[1406] As a concrete example, we will show the implementation of a function related to bicycle-related products.

[1407] Example 1: When a user clicks on an ad for a bicycle and presses the "I Already Have It" button, the server updates the user's profile. From then on, ads for bicycle helmets and lights will be displayed based on the user's emotional data, increasing the likelihood of the user being interested.

[1408] Prompt Sentence Examples

[1409] Below are some example prompts to use with generative AI models (such as GPT-4):

[1410] “Imagine a user already owns a bike. Generate ads for relevant bike accessories (e.g., helmets, lights, custom parts, etc.) for this user. Make the ads interesting by taking into account the user’s sentiment data.”

[1411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1412] Step 1:

[1413] The user's device displays an advertisement. When the user presses the "I Already Have It" button in response to the displayed advertisement, that information is entered into the user's device. The user's device then sends this input information to the server. The specific operations performed on the device side are capturing the button press event and generating the corresponding data packet. The output is the "I Already Have It" button press information.

[1414] Step 2:

[1415] The server receives the "I already have it" information sent from the user's device. The server analyzes this information and processes the data to update the user's profile. Specifically, it adds a new field to the corresponding user profile in the user database and updates the product status. The input is the "I already have it" information, and the output is the updated user profile.

[1416] Step 3:

[1417] The server uses an emotion engine to collect the user's voice data and facial expression data in response to the currently displayed advertisement. The user's device sends this data to the server. The specific operation of emotion data is to capture the user's tone of voice and facial expression using the device's microphone and camera. The input is voice data and facial expression data, and the output is emotion data.

[1418] Step 4:

[1419] The server analyzes the received emotional data using an emotion engine. This analysis uses commercially available emotion analysis software. Specifically, the server identifies the user's emotion (e.g., joy, disgust) from the tone of voice and converts it into numerical data. The input is the collected emotional data, and the output is the analyzed emotional result data.

[1420] Step 5:

[1421] The server inputs the updated user profile and emotional result data into the generative AI model. The generative AI model analyzes this data and selects the relevant ad to display next. At this stage, the machine learning algorithm generates new ad copy using prompt text based on the user's purchase history and emotional data. The input is the updated user profile and emotional result data, and the output is the generated ad copy.

[1422] Step 6:

[1423] The server sends the generated ad copy to the user's device. The next time the user opens the app, the new ad will be displayed. The specific operation performed on the server side is to generate and send packets of ad data. The output is the ad data to be displayed next time.

[1424] Step 7:

[1425] The user's device displays a new ad and records the user's clicks and interactions, providing useful feedback data for future ad displays. The specific actions performed by the device are displaying the ad and capturing the user's interactions. The output is user interaction data.

[1426] The above are the specific processing steps for carrying out the present invention. The operation methods of the hardware and software used in each step and the data flow will become clear, which will help in putting the present invention into practical use.

[1427] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1428] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1429] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1430] [Fourth embodiment]

[1431] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1432] 7, a 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.

[1433] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1434] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1435] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1436] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1437] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1438] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1439] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1440] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1441] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1442] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1443] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1444] preface

[1445] The present invention relates to an advertising recommendation system for maximizing advertising effectiveness. In particular, the system has the function of displaying advertisements related to products that a user already owns. The system is implemented using a server, a terminal, and a generative AI model.

[1446] System Configuration

[1447] The main components of the system are:

[1448] server

[1449] User Device

[1450] User Database

[1451] Generative AI Models

[1452] Advertising Database

[1453] server

[1454] The server is a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[1455] User Device

[1456] The user device is the device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent from the server and has the ability to display the "I Already Have It" button.

[1457] User Database

[1458] A user database is a database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[1459] Generative AI Models

[1460] The generative AI model is an algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavior data and generates relevant product ads to optimize the next ad display.

[1461] Advertising Database

[1462] The advertisement database is a database that stores advertisement information provided by advertisers, including the advertisement content, target products, related products, etc.

[1463] Program processing

[1464] Advertisement display

[1465] The server selects the most suitable advertisement based on the user profile and sends the advertisement data to the user's device.

[1466] The user's device will display the received advertisement along with the "Already Have It" button.

[1467] Pressing the "I already have it" button

[1468] When a user presses the "I already have it" button for a displayed advertisement, the user's device sends this information to the server.

[1469] Analyzing information and updating your profile

[1470] The server updates the user's profile based on the information it receives and already has.

[1471] The generative AI model analyzes the updated profile and selects relevant ads to display next time.

[1472] Selection and delivery of relevant advertisements

[1473] The selected relevant advertisements are sent from the server to the user's device and displayed the next time the user accesses the site.

[1474] The user's device displays relevant ads and records the user's clicks and interactions.

[1475] Specific examples

[1476] Example 1: Purchasing a bicycle

[1477] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button.

[1478] The user terminal transmits this information to the server.

[1479] The server analyzes this information using a generative AI model and updates the profile so that the next time an ad is displayed, it will serve ads for bicycle-related accessories (helmets, lights, custom parts, etc.).

[1480] The next time the server sends an advertisement to the terminal, it may be for bicycle accessories, which may pique the user's interest.

[1481] Example 2: Cookware

[1482] A user clicks on an advertisement for cooking equipment (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button.

[1483] The user device sends the information it already has to the server.

[1484] The server uses a generative AI model to analyze this information and update the profile to show ads for other products in the cookware series (pots, tongs, spatulas, etc.) in future visits.

[1485] The next time the server sends an advertisement to the terminal, it will be about another product of cooking equipment, which may be of interest to the user.

[1486] As described above, the present invention can maximize the effectiveness of advertising and improve advertisers' ROI by displaying only advertisements that users truly need.

[1487] The processing flow will be explained below.

[1488] Step 1:

[1489] When the ad recommendation system starts, the server loads the ad database and the user database, obtains the latest user information and ad information from the database, and initializes the system.

[1490] Step 2:

[1491] The server retrieves target user profiles and behavioral data from a user database, including user purchase history, click history, and viewing history.

[1492] Step 3:

[1493] The server uses a generative AI model to analyze the user's profile and behavioral data and select the most appropriate ad for that user. The AI ​​model then scores the best ad based on past data.

