System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting location information and behavior information from terminals of users; means for analyzing interests and concerns of the users using a generative AI; means for generating an advertisement optimized for a target user based on an analysis result; means for delivering the generated advertisement to the terminals of the users in real time; and means for retraining a model of the generative AI using the collected response information.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] Conventional ad delivery systems tend to deliver static ads without fully understanding user interests, resulting in limited advertising effectiveness. Furthermore, they are unable to respond in real time to changes in user behavior and the environment, making it difficult to provide targeted ads in a timely manner. Furthermore, they lack a mechanism for providing feedback on user reactions to ads and continuously optimizing ads, making it difficult to improve the accuracy and effectiveness of ads. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that stores location information and behavioral data collected from user devices in a database and analyzes user interests and concerns in real time using a generation AI. Furthermore, it dynamically generates advertisements optimized for target users based on the analysis results and delivers these advertisements to user devices in real time. It also collects user response data to advertisements and uses this data to retrain the generation AI model, thereby continuously improving the accuracy and effectiveness of advertisements. It also collects behavioral data when users visit specific websites and uses this data for analysis, thereby achieving more accurate advertisement generation. Furthermore, it stores external data such as local weather, traffic, and event information in the database and reflects this data in the generated advertisements, thereby providing advertisements optimally tailored to the user's environment.

[0006] "User" means an individual or corporation that uses the service or system.

[0007] A "terminal" is a device used by a user, including a smartphone, tablet, computer, or other device that can connect to the Internet.

[0008] "Location information" refers to information about the current location provided by the user's device, including GPS data and Wi-Fi location data.

[0009] "Behavioral Data" is interaction data generated when a user uses a website or application, including clicks, scrolling, time spent, search history, etc.

[0010] A "database" is a system for systematically storing information, and is used to store user data and external data.

[0011] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate advertisements.

[0012] "Analysis" is the process of identifying user behavior patterns and interests based on collected data.

[0013] An "advertisement" is content that introduces a specific product or service to users, and may include text, images, videos, etc.

[0014] "Real-time" refers to the fact that the series of operations from collecting and analyzing data to generating and delivering advertisements is carried out almost instantly.

[0015] "Response data" refers to data on the actions taken by users in response to advertisements, and includes click-through rates, purchasing behavior, display time, and the like.

[0016] "Retraining" is the process of using collected response data to refine the generative AI model and improve the accuracy of advertising.

[0017] "External data" is information collected separately from user data, and includes weather information, traffic information, event information, and the like. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The system of the present invention includes a series of operations that collect location information and behavioral data from user terminals, store them in a database, analyze them, generate advertisements, deliver them in real time, collect user response data, and perform retraining.

[0040] Data collection and storage

[0041] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) and also obtains real-time location information using GPS and Wi-Fi. This data is periodically sent by the device to the server.

[0042] The server stores the collected data in a database, which stores user profiles, past behavioral data, and location information. It also collects external data (weather information, traffic information, event information, etc.) and stores it in the database.

[0043] Data analysis and ad generation

[0044] The server analyzes the data stored in the database using generative AI. The analysis process identifies user behavioral patterns and interests. For example, if a user frequently visits sites related to cafes, a "cafe lover" tag is added to the user profile.

[0045] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day, an advertisement for discounts at a nearby cafe or for rain gear sales may be generated.

[0046] Advertisement delivery and response data collection

[0047] The server delivers the generated advertisements to the user's device in real time. The advertisements are sent along with metadata (target user ID, display time, location, etc.) to ensure proper targeting.

[0048] The user's device displays the received advertisement. After the advertisement is displayed, the device collects user response data (clicks, swipes, purchases, etc.) and sends it to the server.

[0049] Retraining the model

[0050] The server evaluates the effectiveness of the advertisement based on the received user response data. Based on the evaluation results (click rate, purchase rate, etc.), the generation AI model is retrained to improve the accuracy and effectiveness of advertisement generation. The next advertisement is generated using the new retrained model.

[0051] Examples:

[0052] Example 1: Cafe advertising

[0053] If the user is in an office district, the device acquires location information and sends it to the server. Data on the user's frequent visits to cafe-related websites is also stored on the server. Information that the weather is rainy is obtained from external data.

[0054] 1. The server analyzes that the user likes cafes.

[0055] 2. Generate an ad that takes into account the rainy day situation and includes discount information for a nearby cafe.

[0056] 3. The server delivers this advertisement to the user's device in real time.

[0057] 4. The user's device displays the advertisement, and when the user clicks on the advertisement, the response data is sent to the server.

[0058] 5. The server uses this data to evaluate ad click-through rates and retrain the generative AI model.

[0059] Example 2: Concert advertising

[0060] If a user frequently uses a music streaming service, their behavioral data is sent to the server, which analyzes their interest in specific music genres and artists.

[0061] 1. The server identifies the user's musical preferences.

[0062] 2. Information about nearby concerts is obtained from external data.

[0063] 3. The server generates an advertisement containing ticket information for this concert.

[0064] 4. The server delivers the generated advertisement to the user's device in real time.

[0065] 5. The user's device displays the advertisement, and when the user purchases a ticket, the data is sent to the server.

[0066] 6. The server uses this data to evaluate the purchase rate of ads and retrain the generative AI model.

[0067] In this way, the present invention provides a system that analyzes user behavior data and local conditions in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertisements.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] When a user starts using a website or application, the device collects real-time data on the user's behavior (clicks, scrolling, time spent, search history, etc.) and also obtains the user's current location using GPS and Wi-Fi.

[0071] Step 2:

[0072] The device periodically sends collected behavioral data and location information, including timestamps and user IDs, to a server.

[0073] Step 3:

[0074] The server stores the received data in a database, which includes user profiles, behavioral data, location information, and external data (weather, traffic, event information, etc.).

[0075] Step 4:

[0076] The server uses generative AI to analyze the data stored in the database. The purpose of the analysis is to identify the user's behavioral patterns and interests. For example, if a user frequently visits sites related to cafes, a "cafe lover" tag will be added to the user's profile.

[0077] Step 5:

[0078] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day, the server generates advertisements for discounts at nearby cafes or for rain gear sales.

[0079] Step 6:

[0080] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[0081] Step 7:

[0082] The device then displays the received advertisement to the user. When the advertisement is displayed, the device again collects user response data (clicks, swipes, purchases, etc.).

[0083] Step 8:

[0084] The device sends the collected response data to the server, including the ad display status and user actions.

[0085] Step 9:

[0086] The server analyzes the response data and evaluates the effectiveness of the advertisement (e.g., click rate and purchase rate). This evaluation result is used to retrain the generative AI model.

[0087] Step 10:

[0088] The server then uses the new, retrained model to generate the next ad, continually improving the accuracy and effectiveness of the ad, and the entire ad generation and delivery process begins again.

[0089] Example 1

[0090] 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."

[0091] Conventional ad delivery systems simply collect user behavior data and display static ads, failing to fully utilize dynamic factors such as user interests and location information. As a result, advertising effectiveness is low, making it difficult to provide more effective ads to users. Furthermore, there are problems with the inefficient data collection and retraining processes required to evaluate the effectiveness of ads and reflect them in the generation of subsequent ads.

[0092] 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.

[0093] In this invention, the server includes means for collecting location information and behavioral data from user devices, means for storing the collected data in a database, means for analyzing user interests and concerns using a generative artificial intelligence, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to user devices in real time, means for collecting user response data to advertisements, means for retraining the generative artificial intelligence model using the collected response data, means for periodically collecting user location information and generating advertisements based thereon, and means for dynamically generating prompt sentences based on user behavior patterns and using them to generate advertisements. This makes it possible to dynamically analyze user interests and location information and generate and deliver optimal advertisements in real time.

[0094] "User's terminal" refers to an electronic device such as a computer or smartphone used by a user.

[0095] "Location information" means data that indicates the current location of a particular device using GPS, Wi-Fi, or other technologies.

[0096] "Behavioral data" refers to data that includes information such as clicks, scrolls, time spent, and search history when a user visits a website.

[0097] A "database" is a storage device that stores large amounts of data in a structured manner, making it easy to access, manage, and update.

[0098] "Generative AI" refers to AI technology that uses machine learning and deep learning to analyze data and generate new information and content.

[0099] "Analysis" refers to the process of finding patterns and relationships based on collected data and identifying user characteristics.

[0100] "Advertising" refers to information content that promotes a specific product or service and is provided in the form of text, images, video, etc.

[0101] "Real-time" refers to the fact that the entire process, from data collection and analysis to ad generation and delivery, is carried out instantly.

[0102] "Response data" refers to data that records actions such as clicking, swiping, and purchasing when users view an ad.

[0103] "Retraining" refers to the process of retraining a generative AI model with new data to improve the model's accuracy and performance.

[0104] A "prompt" is an instruction entered into a generative AI model that contains the information that will serve as the basis for generating specific content or advertisements.

[0105] The system of the present invention is a technology that generates and distributes optimal advertisements in real time based on user behavior data and location information. This system is realized by the cooperation of multiple hardware and software components.

[0106] First, the user's device obtains real-time location information using technologies such as GPS and Wi-Fi, and also collects behavioral data (clicks, scrolling, time spent, search history, etc.) when visiting websites or using apps. In this case, the location information includes the device's current coordinates, and the behavioral data includes details of what the user did and how. This data is encrypted using a secure protocol (e.g., HTTPS) and sent to a server at regular intervals.

[0107] When the server receives the transmitted data, it first stores the data in a database. A relational database management system (RDBMS) such as MySQL or PostgreSQL is used to manage the database. This database stores the user's location information, behavioral data, past behavioral history, and external data (such as weather information, traffic information, and event information).

[0108] The server then analyzes the collected data using a generative artificial intelligence (AI) model. Deep learning frameworks such as PyTorch and TensorFlow are used to identify user behavioral patterns and interests. For example, if a user frequently visits cafe-related sites, a "cafe lover" label is added to the user profile. Based on the analysis results, ads optimized for the target user are generated. The generative AI model is used to generate ad content in the form of text, images, and videos.

[0109] The generated advertisements are delivered in real time from the server to the user's device. The advertisements also contain metadata such as the target user ID, display time, and location. The user's device displays the received advertisements and collects user response data (e.g., clicks, swipes, purchases, etc.). This response data is also encrypted and sent to the server using a secure protocol.

[0110] The server evaluates the effectiveness of the advertisement based on the received user response data. Based on the evaluation results, such as click-through rate and purchase rate, the generative artificial intelligence (AI) model is retrained. The retraining improves the model's accuracy and performance, which is reflected in the next advertisement generation.

[0111] Specific examples

[0112] Below are some examples of advertisements that are generated and distributed by this system.

[0113] Example 1: Cafe advertising

[0114] If the user is in an office district, the device acquires location information and sends it to the server. The server analyzes that the user has frequently visited cafe-related websites in the past and determines that the user is a cafe lover. It also recognizes from external data that it is raining today. Based on this, the server generates an advertisement containing discount information for a nearby cafe. This advertisement is delivered to the user's device in real time, and when the user clicks on the advertisement, the response data is sent to the server. This allows the click-through rate to be evaluated and the generative AI model to be retrained.

[0115] Example 2: Concert advertising

[0116] When a user frequently uses a music streaming service, that behavioral data is sent to a server. The server analyzes the user's music preferences and determines that the user is interested in specific genres and artists. Information about nearby concerts is obtained from external data, and the server generates an advertisement containing ticket information for that concert. The generated advertisement is delivered to the user's device in real time, and when the user purchases a ticket, the data is sent to the server. This allows the purchase rate to be evaluated and the generative AI model to be retrained.

[0117] Prompt Sentence Examples

[0118] "It's raining today, the user is in an office building, and frequently visits cafe-related sites. What kind of ad should we generate?"

[0119] "If a user listens to a particular artist a lot and there's a concert of that artist nearby, what kind of ad should we generate?"

[0120] In this way, the system of the present invention can dynamically analyze user behavior data and local conditions to generate and deliver optimal advertisements in real time, thereby improving the accuracy and effectiveness of advertisements.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Data collection

[0123] The device collects user location and behavioral data, specifically by using GPS and Wi-Fi to obtain the device's current coordinates and recording user interaction data such as website visit history, clicks, scrolls, dwell time, and search history.

[0124] Input: User location information, behavioral data

[0125] Output: Location and behavior data collected

[0126] Step 2: Send data

[0127] The device encrypts the collected data and sends it to the server using the HTTPS protocol, where the data is encoded in JSON format.

[0128] Input: Collected location and behavioral data

[0129] Output: The encoded and encrypted data is sent to the server.

[0130] Step 3: Save Data

[0131] The server analyzes the received data and stores it in a database. Database management uses an RDBMS such as MySQL or PostgreSQL. The database stores user profiles, location information, behavioral data, and external data (weather, traffic, and event information).

[0132] Input: Data sent to the server

[0133] Output: Data saved to the database

[0134] Step 4: Data analysis

[0135] The server analyzes the stored data using a generative AI model. For example, it uses PyTorch or TensorFlow to identify user behavior patterns and interests. If a user frequently visits cafe-related sites, a "cafe lover" tag is added to the user profile.

[0136] Input: User data in the database

[0137] Output: Analysis results based on user interests

[0138] Step 5: Generate Ads

[0139] The server generates advertisements optimized for the target user based on the results of data analysis. It inputs prompt sentences into the generative AI model and creates advertisement content (text, images, videos).

[0140] Input: Data analysis results, prompt statement

[0141] Output: Ads optimized for the target user

[0142] Step 6: Ad serving

[0143] The server delivers the generated advertisements to the user's device in real time, and the advertisements contain metadata such as the target user ID, display time, and location.

[0144] Input: Generated ads, metadata

[0145] Output: Ad delivered to user device

[0146] Step 7: Reaction data collection

[0147] The user's device collects data on the user's response to the displayed advertisements (clicks, swipes, purchases), which is encrypted and periodically sent to a server.

[0148] Input: User response to the ad

[0149] Output: Encrypted reaction data sent to the server

[0150] Step 8: Retrain the model

[0151] The server retrains the generative AI model based on user response data, evaluating click rates and purchase rates, and retraining the model based on the new evaluation results.

[0152] Input: User response data, evaluation results of advertising effectiveness

[0153] Output: A generative AI model with improved accuracy through retraining

[0154] These processing steps enable the system to dynamically analyze user behavioral data and location information and provide optimal advertisements in real time.

[0155] (Application example 1)

[0156] 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."

[0157] Conventional advertising systems have limited methods for utilizing user location information and behavioral data, making it difficult to generate and deliver advertisements in real time based on user behavior, especially in physical stores. As a result, it has been impossible to provide advertisements optimized for users in a timely manner, resulting in a problem of reduced advertising effectiveness. The present invention aims to solve this problem and provide a system that can deliver advertisements optimized for the user's situation in real time, even when the user is in a physical store.

[0158] 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.

[0159] In this invention, the server includes means for collecting location information and behavioral data from a user's device, means for storing the collected data in a database, means for analyzing user interests and concerns using a generation AI, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to the user's device in real time, means for collecting user response data to the advertisements, means for retraining the generation AI model using the collected response data, and means for generating and delivering advertisements based on location information and related data within a physical store when the user is in the physical store. This enables optimization of advertisements based on user location information and behavioral data and real-time advertisement delivery within the physical store.

[0160] A "user's terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[0161] "Location information" is data that indicates a user's current geographic coordinates or location.

[0162] "Behavioral data" refers to information such as clicks, scrolls, time spent, search history, and locations visited when a user uses a device.

[0163] A "database" is a storage device for storing collected location information, behavioral data, and external data.

[0164] "Generative AI" is artificial intelligence that uses technologies such as machine learning and deep learning to analyze data and generate advertisements.

[0165] "Interests and concerns" refer to the user's preferences and interests that can be identified from the analyzed user's behavioral patterns and past data.

[0166] A "target user" is a specific user for whom an advertisement is generated.

[0167] An "advertisement" is content created for the purpose of conveying information about a product or service to users, and may take the form of text, images, video, etc.

[0168] "Real-time" means that data collection, analysis, ad generation, distribution, and response collection are all carried out simultaneously, with results reflected almost instantly.

[0169] "Response data" refers to feedback information such as clicks, swipes, and purchases made by users in response to an advertisement.

[0170] A "brick and mortar store" is a physical store where users can visit and purchase goods or services in person.

[0171] "Promotion information" is data about promotional activities such as discounts, coupons, and sales offered by stores.

[0172] "Optimization" is the process of generating and delivering the most effective ads based on user behavioral data and location information.

[0173] Embodiments of the invention include a process for efficient data collection, analysis, ad generation and delivery using a server, a user's terminal, and a generative AI model.

[0174] Data collection and storage

[0175] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) and location information (using GPS and Wi-Fi). This data is periodically sent to the server. The server stores the collected data in a database. This database includes user profiles, past behavior data, location information, and external data (weather information, traffic information, event information, promotion information, etc.).

[0176] Data analysis and ad generation

[0177] The server uses generative AI to analyze the data stored in the database. The analysis process identifies the user's behavioral patterns and interests. If the user is in a physical store, an advertisement optimized for the target user is generated based on the user's location information and related data within the store. The generated advertisements are created in the form of text, images, videos, etc.

[0178] Advertisement delivery and response data collection

[0179] The server delivers the generated advertisement to the user's device in real time. The user's device displays the received advertisement and sends the user's response data (clicks, swipes, purchases, etc.) back to the server. The server evaluates the effectiveness of the advertisement based on this response data.

[0180] Retraining the model

[0181] The server retrains the generative AI model using the collected user response data, improving the accuracy and effectiveness of ad generation. The new, retrained model is then used to generate the next ad.

[0182] Hardware and software used

[0183] Hardware: Servers (e.g. AWS EC2), smartphones (iOS / Android)

[0184] Software: Flask (backend framework), MongoDB (database management), TensorFlow / Keras (generative AI models)

[0185] Examples and prompts

[0186] As a specific example, if a user is in a shopping mall, an advertisement for a discount coupon for a fashion brand is generated based on location information and behavioral data collected from the device and delivered in real time.As another specific example, if a user is visiting a cafe-related site on a rainy day, an advertisement containing discount information for a nearby cafe is delivered.

[0187] Example prompt for a generative AI model:

[0188] "A user frequently visits fashion-related sites. Their location indicates they are currently at a shopping mall. Based on information about nearby stores, generate answers to the following prompts: 1. The weather is sunny. Please recommend stores with discounts on summer fashion items. 2. It's raining. Please provide discounts on rain gear and indoor facilities."

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Step 1:

[0191] Data collection

[0192] The user's device collects the user's location information (GPS and Wi-Fi) and interaction data (clicks, scrolls, dwell time, search history, etc.), which is periodically sent from the device to the server.

[0193] Input: User location, interaction data

[0194] Output: Location and interaction data sent to the server

[0195] What it does: Your smartphone obtains your location and collects behavioral data such as clicks and search history.

[0196] Step 2:

[0197] Data storage

[0198] The server stores the received location and interaction data in a database, including user profiles, past behavioral data, and external data (such as weather, traffic, event, and promotion information).

[0199] Input: Location and interaction data sent from your device

[0200] Output: Location and behavior data stored in a database

[0201] Specific operation: The server uses MongoDB to store location information and behavior data in a database.

[0202] Step 3:

[0203] Data analysis

[0204] The server uses a generative AI model to analyze the data stored in the database and identify users' behavioral patterns and interests.

[0205] Input: Location information, behavioral data, external data stored in the database

[0206] Output: Analysis results that identify user interests

[0207] Specific operation: The server analyzes the data using TensorFlow / Keras and identifies user behavior patterns.

[0208] Step 4:

[0209] Ad Generation

[0210] Based on the analysis results of the generative AI model, the server generates ads optimized for the target users, which can be in the form of text, images, videos, etc.

[0211] Input: Analysis results

[0212] Output: Generated ad content

[0213] What happens: The server generates and formats the ad content according to the analysis results.

[0214] Step 5:

[0215] Ad serving

[0216] The server delivers the generated advertisement to the user's terminal in real time, and the user's terminal receives and displays the advertisement.

[0217] Input: Generated ad content

[0218] Output: Ad displayed on the user's device

[0219] Specific operation: The server sends the generated advertisement to the user's smartphone, and the smartphone displays the advertisement.

[0220] Step 6:

[0221] Reaction data collection

[0222] The device collects data on the user's response to the advertisement (clicks, swipes, purchases, etc.) and sends it to the server.

