System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting behavioral data of a user; means for analyzing the collected behavioral data to grasp a user characteristic; means for automatically generating an advertisement based on the user characteristic; means for delivering the generated advertisement to the user; and means for monitoring and optimizing an effect of the advertisement.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] With conventional advertising methods, it is difficult to accurately grasp the different preferences and behavioral patterns of each user, and so uniform ads are often delivered to all users. This reduces the effectiveness of ads and makes it difficult to maximize advertisers' ROI. In addition, creating advertising creatives requires a huge amount of time and cost, and there is a lack of means to evaluate and optimize their effectiveness in real time. To solve these problems, a system is needed to generate personalized ads that reflect each user's consumption behavior and preferences, and to quickly optimize their effectiveness. [Means for solving the problem]

[0005] The present invention is a system including a means for collecting user behavioral data, a means for analyzing the collected behavioral data and understanding user characteristics, a means for automatically generating advertisements based on the user characteristics, a means for delivering the generated advertisements to users, and a means for monitoring the effectiveness of the advertisements and optimizing them.

[0006] Specifically, by further including a means for generating multiple advertising patterns based on user characteristics and conducting A / B testing with those advertising patterns, the most effective advertising pattern can be identified and the advertiser's ROI can be maximized. Also, by including a means for determining the timing of delivery of the generated advertisements based on user behavior patterns, the advertisements can be delivered at the timing when the user is most likely to respond to them. This makes it possible to increase the effectiveness of the advertisements and maximize the advertiser's ROI.

[0007] "User behavioral data" refers to records of a user's digital behavior, such as search history, browsing history, purchase history, and review site browsing history when using the Internet or applications.

[0008] "User characteristics" refers to information such as a user's interests, concerns, preferences, and behavioral patterns, which is obtained by analyzing the user's behavioral data.

[0009] "Means for automatically generating advertisements" refers to technology for automatically combining creative elements such as copy, design, and placement to generate advertisements based on user characteristics.

[0010] The "means for delivering advertisements to users" refers to a technique for displaying the generated advertisements on the user's terminal at an appropriate time and place.

[0011] "Means for monitoring and optimizing the effectiveness of advertising" refers to technology that analyzes performance data such as click rates and conversion rates of delivered advertisements in real time, and optimizes the content and delivery method of advertisements based on the results.

[0012] "Generating multiple ad variations" means generating variations of different creative elements (for example, catchphrases and designs) based on the same user characteristics.

[0013] "AB testing" is a method for identifying the most effective advertisement by simultaneously delivering different variations of advertisement patterns and comparing their effectiveness.

[0014] "Determining the timing of delivery" refers to a technique for identifying the time period and circumstances in which a user is most likely to view an advertisement, and delivering the advertisement at that time. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention provides a system that automatically generates personalized advertisements based on user behavior data and delivers them effectively. This system includes collection of behavioral data by user devices, data analysis by a server, advertisement generation, advertisement delivery, and monitoring and optimization of advertisement effectiveness.

[0037] System program processing explanation

[0038] Data collection

[0039] Device:

[0040] When a user uses a web browser or application, their device collects behavioral data such as their search history, browsing history, purchase history, and review site browsing history. This data is used as the basis for reflecting the user's interests and preferences.

[0041] server:

[0042] The collected behavioral data is transferred to a server and stored in a secure database, which stores huge amounts of big data and is used for analysis.

[0043] Data analysis

[0044] server:

[0045] The server runs a generation AI to analyze the behavioral data. The generation AI analyzes the collected behavioral data in detail and extracts user characteristics. The extracted user characteristics include areas of interest, purchasing tendencies, and behavioral patterns by time of day. This creates a persona for each user.

[0046] Ad Generation

[0047] server:

[0048] Based on the personas, generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.), including an ad set with multiple variations of the ad. The generated ads are then configured for A / B testing, with settings set up to measure the effectiveness of each variation.

[0049] Ad serving

[0050] server:

[0051] The ad distribution engine then runs and determines the optimal timing and location for distribution based on user behavior data. For example, it can deliver a specified ad during times when users are most likely to use a social networking app.

[0052] Device:

[0053] Advertisements are displayed on users' smartphones and PCs, allowing them to interact with the ads at the time when they are most likely to respond.

[0054] Measurement and optimization

[0055] server:

[0056] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of AB tests are analyzed to identify the optimal ad variations. Subsequent ad generation and delivery are optimized based on the identified effective ad variations.