[1494] Step 4:

[1495] The server sends the selected advertisement and the advertisement data, including the "I Already Have It Button," to the user's device. This advertisement data includes the content of the advertisement to be displayed and a script for recording the user's interaction.

[1496] Step 5:

[1497] The device analyzes the advertising data received from the server and displays the advertisement and the "Already Have It Button" on the user's screen. The advertisement is optimized for the user's viewing environment and displayed in a way that is easy for the user to see.

[1498] Step 6:

[1499] The user checks the displayed advertisement and presses the "I already have it" button if they already own it. This action records the user's interaction.

[1500] Step 7:

[1501] The device sends information to the server that the user pressed the "Already Have It Button," including the date and time of the press, the associated advertising ID, and the user ID.

[1502] Step 8:

[1503] The server updates the user profile in the user database based on the received "already owned" information, specifically by marking the items that the user already owns.

[1504] Step 9:

[1505] The server then uses the generative AI model to analyze the updated user profile and select the next ad to display, taking into account the user's "already owned" information and selecting ads for related accessories and complementary products.

[1506] Step 10:

[1507] The server prepares a list of newly selected relevant ads and sends it to the device for subsequent ad delivery. This list will be used the next time the user accesses the site.

[1508] Step 11:

[1509] The next time the user views an ad, the device will display relevant ads based on the new ad list sent from the server, which are likely to increase click-through rates and purchase rates because they are selected based on the user's interests and needs.

[1510] Step 12:

[1511] How the user responds to the new ad is recorded and this information is fed back to the server, which further influences future ad presentations.

[1512] By following these steps, it is possible to display ads that best fit the user's needs, maximizing advertising effectiveness and improving advertisers' ROI.

[1513] Example 1

[1514] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1515] Conventional advertising systems often displayed ads for products that users already owned, which meant they were unable to respond to users' interests and needs. This resulted in lower ad click-through rates and conversion rates, and lower advertiser ROI. In addition, there was a lack of a way to reflect product information that users already owned in the ad display selection process, making it difficult to deliver effective ads.

[1516] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1517] In this invention, the server includes a means for acquiring a user profile and behavioral data from a user database, a means for inputting a prompt sentence into a generative AI model based on the acquired data to generate an optimal advertisement, and a means for transmitting the advertisement data obtained from the generative AI model to a user terminal, thereby making it possible to exclude products already owned by the user and display advertisements that focus on related products.

[1518] The "server" is a device that retrieves user profiles and behavioral data from a user database and uses that data to generate optimal advertisements for the generative AI model.

[1519] A "user terminal" is a device that receives and displays advertising data sent from the server and provides users with interactions such as the "I Already Have It Button."

[1520] "User database" refers to data storage that stores and manages user behavioral data and profile information, such as purchase history, click history, and browsing history.

[1521] The "generative AI model" is an algorithm that selects and generates optimal advertisements based on acquired user data, and generates relevant advertisement information by inputting a prompt text.

[1522] A "prompt" is text data that is input into a generative AI model, and is an instruction to suggest optimal advertisements based on the user's profile and behavioral data.

[1523] The "Already Owned Button" is a user interface element that notifies the server that the user already owns the product in response to a displayed advertisement.

[1524] This invention relates to an advertising recommendation system that maximizes advertising effectiveness by displaying advertisements related to products that users already own. In particular, the system uses a generative AI model based on user behavior data and purchase history to generate optimal product advertisements and provide them to users.

[1525] System Configuration

[1526] The system mainly consists of the following components:

[1527] server

[1528] User Device

[1529] User Database

[1530] Generative AI Models

[1531] Advertising Database

[1532] server

[1533] The server has the following features:

[1534] 1. Retrieve user profile and behavioral data from your user database.

[1535] 2. Based on the acquired data, a prompt sentence is input into the generative AI model to generate the optimal ad.

[1536] 3. The advertising data obtained from the generative AI model is sent to the user's device.

[1537] User Device

[1538] The user terminal has the following features:

[1539] 1. Receives advertising data sent from the server and displays it on the screen.

[1540] 2. Display the "I Already Have It" button and record the user's interaction.

[1541] 3. When the user presses the "I already have it" button, the information is sent to the server.

[1542] User Database

[1543] The user database stores and manages user behavioral data and profile information, such as purchase history, click history, and browsing history, and provides data for selecting the most suitable advertisements for users.

[1544] Generative AI Models

[1545] The generative AI model uses machine learning algorithms to analyze user profile data and generate optimal ads. By inputting prompt text, the generative AI model generates ads that are likely to attract the user's attention.

[1546] Advertising Database

[1547] The advertisement database stores advertisement information provided by advertisers, including the content of the advertisement, target products, related products, etc.

[1548] Specific examples

[1549] Example 1: Purchasing a bicycle

[1550] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button. The user's device sends this information to the server. The server analyzes this information using a generative AI model and updates the user's profile so that the next time ads are displayed, they will be for bicycle accessories (e.g., helmets, lights, custom parts). The next time the server sends an ad to the user's device, it will be for bicycle accessories, which are more likely to interest the user.

[1551] Example 2: Cookware

[1552] A user clicks on an ad for cookware (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button. The user's device sends the "I already have it" information to the server. The server analyzes this information using a generative AI model and updates the profile so that ads for other products in the cookware series (for example, pots, tongs, and spatulas) will be displayed in the future. The next ad the server sends to the user's device will be for a different cookware product and is more likely to interest the user.

[1553] This invention reflects user profiles and behavioral data in real time, allowing for the display of highly relevant ads and improving advertisers' ROI. Using a generative AI model, it is possible to accurately capture user interests and reflect them in subsequent ad displays. This allows for less intrusive ad displays for users.

[1554] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1555] Step 1:

[1556] The server retrieves user profile and behavioral data from a user database. The user ID is used as input. Specifically, it executes an SQL query to retrieve data from the database, which is then output. Based on the retrieved data, the server prepares the basis for determining which ad is most suitable.

[1557] Step 2:

[1558] The server inputs a prompt sentence into the generative AI model based on the acquired data, generating the optimal ad. User profile and behavioral data are used as input. Specifically, the server generates and inputs a prompt sentence such as "Please suggest ads that this user is likely to be interested in" into the generative AI model. The generative AI model then analyzes the prompt sentence and outputs the optimal ad.