[0223] Input: User response (click, swipe, purchase)

[0224] Output: Response data sent to the server

[0225] Specific operation: The user's smartphone collects reaction data and sends it to the server.

[0226] Step 7:

[0227] Retraining the model

[0228] The server uses the collected response data to retrain the generative AI model, thereby improving the accuracy and effectiveness of ad generation.

[0229] Input: User response data

[0230] Output: Retrained generative AI model

[0231] Specific operation: The server retrains the model using TensorFlow / Keras to improve the ad generation logic.

[0232] 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.

[0233] The system of the present invention collects location information and behavioral data from users' devices and combines it with an emotion engine that recognizes users' emotions. The system stores the data in a database, analyzes it using generative AI, generates and distributes advertisements in real time, collects user response data, and performs a series of operations including retraining.

[0234] Data collection and storage

[0235] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) in real time, and also obtains real-time location information using GPS and Wi-Fi. The emotion engine also collects emotional data (facial expressions, voice tone, heart rate, etc.) from the user's biometric sensors and camera.

[0236] The device periodically transmits collected behavioral, location, and emotional data to a server, including a timestamp and user ID.

[0237] The server stores the received data in a database, which includes user profiles, behavioral data, location information, emotional data, and external data (such as weather, traffic, and event information).

[0238] Data analysis and ad generation

[0239] The server uses generative AI to analyze the data stored in the database. The purpose of the analysis is to identify the user's behavioral patterns, interests, and emotional state. For example, if a user frequently visits cafe-related sites and their emotional data indicates that they are in a "feeling like relaxing," tags such as "I like cafes" and "I want to relax" will be added to the user's profile.

[0240] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be created in the form of text, images, videos, etc. For example, if a user is out on a rainy day and feels like "relaxing," the server generates an advertisement for a nearby cafe that offers discount information or a relaxing environment.

[0241] Advertisement delivery and response data collection

[0242] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[0243] The device displays the received advertisement to the user. When the advertisement is displayed, the device again collects the user's response data (clicks, swipes, purchases, etc.) and emotional data.

[0244] The device sends collected reaction and emotion data, including the ad display status and user actions, to a server.

[0245] Retraining the model

[0246] The server analyzes the response data and evaluates the effectiveness of the ad (e.g., click rate and purchase rate). It also evaluates changes in emotion based on the emotion data and analyzes how the ad influenced those emotions. Using the results of this evaluation, the generative AI model is retrained to improve the accuracy and effectiveness of ad generation. The next ad is generated using the new, retrained model.

[0247] Examples:

[0248] Example 1: Cafe advertising

[0249] When the user is in an office district, the device acquires location information and sends it to the server. The server also stores data indicating that the user has frequently visited cafe-related websites in the past and that their emotional data indicates they want to relax. Information indicating that the weather is rainy is obtained from external data.

[0250] 1. The server analyzes that the user likes cafes and is in the mood to relax.

[0251] 2. Considering the situation of a rainy day, generate ads for nearby cafes with discount information and relaxing environments.

[0252] 3. The server delivers this advertisement to the user's device in real time.

[0253] 4. The user's device displays the advertisement, and when the user clicks on the advertisement, the reaction data and emotion data are sent to the server.

[0254] 5. The server uses this data to evaluate ad click-through rates and changes in sentiment to retrain the generative AI model.

[0255] Example 2: Concert advertising

[0256] If a user frequently uses a music streaming service, their behavioral data will be sent to the server, and the user may be interested in a particular music genre or artist, and their emotional data may indicate that they are "excited."

[0257] 1. The server identifies the user's musical preferences and emotional mood.

[0258] 2. Information about nearby concerts is obtained from external data.

[0259] 3. The server generates an advertisement containing ticket information for this concert.

[0260] 4. The server delivers the generated advertisement to the user's device in real time.

[0261] 5. The user's device displays the advertisement, and when the user purchases a ticket, the data and emotional data are sent to the server.

[0262] 6. The server uses this data to evaluate the purchase rate and sentiment of the ads and retrain the generative AI model.

[0263] In this way, the present invention provides a system that analyzes user behavioral data, location information, and emotional state in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertising.

[0264] The processing flow will be explained below.

[0265] Step 1:

[0266] When a user begins using a website or application, the device collects user behavior data (clicks, scrolls, dwell time, search history, etc.) in real time. In addition, the device obtains the user's current location using GPS and Wi-Fi. Using an emotion engine, the device also collects emotional data (facial expressions, voice tone, heart rate, etc.) from biometric sensors and cameras.

[0267] Step 2:

[0268] The device periodically transmits collected behavioral, location, and emotional data to a server, including timestamps and user IDs.

[0269] Step 3:

[0270] The server stores the received data in a database, which includes user profiles, behavioral data, location information, emotional data, and external data (weather, traffic, event information, etc.).

[0271] Step 4:

[0272] The server uses generative AI to analyze the data stored in the database. This analysis identifies the user's behavioral patterns, interests, and emotional state. For example, if a user frequently visits cafe-related sites and the emotional data indicates that they are "in the mood to relax," tags for "cafe lover" and "want to relax" will be added to the user's profile.

[0273] Step 5:

[0274] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day and feels like "relaxing," the server generates advertisements for nearby cafes offering discount information and a relaxing environment.

[0275] Step 6:

[0276] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[0277] Step 7:

[0278] The device displays the received advertisement to the user. When the advertisement is displayed, the device again collects the user's response data (clicks, swipes, purchases, etc.) and new emotional data.

[0279] Step 8:

[0280] The device sends the collected reaction and emotion data to the server, including the display status of advertisements and user actions.

[0281] Step 9:

[0282] The server analyzes the response data and evaluates the effectiveness of the advertisement (click rate, purchase rate, etc.). It also evaluates changes in emotions based on the emotion data and analyzes how the advertisement affected emotions.

[0283] Step 10:

[0284] The server uses the evaluation results to retrain the generative AI model to improve the accuracy and effectiveness of ad generation, and then uses the new retrained model to generate the next ad. This process is repeated to continuously optimize the user experience.

[0285] Example 2

[0286] 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."

[0287] Conventional ad delivery systems have a problem in that they are unable to deliver ads that take into account not only the user's interests and concerns, but also their real-time emotional state. Furthermore, they are also inadequate at optimizing ads by effectively combining external factors (weather, traffic information, event information, etc.). As a result, they fail to attract user attention, resulting in low ad click rates and purchase rates.

[0288] 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.

[0289] In this invention, the server includes means for collecting location information and behavioral data from a user's device, means for storing the collected data in a database, means for analyzing the user's interests and emotional state using a generation AI, means for updating a user profile based on the analysis results, means for generating advertisements optimized for the target user based on the analysis results, means for delivering the generated advertisements to the user's device in real time, means for collecting user response data and emotional data regarding the advertisements, and means for retraining the generation AI model using the collected response data and emotional data. This enables more effective delivery of advertisements that reflect the user's real-time behavioral data and emotional state.

[0290] A "user terminal" is a computer or mobile information terminal operated by a user, and is a device for collecting behavioral data, location information, and emotional data of the user.

[0291] "Location information" is data indicating a user's current location and movement history obtained using GPS or Wi-Fi.

[0292] "Behavioral data" refers to interaction data such as clicks, scrolls, time spent, and search history when a user browses a website.

[0293] "Emotion data" refers to data that indicates the user's emotional state estimated from the user's facial expression, voice tone, heart rate, etc., collected using the user's biometric sensors and camera.

[0294] "Database" refers to a data management system for systematically storing collected behavioral data, location information, emotional data, and external data.

[0295] "Generative AI" is an artificial intelligence technology that analyzes user data, identifies their interests, concerns, and emotional state, and generates advertisements.

[0296] A "user profile" is detailed user information that includes the user's behavioral patterns, interests, concerns, emotional state, etc., which is updated based on the analysis results.

[0297] An "advertisement" is a message containing information (in the form of text, images, or videos) that is generated based on a user's behavioral and emotional data and delivered to the user's device.

[0298] "Response data" refers to behavioral data such as clicks, swipes, and purchases made by users in response to advertisements.

[0299] "Retraining" is the process of using collected reaction and sentiment data to update the generative AI model, improving the accuracy and effectiveness of ad generation.

[0300] The system of the present invention collects location information and behavioral data from the user's device, analyzes the user's interests, concerns, and emotional state using a generation AI, and generates and delivers optimal advertisements. Specific embodiments of the present invention are described below.

[0301] Data collection and transmission

[0302] The user's device collects real-time interaction data such as clicks, scrolls, dwell time, and search history. It also acquires location information using GPS and Wi-Fi. It also collects emotional data such as facial expressions, voice tone, and heart rate using biometric sensors and cameras. This data is sent from the device to the server at regular intervals (for example, every hour). The data is compressed before being sent to reduce communication load.

[0303] Data storage

[0304] The server stores the received data in a database, which includes user profiles, collected behavioral data, location information, emotional data, as well as external data such as weather, traffic, and event information. The data is stored in temporary storage and then integrated into the database through batch processing.

[0305] Data analysis

[0306] The server analyzes the stored data using generative AI models, such as machine learning algorithms, to identify user behavioral patterns, interests, and emotional states. Analysis is performed periodically (e.g., nightly), and data preprocessing includes filling in missing values ​​and removing outliers.

[0307] Ad generation and delivery

[0308] The server generates optimized ads based on the analysis results. Generative AI models (such as GPT-4 and GAN) create ads in text, image, and video formats. A specific prompt is provided: "The user is out on a rainy day, wanting to relax." The generated ads are delivered to the device in real time, along with metadata including the target user ID and delivery timing. For example, an ad could be triggered just before the user arrives in a specific area.

[0309] Response data collection and retraining

[0310] The device collects user response data (clicks, swipes, purchases, etc.) and emotional data (changes in facial expressions and voice tone while the ad is displayed). The display time of the ad and the user's operation log are also recorded at the same time, and the data is sent to the server.

[0311] The server uses the collected response data to retrain the generative AI model. For example, it evaluates changes in ad click rates, purchase rates, and sentiment data to generate a new learning dataset. This new dataset is then used for retraining and the next ad generation.

[0312] Specific examples

[0313] For example, if a user frequently visits a cafe-related website, the device collects that behavioral data and sends it to a server. The server analyzes the data and determines that the user is in the mood to relax. Furthermore, the server determines from external data that the weather for that day is rainy, and generates an advertisement offering discount information for a nearby cafe. The advertisement is delivered to the device when the user is in an office district, and when the user clicks on the advertisement, their reaction and emotional data are sent to the server. The server analyzes the data and retrains the generative AI model.

[0314] Specific prompt examples:

[0315] The situation is "The user is out on a rainy day wanting to relax."

[0316] In this way, the system of the present invention analyzes the user's behavioral data and emotional state in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertisements.

[0317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0318] Step 1:

[0319] Users' devices collect interaction data in real time, including clicks, scrolls, dwell time, and search history.

[0320] Input: Behavioral data about how users interact with websites and applications.

[0321] What it does: The device runs in the background and logs user interactions such as clicks, scrolls, dwell time, and search history.

[0322] Output: Collected real-time behavioral data.

[0323] Step 2:

[0324] The user's device acquires location information using GPS and Wi-Fi, and also collects emotional data using biometric sensors and cameras.

[0325] Input: GPS data, Wi-Fi access point location information, data from biometric sensors, and image and audio data from cameras.

[0326] Specific operation: The device acquires geographical location information, adds a timestamp and user ID to the collected data, and analyzes data from the camera and microphone in real time, converting facial expressions and voice tone into emotional data.

[0327] Output: Location information, emotion data.

[0328] Step 3:

[0329] The device transmits the collected behavioral data, location information, and emotion data to the server at regular intervals (for example, every hour).

[0330] Input: behavioral data, location information, emotion data.

[0331] How it works: Data is batched at regular intervals and compressed for communication. The device then sends the data to the server using a secure communication protocol.

[0332] Output: The data sent to the server.

[0333] Step 4:

[0334] The server stores the received data in a database.

[0335] Input: Behavioral, location, and emotional data sent from the device.

[0336] Specific operation: Incoming data is first stored in temporary storage, then batch-processed and integrated into the database. Any necessary data reformatting and missing value imputation are also performed.

[0337] Output: A set of user data stored in a database.

[0338] Step 5:

[0339] The server uses generative AI to analyze the data in the database.

[0340] Input: User profile, behavioral data, location information, and emotional data stored in a database.

[0341] How it works: Generative AI models use machine learning algorithms to identify user behavioral patterns, interests, and emotional states, including data preprocessing such as missing value imputation and outlier removal.

[0342] Output: Analysis results of user behavior patterns, interests, and emotional state.

[0343] Step 6:

[0344] The server generates an optimized advertisement based on the analysis results.

[0345] Input: Generated analysis results, prompt statement (e.g., "The user is out on a rainy day feeling relaxed").

[0346] How it works: A generative AI model (such as GPT-4 or GAN) creates an ad based on the analysis and prompt. The ad can be in the form of text, image, or video. The ad is then saved as an email or push notification template.

[0347] Output: The generated ad.

[0348] Step 7:

[0349] The server delivers the generated advertisement to the user's terminal in real time.

[0350] Input: Generated ad, target user ID, and delivery timing metadata.

[0351] Specific behavior: Deliver ads at the right time, taking into account the user's current location and behavior. For example, set it to trigger just before the user arrives in a specific area.

[0352] Output: Ads delivered to the user's device.

[0353] Step 8:

[0354] The device collects user response data (clicks, swipes, purchases, etc.) and emotional data (changes in facial expressions and tone of voice while the ad is displayed) to the displayed ad.

[0355] Input: The action the user took on the ad.

[0356] Specific operation: The device records the display time of the advertisement and the user's operation log, and also captures emotional changes through sensor data. This data is then sent back to the server.

[0357] Output: Collected reaction and sentiment data.

[0358] Step 9:

[0359] The server uses the collected reaction and emotion data to retrain the generative AI model.

[0360] Input: Collected reaction data, emotion data.

[0361] How it works: The server analyzes the response data to evaluate the effectiveness of the ad (click-through rate and purchase rate). It also analyzes the emotion data to evaluate the emotional impact of the ad. Based on these results, the generative AI model is retrained to generate a new learning dataset.

[0362] Output: A retrained generative AI model.

[0363] Through the above series of steps, it becomes possible to analyze user behavioral data and emotional data in real time and efficiently deliver optimized advertisements.

[0364] (Application example 2)

[0365] 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."

[0366] In modern society, simply using behavioral data and location information is insufficient to effectively deliver advertisements to consumers; it is also important to consider their emotional state at the time. Conventional advertising delivery systems generate and deliver advertisements based solely on a user's behavioral patterns and location information, but this does not necessarily provide advertisements that match the user's interests and emotions, limiting the effectiveness of the advertisements. The present invention aims to solve these problems and realize more personalized advertisement delivery to users.

[0367] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0368] In this invention, the server includes means for collecting location information and behavioral data from user devices, means for storing the collected data in a database, means for analyzing user interests and concerns using a generation AI, means for collecting emotion data in real time, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to user devices in real time, means for collecting user response data and emotion data regarding the advertisements, and means for retraining the generation AI model using the collected response data and emotion data. This enables the generation and delivery of personalized advertisements that comprehensively take into account the user's behavioral data, location information, and emotional state.

[0369] "Location information" is data indicating the geographic coordinates where the user's terminal is currently located.

[0370] "Behavioral data" refers to data about interactions such as clicks, scrolls, time spent, and search history on a user's device.

[0371] "Emotional data" refers to data about a user's emotional state extracted from facial expressions, voice tone, heart rate, etc. collected using biometric sensors or cameras.

[0372] "Real-time" refers to data being processed and delivered as soon as it is generated or captured.

[0373] "Generative AI" refers to artificial intelligence models used for data analysis and ad generation.

[0374] A "database" is a system for systematically storing and managing collected location information, behavioral data, emotional data, etc.

[0375] A "server" is a computer system that analyzes collected data and generates and distributes advertisements using generative AI.

[0376] "Advertisement" means content generated in the form of text, images or video that presents information intended to promote a product or service.

[0377] "Response data" is data about user behavior in response to an advertisement, such as clicking, swiping, or purchasing.

[0378] "Retraining" refers to the process of updating a generative AI model with new data collected to improve its accuracy and effectiveness.

[0379] A system for implementing this invention collects location information and behavioral data from a user's device and stores it in a database. It then uses a generation AI to analyze the user's interests and concerns. Based on the analysis results, it collects emotional data in real time and generates advertisements optimized for the target user. The generated advertisements are delivered to the user's device in real time, and user response data and emotional data regarding the advertisements are collected. Finally, the collected data is used to retrain the generation AI model.

[0380] The server performs the following processes using a Python program. First, the server collects real-time location and behavior data from the user's device. This data is processed using the geopy library. The collected data is stored in a database in JSON format. Emotion data is collected in real time from biometric sensors and cameras. EmotionRecognizer is used to analyze the user's facial expressions, voice tone, heart rate, etc.

[0381] The generative AI model analyzes user behavior patterns, location information, and emotional data to generate ads optimized for the target user. This is handled by AdGenerator and UserBehaviorAnalyzer. The generated ads are delivered in real time from the server to the user's device.

[0382] The user's device displays the ad, and again collects reaction data, such as clicks and swipes, and emotional data from the user, which is then sent to the server. The server uses this data to retrain the generative AI model and improve the accuracy and effectiveness of the ad.

[0383] As a concrete example, consider a case where a user is at a shopping mall. The server obtains the user's location information and determines from past data that the user is interested in clothes and accessories. If the server detects from emotional data that the user is in a "fun mood," it can generate and deliver immediately available sales information or promotional advertisements for specific brands to the user.

[0384] An example of a prompt is as follows:

[0385] "The user is located in Otemachi, Tokyo, and has visited fashion-related websites more than 10 times in the past month. They seem to be in a fun mood right now. Generate ads for this user with information about sales available right now and promotions for specific brands."

[0386] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0387] Step 1:

[0388] The user's device collects user behavioral data and location information. Behavioral data includes clicks, scrolling, time spent, search history, etc. Location information is obtained using GPS and Wi-Fi. The collected data is temporarily stored on the device.

[0389] Input: User interaction data, location information

[0390] Output: Collected behavioral data, location information

[0391] Step 2:

[0392] The device sends the collected behavioral data and location information to the server. This data is accompanied by a timestamp and user ID. The transmission uses the HTTP protocol.

[0393] Input: Collected behavioral data, location information, timestamp, user ID

[0394] Output: Behavioral data sent to the server, location information, timestamp, user ID

[0395] Step 3:

[0396] The server stores the received data in a database built using SQL or NoSQL, which stores user profiles, behavioral data, and location information.

[0397] Input: Behavioral data sent to the server, location information, timestamp, user ID

[0398] Output: Behavioral data stored in a database, location information, timestamp, and user ID.

[0399] Step 4:

[0400] The device collects real-time emotional data from users using biometric sensors and cameras, and uses EmotionRecognizer to analyze facial expressions, voice tone, heart rate, and more.

[0401] Input: User facial expressions, voice tones, heart rate

[0402] Output: Parsed emotion data

[0403] Step 5:

[0404] The terminal transmits the collected emotion data to the server.

[0405] Input: Parsed emotion data

[0406] Output: Emotion data sent to the server

[0407] Step 6:

[0408] The server uses behavioral, location, and emotional data to run generative AI models to analyze user interests and concerns, using AdGenerator and UserBehaviorAnalyzer.

[0409] Input: Behavioral data, location information, and emotion data stored in a database

[0410] Output: Analysis results (user profile, interests)

[0411] Step 7:

[0412] The server generates ads optimized for the target users based on the analysis results. The ads are generated in text, image, and video formats.

[0413] Input: Analysis results (user profile, interests)

[0414] Output: The generated ad

[0415] Step 8:

[0416] The server delivers the generated advertisement to the user's terminal in real time.

[0417] Input: Generated Ad

[0418] Output: Ads delivered to the device

[0419] Step 9:

[0420] The user's device displays the ads and collects user response data, including clicks, swipes, and purchases.

[0421] Input: Served ad

[0422] Output: Collected reaction data

[0423] Step 10:

[0424] The terminal transmits the collected reaction data and emotion data to the server.

[0425] Input: Reaction data, emotion data

[0426] Output: Reaction data and emotion data sent to the server

[0427] Step 11:

[0428] The server analyzes the collected reaction and sentiment data to evaluate the effectiveness of the advertisements, and retrains the generative AI model based on the evaluation results.

[0429] Input: Reaction data and emotion data sent to the server

[0430] Output: Retrained generative AI model

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Second embodiment]

[0435] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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."

[0447] The system of the present invention includes a series of operations that collect location information and behavioral data from user terminals, store them in a database, analyze them, generate advertisements, deliver them in real time, collect user response data, and perform retraining.

[0448] Data collection and storage

[0449] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) and also obtains real-time location information using GPS and Wi-Fi. This data is periodically sent by the device to the server.

[0450] The server stores the collected data in a database, which stores user profiles, past behavioral data, and location information. It also collects external data (weather information, traffic information, event information, etc.) and stores it in the database.

[0451] Data analysis and ad generation

[0452] The server analyzes the data stored in the database using generative AI. The analysis process identifies user behavioral patterns and interests. For example, if a user frequently visits sites related to cafes, a "cafe lover" tag is added to the user profile.

[0453] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day, an advertisement for discounts at a nearby cafe or for rain gear sales may be generated.

[0454] Advertisement delivery and response data collection

[0455] The server delivers the generated advertisements to the user's device in real time. The advertisements are sent along with metadata (target user ID, display time, location, etc.) to ensure proper targeting.

[0456] The user's device displays the received advertisement. After the advertisement is displayed, the device collects user response data (clicks, swipes, purchases, etc.) and sends it to the server.

[0457] Retraining the model

[0458] The server evaluates the effectiveness of the advertisement based on the received user response data. Based on the evaluation results (click rate, purchase rate, etc.), the generation AI model is retrained to improve the accuracy and effectiveness of advertisement generation. The next advertisement is generated using the new retrained model.

[0459] Examples:

[0460] Example 1: Cafe advertising

[0461] If the user is in an office district, the device acquires location information and sends it to the server. Data on the user's frequent visits to cafe-related websites is also stored on the server. Information that the weather is rainy is obtained from external data.

[0462] 1. The server analyzes that the user likes cafes.

[0463] 2. Generate an ad that takes into account the rainy day situation and includes discount information for a nearby cafe.

[0464] 3. The server delivers this advertisement to the user's device in real time.

[0465] 4. The user's device displays the advertisement, and when the user clicks on the advertisement, the response data is sent to the server.

[0466] 5. The server uses this data to evaluate ad click-through rates and retrain the generative AI model.

[0467] Example 2: Concert advertising

[0468] If a user frequently uses a music streaming service, their behavioral data is sent to the server, which analyzes their interest in specific music genres and artists.

[0469] 1. The server identifies the user's musical preferences.

[0470] 2. Information about nearby concerts is obtained from external data.

[0471] 3. The server generates an advertisement containing ticket information for this concert.

[0472] 4. The server delivers the generated advertisement to the user's device in real time.

[0473] 5. The user's device displays the advertisement, and when the user purchases a ticket, the data is sent to the server.

[0474] 6. The server uses this data to evaluate the purchase rate of ads and retrain the generative AI model.

[0475] In this way, the present invention provides a system that analyzes user behavior data and local conditions in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertisements.

[0476] The processing flow will be explained below.

[0477] Step 1:

[0478] When a user starts using a website or application, the device collects real-time data on the user's behavior (clicks, scrolling, time spent, search history, etc.) and also obtains the user's current location using GPS and Wi-Fi.

[0479] Step 2:

[0480] The device periodically sends collected behavioral data and location information, including timestamps and user IDs, to a server.

[0481] Step 3:

[0482] The server stores the received data in a database, which includes user profiles, behavioral data, location information, and external data (weather, traffic, event information, etc.).

[0483] Step 4:

[0484] The server uses generative AI to analyze the data stored in the database. The purpose of the analysis is to identify the user's behavioral patterns and interests. For example, if a user frequently visits sites related to cafes, a "cafe lover" tag will be added to the user's profile.

[0485] Step 5:

[0486] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day, the server generates advertisements for discounts at nearby cafes or for rain gear sales.

[0487] Step 6:

[0488] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[0489] Step 7:

[0490] The device then displays the received advertisement to the user. When the advertisement is displayed, the device again collects user response data (clicks, swipes, purchases, etc.).

[0491] Step 8:

[0492] The device sends the collected response data to the server, including the ad display status and user actions.

[0493] Step 9:

[0494] The server analyzes the response data and evaluates the effectiveness of the advertisement (e.g., click rate and purchase rate). This evaluation result is used to retrain the generative AI model.

[0495] Step 10:

[0496] The server then uses the new, retrained model to generate the next ad, continually improving the accuracy and effectiveness of the ad, and the entire ad generation and delivery process begins again.

[0497] Example 1

[0498] 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."

[0499] Conventional ad delivery systems simply collect user behavior data and display static ads, failing to fully utilize dynamic factors such as user interests and location information. As a result, advertising effectiveness is low, making it difficult to provide more effective ads to users. Furthermore, there are problems with the inefficient data collection and retraining processes required to evaluate the effectiveness of ads and reflect them in the generation of subsequent ads.

[0500] 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.

[0501] In this invention, the server includes means for collecting location information and behavioral data from user devices, means for storing the collected data in a database, means for analyzing user interests and concerns using a generative artificial intelligence, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to user devices in real time, means for collecting user response data to advertisements, means for retraining the generative artificial intelligence model using the collected response data, means for periodically collecting user location information and generating advertisements based thereon, and means for dynamically generating prompt sentences based on user behavior patterns and using them to generate advertisements. This makes it possible to dynamically analyze user interests and location information and generate and deliver optimal advertisements in real time.

[0502] "User's terminal" refers to an electronic device such as a computer or smartphone used by a user.

[0503] "Location information" means data that indicates the current location of a particular device using GPS, Wi-Fi, or other technologies.

[0504] "Behavioral data" refers to data that includes information such as clicks, scrolls, time spent, and search history when a user visits a website.

[0505] A "database" is a storage device that stores large amounts of data in a structured manner, making it easy to access, manage, and update.

[0506] "Generative AI" refers to AI technology that uses machine learning and deep learning to analyze data and generate new information and content.

[0507] "Analysis" refers to the process of finding patterns and relationships based on collected data and identifying user characteristics.

[0508] "Advertising" refers to information content that promotes a specific product or service and is provided in the form of text, images, video, etc.

[0509] "Real-time" refers to the fact that the entire process, from data collection and analysis to ad generation and delivery, is carried out instantly.

[0510] "Response data" refers to data that records actions such as clicking, swiping, and purchasing when users view an ad.

[0511] "Retraining" refers to the process of retraining a generative AI model with new data to improve the model's accuracy and performance.

[0512] A "prompt" is an instruction entered into a generative AI model that contains the information that will serve as the basis for generating specific content or advertisements.

[0513] The system of the present invention is a technology that generates and distributes optimal advertisements in real time based on user behavior data and location information. This system is realized by the cooperation of multiple hardware and software components.

[0514] First, the user's device obtains real-time location information using technologies such as GPS and Wi-Fi, and also collects behavioral data (clicks, scrolling, time spent, search history, etc.) when visiting websites or using apps. In this case, the location information includes the device's current coordinates, and the behavioral data includes details of what the user did and how. This data is encrypted using a secure protocol (e.g., HTTPS) and sent to a server at regular intervals.

[0515] When the server receives the transmitted data, it first stores the data in a database. A relational database management system (RDBMS) such as MySQL or PostgreSQL is used to manage the database. This database stores the user's location information, behavioral data, past behavioral history, and external data (such as weather information, traffic information, and event information).

[0516] The server then analyzes the collected data using a generative artificial intelligence (AI) model. Deep learning frameworks such as PyTorch and TensorFlow are used to identify user behavioral patterns and interests. For example, if a user frequently visits cafe-related sites, a "cafe lover" label is added to the user profile. Based on the analysis results, ads optimized for the target user are generated. The generative AI model is used to generate ad content in the form of text, images, and videos.

[0517] The generated advertisements are delivered in real time from the server to the user's device. The advertisements also contain metadata such as the target user ID, display time, and location. The user's device displays the received advertisements and collects user response data (e.g., clicks, swipes, purchases, etc.). This response data is also encrypted and sent to the server using a secure protocol.

[0518] The server evaluates the effectiveness of the advertisement based on the received user response data. Based on the evaluation results, such as click-through rate and purchase rate, the generative artificial intelligence (AI) model is retrained. The retraining improves the model's accuracy and performance, which is reflected in the next advertisement generation.

[0519] Specific examples

[0520] Below are some examples of advertisements that are generated and distributed by this system.

[0521] Example 1: Cafe advertising

[0522] If the user is in an office district, the device acquires location information and sends it to the server. The server analyzes that the user has frequently visited cafe-related websites in the past and determines that the user is a cafe lover. It also recognizes from external data that it is raining today. Based on this, the server generates an advertisement containing discount information for a nearby cafe. This advertisement is delivered to the user's device in real time, and when the user clicks on the advertisement, the response data is sent to the server. This allows the click-through rate to be evaluated and the generative AI model to be retrained.

[0523] Example 2: Concert advertising

[0524] When a user frequently uses a music streaming service, that behavioral data is sent to a server. The server analyzes the user's music preferences and determines that the user is interested in specific genres and artists. Information about nearby concerts is obtained from external data, and the server generates an advertisement containing ticket information for that concert. The generated advertisement is delivered to the user's device in real time, and when the user purchases a ticket, the data is sent to the server. This allows the purchase rate to be evaluated and the generative AI model to be retrained.

[0525] Prompt Sentence Examples

[0526] "It's raining today, the user is in an office building, and frequently visits cafe-related sites. What kind of ad should we generate?"

[0527] "If a user listens to a particular artist a lot and there's a concert of that artist nearby, what kind of ad should we generate?"

[0528] In this way, the system of the present invention can dynamically analyze user behavior data and local conditions to generate and deliver optimal advertisements in real time, thereby improving the accuracy and effectiveness of advertisements.

[0529] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0530] Step 1: Data collection

[0531] The device collects user location and behavioral data, specifically by using GPS and Wi-Fi to obtain the device's current coordinates and recording user interaction data such as website visit history, clicks, scrolls, dwell time, and search history.

[0532] Input: User location information, behavioral data

[0533] Output: Location and behavior data collected

[0534] Step 2: Send data

[0535] The device encrypts the collected data and sends it to the server using the HTTPS protocol, where the data is encoded in JSON format.

[0536] Input: Collected location and behavioral data

[0537] Output: The encoded and encrypted data is sent to the server.

[0538] Step 3: Save Data

[0539] The server analyzes the received data and stores it in a database. Database management uses an RDBMS such as MySQL or PostgreSQL. The database stores user profiles, location information, behavioral data, and external data (weather, traffic, and event information).

[0540] Input: Data sent to the server

[0541] Output: Data saved to the database

[0542] Step 4: Data analysis

[0543] The server analyzes the stored data using a generative AI model. For example, it uses PyTorch or TensorFlow to identify user behavior patterns and interests. If a user frequently visits cafe-related sites, a "cafe lover" tag is added to the user profile.

[0544] Input: User data in the database

[0545] Output: Analysis results based on user interests

[0546] Step 5: Generate Ads

[0547] The server generates advertisements optimized for the target user based on the results of data analysis. It inputs prompt sentences into the generative AI model and creates advertisement content (text, images, videos).

[0548] Input: Data analysis results, prompt statement

[0549] Output: Ads optimized for the target user

[0550] Step 6: Ad serving

[0551] The server delivers the generated advertisements to the user's device in real time, and the advertisements contain metadata such as the target user ID, display time, and location.

[0552] Input: Generated ads, metadata

[0553] Output: Ad delivered to user device

[0554] Step 7: Reaction data collection

[0555] The user's device collects data on the user's response to the displayed advertisements (clicks, swipes, purchases), which is encrypted and periodically sent to a server.

[0556] Input: User response to the ad

[0557] Output: Encrypted reaction data sent to the server

[0558] Step 8: Retrain the model

[0559] The server retrains the generative AI model based on user response data, evaluating click rates and purchase rates, and retraining the model based on the new evaluation results.

[0560] Input: User response data, evaluation results of advertising effectiveness

[0561] Output: A generative AI model with improved accuracy through retraining

[0562] These processing steps enable the system to dynamically analyze user behavioral data and location information and provide optimal advertisements in real time.

[0563] (Application example 1)

[0564] 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."

[0565] Conventional advertising systems have limited methods for utilizing user location information and behavioral data, making it difficult to generate and deliver advertisements in real time based on user behavior, especially in physical stores. As a result, it has been impossible to provide advertisements optimized for users in a timely manner, resulting in a problem of reduced advertising effectiveness. The present invention aims to solve this problem and provide a system that can deliver advertisements optimized for the user's situation in real time, even when the user is in a physical store.

[0566] 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.

[0567] In this invention, the server includes means for collecting location information and behavioral data from a user's device, means for storing the collected data in a database, means for analyzing user interests and concerns using a generation AI, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to the user's device in real time, means for collecting user response data to the advertisements, means for retraining the generation AI model using the collected response data, and means for generating and delivering advertisements based on location information and related data within a physical store when the user is in the physical store. This enables optimization of advertisements based on user location information and behavioral data and real-time advertisement delivery within the physical store.

[0568] A "user's terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[0569] "Location information" is data that indicates a user's current geographic coordinates or location.

[0570] "Behavioral data" refers to information such as clicks, scrolls, time spent, search history, and locations visited when a user uses a device.

[0571] A "database" is a storage device for storing collected location information, behavioral data, and external data.

[0572] "Generative AI" is artificial intelligence that uses technologies such as machine learning and deep learning to analyze data and generate advertisements.

[0573] "Interests and concerns" refer to the user's preferences and interests that can be identified from the analyzed user's behavioral patterns and past data.

[0574] A "target user" is a specific user for whom an advertisement is generated.

[0575] An "advertisement" is content created for the purpose of conveying information about a product or service to users, and may take the form of text, images, video, etc.

[0576] "Real-time" means that data collection, analysis, ad generation, distribution, and response collection are all carried out simultaneously, with results reflected almost instantly.

[0577] "Response data" refers to feedback information such as clicks, swipes, and purchases made by users in response to an advertisement.

[0578] A "brick and mortar store" is a physical store where users can visit and purchase goods or services in person.

[0579] "Promotion information" is data about promotional activities such as discounts, coupons, and sales offered by stores.

[0580] "Optimization" is the process of generating and delivering the most effective ads based on user behavioral data and location information.

[0581] Embodiments of the invention include a process for efficient data collection, analysis, ad generation and delivery using a server, a user's terminal, and a generative AI model.

[0582] Data collection and storage

[0583] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) and location information (using GPS and Wi-Fi). This data is periodically sent to the server. The server stores the collected data in a database. This database includes user profiles, past behavior data, location information, and external data (weather information, traffic information, event information, promotion information, etc.).

[0584] Data analysis and ad generation

[0585] The server uses generative AI to analyze the data stored in the database. The analysis process identifies the user's behavioral patterns and interests. If the user is in a physical store, an advertisement optimized for the target user is generated based on the user's location information and related data within the store. The generated advertisements are created in the form of text, images, videos, etc.

[0586] Advertisement delivery and response data collection

[0587] The server delivers the generated advertisement to the user's device in real time. The user's device displays the received advertisement and sends the user's response data (clicks, swipes, purchases, etc.) back to the server. The server evaluates the effectiveness of the advertisement based on this response data.

[0588] Retraining the model

[0589] The server retrains the generative AI model using the collected user response data, improving the accuracy and effectiveness of ad generation. The new, retrained model is then used to generate the next ad.

[0590] Hardware and software used

[0591] Hardware: Servers (e.g. AWS EC2), smartphones (iOS / Android)

[0592] Software: Flask (backend framework), MongoDB (database management), TensorFlow / Keras (generative AI models)

[0593] Examples and prompts

[0594] As a specific example, if a user is in a shopping mall, an advertisement for a discount coupon for a fashion brand is generated based on location information and behavioral data collected from the device and delivered in real time.As another specific example, if a user is visiting a cafe-related site on a rainy day, an advertisement containing discount information for a nearby cafe is delivered.

[0595] Example prompt for a generative AI model:

[0596] "A user frequently visits fashion-related sites. Their location indicates they are currently at a shopping mall. Based on information about nearby stores, generate answers to the following prompts: 1. The weather is sunny. Please recommend stores with discounts on summer fashion items. 2. It's raining. Please provide discounts on rain gear and indoor facilities."

[0597] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0598] Step 1:

[0599] Data collection

[0600] The user's device collects the user's location information (GPS and Wi-Fi) and interaction data (clicks, scrolls, dwell time, search history, etc.), which is periodically sent from the device to the server.

[0601] Input: User location, interaction data

[0602] Output: Location and interaction data sent to the server

[0603] What it does: Your smartphone obtains your location and collects behavioral data such as clicks and search history.

[0604] Step 2:

[0605] Data storage

[0606] The server stores the received location and interaction data in a database, including user profiles, past behavioral data, and external data (such as weather, traffic, event, and promotion information).

[0607] Input: Location and interaction data sent from your device

[0608] Output: Location and behavior data stored in a database

[0609] Specific operation: The server uses MongoDB to store location information and behavior data in a database.

[0610] Step 3:

[0611] Data analysis

[0612] The server uses a generative AI model to analyze the data stored in the database and identify users' behavioral patterns and interests.

[0613] Input: Location information, behavioral data, external data stored in the database

[0614] Output: Analysis results that identify user interests

[0615] Specific operation: The server analyzes the data using TensorFlow / Keras and identifies user behavior patterns.

[0616] Step 4:

[0617] Ad Generation

[0618] Based on the analysis results of the generative AI model, the server generates ads optimized for the target users, which can be in the form of text, images, videos, etc.

[0619] Input: Analysis results

[0620] Output: Generated ad content

[0621] What happens: The server generates and formats the ad content according to the analysis results.

[0622] Step 5:

[0623] Ad serving

[0624] The server delivers the generated advertisement to the user's terminal in real time, and the user's terminal receives and displays the advertisement.

[0625] Input: Generated ad content

[0626] Output: Ad displayed on the user's device

[0627] Specific operation: The server sends the generated advertisement to the user's smartphone, and the smartphone displays the advertisement.

[0628] Step 6:

[0629] Reaction data collection

[0630] The device collects data on the user's response to the advertisement (clicks, swipes, purchases, etc.) and sends it to the server.

[0631] Input: User response (click, swipe, purchase)

[0632] Output: Response data sent to the server

[0633] Specific operation: The user's smartphone collects reaction data and sends it to the server.

[0634] Step 7:

[0635] Retraining the model

[0636] The server uses the collected response data to retrain the generative AI model, thereby improving the accuracy and effectiveness of ad generation.

[0637] Input: User response data

[0638] Output: Retrained generative AI model

[0639] Specific operation: The server retrains the model using TensorFlow / Keras to improve the ad generation logic.

[0640] 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.

[0641] The system of the present invention collects location information and behavioral data from users' devices and combines it with an emotion engine that recognizes users' emotions. The system stores the data in a database, analyzes it using generative AI, generates and distributes advertisements in real time, collects user response data, and performs a series of operations including retraining.

[0642] Data collection and storage

[0643] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) in real time, and also obtains real-time location information using GPS and Wi-Fi. The emotion engine also collects emotional data (facial expressions, voice tone, heart rate, etc.) from the user's biometric sensors and camera.

[0644] The device periodically transmits collected behavioral, location, and emotional data to a server, including a timestamp and user ID.

[0645] The server stores the received data in a database, which includes user profiles, behavioral data, location information, emotional data, and external data (such as weather, traffic, and event information).

[0646] Data analysis and ad generation

[0647] The server uses generative AI to analyze the data stored in the database. The purpose of the analysis is to identify the user's behavioral patterns, interests, and emotional state. For example, if a user frequently visits cafe-related sites and their emotional data indicates that they are in a "feeling like relaxing," tags such as "I like cafes" and "I want to relax" will be added to the user's profile.

[0648] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be created in the form of text, images, videos, etc. For example, if a user is out on a rainy day and feels like "relaxing," the server generates an advertisement for a nearby cafe that offers discount information or a relaxing environment.

[0649] Advertisement delivery and response data collection

[0650] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[0651] The device displays the received advertisement to the user. When the advertisement is displayed, the device again collects the user's response data (clicks, swipes, purchases, etc.) and emotional data.