[1559] Step 3:

[1560] The server sends the advertising data obtained from the generative AI model to the user device. The optimal advertising data output by the generative AI model is used as input. Specifically, the advertising data is sent using an HTTP request. The advertising data is sent to the user device, and this becomes the output.

[1561] Step 4:

[1562] The user's device displays the received advertising data along with the "I already have it button." The advertising data sent from the server is used as input. Specifically, the process of displaying the advertisement and button is carried out using front-end technologies (HTML, CSS, JavaScript). The output is the advertisement and button displayed on the screen.

[1563] Step 5:

[1564] The user presses the "I already have it" button for an ad. The user's click action is used as input. Specifically, when the user clicks the button, a click event occurs, which becomes the output.

[1565] Step 6:

[1566] The user device catches the button click event and sends that information to the server. The button click event is used as input. Specifically, the button click event is detected using JavaScript and the information is sent to the server using an AJAX request. The information sent to the server becomes the output.

[1567] Step 7:

[1568] When the server receives the "already have" information, it updates the corresponding user profile in the user database. The "already have" information is used as input. Specifically, an SQL query is used to update the user profile data. The update to the user database is the output.

[1569] Step 8:

[1570] The generative AI model analyzes the newly updated user data and selects the relevant ad to display next. The updated user data is used as input. Specifically, it generates and inputs a prompt such as "Please suggest ads for related products" based on the new profile data. The generated relevant ad is the output.

[1571] Step 9:

[1572] The server sends the selected relevant advertising data to the user device. The relevant advertising data selected by the generative AI model is used as input. Specifically, the relevant advertising data is sent using an HTTP request. The data is sent to the user device as output.

[1573] Step 10:

[1574] The user's device displays relevant ads the next time they access the site and records the user's clicks and interactions. The input is the relevant ad data sent from the server. Specifically, front-end technology is used to display ads, and if the user clicks on an ad, that information is sent back to the server. The output is a record of the user's interactions.

[1575] (Application example 1)

[1576] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1577] Conventional advertising systems have not provided sufficient methods for effectively displaying advertisements related to products that users already own. As a result, irrelevant or overlapping advertisements are displayed to users, reducing advertising effectiveness. Furthermore, there is a need for a method to increase the relevance of advertisements by dynamically displaying advertisements related to products that users already own, especially in new shopping experiences using virtual stores and smart devices.

[1578] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1579] In this invention, the server includes: means for a user to press an "Already Have It" button to indicate that the user already owns the displayed advertisement; means for transmitting information that the "Already Have It" button has been pressed to the server; means for updating the user's profile based on the information received by the server; means for selecting a relevant new advertisement based on the user's profile using a generative AI model; means for delivering the selected new advertisement to the user from the next time onward; means for dynamically displaying advertisements related to products owned by the user in a virtual store; and means for recognizing the user through the smart glasses and requesting advertisements based on the user's profile data. This makes it possible to dynamically display advertisements that are highly relevant to the user, thereby maximizing advertising effectiveness.

[1580] 1. "An 'I Already Have It' button that allows users to indicate that they already own the product when presented with an ad" is an interface element that allows users to indicate that they already own the product when presented with an ad.

[1581] 2. "Information indicating that the 'I already have it' button was pressed" refers to data generated when a user presses the 'I already have it' button, and is information indicating that the user already owns the product.

[1582] 3. "Means for transmitting to the server" means a communication system for transmitting data from the user terminal to the server.

[1583] 4. "Means for updating a user's profile based on information received by the server" refers to a function that analyzes the information received by the server and keeps the profile information up to date based on the user's purchasing history and behavioral data.

[1584] 5. "Generative AI model" is an artificial intelligence algorithm that analyzes user profile data and behavioral data to generate and select optimal advertisements.

[1585] 6. "Means for selecting new relevant ads" means a function that uses a generative AI model to select new ads that are highly relevant to a user based on the user's profile.

[1586] 7. "Means for delivering the selected new advertisement to the user from the next time onwards" means a system in which the server sends the selected advertisement to the user's terminal so that it will be displayed the next time the user accesses the site.

[1587] 8. "Virtual store" means an online shopping platform where users can browse and purchase products via the Internet.

[1588] 9. "Means for dynamically displaying advertisements related to products owned by a user" means a system or function for displaying new advertisements related to products already owned by a user in real time.

[1589] 10. "Smart glasses" are wearable devices that have augmented reality or virtual reality capabilities and display information to the user.

[1590] 11. "Means for recognizing users and requesting advertisements based on their profile data" means the functionality of devices such as smart glasses to identify users and retrieve appropriate advertisements based on their user profile.

[1591] This invention is an advertisement recommendation system that updates a user's profile and displays new related advertisements when the user presses an "I already have it" button to indicate that they already own the product displayed in a virtual store. Specifically, it is composed of the following elements:

[1592] server

[1593] The server acts as the central management system and performs the following main functions:

[1594] 1. Receiving information and updating user profile: The server receives information from the user device when the "I already have it" button is pressed. Based on the received information, the server updates the user database and clarifies the product information the user owns.

[1595] 2. Use of generative AI models: The server uses generative AI models to analyze user profile data and behavioral data and select new ads that are highly relevant to the user.

[1596] 3. Advertisement delivery: The selected new advertisement is delivered to the user from the next time onwards. The server displays the advertisement at the appropriate time when the user accesses the website.

[1597] User Device

[1598] The user device is the device on which the user sees and interacts with the advertisement, typically a pair of smart glasses or a smartphone.

[1599] 1. Displaying Ads: While users are browsing the virtual store, relevant ads sent from the server are displayed, including an "Already Have It" button.

[1600] 2. Information transmission: When the user presses the "I already have it" button, the information is transmitted to the server.

[1601] 3. User Recognition: Recognizing users and obtaining their profiles through devices such as smart glasses using facial recognition technology and other identification technologies.

[1602] Generative AI Models

[1603] A generative AI model is a system that uses machine learning algorithms to analyze user profile and behavioral data, dynamically generating and selecting the most relevant ads.

[1604] 1. Profile Analysis: Analyzes updated user profile data to identify user interests and purchasing patterns.