[0652] The device sends collected reaction and emotion data, including the ad display status and user actions, to a server.

[0653] Retraining the model

[0654] The server analyzes the response data and evaluates the effectiveness of the ad (e.g., click rate and purchase rate). It also evaluates changes in emotion based on the emotion data and analyzes how the ad influenced those emotions. Using the results of this evaluation, the generative AI model is retrained to improve the accuracy and effectiveness of ad generation. The next ad is generated using the new, retrained model.

[0655] Examples:

[0656] Example 1: Cafe advertising

[0657] When the user is in an office district, the device acquires location information and sends it to the server. The server also stores data indicating that the user has frequently visited cafe-related websites in the past and that their emotional data indicates they want to relax. Information indicating that the weather is rainy is obtained from external data.

[0658] 1. The server analyzes that the user likes cafes and is in the mood to relax.

[0659] 2. Considering the situation of a rainy day, generate ads for nearby cafes with discount information and relaxing environments.

[0660] 3. The server delivers this advertisement to the user's device in real time.

[0661] 4. The user's device displays the advertisement, and when the user clicks on the advertisement, the reaction data and emotion data are sent to the server.

[0662] 5. The server uses this data to evaluate ad click-through rates and changes in sentiment to retrain the generative AI model.

[0663] Example 2: Concert advertising

[0664] If a user frequently uses a music streaming service, their behavioral data will be sent to the server, and the user may be interested in a particular music genre or artist, and their emotional data may indicate that they are "excited."

[0665] 1. The server identifies the user's musical preferences and emotional mood.

[0666] 2. Information about nearby concerts is obtained from external data.

[0667] 3. The server generates an advertisement containing ticket information for this concert.

[0668] 4. The server delivers the generated advertisement to the user's device in real time.

[0669] 5. The user's device displays the advertisement, and when the user purchases a ticket, the data and emotional data are sent to the server.

[0670] 6. The server uses this data to evaluate the purchase rate and sentiment of the ads and retrain the generative AI model.

[0671] In this way, the present invention provides a system that analyzes user behavioral data, location information, and emotional state in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertising.

[0672] The processing flow will be explained below.

[0673] Step 1:

[0674] When a user begins using a website or application, the device collects user behavior data (clicks, scrolls, dwell time, search history, etc.) in real time. In addition, the device obtains the user's current location using GPS and Wi-Fi. Using an emotion engine, the device also collects emotional data (facial expressions, voice tone, heart rate, etc.) from biometric sensors and cameras.

[0675] Step 2:

[0676] The device periodically transmits collected behavioral, location, and emotional data to a server, including timestamps and user IDs.

[0677] Step 3:

[0678] The server stores the received data in a database, which includes user profiles, behavioral data, location information, emotional data, and external data (weather, traffic, event information, etc.).

[0679] Step 4:

[0680] The server uses generative AI to analyze the data stored in the database. This analysis identifies the user's behavioral patterns, interests, and emotional state. For example, if a user frequently visits cafe-related sites and the emotional data indicates that they are "in the mood to relax," tags for "cafe lover" and "want to relax" will be added to the user's profile.

[0681] Step 5:

[0682] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day and feels like "relaxing," the server generates advertisements for nearby cafes offering discount information and a relaxing environment.

[0683] Step 6:

[0684] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[0685] Step 7:

[0686] The device displays the received advertisement to the user. When the advertisement is displayed, the device again collects the user's response data (clicks, swipes, purchases, etc.) and new emotional data.

[0687] Step 8:

[0688] The device sends the collected reaction and emotion data to the server, including the display status of advertisements and user actions.

[0689] Step 9:

[0690] The server analyzes the response data and evaluates the effectiveness of the advertisement (click rate, purchase rate, etc.). It also evaluates changes in emotions based on the emotion data and analyzes how the advertisement affected emotions.

[0691] Step 10:

[0692] The server uses the evaluation results to retrain the generative AI model to improve the accuracy and effectiveness of ad generation, and then uses the new retrained model to generate the next ad. This process is repeated to continuously optimize the user experience.

[0693] Example 2

[0694] 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."

[0695] Conventional ad delivery systems have a problem in that they are unable to deliver ads that take into account not only the user's interests and concerns, but also their real-time emotional state. Furthermore, they are also inadequate at optimizing ads by effectively combining external factors (weather, traffic information, event information, etc.). As a result, they fail to attract user attention, resulting in low ad click rates and purchase rates.

[0696] 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.

[0697] In this invention, the server includes means for collecting location information and behavioral data from a user's device, means for storing the collected data in a database, means for analyzing the user's interests and emotional state using a generation AI, means for updating a user profile based on the analysis results, means for generating advertisements optimized for the target user based on the analysis results, means for delivering the generated advertisements to the user's device in real time, means for collecting user response data and emotional data regarding the advertisements, and means for retraining the generation AI model using the collected response data and emotional data. This enables more effective delivery of advertisements that reflect the user's real-time behavioral data and emotional state.

[0698] A "user terminal" is a computer or mobile information terminal operated by a user, and is a device for collecting behavioral data, location information, and emotional data of the user.

[0699] "Location information" is data indicating a user's current location and movement history obtained using GPS or Wi-Fi.

[0700] "Behavioral data" refers to interaction data such as clicks, scrolls, time spent, and search history when a user browses a website.

[0701] "Emotion data" refers to data that indicates the user's emotional state estimated from the user's facial expression, voice tone, heart rate, etc., collected using the user's biometric sensors and camera.

[0702] "Database" refers to a data management system for systematically storing collected behavioral data, location information, emotional data, and external data.

[0703] "Generative AI" is an artificial intelligence technology that analyzes user data, identifies their interests, concerns, and emotional state, and generates advertisements.

[0704] A "user profile" is detailed user information that includes the user's behavioral patterns, interests, concerns, emotional state, etc., which is updated based on the analysis results.

[0705] An "advertisement" is a message containing information (in the form of text, images, or videos) that is generated based on a user's behavioral and emotional data and delivered to the user's device.

[0706] "Response data" refers to behavioral data such as clicks, swipes, and purchases made by users in response to advertisements.

[0707] "Retraining" is the process of using collected reaction and sentiment data to update the generative AI model, improving the accuracy and effectiveness of ad generation.

[0708] The system of the present invention collects location information and behavioral data from the user's device, analyzes the user's interests, concerns, and emotional state using a generation AI, and generates and delivers optimal advertisements. Specific embodiments of the present invention are described below.

[0709] Data collection and transmission

[0710] The user's device collects real-time interaction data such as clicks, scrolls, dwell time, and search history. It also acquires location information using GPS and Wi-Fi. It also collects emotional data such as facial expressions, voice tone, and heart rate using biometric sensors and cameras. This data is sent from the device to the server at regular intervals (for example, every hour). The data is compressed before being sent to reduce communication load.

[0711] Data storage

[0712] The server stores the received data in a database, which includes user profiles, collected behavioral data, location information, emotional data, as well as external data such as weather, traffic, and event information. The data is stored in temporary storage and then integrated into the database through batch processing.

[0713] Data analysis

[0714] The server analyzes the stored data using generative AI models, such as machine learning algorithms, to identify user behavioral patterns, interests, and emotional states. Analysis is performed periodically (e.g., nightly), and data preprocessing includes filling in missing values ​​and removing outliers.

[0715] Ad generation and delivery

[0716] The server generates optimized ads based on the analysis results. Generative AI models (such as GPT-4 and GAN) create ads in text, image, and video formats. A specific prompt is provided: "The user is out on a rainy day, wanting to relax." The generated ads are delivered to the device in real time, along with metadata including the target user ID and delivery timing. For example, an ad could be triggered just before the user arrives in a specific area.

[0717] Response data collection and retraining

[0718] The device collects user response data (clicks, swipes, purchases, etc.) and emotional data (changes in facial expressions and voice tone while the ad is displayed). The display time of the ad and the user's operation log are also recorded at the same time, and the data is sent to the server.

[0719] The server uses the collected response data to retrain the generative AI model. For example, it evaluates changes in ad click rates, purchase rates, and sentiment data to generate a new learning dataset. This new dataset is then used for retraining and the next ad generation.

[0720] Specific examples

[0721] For example, if a user frequently visits a cafe-related website, the device collects that behavioral data and sends it to a server. The server analyzes the data and determines that the user is in the mood to relax. Furthermore, the server determines from external data that the weather for that day is rainy, and generates an advertisement offering discount information for a nearby cafe. The advertisement is delivered to the device when the user is in an office district, and when the user clicks on the advertisement, their reaction and emotional data are sent to the server. The server analyzes the data and retrains the generative AI model.

[0722] Specific prompt examples:

[0723] The situation is "The user is out on a rainy day wanting to relax."

[0724] In this way, the system of the present invention analyzes the user's behavioral data and emotional state in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertisements.

[0725] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0726] Step 1:

[0727] Users' devices collect interaction data in real time, including clicks, scrolls, dwell time, and search history.

[0728] Input: Behavioral data about how users interact with websites and applications.

[0729] What it does: The device runs in the background and logs user interactions such as clicks, scrolls, dwell time, and search history.

[0730] Output: Collected real-time behavioral data.

[0731] Step 2:

[0732] The user's device acquires location information using GPS and Wi-Fi, and also collects emotional data using biometric sensors and cameras.

[0733] Input: GPS data, Wi-Fi access point location information, data from biometric sensors, and image and audio data from cameras.

[0734] Specific operation: The device acquires geographical location information, adds a timestamp and user ID to the collected data, and analyzes data from the camera and microphone in real time, converting facial expressions and voice tone into emotional data.

[0735] Output: Location information, emotion data.

[0736] Step 3:

[0737] The device transmits the collected behavioral data, location information, and emotion data to the server at regular intervals (for example, every hour).

[0738] Input: behavioral data, location information, emotion data.

[0739] How it works: Data is batched at regular intervals and compressed for communication. The device then sends the data to the server using a secure communication protocol.

[0740] Output: The data sent to the server.

[0741] Step 4:

[0742] The server stores the received data in a database.

[0743] Input: Behavioral, location, and emotional data sent from the device.

[0744] Specific operation: Incoming data is first stored in temporary storage, then batch-processed and integrated into the database. Any necessary data reformatting and missing value imputation are also performed.

[0745] Output: A set of user data stored in a database.

[0746] Step 5:

[0747] The server uses generative AI to analyze the data in the database.

[0748] Input: User profile, behavioral data, location information, and emotional data stored in a database.

[0749] How it works: Generative AI models use machine learning algorithms to identify user behavioral patterns, interests, and emotional states, including data preprocessing such as missing value imputation and outlier removal.

[0750] Output: Analysis results of user behavior patterns, interests, and emotional state.

[0751] Step 6:

[0752] The server generates an optimized advertisement based on the analysis results.

[0753] Input: Generated analysis results, prompt statement (e.g., "The user is out on a rainy day feeling relaxed").

[0754] How it works: A generative AI model (such as GPT-4 or GAN) creates an ad based on the analysis and prompt. The ad can be in the form of text, image, or video. The ad is then saved as an email or push notification template.

[0755] Output: The generated ad.

[0756] Step 7:

[0757] The server delivers the generated advertisement to the user's terminal in real time.

[0758] Input: Generated ad, target user ID, and delivery timing metadata.

[0759] Specific behavior: Deliver ads at the right time, taking into account the user's current location and behavior. For example, set it to trigger just before the user arrives in a specific area.

[0760] Output: Ads delivered to the user's device.

[0761] Step 8:

[0762] The device collects user response data (clicks, swipes, purchases, etc.) and emotional data (changes in facial expressions and tone of voice while the ad is displayed) to the displayed ad.

[0763] Input: The action the user took on the ad.

[0764] Specific operation: The device records the display time of the advertisement and the user's operation log, and also captures emotional changes through sensor data. This data is then sent back to the server.

[0765] Output: Collected reaction and sentiment data.

[0766] Step 9:

[0767] The server uses the collected reaction and emotion data to retrain the generative AI model.

[0768] Input: Collected reaction data, emotion data.

[0769] How it works: The server analyzes the response data to evaluate the effectiveness of the ad (click-through rate and purchase rate). It also analyzes the emotion data to evaluate the emotional impact of the ad. Based on these results, the generative AI model is retrained to generate a new learning dataset.

[0770] Output: A retrained generative AI model.

[0771] Through the above series of steps, it becomes possible to analyze user behavioral data and emotional data in real time and efficiently deliver optimized advertisements.

[0772] (Application example 2)

[0773] 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."

[0774] In modern society, simply using behavioral data and location information is insufficient to effectively deliver advertisements to consumers; it is also important to consider their emotional state at the time. Conventional advertising delivery systems generate and deliver advertisements based solely on a user's behavioral patterns and location information, but this does not necessarily provide advertisements that match the user's interests and emotions, limiting the effectiveness of the advertisements. The present invention aims to solve these problems and realize more personalized advertisement delivery to users.

[0775] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0776] In this invention, the server includes means for collecting location information and behavioral data from user devices, means for storing the collected data in a database, means for analyzing user interests and concerns using a generation AI, means for collecting emotion data in real time, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to user devices in real time, means for collecting user response data and emotion data regarding the advertisements, and means for retraining the generation AI model using the collected response data and emotion data. This enables the generation and delivery of personalized advertisements that comprehensively take into account the user's behavioral data, location information, and emotional state.

[0777] "Location information" is data indicating the geographic coordinates where the user's terminal is currently located.

[0778] "Behavioral data" refers to data about interactions such as clicks, scrolls, time spent, and search history on a user's device.

[0779] "Emotional data" refers to data about a user's emotional state extracted from facial expressions, voice tone, heart rate, etc. collected using biometric sensors or cameras.

[0780] "Real-time" refers to data being processed and delivered as soon as it is generated or captured.

[0781] "Generative AI" refers to artificial intelligence models used for data analysis and ad generation.

[0782] A "database" is a system for systematically storing and managing collected location information, behavioral data, emotional data, etc.

[0783] A "server" is a computer system that analyzes collected data and generates and distributes advertisements using generative AI.

[0784] "Advertisement" means content generated in the form of text, images or video that presents information intended to promote a product or service.

[0785] "Response data" is data about user behavior in response to an advertisement, such as clicking, swiping, or purchasing.

[0786] "Retraining" refers to the process of updating a generative AI model with new data collected to improve its accuracy and effectiveness.

[0787] A system for implementing this invention collects location information and behavioral data from a user's device and stores it in a database. It then uses a generation AI to analyze the user's interests and concerns. Based on the analysis results, it collects emotional data in real time and generates advertisements optimized for the target user. The generated advertisements are delivered to the user's device in real time, and user response data and emotional data regarding the advertisements are collected. Finally, the collected data is used to retrain the generation AI model.

[0788] The server performs the following processes using a Python program. First, the server collects real-time location and behavior data from the user's device. This data is processed using the geopy library. The collected data is stored in a database in JSON format. Emotion data is collected in real time from biometric sensors and cameras. EmotionRecognizer is used to analyze the user's facial expressions, voice tone, heart rate, etc.

[0789] The generative AI model analyzes user behavior patterns, location information, and emotional data to generate ads optimized for the target user. This is handled by AdGenerator and UserBehaviorAnalyzer. The generated ads are delivered in real time from the server to the user's device.

[0790] The user's device displays the ad, and again collects reaction data, such as clicks and swipes, and emotional data from the user, which is then sent to the server. The server uses this data to retrain the generative AI model and improve the accuracy and effectiveness of the ad.

[0791] As a concrete example, consider a case where a user is at a shopping mall. The server obtains the user's location information and determines from past data that the user is interested in clothes and accessories. If the server detects from emotional data that the user is in a "fun mood," it can generate and deliver immediately available sales information or promotional advertisements for specific brands to the user.

[0792] An example of a prompt is as follows:

[0793] "The user is located in Otemachi, Tokyo, and has visited fashion-related websites more than 10 times in the past month. They seem to be in a fun mood right now. Generate ads for this user with information about sales available right now and promotions for specific brands."

[0794] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0795] Step 1:

[0796] The user's device collects user behavioral data and location information. Behavioral data includes clicks, scrolling, time spent, search history, etc. Location information is obtained using GPS and Wi-Fi. The collected data is temporarily stored on the device.

[0797] Input: User interaction data, location information

[0798] Output: Collected behavioral data, location information

[0799] Step 2:

[0800] The device sends the collected behavioral data and location information to the server. This data is accompanied by a timestamp and user ID. The transmission uses the HTTP protocol.

[0801] Input: Collected behavioral data, location information, timestamp, user ID

[0802] Output: Behavioral data sent to the server, location information, timestamp, user ID

[0803] Step 3:

[0804] The server stores the received data in a database built using SQL or NoSQL, which stores user profiles, behavioral data, and location information.

[0805] Input: Behavioral data sent to the server, location information, timestamp, user ID

[0806] Output: Behavioral data stored in a database, location information, timestamp, and user ID.

[0807] Step 4:

[0808] The device collects real-time emotional data from users using biometric sensors and cameras, and uses EmotionRecognizer to analyze facial expressions, voice tone, heart rate, and more.

[0809] Input: User facial expressions, voice tones, heart rate

[0810] Output: Parsed emotion data

[0811] Step 5:

[0812] The terminal transmits the collected emotion data to the server.

[0813] Input: Parsed emotion data

[0814] Output: Emotion data sent to the server

[0815] Step 6:

[0816] The server uses behavioral, location, and emotional data to run generative AI models to analyze user interests and concerns, using AdGenerator and UserBehaviorAnalyzer.

[0817] Input: Behavioral data, location information, and emotion data stored in a database

[0818] Output: Analysis results (user profile, interests)

[0819] Step 7:

[0820] The server generates ads optimized for the target users based on the analysis results. The ads are generated in text, image, and video formats.

[0821] Input: Analysis results (user profile, interests)

[0822] Output: The generated ad

[0823] Step 8:

[0824] The server delivers the generated advertisement to the user's terminal in real time.

[0825] Input: Generated Ad

[0826] Output: Ads delivered to the device

[0827] Step 9:

[0828] The user's device displays the ads and collects user response data, including clicks, swipes, and purchases.

[0829] Input: Served ad

[0830] Output: Collected reaction data

[0831] Step 10:

[0832] The terminal transmits the collected reaction data and emotion data to the server.

[0833] Input: Reaction data, emotion data

[0834] Output: Reaction data and emotion data sent to the server

[0835] Step 11:

[0836] The server analyzes the collected reaction and sentiment data to evaluate the effectiveness of the advertisements, and retrains the generative AI model based on the evaluation results.

[0837] Input: Reaction data and emotion data sent to the server

[0838] Output: Retrained generative AI model

[0839] 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.

[0840] 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.

[0841] 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.

[0842] [Third embodiment]

[0843] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0844] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0845] 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).

[0846] 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.

[0847] 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.

[0848] 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).

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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."

[0855] The system of the present invention includes a series of operations that collect location information and behavioral data from user terminals, store them in a database, analyze them, generate advertisements, deliver them in real time, collect user response data, and perform retraining.

[0856] Data collection and storage

[0857] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) and also obtains real-time location information using GPS and Wi-Fi. This data is periodically sent by the device to the server.

[0858] The server stores the collected data in a database, which stores user profiles, past behavioral data, and location information. It also collects external data (weather information, traffic information, event information, etc.) and stores it in the database.

[0859] Data analysis and ad generation

[0860] The server analyzes the data stored in the database using generative AI. The analysis process identifies user behavioral patterns and interests. For example, if a user frequently visits sites related to cafes, a "cafe lover" tag is added to the user profile.

[0861] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day, an advertisement for discounts at a nearby cafe or for rain gear sales may be generated.

[0862] Advertisement delivery and response data collection

[0863] The server delivers the generated advertisements to the user's device in real time. The advertisements are sent along with metadata (target user ID, display time, location, etc.) to ensure proper targeting.

[0864] The user's device displays the received advertisement. After the advertisement is displayed, the device collects user response data (clicks, swipes, purchases, etc.) and sends it to the server.

[0865] Retraining the model

[0866] The server evaluates the effectiveness of the advertisement based on the received user response data. Based on the evaluation results (click rate, purchase rate, etc.), the generation AI model is retrained to improve the accuracy and effectiveness of advertisement generation. The next advertisement is generated using the new retrained model.

[0867] Examples:

[0868] Example 1: Cafe advertising

[0869] If the user is in an office district, the device acquires location information and sends it to the server. Data on the user's frequent visits to cafe-related websites is also stored on the server. Information that the weather is rainy is obtained from external data.