[1605] 2. Ad generation: Generate relevant ads based on user profiles.

[1606] Advertising Database

[1607] It is a database that works with a server and stores advertisements for use by generative AI models, including the content of the advertisement, target audience, related products, etc.

[1608] Specific Examples

[1609] 1. A user is browsing the bicycle accessories section in a "virtual store":

[1610] User recognition: The smart glasses recognize User A and identify his User ID as 'd741f2'.

[1611] Get profile data: The server gets the profile data of User A from the user database and finds out that the user owns an expensive bicycle.

[1612] Ad request and display: The server uses the generative AI model to select an ad for an accessory (e.g., bike light) that is highly relevant to User A and displays it on the smart glasses.

[1613] Pressing the "I already have it" button: User A sees the advertisement and presses the "I already have it" button because he already owns the light. This information is sent to the server and reflected in User A's profile.

[1614] Prompt Sentence Examples

[1615] "User d741f2 has clicked 'already_have' on ad_id 12345. Update their profile to show related bicycle accessories next time."

[1616] This system will dynamically display ads that are highly relevant to users, maximizing advertising effectiveness.

[1617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1618] Step 1:

[1619] User Awareness

[1620] The smart glasses recognize the user. As input, they use facial recognition data from the smart glasses' camera. They use a facial recognition API (e.g., Face++, Azure Face API, etc.) to identify the user ID. The output is the identified user ID (e.g., d741f2).

[1621] Step 2:

[1622] Profile Data Acquisition

[1623] The server retrieves profile data corresponding to the identified user ID from a user database. It executes a database query using the user ID as input. The output is profile data including the user's purchasing history and behavioral data.

[1624] Step 3:

[1625] Ad request

[1626] The server sends the data necessary to generate advertisements to the generative AI model based on the user's profile data. The generative AI model analyzes the input profile data and selects the advertisements most relevant to the user. The output is the selected advertisement data.

[1627] Step 4:

[1628] Advertisement display

[1629] The user device (smart glasses) displays the advertising data received from the server, which includes the advertising content and an "I already have it" button. The input is the advertising data, and the output is the advertisement displayed to the user.

[1630] Step 5:

[1631] Pressing the "I already have it" button

[1632] The user presses the "I already have it" button for the displayed ad. The input is the user interaction, which triggers a button press event. The output is the transmission of this information from the device to the server.

[1633] Step 6:

[1634] Information transmission

[1635] The user terminal sends information that the "I already have it" button has been pressed to the server. The button press event data is used as input, and the information is transmitted to the server via a data transmission protocol. The output is the user's "I already have it" information received by the server.

[1636] Step 7:

[1637] Profile Update

[1638] The server updates the user database based on the "already-held" information it receives. The input is the "already-held" information, and updates the user's profile data accordingly. The output is the updated user profile.

[1639] Step 8:

[1640] Next ad selection

[1641] The server requests the generative AI model to select future advertisements based on the updated user profile. The generative AI model analyzes the latest profile data and selects future advertisements to display. The input is the updated profile data, and the output is future advertisement data.

[1642] Step 9:

[1643] Next ad delivery

[1644] The server delivers the selected new advertisement the next time the user visits the store. The input is the new advertisement data, and the output is the advertisement to be displayed the next time the user visits the store. This process is triggered the next time the user visits the virtual store.

[1645] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1646] preface

[1647] This invention relates to an advertising recommendation system for maximizing advertising effectiveness. In particular, this system has the function of displaying advertisements related to products that a user already owns, and also combines an emotion engine that recognizes the user's emotions. This system is implemented using a server, a terminal, a generative AI model, and an emotion engine.

[1648] System Configuration

[1649] The main components of the system are:

[1650] server

[1651] User Device

[1652] User Database

[1653] Generative AI Models

[1654] Emotion Engine

[1655] Advertising Database

[1656] server

[1657] The server is a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[1658] User Device

[1659] The user device is the device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent from the server and has the ability to display the "I Already Have It" button.

[1660] User Database

[1661] A user database is a database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[1662] Generative AI Models

[1663] The generative AI model is an algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavioral and emotional data to generate relevant product ads to optimize the next ad display.

[1664] Emotion Engine

[1665] The emotion engine is a system for recognizing user emotions and including emotional data in user profiles. The emotion engine analyzes emotional data from users' voices, facial expressions, text inputs, etc., and provides this information to generative AI models.

[1666] Advertising Database

[1667] The advertisement database is a database that stores advertisement information provided by advertisers, including the advertisement content, target products, related products, etc.

[1668] Program processing

[1669] Advertisement display

[1670] The server selects the most suitable advertisement based on the user profile and sends the advertisement data to the user's device.

[1671] The user's device will display the received advertisement along with the "Already Have It" button.

[1672] Pressing the "I already have it" button

[1673] When a user presses the "I already have it" button for a displayed advertisement, the user's device sends this information to the server.

[1674] Analyzing information and updating your profile

[1675] The server updates the user's profile based on the information it receives and already has.

[1676] The generative AI model analyzes the updated profile and selects relevant ads to display next time.

[1677] Acquiring emotion data

[1678] The emotion engine analyzes the user's voice, facial expressions, and text input to obtain emotional data about the ad they are viewing, which indicates whether the user is interested in the ad or dislikes it.

[1679] Use of Emotional Data

[1680] The emotion engine transmits the acquired emotion data to the server, which then updates the corresponding user profile in the user database.

[1681] The generative AI model analyzes the updated profile and sentiment data to further optimize the next ad shown.

[1682] Selection and delivery of relevant advertisements

[1683] The selected relevant advertisements are sent from the server to the user's device and displayed the next time the user accesses the site.

[1684] The user's device displays relevant ads and records the user's clicks and interactions.

[1685] Specific examples

[1686] Example 1: Bicycle purchase and sentiment data

[1687] A user clicks on an ad for a bicycle and, because they already own the product, presses the "I already have it" button.

[1688] The user terminal transmits this information to the server.

[1689] The server analyzes this information using a generative AI model and updates the profile so that the next time an ad is displayed, it will serve ads for bicycle-related accessories (helmets, lights, custom parts, etc.).

[1690] The emotion engine analyzes the user's emotions regarding the ad being displayed, and if it detects a positive emotion, it sets the display of related ads from the next time onwards. The next ad sent by the server to the device may be for bicycle accessories, which may pique the user's interest.

[1691] Example 2: Cookware and Emotional Data

[1692] A user clicks on an advertisement for cooking equipment (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button.

[1693] The user device sends the information it already has to the server.

[1694] The server uses a generative AI model to analyze this information and update the profile to show ads for other products in the cookware series (pots, tongs, spatulas, etc.) in future visits.

[1695] If the emotion engine detects a happy emotion from the user's voice or facial expression while viewing an ad, it will prioritize displaying related ads the next time. The next ad sent by the server to the device may be about another product in the same series of cookware, which may pique the user's interest.

[1696] As described above, the present invention can maximize the effectiveness of advertising and improve advertisers' ROI by displaying advertisements based on the user's needs and emotions.

[1697] The processing flow will be explained below.

[1698] Step 1:

[1699] When the ad recommendation system starts, the server loads the ad database and the user database, obtains the latest user information and ad information from the database, and initializes the system.

[1700] Step 2:

[1701] The server retrieves target user profiles and behavioral data from a user database, including user purchase history, click history, and viewing history.

[1702] Step 3:

[1703] The server uses a generative AI model to analyze the user's profile and behavioral data and select the most appropriate ad for that user. The AI ​​model then scores the best ad based on past data.

[1704] Step 4:

[1705] The server sends the selected advertisement and the advertisement data, including the "I Already Have It Button," to the user's device. This advertisement data includes the content of the advertisement to be displayed and a script for recording the user's interaction.

[1706] Step 5:

[1707] The device analyzes the advertising data received from the server and displays the advertisement and the "Already Have It Button" on the user's screen. The advertisement is optimized for the user's viewing environment and displayed in a way that is easy for the user to see.

[1708] Step 6:

[1709] The user checks the displayed advertisement and presses the "I already have it" button if they already own it. This action records the user's interaction.

[1710] Step 7:

[1711] The device sends information to the server that the user pressed the "Already Have It Button," including the date and time of the press, the associated advertising ID, and the user ID.

[1712] Step 8:

[1713] The server updates the user profile in the user database based on the received "already owned" information, specifically by marking the items that the user already owns.

[1714] Step 9:

[1715] The server then uses the generative AI model to analyze the updated user profile and select the next relevant ad to display, taking into account the user's "already owned" information and selecting ads for related accessories and complementary products.

[1716] Step 10:

[1717] The server prepares a list of newly selected relevant ads and sends it to the device for subsequent ad delivery. This list will be used the next time the user accesses the site.

[1718] Step 11:

[1719] The next time the user views an ad, the device will display relevant ads based on the new ad list sent from the server, which are likely to increase click-through rates and purchase rates because they are selected based on the user's interests and needs.

[1720] Step 12:

[1721] How the user responds to the new ad is recorded and this information is fed back to the server, which further influences future ad presentations.

[1722] Step 13:

[1723] The emotion engine analyzes the user's voice, facial expressions, and text input while viewing the advertisement to obtain the user's emotion data, which indicates the user's emotional state, such as joy, interest, or disgust.

[1724] Step 14:

[1725] The emotion engine sends the acquired emotion data to the server, which details the user's emotional response to the advertisement.

[1726] Step 15:

[1727] The server stores the emotion data in a user database and provides it to a generative AI model, which uses this information to select more relevant ads the next time the user sees them.

[1728] Step 16:

[1729] The generative AI model analyzes user profile and sentiment data to further optimize the ads that will be shown next, prioritizing ads that users responded positively to and ads for related products based on sentiment data.

[1730] Step 17:

[1731] The server then sends the selected new ad to the device for subsequent ad delivery. Through this process, it becomes possible to provide ads that best fit the user's needs and emotions.

[1732] Specifically, if the emotion engine detects the emotion of joy while a user is viewing a food advertisement, the server will select and deliver the same brand of food or recipe video for the next advertisement. In this way, it becomes possible to deliver advertisements that reflect the user's emotional state, maximizing the effectiveness of the advertisement.

[1733] Example 2

[1734] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1735] Conventional ad recommendation systems often display unnecessary ads for products that users already own, reducing the effectiveness of advertising. Additionally, there is a lack of systems that optimize ads by taking into account user emotional data, making it difficult to accurately grasp users' interests.

[1736] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1737] In this invention, the server includes means for allowing a user to press an "Already Have It" button to indicate that the user already owns the displayed advertisement, means for transmitting information that the "Already Have It" button has been pressed to the server, means for updating the user's profile based on the information received by the server, means for selecting a relevant new advertisement based on the user's profile using a generative AI model, means for delivering the selected new advertisement to the user from the next time onwards, means for acquiring user emotion data using an emotion engine, and means for optimizing the advertisement to be displayed next by the generative AI model using the emotion data. This enables optimal advertisement delivery that reflects the user's ownership status and emotions.

[1738] The "I Already Have It Button" is an interface button that users can use to indicate that they already own the displayed advertisement.

[1739] "Server" refers to a computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements.

[1740] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and select and generate optimal advertisements.

[1741] An "emotion engine" is a system that analyzes emotional data from a user's voice, facial expressions, text input, etc., and includes this information in the user profile.

[1742] A "profile" is an individual data set that includes a user's purchasing history, browsing history, click history, emotional data, etc.

[1743] An "advertising database" is a database that stores advertising information provided by advertisers.

[1744] "User database" refers to a database for storing and managing user profiles and behavioral data.

[1745] "Means for transmitting information" refers to a communication means for transmitting data from a user terminal to a server.

[1746] The "means for receiving information" refers to a communication means by which the server receives data sent from the user terminal.

[1747] A "profile update method" is a process for updating a user's profile in a user database with new information.

[1748] "Means for delivering advertisements" refers to the process by which the server sends the advertisement data generated by the server to the user terminal so that it can be displayed.

[1749] "Means for selecting new advertisements" means a process for using a generative AI model to select relevant new advertisements based on a user's profile.

[1750] MODE FOR CARRYING OUT THE INVENTION

[1751] This invention relates to an advertising recommendation system for maximizing advertising effectiveness. This system displays advertisements related to products that a user already owns and has the function of recognizing the user's emotions to optimize the advertisements. Specifically, this system is implemented using a server, a terminal, a generative AI model, and an emotion engine.