[0870] 1. The server analyzes that the user likes cafes.

[0871] 2. Generate an ad that takes into account the rainy day situation and includes discount information for a nearby cafe.

[0872] 3. The server delivers this advertisement to the user's device in real time.

[0873] 4. The user's device displays the advertisement, and when the user clicks on the advertisement, the response data is sent to the server.

[0874] 5. The server uses this data to evaluate ad click-through rates and retrain the generative AI model.

[0875] Example 2: Concert advertising

[0876] If a user frequently uses a music streaming service, their behavioral data is sent to the server, which analyzes their interest in specific music genres and artists.

[0877] 1. The server identifies the user's musical preferences.

[0878] 2. Information about nearby concerts is obtained from external data.

[0879] 3. The server generates an advertisement containing ticket information for this concert.

[0880] 4. The server delivers the generated advertisement to the user's device in real time.

[0881] 5. The user's device displays the advertisement, and when the user purchases a ticket, the data is sent to the server.

[0882] 6. The server uses this data to evaluate the purchase rate of ads and retrain the generative AI model.

[0883] In this way, the present invention provides a system that analyzes user behavior data and local conditions in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertisements.

[0884] The processing flow will be explained below.

[0885] Step 1:

[0886] When a user starts using a website or application, the device collects real-time data on the user's behavior (clicks, scrolling, time spent, search history, etc.) and also obtains the user's current location using GPS and Wi-Fi.

[0887] Step 2:

[0888] The device periodically sends collected behavioral data and location information, including timestamps and user IDs, to a server.

[0889] Step 3:

[0890] The server stores the received data in a database, which includes user profiles, behavioral data, location information, and external data (weather, traffic, event information, etc.).

[0891] Step 4:

[0892] The server uses generative AI to analyze the data stored in the database. The purpose of the analysis is to identify the user's behavioral patterns and interests. For example, if a user frequently visits sites related to cafes, a "cafe lover" tag will be added to the user's profile.

[0893] Step 5:

[0894] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day, the server generates advertisements for discounts at nearby cafes or for rain gear sales.

[0895] Step 6:

[0896] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[0897] Step 7:

[0898] The device then displays the received advertisement to the user. When the advertisement is displayed, the device again collects user response data (clicks, swipes, purchases, etc.).

[0899] Step 8:

[0900] The device sends the collected response data to the server, including the ad display status and user actions.

[0901] Step 9:

[0902] The server analyzes the response data and evaluates the effectiveness of the advertisement (e.g., click rate and purchase rate). This evaluation result is used to retrain the generative AI model.

[0903] Step 10:

[0904] The server then uses the new, retrained model to generate the next ad, continually improving the accuracy and effectiveness of the ad, and the entire ad generation and delivery process begins again.

[0905] Example 1

[0906] 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."

[0907] Conventional ad delivery systems simply collect user behavior data and display static ads, failing to fully utilize dynamic factors such as user interests and location information. As a result, advertising effectiveness is low, making it difficult to provide more effective ads to users. Furthermore, there are problems with the inefficient data collection and retraining processes required to evaluate the effectiveness of ads and reflect them in the generation of subsequent ads.

[0908] 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.

[0909] In this invention, the server includes means for collecting location information and behavioral data from user devices, means for storing the collected data in a database, means for analyzing user interests and concerns using a generative artificial intelligence, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to user devices in real time, means for collecting user response data to advertisements, means for retraining the generative artificial intelligence model using the collected response data, means for periodically collecting user location information and generating advertisements based thereon, and means for dynamically generating prompt sentences based on user behavior patterns and using them to generate advertisements. This makes it possible to dynamically analyze user interests and location information and generate and deliver optimal advertisements in real time.

[0910] "User's terminal" refers to an electronic device such as a computer or smartphone used by a user.

[0911] "Location information" means data that indicates the current location of a particular device using GPS, Wi-Fi, or other technologies.

[0912] "Behavioral data" refers to data that includes information such as clicks, scrolls, time spent, and search history when a user visits a website.

[0913] A "database" is a storage device that stores large amounts of data in a structured manner, making it easy to access, manage, and update.

[0914] "Generative AI" refers to AI technology that uses machine learning and deep learning to analyze data and generate new information and content.

[0915] "Analysis" refers to the process of finding patterns and relationships based on collected data and identifying user characteristics.

[0916] "Advertising" refers to information content that promotes a specific product or service and is provided in the form of text, images, video, etc.

[0917] "Real-time" refers to the fact that the entire process, from data collection and analysis to ad generation and delivery, is carried out instantly.

[0918] "Response data" refers to data that records actions such as clicking, swiping, and purchasing when users view an ad.

[0919] "Retraining" refers to the process of retraining a generative AI model with new data to improve the model's accuracy and performance.

[0920] A "prompt" is an instruction entered into a generative AI model that contains the information that will serve as the basis for generating specific content or advertisements.

[0921] The system of the present invention is a technology that generates and distributes optimal advertisements in real time based on user behavior data and location information. This system is realized by the cooperation of multiple hardware and software components.

[0922] First, the user's device obtains real-time location information using technologies such as GPS and Wi-Fi, and also collects behavioral data (clicks, scrolling, time spent, search history, etc.) when visiting websites or using apps. In this case, the location information includes the device's current coordinates, and the behavioral data includes details of what the user did and how. This data is encrypted using a secure protocol (e.g., HTTPS) and sent to a server at regular intervals.

[0923] When the server receives the transmitted data, it first stores the data in a database. A relational database management system (RDBMS) such as MySQL or PostgreSQL is used to manage the database. This database stores the user's location information, behavioral data, past behavioral history, and external data (such as weather information, traffic information, and event information).

[0924] The server then analyzes the collected data using a generative artificial intelligence (AI) model. Deep learning frameworks such as PyTorch and TensorFlow are used to identify user behavioral patterns and interests. For example, if a user frequently visits cafe-related sites, a "cafe lover" label is added to the user profile. Based on the analysis results, ads optimized for the target user are generated. The generative AI model is used to generate ad content in the form of text, images, and videos.

[0925] The generated advertisements are delivered in real time from the server to the user's device. The advertisements also contain metadata such as the target user ID, display time, and location. The user's device displays the received advertisements and collects user response data (e.g., clicks, swipes, purchases, etc.). This response data is also encrypted and sent to the server using a secure protocol.

[0926] The server evaluates the effectiveness of the advertisement based on the received user response data. Based on the evaluation results, such as click-through rate and purchase rate, the generative artificial intelligence (AI) model is retrained. The retraining improves the model's accuracy and performance, which is reflected in the next advertisement generation.

[0927] Specific examples

[0928] Below are some examples of advertisements that are generated and distributed by this system.

[0929] Example 1: Cafe advertising

[0930] If the user is in an office district, the device acquires location information and sends it to the server. The server analyzes that the user has frequently visited cafe-related websites in the past and determines that the user is a cafe lover. It also recognizes from external data that it is raining today. Based on this, the server generates an advertisement containing discount information for a nearby cafe. This advertisement is delivered to the user's device in real time, and when the user clicks on the advertisement, the response data is sent to the server. This allows the click-through rate to be evaluated and the generative AI model to be retrained.

[0931] Example 2: Concert advertising

[0932] When a user frequently uses a music streaming service, that behavioral data is sent to a server. The server analyzes the user's music preferences and determines that the user is interested in specific genres and artists. Information about nearby concerts is obtained from external data, and the server generates an advertisement containing ticket information for that concert. The generated advertisement is delivered to the user's device in real time, and when the user purchases a ticket, the data is sent to the server. This allows the purchase rate to be evaluated and the generative AI model to be retrained.

[0933] Prompt Sentence Examples

[0934] "It's raining today, the user is in an office building, and frequently visits cafe-related sites. What kind of ad should we generate?"

[0935] "If a user listens to a particular artist a lot and there's a concert of that artist nearby, what kind of ad should we generate?"

[0936] In this way, the system of the present invention can dynamically analyze user behavior data and local conditions to generate and deliver optimal advertisements in real time, thereby improving the accuracy and effectiveness of advertisements.

[0937] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0938] Step 1: Data collection

[0939] The device collects user location and behavioral data, specifically by using GPS and Wi-Fi to obtain the device's current coordinates and recording user interaction data such as website visit history, clicks, scrolls, dwell time, and search history.

[0940] Input: User location information, behavioral data

[0941] Output: Location and behavior data collected

[0942] Step 2: Send data

[0943] The device encrypts the collected data and sends it to the server using the HTTPS protocol, where the data is encoded in JSON format.

[0944] Input: Collected location and behavioral data

[0945] Output: The encoded and encrypted data is sent to the server.

[0946] Step 3: Save Data

[0947] The server analyzes the received data and stores it in a database. Database management uses an RDBMS such as MySQL or PostgreSQL. The database stores user profiles, location information, behavioral data, and external data (weather, traffic, and event information).

[0948] Input: Data sent to the server

[0949] Output: Data saved to the database

[0950] Step 4: Data analysis

[0951] The server analyzes the stored data using a generative AI model. For example, it uses PyTorch or TensorFlow to identify user behavior patterns and interests. If a user frequently visits cafe-related sites, a "cafe lover" tag is added to the user profile.

[0952] Input: User data in the database

[0953] Output: Analysis results based on user interests

[0954] Step 5: Generate Ads

[0955] The server generates advertisements optimized for the target user based on the results of data analysis. It inputs prompt sentences into the generative AI model and creates advertisement content (text, images, videos).

[0956] Input: Data analysis results, prompt statement

[0957] Output: Ads optimized for the target user

[0958] Step 6: Ad serving

[0959] The server delivers the generated advertisements to the user's device in real time, and the advertisements contain metadata such as the target user ID, display time, and location.

[0960] Input: Generated ads, metadata

[0961] Output: Ad delivered to user device

[0962] Step 7: Reaction data collection

[0963] The user's device collects data on the user's response to the displayed advertisements (clicks, swipes, purchases), which is encrypted and periodically sent to a server.

[0964] Input: User response to the ad

[0965] Output: Encrypted reaction data sent to the server

[0966] Step 8: Retrain the model

[0967] The server retrains the generative AI model based on user response data, evaluating click rates and purchase rates, and retraining the model based on the new evaluation results.

[0968] Input: User response data, evaluation results of advertising effectiveness

[0969] Output: A generative AI model with improved accuracy through retraining

[0970] These processing steps enable the system to dynamically analyze user behavioral data and location information and provide optimal advertisements in real time.

[0971] (Application example 1)

[0972] 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."

[0973] Conventional advertising systems have limited methods for utilizing user location information and behavioral data, making it difficult to generate and deliver advertisements in real time based on user behavior, especially in physical stores. As a result, it has been impossible to provide advertisements optimized for users in a timely manner, resulting in a problem of reduced advertising effectiveness. The present invention aims to solve this problem and provide a system that can deliver advertisements optimized for the user's situation in real time, even when the user is in a physical store.

[0974] 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.

[0975] In this invention, the server includes means for collecting location information and behavioral data from a user's device, means for storing the collected data in a database, means for analyzing user interests and concerns using a generation AI, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to the user's device in real time, means for collecting user response data to the advertisements, means for retraining the generation AI model using the collected response data, and means for generating and delivering advertisements based on location information and related data within a physical store when the user is in the physical store. This enables optimization of advertisements based on user location information and behavioral data and real-time advertisement delivery within the physical store.

[0976] A "user's terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[0977] "Location information" is data that indicates a user's current geographic coordinates or location.

[0978] "Behavioral data" refers to information such as clicks, scrolls, time spent, search history, and locations visited when a user uses a device.

[0979] A "database" is a storage device for storing collected location information, behavioral data, and external data.

[0980] "Generative AI" is artificial intelligence that uses technologies such as machine learning and deep learning to analyze data and generate advertisements.

[0981] "Interests and concerns" refer to the user's preferences and interests that can be identified from the analyzed user's behavioral patterns and past data.

[0982] A "target user" is a specific user for whom an advertisement is generated.

[0983] An "advertisement" is content created for the purpose of conveying information about a product or service to users, and may take the form of text, images, video, etc.

[0984] "Real-time" means that data collection, analysis, ad generation, distribution, and response collection are all carried out simultaneously, with results reflected almost instantly.

[0985] "Response data" refers to feedback information such as clicks, swipes, and purchases made by users in response to an advertisement.

[0986] A "brick and mortar store" is a physical store where users can visit and purchase goods or services in person.

[0987] "Promotion information" is data about promotional activities such as discounts, coupons, and sales offered by stores.

[0988] "Optimization" is the process of generating and delivering the most effective ads based on user behavioral data and location information.

[0989] Embodiments of the invention include a process for efficient data collection, analysis, ad generation and delivery using a server, a user's terminal, and a generative AI model.

[0990] Data collection and storage

[0991] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) and location information (using GPS and Wi-Fi). This data is periodically sent to the server. The server stores the collected data in a database. This database includes user profiles, past behavior data, location information, and external data (weather information, traffic information, event information, promotion information, etc.).

[0992] Data analysis and ad generation

[0993] The server uses generative AI to analyze the data stored in the database. The analysis process identifies the user's behavioral patterns and interests. If the user is in a physical store, an advertisement optimized for the target user is generated based on the user's location information and related data within the store. The generated advertisements are created in the form of text, images, videos, etc.

[0994] Advertisement delivery and response data collection

[0995] The server delivers the generated advertisement to the user's device in real time. The user's device displays the received advertisement and sends the user's response data (clicks, swipes, purchases, etc.) back to the server. The server evaluates the effectiveness of the advertisement based on this response data.

[0996] Retraining the model

[0997] The server retrains the generative AI model using the collected user response data, improving the accuracy and effectiveness of ad generation. The new, retrained model is then used to generate the next ad.

[0998] Hardware and software used

[0999] Hardware: Servers (e.g. AWS EC2), smartphones (iOS / Android)

[1000] Software: Flask (backend framework), MongoDB (database management), TensorFlow / Keras (generative AI models)

[1001] Examples and prompts

[1002] As a specific example, if a user is in a shopping mall, an advertisement for a discount coupon for a fashion brand is generated based on location information and behavioral data collected from the device and delivered in real time.As another specific example, if a user is visiting a cafe-related site on a rainy day, an advertisement containing discount information for a nearby cafe is delivered.

[1003] Example prompt for a generative AI model:

[1004] "A user frequently visits fashion-related sites. Their location indicates they are currently at a shopping mall. Based on information about nearby stores, generate answers to the following prompts: 1. The weather is sunny. Please recommend stores with discounts on summer fashion items. 2. It's raining. Please provide discounts on rain gear and indoor facilities."

[1005] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1006] Step 1:

[1007] Data collection

[1008] The user's device collects the user's location information (GPS and Wi-Fi) and interaction data (clicks, scrolls, dwell time, search history, etc.), which is periodically sent from the device to the server.

[1009] Input: User location, interaction data

[1010] Output: Location and interaction data sent to the server

[1011] What it does: Your smartphone obtains your location and collects behavioral data such as clicks and search history.

[1012] Step 2:

[1013] Data storage

[1014] The server stores the received location and interaction data in a database, including user profiles, past behavioral data, and external data (such as weather, traffic, event, and promotion information).

[1015] Input: Location and interaction data sent from your device

[1016] Output: Location and behavior data stored in a database

[1017] Specific operation: The server uses MongoDB to store location information and behavior data in a database.

[1018] Step 3:

[1019] Data analysis

[1020] The server uses a generative AI model to analyze the data stored in the database and identify users' behavioral patterns and interests.

[1021] Input: Location information, behavioral data, external data stored in the database

[1022] Output: Analysis results that identify user interests

[1023] Specific operation: The server analyzes the data using TensorFlow / Keras and identifies user behavior patterns.

[1024] Step 4:

[1025] Ad Generation

[1026] Based on the analysis results of the generative AI model, the server generates ads optimized for the target users, which can be in the form of text, images, videos, etc.

[1027] Input: Analysis results

[1028] Output: Generated ad content

[1029] What happens: The server generates and formats the ad content according to the analysis results.

[1030] Step 5:

[1031] Ad serving

[1032] The server delivers the generated advertisement to the user's terminal in real time, and the user's terminal receives and displays the advertisement.

[1033] Input: Generated ad content

[1034] Output: Ad displayed on the user's device

[1035] Specific operation: The server sends the generated advertisement to the user's smartphone, and the smartphone displays the advertisement.

[1036] Step 6:

[1037] Reaction data collection

[1038] The device collects data on the user's response to the advertisement (clicks, swipes, purchases, etc.) and sends it to the server.

[1039] Input: User response (click, swipe, purchase)

[1040] Output: Response data sent to the server

[1041] Specific operation: The user's smartphone collects reaction data and sends it to the server.

[1042] Step 7:

[1043] Retraining the model

[1044] The server uses the collected response data to retrain the generative AI model, thereby improving the accuracy and effectiveness of ad generation.

[1045] Input: User response data

[1046] Output: Retrained generative AI model

[1047] Specific operation: The server retrains the model using TensorFlow / Keras to improve the ad generation logic.

[1048] 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.

[1049] The system of the present invention collects location information and behavioral data from users' devices and combines it with an emotion engine that recognizes users' emotions. The system stores the data in a database, analyzes it using generative AI, generates and distributes advertisements in real time, collects user response data, and performs a series of operations including retraining.

[1050] Data collection and storage

[1051] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) in real time, and also obtains real-time location information using GPS and Wi-Fi. The emotion engine also collects emotional data (facial expressions, voice tone, heart rate, etc.) from the user's biometric sensors and camera.

[1052] The device periodically transmits collected behavioral, location, and emotional data to a server, including a timestamp and user ID.

[1053] The server stores the received data in a database, which includes user profiles, behavioral data, location information, emotional data, and external data (such as weather, traffic, and event information).

[1054] Data analysis and ad generation

[1055] The server uses generative AI to analyze the data stored in the database. The purpose of the analysis is to identify the user's behavioral patterns, interests, and emotional state. For example, if a user frequently visits cafe-related sites and their emotional data indicates that they are in a "feeling like relaxing," tags such as "I like cafes" and "I want to relax" will be added to the user's profile.

[1056] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be created in the form of text, images, videos, etc. For example, if a user is out on a rainy day and feels like "relaxing," the server generates an advertisement for a nearby cafe that offers discount information or a relaxing environment.

[1057] Advertisement delivery and response data collection

[1058] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[1059] The device displays the received advertisement to the user. When the advertisement is displayed, the device again collects the user's response data (clicks, swipes, purchases, etc.) and emotional data.

[1060] The device sends collected reaction and emotion data, including the ad display status and user actions, to a server.

[1061] Retraining the model

[1062] The server analyzes the response data and evaluates the effectiveness of the ad (e.g., click rate and purchase rate). It also evaluates changes in emotion based on the emotion data and analyzes how the ad influenced those emotions. Using the results of this evaluation, the generative AI model is retrained to improve the accuracy and effectiveness of ad generation. The next ad is generated using the new, retrained model.

[1063] Examples:

[1064] Example 1: Cafe advertising

[1065] When the user is in an office district, the device acquires location information and sends it to the server. The server also stores data indicating that the user has frequently visited cafe-related websites in the past and that their emotional data indicates they want to relax. Information indicating that the weather is rainy is obtained from external data.

[1066] 1. The server analyzes that the user likes cafes and is in the mood to relax.

[1067] 2. Considering the situation of a rainy day, generate ads for nearby cafes with discount information and relaxing environments.

[1068] 3. The server delivers this advertisement to the user's device in real time.

[1069] 4. The user's device displays the advertisement, and when the user clicks on the advertisement, the reaction data and emotion data are sent to the server.

[1070] 5. The server uses this data to evaluate ad click-through rates and changes in sentiment to retrain the generative AI model.

[1071] Example 2: Concert advertising

[1072] If a user frequently uses a music streaming service, their behavioral data will be sent to the server, and the user may be interested in a particular music genre or artist, and their emotional data may indicate that they are "excited."

[1073] 1. The server identifies the user's musical preferences and emotional mood.

[1074] 2. Information about nearby concerts is obtained from external data.

[1075] 3. The server generates an advertisement containing ticket information for this concert.

[1076] 4. The server delivers the generated advertisement to the user's device in real time.

[1077] 5. The user's device displays the advertisement, and when the user purchases a ticket, the data and emotional data are sent to the server.

[1078] 6. The server uses this data to evaluate the purchase rate and sentiment of the ads and retrain the generative AI model.