[1752] System Configuration

[1753] The main components of this system are:

[1754] Server: A computer system that manages advertising information and user profile information, receives and analyzes information from user devices, and delivers advertisements. The server optimizes advertisements based on user behavior data and purchase history, and generates highly relevant advertisements using a generative AI model.

[1755] User Device: The device that displays the advertisements and records the user's interactions. It typically operates through a web browser or a mobile application. The user device displays the advertisements sent by the server and has the ability to display the "I Already Have It" button.

[1756] User database: A database for storing and managing user profiles and behavioral data, including user purchase history, browsing history, click history, etc.

[1757] Generative AI model: An algorithm that uses machine learning to analyze user profile data and select and generate optimal ads. This model analyzes user behavioral and emotional data to generate relevant product ads to optimize the next ad display.

[1758] Emotion engine: A system for recognizing user emotions and including emotional data in user profiles. The emotion engine analyzes emotional data from users' voices, facial expressions, text inputs, etc., and provides this information to generative AI models.

[1759] Advertising database: A database that stores advertising information provided by advertisers, including the content of the advertisement, target products, related products, etc.

[1760] Specific examples

[1761] Example 1: Bicycle purchase and sentiment data

[1762] A user clicks on a bicycle ad and, because they already own the product, presses the "I already have it" button. The user's device sends this information to the server. The server analyzes this information using a generative AI model and updates the profile so that the next time ads are displayed, ads for bicycle-related accessories (helmets, lights, custom parts, etc.) are delivered. The emotion engine analyzes the user's emotions regarding the currently displayed ad, and if a positive emotion is detected, it sets the device to display related ads in the future. The next ad the server sends to the device will be for bicycle accessories, which may pique the user's interest.

[1763] Example 2: Cookware and Emotional Data

[1764] A user clicks on an ad for cooking utensils (for example, a frying pan) and, since they have already purchased it, presses the "I already have it" button. The user's device sends the "I already have it" information to the server. The server analyzes this information using a generative AI model and updates the profile so that ads for other products in the cooking utensil series (pots, tongs, spatulas, etc.) will be displayed from the next time onwards. If the emotion engine detects an emotion of joy from the user's voice or facial expression regarding the currently displayed ad, it will prioritize displaying related ads next time. Next time, the server will send the generated advertisement for cooking utensils to the user's device.

[1765] Prompt Sentence Examples

[1766] Examples of prompts to be input to a generative AI model include:

[1767] "If a user clicks on an ad for a bicycle and hits the 'I already have it' button because they already own one, generate an ad for a related bicycle accessory."

[1768] "When a user clicks on an ad for a frying pan and presses the 'I already have it' button, generate an ad for other cookware in the same series."

[1769] "Next time, show me the ad where the sentiment engine detects a positive sentiment in the user."

[1770] With the above configuration, the present invention can maximize advertising effectiveness and improve advertisers' ROI by displaying optimal advertisements that reflect the user's possession status and emotions.

[1771] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1772] Program processing flow

[1773] Step 1: Prepare user profiles and advertising data

[1774] The server retrieves data from a user database and an advertising database. The user profile includes purchase history, browsing history, click history, etc., while the advertising database stores information on advertising content and related products.

[1775] Specific behavior:

[1776] Input: User database, Ad database

[1777] Data processing: The server uses SQL queries to retrieve user profile information and advertising information.

[1778] Output: User profile data, advertising data

[1779] Step 2: Ad selection using generative AI models

[1780] Based on the acquired data, the server sends prompt text to the generative AI model, causing it to generate the optimal advertisement.

[1781] Specific behavior:

[1782] Input: User profile data, advertising data

[1783] Data processing: The server sends a prompt to the generative AI model saying, "Please generate the most appropriate ad based on the user profile." The generative AI model analyzes this and selects the most relevant ad.

[1784] Output: Selected advertising data

[1785] Step 3: Displaying ads on user devices

[1786] The server sends the advertising data received from the generative AI model to the user's device, which displays the received advertisement along with the "Already Have It" button.

[1787] Specific behavior:

[1788] Input: Selected advertising data

[1789] Data processing: The server sends the generated advertising data to the user's device as an HTTP response.

[1790] Output: Advertisement display screen

[1791] Step 4: Press the "I already have it" button

[1792] If a user sees an advertisement for a product that they already own, they press the "I already own it" button, and the user's device sends this information to the server.

[1793] Specific behavior:

[1794] Input: "I already have it" button press data

[1795] Data processing: The user device sends a POST request to the server by pressing a button.

[1796] Output: Send "I already have it" information

[1797] Step 5: Analyze information and update your profile

[1798] The server analyzes the information it already has and updates the user's profile.

[1799] Specific behavior:

[1800] Input: Information you already have

[1801] Data processing: The server updates the user database profile based on the received data.

[1802] Output: Updated profile data

[1803] Step 6: Next ad selection by generative AI model

[1804] The generative AI model analyzes the new profile and selects relevant product ads for the next ad display.

[1805] Specific behavior:

[1806] Input: Updated profile data

[1807] Data processing: The server sends a prompt to the generative AI model saying, "Please generate an ad based on a new profile." The generative AI model analyzes this and selects the next ad.

[1808] Output: Ad data to be displayed next time

[1809] Step 7: Acquire and analyze emotion data

[1810] The emotion engine analyzes the user's voice, facial expressions, and text input while the ad is being displayed to obtain emotional data.

[1811] Specific behavior:

[1812] Input: User voice, facial expressions, and text data

[1813] Data processing: The emotion engine analyzes these input data and generates emotion data.

[1814] Output: Emotion data

[1815] Step 8: Use emotion data and update your profile

[1816] The emotion engine transmits the acquired emotion data to the server, which then updates the corresponding user profile in the user database.

[1817] Specific behavior:

[1818] Input: Emotion data

[1819] Data processing: The server stores the emotion data in the user database and updates the user profile.

[1820] Output: Updated profile data

[1821] Step 9: Select and deliver relevant ads

[1822] The server selects an optimized advertisement for the next display based on the data obtained by the generative AI model and sends it to the user's device.

[1823] Specific behavior:

[1824] Input: Updated profile data and emotion data

[1825] Data processing: The server sends the prompt text to the generative AI model again to generate the optimal ad. The ad data is saved to be displayed the next time the user accesses the site.

[1826] Output: Optimized advertising data

[1827] Step 10: Optimized Ad Display

[1828] The selected advertisement is sent from the server to the user's device, which displays the relevant advertisement on the user's next visit, and records the user's clicks and interactions.

[1829] Specific behavior:

[1830] Input: Optimized advertising data

[1831] Data processing: The server sends the advertising data to the user's device, and the user's device displays the received advertisement.

[1832] Output: Advertisement display screen

[1833] Through these steps, the system can deliver optimal advertisements that reflect the user's ownership status and emotions.

[1834] (Application example 2)

[1835] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1836] Conventional advertising systems deliver advertisements based on a user's purchasing behavior and browsing history, but they are inadequate in responding to user emotions or when the user already owns a product. As a result, advertisements that do not interest the user or advertisements related to products the user already owns are often displayed, reducing the effectiveness of the advertisements. The objective of this invention is to maximize the effectiveness of advertisements by utilizing user emotional data to display more personalized advertisements.

[1837] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for pressing an "Already Have It" button to indicate that the user already owns a displayed advertisement, means for transmitting information that the "Already Have It" button has been pressed to the server, means for updating the user's profile based on the information received by the server, means for selecting a relevant new advertisement based on the user's profile using a generative AI model, means for using an emotion engine that acquires and analyzes user emotion data, means for the generative AI model to optimize advertisement display based on the emotion data analyzed by the emotion engine, and means for delivering the selected new advertisement to the user from the next time onwards. This makes it possible to optimize advertisement display based on user emotion data.

[1838] The "I Already Have It" button is an interface that users can press to indicate that they already own a product.

[1839] A "server" is a computer system that manages user behavioral and emotional data and delivers advertisements.

[1840] A "user profile" is a collection of information about an individual user, including the user's purchasing history, behavioral data, emotional data, etc.

[1841] A "generative AI model" is an algorithm that uses machine learning to analyze user profile data and generate and select optimal advertisements.

[1842] An "emotion engine" is a system that analyzes a user's voice, facial expressions, and text input to obtain emotional data.

[1843] An "advertising database" is a database that stores advertising information provided by advertisers.

[1844] "Ad display optimization" is the process of selecting the most appropriate ads to display based on user profile and emotional data.

[1845] "Relevant new ads" are ads that are predicted to be of interest to the user based on the user's profile.

[1846] This invention is a system for displaying advertisements related to products that a user already owns and optimizing the advertisements based on emotion data. The system's main components include a server, a user terminal, a user database, a generative AI model, an emotion engine, and an advertisement database.

[1847] System configuration

[1848] The system is configured as follows:

[1849] 1. Server:

[1850] The server is a computer system that manages user behavior data and purchase history and delivers advertisements.

[1851] The server maintains a user database and stores the received "already held" information and emotional data.

[1852] 2. User Device:

[1853] The user device is a device such as a smartphone or tablet that displays the advertisement and displays the "I Already Have It Button."

[1854] The user device displays advertisements, records user interactions, and can also collect voice and facial expression data.

[1855] 3. User Database:

[1856] The user database is a database that stores and manages user profiles, emotional data, purchase history, and browsing history.

[1857] 4. Generative AI Models:

[1858] A generative AI model is an algorithm that uses machine learning to analyze user profile data and generate and select optimal ads.

[1859] 5. Emotion Engine:

[1860] The emotion engine is a system that analyzes the user's voice, facial expressions, and text input to obtain emotional data. This part can use external services such as the Emotion API.

[1861] 6. Advertising Database:

[1862] The advertisement database is a database that stores advertisement information provided by advertisers.

[1863] What the program does

[1864] 1. Data Collection:

[1865] The user device collects information on when the "I already have it" button is pressed, voice data, facial expression data, and purchase history, and sends the collected data to the server.

[1866] 2. Data transmission:

[1867] The information collected on the user's device is sent in real time to a server, which analyzes and stores it.

[1868] 3. Emotion analysis:

[1869] Once the voice and facial expression data arrives at the server, it is analyzed by an emotion engine, which uses Microsoft Azure's Emotion API and other emotion analysis tools.

[1870] 4. Ad generation using generative AI models:

[1871] Based on the sentiment data and user profile data, a generative AI model, such as GPT-4 or BERT, selects the optimal ad.

[1872] 5. Advertising:

[1873] The selected advertisement is delivered from the server to the user's device and displayed the next time the user uses the device. The advertisement also includes an "Already Have It" button.

[1874] 6. Use of Feedback:

[1875] After the ad is displayed, the user's emotional data is collected and analyzed again, and this information is used to select the next ad.

[1876] Specific examples

[1877] As a concrete example, we will show the implementation of a function related to bicycle-related products.

[1878] Example 1: When a user clicks on an ad for a bicycle and presses the "I Already Have It" button, the server updates the user's profile. From then on, ads for bicycle helmets and lights will be displayed based on the user's emotional data, increasing the likelihood of the user being interested.

[1879] Prompt Sentence Examples

[1880] Below are some example prompts to use with generative AI models (such as GPT-4):

[1881] “Imagine a user already owns a bike. Generate ads for relevant bike accessories (e.g., helmets, lights, custom parts, etc.) for this user. Make the ads interesting by taking into account the user’s sentiment data.”

[1882] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1883] Step 1:

[1884] The user's device displays an advertisement. When the user presses the "I Already Have It" button in response to the displayed advertisement, that information is entered into the user's device. The user's device then sends this input information to the server. The specific operations performed on the device side are capturing the button press event and generating the corresponding data packet. The output is the "I Already Have It" button press information.

[1885] Step 2:

[1886] The server receives the "I already have it" information sent from the user's device. The server analyzes this information and processes the data to update the user's profile. Specifically, it adds a new field to the corresponding user profile in the user database and updates the product status. The input is the "I already have it" information, and the output is the updated user profile.

[1887] Step 3:

[1888] The server uses an emotion engine to collect the user's voice data and facial expression data in response to the currently displayed advertisement. The user's device sends this data to the server. The specific operation of emotion data is to capture the user's tone of voice and facial expression using the device's microphone and camera. The input is voice data and facial expression data, and the output is emotion data.