[1079] In this way, the present invention provides a system that analyzes user behavioral data, location information, and emotional state in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertising.

[1080] The processing flow will be explained below.

[1081] Step 1:

[1082] When a user begins using a website or application, the device collects user behavior data (clicks, scrolls, dwell time, search history, etc.) in real time. In addition, the device obtains the user's current location using GPS and Wi-Fi. Using an emotion engine, the device also collects emotional data (facial expressions, voice tone, heart rate, etc.) from biometric sensors and cameras.

[1083] Step 2:

[1084] The device periodically transmits collected behavioral, location, and emotional data to a server, including timestamps and user IDs.

[1085] Step 3:

[1086] The server stores the received data in a database, which includes user profiles, behavioral data, location information, emotional data, and external data (weather, traffic, event information, etc.).

[1087] Step 4:

[1088] The server uses generative AI to analyze the data stored in the database. This analysis identifies the user's behavioral patterns, interests, and emotional state. For example, if a user frequently visits cafe-related sites and the emotional data indicates that they are "in the mood to relax," tags for "cafe lover" and "want to relax" will be added to the user's profile.

[1089] Step 5:

[1090] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day and feels like "relaxing," the server generates advertisements for nearby cafes offering discount information and a relaxing environment.

[1091] Step 6:

[1092] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[1093] Step 7:

[1094] The device displays the received advertisement to the user. When the advertisement is displayed, the device again collects the user's response data (clicks, swipes, purchases, etc.) and new emotional data.

[1095] Step 8:

[1096] The device sends the collected reaction and emotion data to the server, including the display status of advertisements and user actions.

[1097] Step 9:

[1098] The server analyzes the response data and evaluates the effectiveness of the advertisement (click rate, purchase rate, etc.). It also evaluates changes in emotions based on the emotion data and analyzes how the advertisement affected emotions.

[1099] Step 10:

[1100] The server uses the evaluation results to retrain the generative AI model to improve the accuracy and effectiveness of ad generation, and then uses the new retrained model to generate the next ad. This process is repeated to continuously optimize the user experience.

[1101] Example 2

[1102] 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."

[1103] Conventional ad delivery systems have a problem in that they are unable to deliver ads that take into account not only the user's interests and concerns, but also their real-time emotional state. Furthermore, they are also inadequate at optimizing ads by effectively combining external factors (weather, traffic information, event information, etc.). As a result, they fail to attract user attention, resulting in low ad click rates and purchase rates.

[1104] 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.

[1105] In this invention, the server includes means for collecting location information and behavioral data from a user's device, means for storing the collected data in a database, means for analyzing the user's interests and emotional state using a generation AI, means for updating a user profile based on the analysis results, means for generating advertisements optimized for the target user based on the analysis results, means for delivering the generated advertisements to the user's device in real time, means for collecting user response data and emotional data regarding the advertisements, and means for retraining the generation AI model using the collected response data and emotional data. This enables more effective delivery of advertisements that reflect the user's real-time behavioral data and emotional state.

[1106] A "user terminal" is a computer or mobile information terminal operated by a user, and is a device for collecting behavioral data, location information, and emotional data of the user.

[1107] "Location information" is data indicating a user's current location and movement history obtained using GPS or Wi-Fi.

[1108] "Behavioral data" refers to interaction data such as clicks, scrolls, time spent, and search history when a user browses a website.

[1109] "Emotion data" refers to data that indicates the user's emotional state estimated from the user's facial expression, voice tone, heart rate, etc., collected using the user's biometric sensors and camera.

[1110] "Database" refers to a data management system for systematically storing collected behavioral data, location information, emotional data, and external data.

[1111] "Generative AI" is an artificial intelligence technology that analyzes user data, identifies their interests, concerns, and emotional state, and generates advertisements.

[1112] A "user profile" is detailed user information that includes the user's behavioral patterns, interests, concerns, emotional state, etc., which is updated based on the analysis results.

[1113] An "advertisement" is a message containing information (in the form of text, images, or videos) that is generated based on a user's behavioral and emotional data and delivered to the user's device.

[1114] "Response data" refers to behavioral data such as clicks, swipes, and purchases made by users in response to advertisements.

[1115] "Retraining" is the process of using collected reaction and sentiment data to update the generative AI model, improving the accuracy and effectiveness of ad generation.

[1116] The system of the present invention collects location information and behavioral data from the user's device, analyzes the user's interests, concerns, and emotional state using a generation AI, and generates and delivers optimal advertisements. Specific embodiments of the present invention are described below.

[1117] Data collection and transmission

[1118] The user's device collects real-time interaction data such as clicks, scrolls, dwell time, and search history. It also acquires location information using GPS and Wi-Fi. It also collects emotional data such as facial expressions, voice tone, and heart rate using biometric sensors and cameras. This data is sent from the device to the server at regular intervals (for example, every hour). The data is compressed before being sent to reduce communication load.

[1119] Data storage

[1120] The server stores the received data in a database, which includes user profiles, collected behavioral data, location information, emotional data, as well as external data such as weather, traffic, and event information. The data is stored in temporary storage and then integrated into the database through batch processing.

[1121] Data analysis

[1122] The server analyzes the stored data using generative AI models, such as machine learning algorithms, to identify user behavioral patterns, interests, and emotional states. Analysis is performed periodically (e.g., nightly), and data preprocessing includes filling in missing values ​​and removing outliers.

[1123] Ad generation and delivery

[1124] The server generates optimized ads based on the analysis results. Generative AI models (such as GPT-4 and GAN) create ads in text, image, and video formats. A specific prompt is provided: "The user is out on a rainy day, wanting to relax." The generated ads are delivered to the device in real time, along with metadata including the target user ID and delivery timing. For example, an ad could be triggered just before the user arrives in a specific area.

[1125] Response data collection and retraining

[1126] The device collects user response data (clicks, swipes, purchases, etc.) and emotional data (changes in facial expressions and voice tone while the ad is displayed). The display time of the ad and the user's operation log are also recorded at the same time, and the data is sent to the server.

[1127] The server uses the collected response data to retrain the generative AI model. For example, it evaluates changes in ad click rates, purchase rates, and sentiment data to generate a new learning dataset. This new dataset is then used for retraining and the next ad generation.

[1128] Specific examples

[1129] For example, if a user frequently visits a cafe-related website, the device collects that behavioral data and sends it to a server. The server analyzes the data and determines that the user is in the mood to relax. Furthermore, the server determines from external data that the weather for that day is rainy, and generates an advertisement offering discount information for a nearby cafe. The advertisement is delivered to the device when the user is in an office district, and when the user clicks on the advertisement, their reaction and emotional data are sent to the server. The server analyzes the data and retrains the generative AI model.

[1130] Specific prompt examples:

[1131] The situation is "The user is out on a rainy day wanting to relax."

[1132] In this way, the system of the present invention analyzes the user's behavioral data and emotional state in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertisements.

[1133] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1134] Step 1:

[1135] Users' devices collect interaction data in real time, including clicks, scrolls, dwell time, and search history.

[1136] Input: Behavioral data about how users interact with websites and applications.

[1137] What it does: The device runs in the background and logs user interactions such as clicks, scrolls, dwell time, and search history.

[1138] Output: Collected real-time behavioral data.

[1139] Step 2:

[1140] The user's device acquires location information using GPS and Wi-Fi, and also collects emotional data using biometric sensors and cameras.

[1141] Input: GPS data, Wi-Fi access point location information, data from biometric sensors, and image and audio data from cameras.

[1142] Specific operation: The device acquires geographical location information, adds a timestamp and user ID to the collected data, and analyzes data from the camera and microphone in real time, converting facial expressions and voice tone into emotional data.

[1143] Output: Location information, emotion data.

[1144] Step 3:

[1145] The device transmits the collected behavioral data, location information, and emotion data to the server at regular intervals (for example, every hour).

[1146] Input: behavioral data, location information, emotion data.

[1147] How it works: Data is batched at regular intervals and compressed for communication. The device then sends the data to the server using a secure communication protocol.

[1148] Output: The data sent to the server.

[1149] Step 4:

[1150] The server stores the received data in a database.

[1151] Input: Behavioral, location, and emotional data sent from the device.

[1152] Specific operation: Incoming data is first stored in temporary storage, then batch-processed and integrated into the database. Any necessary data reformatting and missing value imputation are also performed.

[1153] Output: A set of user data stored in a database.

[1154] Step 5:

[1155] The server uses generative AI to analyze the data in the database.

[1156] Input: User profile, behavioral data, location information, and emotional data stored in a database.

[1157] How it works: Generative AI models use machine learning algorithms to identify user behavioral patterns, interests, and emotional states, including data preprocessing such as missing value imputation and outlier removal.

[1158] Output: Analysis results of user behavior patterns, interests, and emotional state.

[1159] Step 6:

[1160] The server generates an optimized advertisement based on the analysis results.

[1161] Input: Generated analysis results, prompt statement (e.g., "The user is out on a rainy day feeling relaxed").

[1162] How it works: A generative AI model (such as GPT-4 or GAN) creates an ad based on the analysis and prompt. The ad can be in the form of text, image, or video. The ad is then saved as an email or push notification template.

[1163] Output: The generated ad.

[1164] Step 7:

[1165] The server delivers the generated advertisement to the user's terminal in real time.

[1166] Input: Generated ad, target user ID, and delivery timing metadata.

[1167] Specific behavior: Deliver ads at the right time, taking into account the user's current location and behavior. For example, set it to trigger just before the user arrives in a specific area.

[1168] Output: Ads delivered to the user's device.

[1169] Step 8:

[1170] The device collects user response data (clicks, swipes, purchases, etc.) and emotional data (changes in facial expressions and tone of voice while the ad is displayed) to the displayed ad.

[1171] Input: The action the user took on the ad.

[1172] Specific operation: The device records the display time of the advertisement and the user's operation log, and also captures emotional changes through sensor data. This data is then sent back to the server.

[1173] Output: Collected reaction and sentiment data.

[1174] Step 9:

[1175] The server uses the collected reaction and emotion data to retrain the generative AI model.

[1176] Input: Collected reaction data, emotion data.

[1177] How it works: The server analyzes the response data to evaluate the effectiveness of the ad (click-through rate and purchase rate). It also analyzes the emotion data to evaluate the emotional impact of the ad. Based on these results, the generative AI model is retrained to generate a new learning dataset.

[1178] Output: A retrained generative AI model.

[1179] Through the above series of steps, it becomes possible to analyze user behavioral data and emotional data in real time and efficiently deliver optimized advertisements.

[1180] (Application example 2)

[1181] 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."

[1182] In modern society, simply using behavioral data and location information is insufficient to effectively deliver advertisements to consumers; it is also important to consider their emotional state at the time. Conventional advertising delivery systems generate and deliver advertisements based solely on a user's behavioral patterns and location information, but this does not necessarily provide advertisements that match the user's interests and emotions, limiting the effectiveness of the advertisements. The present invention aims to solve these problems and realize more personalized advertisement delivery to users.

[1183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1184] In this invention, the server includes means for collecting location information and behavioral data from user devices, means for storing the collected data in a database, means for analyzing user interests and concerns using a generation AI, means for collecting emotion data in real time, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to user devices in real time, means for collecting user response data and emotion data regarding the advertisements, and means for retraining the generation AI model using the collected response data and emotion data. This enables the generation and delivery of personalized advertisements that comprehensively take into account the user's behavioral data, location information, and emotional state.

[1185] "Location information" is data indicating the geographic coordinates where the user's terminal is currently located.

[1186] "Behavioral data" refers to data about interactions such as clicks, scrolls, time spent, and search history on a user's device.

[1187] "Emotional data" refers to data about a user's emotional state extracted from facial expressions, voice tone, heart rate, etc. collected using biometric sensors or cameras.

[1188] "Real-time" refers to data being processed and delivered as soon as it is generated or captured.

[1189] "Generative AI" refers to artificial intelligence models used for data analysis and ad generation.

[1190] A "database" is a system for systematically storing and managing collected location information, behavioral data, emotional data, etc.

[1191] A "server" is a computer system that analyzes collected data and generates and distributes advertisements using generative AI.

[1192] "Advertisement" means content generated in the form of text, images or video that presents information intended to promote a product or service.

[1193] "Response data" is data about user behavior in response to an advertisement, such as clicking, swiping, or purchasing.

[1194] "Retraining" refers to the process of updating a generative AI model with new data collected to improve its accuracy and effectiveness.

[1195] A system for implementing this invention collects location information and behavioral data from a user's device and stores it in a database. It then uses a generation AI to analyze the user's interests and concerns. Based on the analysis results, it collects emotional data in real time and generates advertisements optimized for the target user. The generated advertisements are delivered to the user's device in real time, and user response data and emotional data regarding the advertisements are collected. Finally, the collected data is used to retrain the generation AI model.

[1196] The server performs the following processes using a Python program. First, the server collects real-time location and behavior data from the user's device. This data is processed using the geopy library. The collected data is stored in a database in JSON format. Emotion data is collected in real time from biometric sensors and cameras. EmotionRecognizer is used to analyze the user's facial expressions, voice tone, heart rate, etc.

[1197] The generative AI model analyzes user behavior patterns, location information, and emotional data to generate ads optimized for the target user. This is handled by AdGenerator and UserBehaviorAnalyzer. The generated ads are delivered in real time from the server to the user's device.

[1198] The user's device displays the ad, and again collects reaction data, such as clicks and swipes, and emotional data from the user, which is then sent to the server. The server uses this data to retrain the generative AI model and improve the accuracy and effectiveness of the ad.

[1199] As a concrete example, consider a case where a user is at a shopping mall. The server obtains the user's location information and determines from past data that the user is interested in clothes and accessories. If the server detects from emotional data that the user is in a "fun mood," it can generate and deliver immediately available sales information or promotional advertisements for specific brands to the user.

[1200] An example of a prompt is as follows:

[1201] "The user is located in Otemachi, Tokyo, and has visited fashion-related websites more than 10 times in the past month. They seem to be in a fun mood right now. Generate ads for this user with information about sales available right now and promotions for specific brands."

[1202] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1203] Step 1:

[1204] The user's device collects user behavioral data and location information. Behavioral data includes clicks, scrolling, time spent, search history, etc. Location information is obtained using GPS and Wi-Fi. The collected data is temporarily stored on the device.

[1205] Input: User interaction data, location information

[1206] Output: Collected behavioral data, location information

[1207] Step 2:

[1208] The device sends the collected behavioral data and location information to the server. This data is accompanied by a timestamp and user ID. The transmission uses the HTTP protocol.

[1209] Input: Collected behavioral data, location information, timestamp, user ID

[1210] Output: Behavioral data sent to the server, location information, timestamp, user ID

[1211] Step 3:

[1212] The server stores the received data in a database built using SQL or NoSQL, which stores user profiles, behavioral data, and location information.

[1213] Input: Behavioral data sent to the server, location information, timestamp, user ID

[1214] Output: Behavioral data stored in a database, location information, timestamp, and user ID.

[1215] Step 4:

[1216] The device collects real-time emotional data from users using biometric sensors and cameras, and uses EmotionRecognizer to analyze facial expressions, voice tone, heart rate, and more.

[1217] Input: User facial expressions, voice tones, heart rate

[1218] Output: Parsed emotion data

[1219] Step 5:

[1220] The terminal transmits the collected emotion data to the server.

[1221] Input: Parsed emotion data

[1222] Output: Emotion data sent to the server

[1223] Step 6:

[1224] The server uses behavioral, location, and emotional data to run generative AI models to analyze user interests and concerns, using AdGenerator and UserBehaviorAnalyzer.

[1225] Input: Behavioral data, location information, and emotion data stored in a database

[1226] Output: Analysis results (user profile, interests)

[1227] Step 7:

[1228] The server generates ads optimized for the target users based on the analysis results. The ads are generated in text, image, and video formats.

[1229] Input: Analysis results (user profile, interests)

[1230] Output: The generated ad

[1231] Step 8:

[1232] The server delivers the generated advertisement to the user's terminal in real time.

[1233] Input: Generated Ad

[1234] Output: Ads delivered to the device

[1235] Step 9:

[1236] The user's device displays the ads and collects user response data, including clicks, swipes, and purchases.

[1237] Input: Served ad

[1238] Output: Collected reaction data

[1239] Step 10:

[1240] The terminal transmits the collected reaction data and emotion data to the server.

[1241] Input: Reaction data, emotion data

[1242] Output: Reaction data and emotion data sent to the server

[1243] Step 11:

[1244] The server analyzes the collected reaction and sentiment data to evaluate the effectiveness of the advertisements, and retrains the generative AI model based on the evaluation results.

[1245] Input: Reaction data and emotion data sent to the server

[1246] Output: Retrained generative AI model

[1247] 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.

[1248] 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.

[1249] 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.

[1250] [Fourth embodiment]

[1251] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1252] 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.

[1253] 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).

[1254] 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.

[1255] 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.

[1256] 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).

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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."

[1264] The system of the present invention includes a series of operations that collect location information and behavioral data from user terminals, store them in a database, analyze them, generate advertisements, deliver them in real time, collect user response data, and perform retraining.

[1265] Data collection and storage

[1266] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) and also obtains real-time location information using GPS and Wi-Fi. This data is periodically sent by the device to the server.

[1267] The server stores the collected data in a database, which stores user profiles, past behavioral data, and location information. It also collects external data (weather information, traffic information, event information, etc.) and stores it in the database.

[1268] Data analysis and ad generation

[1269] The server analyzes the data stored in the database using generative AI. The analysis process identifies user behavioral patterns and interests. For example, if a user frequently visits sites related to cafes, a "cafe lover" tag is added to the user profile.

[1270] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day, an advertisement for discounts at a nearby cafe or for rain gear sales may be generated.

[1271] Advertisement delivery and response data collection

[1272] The server delivers the generated advertisements to the user's device in real time. The advertisements are sent along with metadata (target user ID, display time, location, etc.) to ensure proper targeting.

[1273] The user's device displays the received advertisement. After the advertisement is displayed, the device collects user response data (clicks, swipes, purchases, etc.) and sends it to the server.

[1274] Retraining the model

[1275] The server evaluates the effectiveness of the advertisement based on the received user response data. Based on the evaluation results (click rate, purchase rate, etc.), the generation AI model is retrained to improve the accuracy and effectiveness of advertisement generation. The next advertisement is generated using the new retrained model.

[1276] Examples:

[1277] Example 1: Cafe advertising

[1278] If the user is in an office district, the device acquires location information and sends it to the server. Data on the user's frequent visits to cafe-related websites is also stored on the server. Information that the weather is rainy is obtained from external data.

[1279] 1. The server analyzes that the user likes cafes.

[1280] 2. Generate an ad that takes into account the rainy day situation and includes discount information for a nearby cafe.

[1281] 3. The server delivers this advertisement to the user's device in real time.

[1282] 4. The user's device displays the advertisement, and when the user clicks on the advertisement, the response data is sent to the server.

[1283] 5. The server uses this data to evaluate ad click-through rates and retrain the generative AI model.

[1284] Example 2: Concert advertising

[1285] If a user frequently uses a music streaming service, their behavioral data is sent to the server, which analyzes their interest in specific music genres and artists.

[1286] 1. The server identifies the user's musical preferences.

[1287] 2. Information about nearby concerts is obtained from external data.

[1288] 3. The server generates an advertisement containing ticket information for this concert.

[1289] 4. The server delivers the generated advertisement to the user's device in real time.

[1290] 5. The user's device displays the advertisement, and when the user purchases a ticket, the data is sent to the server.

[1291] 6. The server uses this data to evaluate the purchase rate of ads and retrain the generative AI model.

[1292] In this way, the present invention provides a system that analyzes user behavior data and local conditions in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertisements.

[1293] The processing flow will be explained below.

[1294] Step 1:

[1295] When a user starts using a website or application, the device collects real-time data on the user's behavior (clicks, scrolling, time spent, search history, etc.) and also obtains the user's current location using GPS and Wi-Fi.

[1296] Step 2:

[1297] The device periodically sends collected behavioral data and location information, including timestamps and user IDs, to a server.

[1298] Step 3:

[1299] The server stores the received data in a database, which includes user profiles, behavioral data, location information, and external data (weather, traffic, event information, etc.).

[1300] Step 4:

[1301] The server uses generative AI to analyze the data stored in the database. The purpose of the analysis is to identify the user's behavioral patterns and interests. For example, if a user frequently visits sites related to cafes, a "cafe lover" tag will be added to the user's profile.

[1302] Step 5:

[1303] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day, the server generates advertisements for discounts at nearby cafes or for rain gear sales.

[1304] Step 6:

[1305] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[1306] Step 7:

[1307] The device then displays the received advertisement to the user. When the advertisement is displayed, the device again collects user response data (clicks, swipes, purchases, etc.).

[1308] Step 8:

[1309] The device sends the collected response data to the server, including the ad display status and user actions.

[1310] Step 9:

[1311] The server analyzes the response data and evaluates the effectiveness of the advertisement (e.g., click rate and purchase rate). This evaluation result is used to retrain the generative AI model.

[1312] Step 10:

[1313] The server then uses the new, retrained model to generate the next ad, continually improving the accuracy and effectiveness of the ad, and the entire ad generation and delivery process begins again.

[1314] Example 1

[1315] 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."

[1316] Conventional ad delivery systems simply collect user behavior data and display static ads, failing to fully utilize dynamic factors such as user interests and location information. As a result, advertising effectiveness is low, making it difficult to provide more effective ads to users. Furthermore, there are problems with the inefficient data collection and retraining processes required to evaluate the effectiveness of ads and reflect them in the generation of subsequent ads.

[1317] 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.

[1318] In this invention, the server includes means for collecting location information and behavioral data from user devices, means for storing the collected data in a database, means for analyzing user interests and concerns using a generative artificial intelligence, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to user devices in real time, means for collecting user response data to advertisements, means for retraining the generative artificial intelligence model using the collected response data, means for periodically collecting user location information and generating advertisements based thereon, and means for dynamically generating prompt sentences based on user behavior patterns and using them to generate advertisements. This makes it possible to dynamically analyze user interests and location information and generate and deliver optimal advertisements in real time.

[1319] "User's terminal" refers to an electronic device such as a computer or smartphone used by a user.

[1320] "Location information" means data that indicates the current location of a particular device using GPS, Wi-Fi, or other technologies.

[1321] "Behavioral data" refers to data that includes information such as clicks, scrolls, time spent, and search history when a user visits a website.

[1322] A "database" is a storage device that stores large amounts of data in a structured manner, making it easy to access, manage, and update.

[1323] "Generative AI" refers to AI technology that uses machine learning and deep learning to analyze data and generate new information and content.

[1324] "Analysis" refers to the process of finding patterns and relationships based on collected data and identifying user characteristics.

[1325] "Advertising" refers to information content that promotes a specific product or service and is provided in the form of text, images, video, etc.

[1326] "Real-time" refers to the fact that the entire process, from data collection and analysis to ad generation and delivery, is carried out instantly.

[1327] "Response data" refers to data that records actions such as clicking, swiping, and purchasing when users view an ad.

[1328] "Retraining" refers to the process of retraining a generative AI model with new data to improve the model's accuracy and performance.

[1329] A "prompt" is an instruction entered into a generative AI model that contains the information that will serve as the basis for generating specific content or advertisements.

[1330] The system of the present invention is a technology that generates and distributes optimal advertisements in real time based on user behavior data and location information. This system is realized by the cooperation of multiple hardware and software components.

[1331] First, the user's device obtains real-time location information using technologies such as GPS and Wi-Fi, and also collects behavioral data (clicks, scrolling, time spent, search history, etc.) when visiting websites or using apps. In this case, the location information includes the device's current coordinates, and the behavioral data includes details of what the user did and how. This data is encrypted using a secure protocol (e.g., HTTPS) and sent to a server at regular intervals.

[1332] When the server receives the transmitted data, it first stores the data in a database. A relational database management system (RDBMS) such as MySQL or PostgreSQL is used to manage the database. This database stores the user's location information, behavioral data, past behavioral history, and external data (such as weather information, traffic information, and event information).

[1333] The server then analyzes the collected data using a generative artificial intelligence (AI) model. Deep learning frameworks such as PyTorch and TensorFlow are used to identify user behavioral patterns and interests. For example, if a user frequently visits cafe-related sites, a "cafe lover" label is added to the user profile. Based on the analysis results, ads optimized for the target user are generated. The generative AI model is used to generate ad content in the form of text, images, and videos.

[1334] The generated advertisements are delivered in real time from the server to the user's device. The advertisements also contain metadata such as the target user ID, display time, and location. The user's device displays the received advertisements and collects user response data (e.g., clicks, swipes, purchases, etc.). This response data is also encrypted and sent to the server using a secure protocol.

[1335] The server evaluates the effectiveness of the advertisement based on the received user response data. Based on the evaluation results, such as click-through rate and purchase rate, the generative artificial intelligence (AI) model is retrained. The retraining improves the model's accuracy and performance, which is reflected in the next advertisement generation.

[1336] Specific examples

[1337] Below are some examples of advertisements that are generated and distributed by this system.

[1338] Example 1: Cafe advertising

[1339] If the user is in an office district, the device acquires location information and sends it to the server. The server analyzes that the user has frequently visited cafe-related websites in the past and determines that the user is a cafe lover. It also recognizes from external data that it is raining today. Based on this, the server generates an advertisement containing discount information for a nearby cafe. This advertisement is delivered to the user's device in real time, and when the user clicks on the advertisement, the response data is sent to the server. This allows the click-through rate to be evaluated and the generative AI model to be retrained.

[1340] Example 2: Concert advertising

[1341] When a user frequently uses a music streaming service, that behavioral data is sent to a server. The server analyzes the user's music preferences and determines that the user is interested in specific genres and artists. Information about nearby concerts is obtained from external data, and the server generates an advertisement containing ticket information for that concert. The generated advertisement is delivered to the user's device in real time, and when the user purchases a ticket, the data is sent to the server. This allows the purchase rate to be evaluated and the generative AI model to be retrained.

[1342] Prompt Sentence Examples

[1343] "It's raining today, the user is in an office building, and frequently visits cafe-related sites. What kind of ad should we generate?"

[1344] "If a user listens to a particular artist a lot and there's a concert of that artist nearby, what kind of ad should we generate?"

[1345] In this way, the system of the present invention can dynamically analyze user behavior data and local conditions to generate and deliver optimal advertisements in real time, thereby improving the accuracy and effectiveness of advertisements.

[1346] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1347] Step 1: Data collection

[1348] The device collects user location and behavioral data, specifically by using GPS and Wi-Fi to obtain the device's current coordinates and recording user interaction data such as website visit history, clicks, scrolls, dwell time, and search history.

[1349] Input: User location information, behavioral data

[1350] Output: Location and behavior data collected

[1351] Step 2: Send data

[1352] The device encrypts the collected data and sends it to the server using the HTTPS protocol, where the data is encoded in JSON format.

[1353] Input: Collected location and behavioral data

[1354] Output: The encoded and encrypted data is sent to the server.

[1355] Step 3: Save Data

[1356] The server analyzes the received data and stores it in a database. Database management uses an RDBMS such as MySQL or PostgreSQL. The database stores user profiles, location information, behavioral data, and external data (weather, traffic, and event information).

[1357] Input: Data sent to the server

[1358] Output: Data saved to the database

[1359] Step 4: Data analysis

[1360] The server analyzes the stored data using a generative AI model. For example, it uses PyTorch or TensorFlow to identify user behavior patterns and interests. If a user frequently visits cafe-related sites, a "cafe lover" tag is added to the user profile.

[1361] Input: User data in the database

[1362] Output: Analysis results based on user interests

[1363] Step 5: Generate Ads

[1364] The server generates advertisements optimized for the target user based on the results of data analysis. It inputs prompt sentences into the generative AI model and creates advertisement content (text, images, videos).

[1365] Input: Data analysis results, prompt statement

[1366] Output: Ads optimized for the target user

[1367] Step 6: Ad serving

[1368] The server delivers the generated advertisements to the user's device in real time, and the advertisements contain metadata such as the target user ID, display time, and location.

[1369] Input: Generated ads, metadata

[1370] Output: Ad delivered to user device

[1371] Step 7: Reaction data collection

[1372] The user's device collects data on the user's response to the displayed advertisements (clicks, swipes, purchases), which is encrypted and periodically sent to a server.

[1373] Input: User response to the ad

[1374] Output: Encrypted reaction data sent to the server

[1375] Step 8: Retrain the model

[1376] The server retrains the generative AI model based on user response data, evaluating click rates and purchase rates, and retraining the model based on the new evaluation results.

[1377] Input: User response data, evaluation results of advertising effectiveness

[1378] Output: A generative AI model with improved accuracy through retraining

[1379] These processing steps enable the system to dynamically analyze user behavioral data and location information and provide optimal advertisements in real time.

[1380] (Application example 1)

[1381] 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."

[1382] Conventional advertising systems have limited methods for utilizing user location information and behavioral data, making it difficult to generate and deliver advertisements in real time based on user behavior, especially in physical stores. As a result, it has been impossible to provide advertisements optimized for users in a timely manner, resulting in a problem of reduced advertising effectiveness. The present invention aims to solve this problem and provide a system that can deliver advertisements optimized for the user's situation in real time, even when the user is in a physical store.

[1383] 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.

[1384] In this invention, the server includes means for collecting location information and behavioral data from a user's device, means for storing the collected data in a database, means for analyzing user interests and concerns using a generation AI, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to the user's device in real time, means for collecting user response data to the advertisements, means for retraining the generation AI model using the collected response data, and means for generating and delivering advertisements based on location information and related data within a physical store when the user is in the physical store. This enables optimization of advertisements based on user location information and behavioral data and real-time advertisement delivery within the physical store.

[1385] A "user's terminal" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[1386] "Location information" is data that indicates a user's current geographic coordinates or location.

[1387] "Behavioral data" refers to information such as clicks, scrolls, time spent, search history, and locations visited when a user uses a device.

[1388] A "database" is a storage device for storing collected location information, behavioral data, and external data.

[1389] "Generative AI" is artificial intelligence that uses technologies such as machine learning and deep learning to analyze data and generate advertisements.

[1390] "Interests and concerns" refer to the user's preferences and interests that can be identified from the analyzed user's behavioral patterns and past data.

[1391] A "target user" is a specific user for whom an advertisement is generated.

[1392] An "advertisement" is content created for the purpose of conveying information about a product or service to users, and may take the form of text, images, video, etc.

[1393] "Real-time" means that data collection, analysis, ad generation, distribution, and response collection are all carried out simultaneously, with results reflected almost instantly.

[1394] "Response data" refers to feedback information such as clicks, swipes, and purchases made by users in response to an advertisement.

[1395] A "brick and mortar store" is a physical store where users can visit and purchase goods or services in person.

[1396] "Promotion information" is data about promotional activities such as discounts, coupons, and sales offered by stores.

[1397] "Optimization" is the process of generating and delivering the most effective ads based on user behavioral data and location information.

[1398] Embodiments of the invention include a process for efficient data collection, analysis, ad generation and delivery using a server, a user's terminal, and a generative AI model.

[1399] Data collection and storage

[1400] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) and location information (using GPS and Wi-Fi). This data is periodically sent to the server. The server stores the collected data in a database. This database includes user profiles, past behavior data, location information, and external data (weather information, traffic information, event information, promotion information, etc.).

[1401] Data analysis and ad generation

[1402] The server uses generative AI to analyze the data stored in the database. The analysis process identifies the user's behavioral patterns and interests. If the user is in a physical store, an advertisement optimized for the target user is generated based on the user's location information and related data within the store. The generated advertisements are created in the form of text, images, videos, etc.

[1403] Advertisement delivery and response data collection

[1404] The server delivers the generated advertisement to the user's device in real time. The user's device displays the received advertisement and sends the user's response data (clicks, swipes, purchases, etc.) back to the server. The server evaluates the effectiveness of the advertisement based on this response data.

[1405] Retraining the model

[1406] The server retrains the generative AI model using the collected user response data, improving the accuracy and effectiveness of ad generation. The new, retrained model is then used to generate the next ad.

[1407] Hardware and software used

[1408] Hardware: Servers (e.g. AWS EC2), smartphones (iOS / Android)

[1409] Software: Flask (backend framework), MongoDB (database management), TensorFlow / Keras (generative AI models)

[1410] Examples and prompts

[1411] As a specific example, if a user is in a shopping mall, an advertisement for a discount coupon for a fashion brand is generated based on location information and behavioral data collected from the device and delivered in real time.As another specific example, if a user is visiting a cafe-related site on a rainy day, an advertisement containing discount information for a nearby cafe is delivered.

[1412] Example prompt for a generative AI model:

[1413] "A user frequently visits fashion-related sites. Their location indicates they are currently at a shopping mall. Based on information about nearby stores, generate answers to the following prompts: 1. The weather is sunny. Please recommend stores with discounts on summer fashion items. 2. It's raining. Please provide discounts on rain gear and indoor facilities."

[1414] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1415] Step 1:

[1416] Data collection

[1417] The user's device collects the user's location information (GPS and Wi-Fi) and interaction data (clicks, scrolls, dwell time, search history, etc.), which is periodically sent from the device to the server.

[1418] Input: User location, interaction data

[1419] Output: Location and interaction data sent to the server

[1420] What it does: Your smartphone obtains your location and collects behavioral data such as clicks and search history.

[1421] Step 2:

[1422] Data storage

[1423] The server stores the received location and interaction data in a database, including user profiles, past behavioral data, and external data (such as weather, traffic, event, and promotion information).

[1424] Input: Location and interaction data sent from your device

[1425] Output: Location and behavior data stored in a database

[1426] Specific operation: The server uses MongoDB to store location information and behavior data in a database.

[1427] Step 3:

[1428] Data analysis

[1429] The server uses a generative AI model to analyze the data stored in the database and identify users' behavioral patterns and interests.

[1430] Input: Location information, behavioral data, external data stored in the database

[1431] Output: Analysis results that identify user interests

[1432] Specific operation: The server analyzes the data using TensorFlow / Keras and identifies user behavior patterns.

[1433] Step 4:

[1434] Ad Generation

[1435] Based on the analysis results of the generative AI model, the server generates ads optimized for the target users, which can be in the form of text, images, videos, etc.

[1436] Input: Analysis results

[1437] Output: Generated ad content

[1438] What happens: The server generates and formats the ad content according to the analysis results.

[1439] Step 5:

[1440] Ad serving

[1441] The server delivers the generated advertisement to the user's terminal in real time, and the user's terminal receives and displays the advertisement.

[1442] Input: Generated ad content

[1443] Output: Ad displayed on the user's device

[1444] Specific operation: The server sends the generated advertisement to the user's smartphone, and the smartphone displays the advertisement.

[1445] Step 6:

[1446] Reaction data collection

[1447] The device collects data on the user's response to the advertisement (clicks, swipes, purchases, etc.) and sends it to the server.

[1448] Input: User response (click, swipe, purchase)

[1449] Output: Response data sent to the server

[1450] Specific operation: The user's smartphone collects reaction data and sends it to the server.

[1451] Step 7:

[1452] Retraining the model

[1453] The server uses the collected response data to retrain the generative AI model, thereby improving the accuracy and effectiveness of ad generation.

[1454] Input: User response data

[1455] Output: Retrained generative AI model

[1456] Specific operation: The server retrains the model using TensorFlow / Keras to improve the ad generation logic.

[1457] 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.

[1458] The system of the present invention collects location information and behavioral data from users' devices and combines it with an emotion engine that recognizes users' emotions. The system stores the data in a database, analyzes it using generative AI, generates and distributes advertisements in real time, collects user response data, and performs a series of operations including retraining.

[1459] Data collection and storage

[1460] The user's device collects interaction data (clicks, scrolls, dwell time, search history, etc.) in real time, and also obtains real-time location information using GPS and Wi-Fi. The emotion engine also collects emotional data (facial expressions, voice tone, heart rate, etc.) from the user's biometric sensors and camera.

[1461] The device periodically transmits collected behavioral, location, and emotional data to a server, including a timestamp and user ID.

[1462] The server stores the received data in a database, which includes user profiles, behavioral data, location information, emotional data, and external data (such as weather, traffic, and event information).

[1463] Data analysis and ad generation

[1464] The server uses generative AI to analyze the data stored in the database. The purpose of the analysis is to identify the user's behavioral patterns, interests, and emotional state. For example, if a user frequently visits cafe-related sites and their emotional data indicates that they are in a "feeling like relaxing," tags such as "I like cafes" and "I want to relax" will be added to the user's profile.

[1465] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be created in the form of text, images, videos, etc. For example, if a user is out on a rainy day and feels like "relaxing," the server generates an advertisement for a nearby cafe that offers discount information or a relaxing environment.

[1466] Advertisement delivery and response data collection

[1467] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[1468] The device displays the received advertisement to the user. When the advertisement is displayed, the device again collects the user's response data (clicks, swipes, purchases, etc.) and emotional data.

[1469] The device sends collected reaction and emotion data, including the ad display status and user actions, to a server.

[1470] Retraining the model

[1471] The server analyzes the response data and evaluates the effectiveness of the ad (e.g., click rate and purchase rate). It also evaluates changes in emotion based on the emotion data and analyzes how the ad influenced those emotions. Using the results of this evaluation, the generative AI model is retrained to improve the accuracy and effectiveness of ad generation. The next ad is generated using the new, retrained model.

[1472] Examples:

[1473] Example 1: Cafe advertising

[1474] When the user is in an office district, the device acquires location information and sends it to the server. The server also stores data indicating that the user has frequently visited cafe-related websites in the past and that their emotional data indicates they want to relax. Information indicating that the weather is rainy is obtained from external data.

[1475] 1. The server analyzes that the user likes cafes and is in the mood to relax.

[1476] 2. Considering the situation of a rainy day, generate ads for nearby cafes with discount information and relaxing environments.

[1477] 3. The server delivers this advertisement to the user's device in real time.

[1478] 4. The user's device displays the advertisement, and when the user clicks on the advertisement, the reaction data and emotion data are sent to the server.

[1479] 5. The server uses this data to evaluate ad click-through rates and changes in sentiment to retrain the generative AI model.

[1480] Example 2: Concert advertising

[1481] If a user frequently uses a music streaming service, their behavioral data will be sent to the server, and the user may be interested in a particular music genre or artist, and their emotional data may indicate that they are "excited."

[1482] 1. The server identifies the user's musical preferences and emotional mood.

[1483] 2. Information about nearby concerts is obtained from external data.

[1484] 3. The server generates an advertisement containing ticket information for this concert.

[1485] 4. The server delivers the generated advertisement to the user's device in real time.

[1486] 5. The user's device displays the advertisement, and when the user purchases a ticket, the data and emotional data are sent to the server.

[1487] 6. The server uses this data to evaluate the purchase rate and sentiment of the ads and retrain the generative AI model.

[1488] In this way, the present invention provides a system that analyzes user behavioral data, location information, and emotional state in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertising.

[1489] The processing flow will be explained below.

[1490] Step 1:

[1491] When a user begins using a website or application, the device collects user behavior data (clicks, scrolls, dwell time, search history, etc.) in real time. In addition, the device obtains the user's current location using GPS and Wi-Fi. Using an emotion engine, the device also collects emotional data (facial expressions, voice tone, heart rate, etc.) from biometric sensors and cameras.

[1492] Step 2:

[1493] The device periodically transmits collected behavioral, location, and emotional data to a server, including timestamps and user IDs.

[1494] Step 3:

[1495] The server stores the received data in a database, which includes user profiles, behavioral data, location information, emotional data, and external data (weather, traffic, event information, etc.).

[1496] Step 4:

[1497] The server uses generative AI to analyze the data stored in the database. This analysis identifies the user's behavioral patterns, interests, and emotional state. For example, if a user frequently visits cafe-related sites and the emotional data indicates that they are "in the mood to relax," tags for "cafe lover" and "want to relax" will be added to the user's profile.

[1498] Step 5:

[1499] Based on the analysis results, the server generates advertisements optimized for the target user. These advertisements can be in the form of text, images, videos, etc. For example, if a user is out on a rainy day and feels like "relaxing," the server generates advertisements for nearby cafes offering discount information and a relaxing environment.

[1500] Step 6:

[1501] The server delivers the generated advertisement to the user's device in real time along with metadata, which includes the target user ID, display time, location, etc.

[1502] Step 7:

[1503] The device displays the received advertisement to the user. When the advertisement is displayed, the device again collects the user's response data (clicks, swipes, purchases, etc.) and new emotional data.