[1889] Step 4:

[1890] The server analyzes the received emotional data using an emotion engine. This analysis uses commercially available emotion analysis software. Specifically, the server identifies the user's emotion (e.g., joy, disgust) from the tone of voice and converts it into numerical data. The input is the collected emotional data, and the output is the analyzed emotional result data.

[1891] Step 5:

[1892] The server inputs the updated user profile and emotional result data into the generative AI model. The generative AI model analyzes this data and selects the relevant ad to display next. At this stage, the machine learning algorithm generates new ad copy using prompt text based on the user's purchase history and emotional data. The input is the updated user profile and emotional result data, and the output is the generated ad copy.

[1893] Step 6:

[1894] The server sends the generated ad copy to the user's device. The next time the user opens the app, the new ad will be displayed. The specific operation performed on the server side is to generate and send packets of ad data. The output is the ad data to be displayed next time.

[1895] Step 7:

[1896] The user's device displays a new ad and records the user's clicks and interactions, providing useful feedback data for future ad displays. The specific actions performed by the device are displaying the ad and capturing the user's interactions. The output is user interaction data.

[1897] The above are the specific processing steps for carrying out the present invention. The operation methods of the hardware and software used in each step and the data flow will become clear, which will help in putting the present invention into practical use.

[1898] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1899] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1900] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1901] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1902] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1903] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1904] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1905] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1906] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1907] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1908] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1909] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1910] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1911] 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.

[1912] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1913] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1914] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1915] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1916] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1917] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1918] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1919] The following is further disclosed regarding the above embodiment.

[1920] (Claim 1)

[1921] A way for users to click an "I already have it" button to indicate that they already own the ad shown to them;

[1922] means for transmitting information that the "I already have it" button has been pressed to a server;

[1923] means for updating a user profile based on the information received by the server;

[1924] means for selecting relevant new advertisements based on the user's profile using a generative AI model;

[1925] The system includes a means for delivering the selected new advertisement to the user from the next time onwards.

[1926] (Claim 2)

[1927] The system of claim 1 , wherein the generative AI model analyzes user purchasing history and behavioral data.

[1928] (Claim 3)

[1929] 2. The system of claim 1, wherein the server maintains a user database and stores the "I already have" information in the database.

[1930] "Example 1"

[1931] (Claim 1)

[1932] A way for users to click an "I already have it" button to indicate that they already own the ad shown to them;

[1933] means for transmitting information that the "I already have it" button has been pressed to a server;

[1934] means for said server to retrieve user profile and behavioral data from a user database;

[1935] A means for the server to input a prompt sentence into a generation AI model based on the acquired data to generate an optimal advertisement;

[1936] A means for transmitting advertising data obtained by the server from the generating AI model to a user terminal;

[1937] means for displaying the advertising data received by the user terminal and also displaying an "Already Have It" button;

[1938] means for transmitting information from the user terminal to a server after the button is pressed;

[1939] means for said server to update user profiles based on the information it "already has";

[1940] a means for the generating AI model to select relevant advertisements to be displayed next based on the updated profile data;

[1941] The system includes a means for the server to transmit the selected relevant advertisements to the user terminal.

[1942] (Claim 2)

[1943] The system of claim 1, wherein the generative AI model generates optimal advertisements by analyzing user purchasing history and behavioral data.

[1944] (Claim 3)

[1945] 2. The system of claim 1, wherein the server maintains a user database and stores the "I already have" information in the database.

[1946] "Application Example 1"

[1947] (Claim 1)

[1948] A way for users to click an "I already have it" button to indicate that they already own the ad shown to them;

[1949] means for transmitting information that the "I already have it" button has been pressed to a server;

[1950] means for updating a user profile based on the information received by the server;

[1951] means for selecting relevant new advertisements based on the user's profile using a generative AI model;

[1952] A means to deliver the selected new advertisement to users from the next time onwards,

[1953] A means for dynamically displaying advertisements related to products owned by a user in a virtual store;

[1954] means for recognizing a user through the smart glasses and requesting advertisements based on their profile data;

[1955] A system including:

[1956] (Claim 2)

[1957] 10. The system of claim 1, wherein the generative AI model analyzes user purchasing history and behavioral data.

[1958] (Claim 3)

[1959] 2. The system of claim 1, wherein the server maintains a user database and stores the "I already have" information in the database.

[1960] "Example 2: Combining Emotion Engines"

[1961] (Claim 1)

[1962] A way for users to click an "I already have it" button to indicate that they already own the ad shown to them;

[1963] means for transmitting information that the "I already have it" button has been pressed to a server;

[1964] means for updating a user profile based on the information received by the server;

[1965] means for selecting relevant new advertisements based on the user's profile using a generative AI model;

[1966] A means to deliver the selected new advertisement to users from the next time onwards,

[1967] A means for acquiring user emotion data using an emotion engine;

[1968] The system includes a means for optimizing the next advertisement to be displayed by the generative AI model using the emotion data.

[1969] (Claim 2)

[1970] The system of claim 1 , wherein the generative AI model analyzes user purchasing history and behavioral data.

[1971] (Claim 3)

[1972] 2. The system of claim 1, wherein the server maintains a user database and stores the "I already have" information and emotion data in the database.

[1973] "Application example 2 when combining emotion engines"

[1974] (Claim 1)

[1975] A way for users to click an "I already have it" button to indicate that they already own the ad shown to them;

[1976] means for transmitting information that the "I already have it" button has been pressed to a server;

[1977] means for updating a user profile based on the information received by the server;

[1978] means for selecting rel...

Claims

1. A way for users to click an "I already have it" button to indicate that they already own the ad shown to them; means for transmitting information that the "I already have it" button has been pressed to a server; means for updating a user profile based on the information received by the server; means for selecting relevant new advertisements based on the user's profile using a generative AI model; The system includes a means for delivering the selected new advertisement to the user from the next time onwards.

2. The system of claim 1 , wherein the generative AI model analyzes user purchasing history and behavioral data.

3. 2. The system of claim 1, wherein said server maintains a user database and stores said "I already have" information in said database.

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