[1504] Step 8:

[1505] The device sends the collected reaction and emotion data to the server, including the display status of advertisements and user actions.

[1506] Step 9:

[1507] The server analyzes the response data and evaluates the effectiveness of the advertisement (click rate, purchase rate, etc.). It also evaluates changes in emotions based on the emotion data and analyzes how the advertisement affected emotions.

[1508] Step 10:

[1509] The server uses the evaluation results to retrain the generative AI model to improve the accuracy and effectiveness of ad generation, and then uses the new retrained model to generate the next ad. This process is repeated to continuously optimize the user experience.

[1510] Example 2

[1511] 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."

[1512] Conventional ad delivery systems have a problem in that they are unable to deliver ads that take into account not only the user's interests and concerns, but also their real-time emotional state. Furthermore, they are also inadequate at optimizing ads by effectively combining external factors (weather, traffic information, event information, etc.). As a result, they fail to attract user attention, resulting in low ad click rates and purchase rates.

[1513] 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.

[1514] In this invention, the server includes means for collecting location information and behavioral data from a user's device, means for storing the collected data in a database, means for analyzing the user's interests and emotional state using a generation AI, means for updating a user profile based on the analysis results, means for generating advertisements optimized for the target user based on the analysis results, means for delivering the generated advertisements to the user's device in real time, means for collecting user response data and emotional data regarding the advertisements, and means for retraining the generation AI model using the collected response data and emotional data. This enables more effective delivery of advertisements that reflect the user's real-time behavioral data and emotional state.

[1515] A "user terminal" is a computer or mobile information terminal operated by a user, and is a device for collecting behavioral data, location information, and emotional data of the user.

[1516] "Location information" is data indicating a user's current location and movement history obtained using GPS or Wi-Fi.

[1517] "Behavioral data" refers to interaction data such as clicks, scrolls, time spent, and search history when a user browses a website.

[1518] "Emotion data" refers to data that indicates the user's emotional state estimated from the user's facial expression, voice tone, heart rate, etc., collected using the user's biometric sensors and camera.

[1519] "Database" refers to a data management system for systematically storing collected behavioral data, location information, emotional data, and external data.

[1520] "Generative AI" is an artificial intelligence technology that analyzes user data, identifies their interests, concerns, and emotional state, and generates advertisements.

[1521] A "user profile" is detailed user information that includes the user's behavioral patterns, interests, concerns, emotional state, etc., which is updated based on the analysis results.

[1522] An "advertisement" is a message containing information (in the form of text, images, or videos) that is generated based on a user's behavioral and emotional data and delivered to the user's device.

[1523] "Response data" refers to behavioral data such as clicks, swipes, and purchases made by users in response to advertisements.

[1524] "Retraining" is the process of using collected reaction and sentiment data to update the generative AI model, improving the accuracy and effectiveness of ad generation.

[1525] The system of the present invention collects location information and behavioral data from the user's device, analyzes the user's interests, concerns, and emotional state using a generation AI, and generates and delivers optimal advertisements. Specific embodiments of the present invention are described below.

[1526] Data collection and transmission

[1527] The user's device collects real-time interaction data such as clicks, scrolls, dwell time, and search history. It also acquires location information using GPS and Wi-Fi. It also collects emotional data such as facial expressions, voice tone, and heart rate using biometric sensors and cameras. This data is sent from the device to the server at regular intervals (for example, every hour). The data is compressed before being sent to reduce communication load.

[1528] Data storage

[1529] The server stores the received data in a database, which includes user profiles, collected behavioral data, location information, emotional data, as well as external data such as weather, traffic, and event information. The data is stored in temporary storage and then integrated into the database through batch processing.

[1530] Data analysis

[1531] The server analyzes the stored data using generative AI models, such as machine learning algorithms, to identify user behavioral patterns, interests, and emotional states. Analysis is performed periodically (e.g., nightly), and data preprocessing includes filling in missing values ​​and removing outliers.

[1532] Ad generation and delivery

[1533] The server generates optimized ads based on the analysis results. Generative AI models (such as GPT-4 and GAN) create ads in text, image, and video formats. A specific prompt is provided: "The user is out on a rainy day, wanting to relax." The generated ads are delivered to the device in real time, along with metadata including the target user ID and delivery timing. For example, an ad could be triggered just before the user arrives in a specific area.

[1534] Response data collection and retraining

[1535] The device collects user response data (clicks, swipes, purchases, etc.) and emotional data (changes in facial expressions and voice tone while the ad is displayed). The display time of the ad and the user's operation log are also recorded at the same time, and the data is sent to the server.

[1536] The server uses the collected response data to retrain the generative AI model. For example, it evaluates changes in ad click rates, purchase rates, and sentiment data to generate a new learning dataset. This new dataset is then used for retraining and the next ad generation.

[1537] Specific examples

[1538] For example, if a user frequently visits a cafe-related website, the device collects that behavioral data and sends it to a server. The server analyzes the data and determines that the user is in the mood to relax. Furthermore, the server determines from external data that the weather for that day is rainy, and generates an advertisement offering discount information for a nearby cafe. The advertisement is delivered to the device when the user is in an office district, and when the user clicks on the advertisement, their reaction and emotional data are sent to the server. The server analyzes the data and retrains the generative AI model.

[1539] Specific prompt examples:

[1540] The situation is "The user is out on a rainy day wanting to relax."

[1541] In this way, the system of the present invention analyzes the user's behavioral data and emotional state in real time, and constantly generates and provides optimal advertisements, thereby improving the accuracy and effectiveness of advertisements.

[1542] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1543] Step 1:

[1544] Users' devices collect interaction data in real time, including clicks, scrolls, dwell time, and search history.

[1545] Input: Behavioral data about how users interact with websites and applications.

[1546] What it does: The device runs in the background and logs user interactions such as clicks, scrolls, dwell time, and search history.

[1547] Output: Collected real-time behavioral data.

[1548] Step 2:

[1549] The user's device acquires location information using GPS and Wi-Fi, and also collects emotional data using biometric sensors and cameras.

[1550] Input: GPS data, Wi-Fi access point location information, data from biometric sensors, and image and audio data from cameras.

[1551] Specific operation: The device acquires geographical location information, adds a timestamp and user ID to the collected data, and analyzes data from the camera and microphone in real time, converting facial expressions and voice tone into emotional data.

[1552] Output: Location information, emotion data.

[1553] Step 3:

[1554] The device transmits the collected behavioral data, location information, and emotion data to the server at regular intervals (for example, every hour).

[1555] Input: behavioral data, location information, emotion data.

[1556] How it works: Data is batched at regular intervals and compressed for communication. The device then sends the data to the server using a secure communication protocol.

[1557] Output: The data sent to the server.

[1558] Step 4:

[1559] The server stores the received data in a database.

[1560] Input: Behavioral, location, and emotional data sent from the device.

[1561] Specific operation: Incoming data is first stored in temporary storage, then batch-processed and integrated into the database. Any necessary data reformatting and missing value imputation are also performed.

[1562] Output: A set of user data stored in a database.

[1563] Step 5:

[1564] The server uses generative AI to analyze the data in the database.

[1565] Input: User profile, behavioral data, location information, and emotional data stored in a database.

[1566] How it works: Generative AI models use machine learning algorithms to identify user behavioral patterns, interests, and emotional states, including data preprocessing such as missing value imputation and outlier removal.

[1567] Output: Analysis results of user behavior patterns, interests, and emotional state.

[1568] Step 6:

[1569] The server generates an optimized advertisement based on the analysis results.

[1570] Input: Generated analysis results, prompt statement (e.g., "The user is out on a rainy day feeling relaxed").

[1571] How it works: A generative AI model (such as GPT-4 or GAN) creates an ad based on the analysis and prompt. The ad can be in the form of text, image, or video. The ad is then saved as an email or push notification template.

[1572] Output: The generated ad.

[1573] Step 7:

[1574] The server delivers the generated advertisement to the user's terminal in real time.

[1575] Input: Generated ad, target user ID, and delivery timing metadata.

[1576] Specific behavior: Deliver ads at the right time, taking into account the user's current location and behavior. For example, set it to trigger just before the user arrives in a specific area.

[1577] Output: Ads delivered to the user's device.

[1578] Step 8:

[1579] The device collects user response data (clicks, swipes, purchases, etc.) and emotional data (changes in facial expressions and tone of voice while the ad is displayed) to the displayed ad.

[1580] Input: The action the user took on the ad.

[1581] Specific operation: The device records the display time of the advertisement and the user's operation log, and also captures emotional changes through sensor data. This data is then sent back to the server.

[1582] Output: Collected reaction and sentiment data.

[1583] Step 9:

[1584] The server uses the collected reaction and emotion data to retrain the generative AI model.

[1585] Input: Collected reaction data, emotion data.

[1586] How it works: The server analyzes the response data to evaluate the effectiveness of the ad (click-through rate and purchase rate). It also analyzes the emotion data to evaluate the emotional impact of the ad. Based on these results, the generative AI model is retrained to generate a new learning dataset.

[1587] Output: A retrained generative AI model.

[1588] Through the above series of steps, it becomes possible to analyze user behavioral data and emotional data in real time and efficiently deliver optimized advertisements.

[1589] (Application example 2)

[1590] 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."

[1591] In modern society, simply using behavioral data and location information is insufficient to effectively deliver advertisements to consumers; it is also important to consider their emotional state at the time. Conventional advertising delivery systems generate and deliver advertisements based solely on a user's behavioral patterns and location information, but this does not necessarily provide advertisements that match the user's interests and emotions, limiting the effectiveness of the advertisements. The present invention aims to solve these problems and realize more personalized advertisement delivery to users.

[1592] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1593] In this invention, the server includes means for collecting location information and behavioral data from user devices, means for storing the collected data in a database, means for analyzing user interests and concerns using a generation AI, means for collecting emotion data in real time, means for generating advertisements optimized for target users based on the analysis results, means for delivering the generated advertisements to user devices in real time, means for collecting user response data and emotion data regarding the advertisements, and means for retraining the generation AI model using the collected response data and emotion data. This enables the generation and delivery of personalized advertisements that comprehensively take into account the user's behavioral data, location information, and emotional state.

[1594] "Location information" is data indicating the geographic coordinates where the user's terminal is currently located.

[1595] "Behavioral data" refers to data about interactions such as clicks, scrolls, time spent, and search history on a user's device.

[1596] "Emotional data" refers to data about a user's emotional state extracted from facial expressions, voice tone, heart rate, etc. collected using biometric sensors or cameras.

[1597] "Real-time" refers to data being processed and delivered as soon as it is generated or captured.

[1598] "Generative AI" refers to artificial intelligence models used for data analysis and ad generation.

[1599] A "database" is a system for systematically storing and managing collected location information, behavioral data, emotional data, etc.

[1600] A "server" is a computer system that analyzes collected data and generates and distributes advertisements using generative AI.

[1601] "Advertisement" means content generated in the form of text, images or video that presents information intended to promote a product or service.

[1602] "Response data" is data about user behavior in response to an advertisement, such as clicking, swiping, or purchasing.

[1603] "Retraining" refers to the process of updating a generative AI model with new data collected to improve its accuracy and effectiveness.

[1604] A system for implementing this invention collects location information and behavioral data from a user's device and stores it in a database. It then uses a generation AI to analyze the user's interests and concerns. Based on the analysis results, it collects emotional data in real time and generates advertisements optimized for the target user. The generated advertisements are delivered to the user's device in real time, and user response data and emotional data regarding the advertisements are collected. Finally, the collected data is used to retrain the generation AI model.

[1605] The server performs the following processes using a Python program. First, the server collects real-time location and behavior data from the user's device. This data is processed using the geopy library. The collected data is stored in a database in JSON format. Emotion data is collected in real time from biometric sensors and cameras. EmotionRecognizer is used to analyze the user's facial expressions, voice tone, heart rate, etc.

[1606] The generative AI model analyzes user behavior patterns, location information, and emotional data to generate ads optimized for the target user. This is handled by AdGenerator and UserBehaviorAnalyzer. The generated ads are delivered in real time from the server to the user's device.

[1607] The user's device displays the ad, and again collects reaction data, such as clicks and swipes, and emotional data from the user, which is then sent to the server. The server uses this data to retrain the generative AI model and improve the accuracy and effectiveness of the ad.

[1608] As a concrete example, consider a case where a user is at a shopping mall. The server obtains the user's location information and determines from past data that the user is interested in clothes and accessories. If the server detects from emotional data that the user is in a "fun mood," it can generate and deliver immediately available sales information or promotional advertisements for specific brands to the user.

[1609] An example of a prompt is as follows:

[1610] "The user is located in Otemachi, Tokyo, and has visited fashion-related websites more than 10 times in the past month. They seem to be in a fun mood right now. Generate ads for this user with information about sales available right now and promotions for specific brands."

[1611] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1612] Step 1:

[1613] The user's device collects user behavioral data and location information. Behavioral data includes clicks, scrolling, time spent, search history, etc. Location information is obtained using GPS and Wi-Fi. The collected data is temporarily stored on the device.

[1614] Input: User interaction data, location information

[1615] Output: Collected behavioral data, location information

[1616] Step 2:

[1617] The device sends the collected behavioral data and location information to the server. This data is accompanied by a timestamp and user ID. The transmission uses the HTTP protocol.

[1618] Input: Collected behavioral data, location information, timestamp, user ID

[1619] Output: Behavioral data sent to the server, location information, timestamp, user ID

[1620] Step 3:

[1621] The server stores the received data in a database built using SQL or NoSQL, which stores user profiles, behavioral data, and location information.

[1622] Input: Behavioral data sent to the server, location information, timestamp, user ID

[1623] Output: Behavioral data stored in a database, location information, timestamp, and user ID.

[1624] Step 4:

[1625] The device collects real-time emotional data from users using biometric sensors and cameras, and uses EmotionRecognizer to analyze facial expressions, voice tone, heart rate, and more.

[1626] Input: User facial expressions, voice tones, heart rate

[1627] Output: Parsed emotion data

[1628] Step 5:

[1629] The terminal transmits the collected emotion data to the server.

[1630] Input: Parsed emotion data

[1631] Output: Emotion data sent to the server

[1632] Step 6:

[1633] The server uses behavioral, location, and emotional data to run generative AI models to analyze user interests and concerns, using AdGenerator and UserBehaviorAnalyzer.

[1634] Input: Behavioral data, location information, and emotion data stored in a database

[1635] Output: Analysis results (user profile, interests)

[1636] Step 7:

[1637] The server generates ads optimized for the target users based on the analysis results. The ads are generated in text, image, and video formats.

[1638] Input: Analysis results (user profile, interests)

[1639] Output: The generated ad

[1640] Step 8:

[1641] The server delivers the generated advertisement to the user's terminal in real time.

[1642] Input: Generated Ad

[1643] Output: Ads delivered to the device

[1644] Step 9:

[1645] The user's device displays the ads and collects user response data, including clicks, swipes, and purchases.

[1646] Input: Served ad

[1647] Output: Collected reaction data

[1648] Step 10:

[1649] The terminal transmits the collected reaction data and emotion data to the server.

[1650] Input: Reaction data, emotion data

[1651] Output: Reaction data and emotion data sent to the server

[1652] Step 11:

[1653] The server analyzes the collected reaction and sentiment data to evaluate the effectiveness of the advertisements, and retrains the generative AI model based on the evaluation results.

[1654] Input: Reaction data and emotion data sent to the server

[1655] Output: Retrained generative AI model

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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).

[1663] 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.

[1664] 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."

[1665] 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.

[1666] 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).

[1667] 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.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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.

[1677] The following is further disclosed regarding the above embodiment.

[1678] (Claim 1)

[1679] means for collecting location information and behavioral data from a user's device;

[1680] means for storing the collected data in a database;

[1681] A means of analyzing user interests and concerns using generative AI,

[1682] A means for generating an advertisement optimized for the target user based on the analysis result;

[1683] means for delivering the generated advertisement to a user's terminal in real time;

[1684] A means for collecting user response data to advertisements;

[1685] a means for retraining the generative AI model using the collected response data; and

[1686] A system including:

[1687] (Claim 2)

[1688] 10. The system of claim 1, further comprising means for collecting behavioral data when a user visits a particular website.

[1689] (Claim 3)

[1690] 10. The system of claim 1, further comprising means for storing external data in said database, such as local weather, traffic, and event information.

[1691] "Example 1"

[1692] (Claim 1)

[1693] means for collecting location information and behavioral data from a user's device;

[1694] means for storing the collected data in a database;

[1695] A means for analyzing user interests and concerns using generative artificial intelligence;

[1696] A means for generating an advertisement optimized for the target user based on the analysis result;

[1697] means for delivering the generated advertisement to a user's terminal in real time;

[1698] A means for collecting user response data to advertisements;

[1699] means for retraining a generative artificial intelligence model using the collected response data;

[1700] A means for periodically collecting user location information and generating advertisements based thereon;

[1701] A means for dynamically generating prompt sentences based on user behavior patterns and using the prompts to generate advertisements;

[1702] A system including:

[1703] (Claim 2)

[1704] 10. The system of claim 1, further comprising means for collecting behavioral data when a user visits a particular website.

[1705] (Claim 3)

[1706] 10. The system of claim 1, further comprising means for storing external data in said database, such as local weather, traffic, and event information.

[1707] "Application Example 1"

[1708] (Claim 1)

[1709] means for collecting location information and behavioral data from a user's device;

[1710] means for storing the collected data in a database;

[1711] A means of analyzing user interests and concerns using generative AI,

[1712] A means for generating an advertisement optimized for the target user based on the analysis result;

[1713] means for delivering the generated advertisement to a user's terminal in real time;

[1714] A means for collecting user response data to advertisements;

[1715] a means for retraining the generative AI model using the collected response data; and

[1716] means for generating and delivering advertisements based on location information and related data within a physical store when the user is present in the physical store;

[1717] A system including:

[1718] (Claim 2)

[1719] 10. The system of claim 1, further comprising means for collecting behavioral data when a user visits a particular website.

[1720] (Claim 3)

[1721] 10. The system of claim 1, further comprising means for storing external data in the database, such as local weather, traffic, event, and brick-and-mortar promotion information.

[1722] "Example 2: Combining Emotion Engines"

[1723] (Claim 1)

[1724] means for collecting location information and behavioral data from a user's device;

[1725] means for storing the collected data in a database;

[1726] A means for analyzing a user's interests and concerns, as well as their emotional state, using a generative AI;

[1727] means for updating a user profile based on the analysis results;

[1728] A means for generating an advertisement optimized for the target user based on the analysis result;

[1729] means for delivering the generated advertisement to a user's terminal in real time;

[1730] A means for collecting user response data and sentiment data regarding advertisements;

[1731] a means for retraining the generative AI model using the collected reaction and emotion data; and

[1732] A system including:

[1733] (Claim 2)

[1734] 10. The system of claim 1, further comprising means for collecting behavioral data when a user visits a particular website.

[1735] (Claim 3)

[1736] 10. The system of claim 1, further comprising means for storing external data in said database, such as local weather, traffic, and event information.

[1737] "Application example 2 when combining emotion engines"

[1738] (Claim 1)

[1739] means for collecting location information and behavioral data from a user's device;

[1740] means for storing the collected data in a database;

[1741] A means of analyzing user interests and concerns using generative AI,

[1742] a means of collecting real-time emotional data;

[1743] A means for generating an advertisement optimized for the target user based on the analysis result;

[1744] means for delivering the generated advertisement to a user's terminal in real time;

[1745] A means for collecting user response data and sentiment data regarding advertisements;

[1746] a means for retraining the generative AI model using the collected reaction and emotion data; and

[1747] A system including:

[1748] (Claim 2)

[1749] 10. The system of claim 1, further comprising means for collecting behavioral data when a user visits a particular website.

[1750] (Claim 3)

[1751] 10. The system of claim 1, further comprising means for storing external data in said database, such as local weather, traffic, and event information. [Explanation of symbols]

[1752] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting location information and behavioral data from a user's device; means for storing the collected data in a database; A means of analyzing user interests and concerns using generative AI, A means for generating an advertisement optimized for the target user based on the analysis result; means for delivering the generated advertisement to a user's terminal in real time; A means for collecting user response data to advertisements; a means for retraining the generative AI model using the collected response data; and A system including:

2. The system of claim 1 , further comprising means for collecting behavioral data when a user visits a particular website.

3. 10. The system of claim 1, further comprising means for storing external data in said database, such as local weather, traffic, and event information.

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