[0057] Specific examples

[0058] For User A

[0059] 1. Data Collection:

[0060] Device: User A searches for "latest smartwatches" on their smartphone, browses products on multiple online shops, and purchases one. User A also browses reviews of smartwatches on a review site.

[0061] 2. Data Analysis:

[0062] Server: The server analyzes User A's search history, purchase history, and browsing history on review sites to determine that User A has a high interest in technology gadgets. A persona is created for User A to reflect his interest in new gadgets and reviews.

[0063] 3. Ad generation:

[0064] Server: Automatically generate a new smartphone ad based on User A's persona. Multiple ad variations (different copy and design) are created and set up for A / B testing.

[0065] 4. Advertisement Delivery:

[0066] Server: Identify the time of day when User A uses the SNS app and deliver the optimal ad variation at that time.

[0067] Device: User A's smartphone receives an advertisement for a new smartphone during commuting hours.

[0068] 5. Measurement and optimization:

[0069] Server: Monitors the click-through rate and conversion rate of delivered ads in real time, identifies the most effective ad pattern, and optimizes future ad delivery based on the results.

[0070] The present invention makes it possible to realize effective advertisement delivery based on user characteristics, and significantly improve advertisers' ROI.

[0071] The processing flow will be explained below.

[0072] Step 1: Data collection

[0073] Device: When a user uses a web browser or application, their search history, browsing history, purchase history, and review site browsing history are recorded.

[0074] Device: Periodically transmits collected behavioral data to the server.

[0075] Step 2: Save data

[0076] Server: Receives behavioral data sent from the device and stores it in a secure database, including historical data.

[0077] Step 3: Data analysis

[0078] Server: Runs generative AI algorithms to analyze stored behavioral data.

[0079] Server: Generative AI extracts user characteristics (interests, concerns, purchasing tendencies, etc.) based on behavioral data and creates a persona for each user.

[0080] Step 4: Generate Ads

[0081] Server: Based on the generated personas, the generative AI creates the creative elements of the ad (copy, design, placement, etc.).

[0082] Server: Generates different variations of ads (e.g., multiple copy and designs) and sets them up for A / B testing.

[0083] Step 5: Decide the timing of ad delivery

[0084] Server: Analyzes user behavior patterns (such as the time of day and frequency of device use) to determine the optimal timing for delivering advertisements.

[0085] Step 6: Ad serving

[0086] Server: At the specified time, deliver the optimal ad variation to the user's device.

[0087] Device: Advertisements are displayed on users' smartphones or PCs. For example, specific ads may be displayed within social media apps during the morning commute.

[0088] Step 7: Measure your results

[0089] Server: Monitors user responses to delivered ads (click-through rate, conversion rate, etc.) in real time.

[0090] Server: Analyzes the monitoring results and identifies the most effective advertising patterns.

[0091] Step 8: Optimize your ads

[0092] Server: Optimize ad generation and distribution strategies based on the results of effectiveness measurement. This includes strengthening effective elements and improving weak areas.

[0093] Server: Reflects the optimized advertising strategy in subsequent ad generation and delivery.

[0094] By repeating these steps, you can maximize the effectiveness of your ads and significantly increase your ROI.

[0095] Example 1

[0096] 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."

[0097] In today's online advertising market, there is a demand for personalized ad delivery based on user interests and behavior. However, conventional methods lack sufficient accuracy in analyzing user behavior data and optimizing ad generation and delivery, making effective ad delivery difficult. Furthermore, while there is a demand for optimizing the timing and location of ad delivery, as well as real-time monitoring of ad effectiveness and rapid optimization based on that monitoring, these methods also have limitations. Therefore, there is a need for a system that can effectively utilize user behavior data to generate and deliver ads that are optimal for each individual user, thereby maximizing advertising effectiveness.

[0098] 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.

[0099] In this invention, the server includes means for collecting user behavioral data, means for analyzing the collected behavioral data and grasping user characteristics, means for using a generative AI model to automatically generate advertisements based on the user characteristics, means for delivering the generated advertisements to users at appropriate times, and means for monitoring and optimizing the effectiveness of the advertisements. This makes it possible to generate and deliver personalized advertisements based on the user's interests and behavior, thereby maximizing the effectiveness of the advertisements.

[0100] "User behavior data" refers to data such as search history, browsing history, purchase history, and review site browsing history that is recorded when a user uses a web browser or application.

[0101] "Means for collection" refers to the function for recording user behavior data on the terminal and transferring it to the server.

[0102] "Analysis means" refers to the function that performs processing to extract user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.) based on collected behavioral data.

[0103] "Target characteristics" refer to characteristics such as user interests, preferences, purchasing tendencies, and behavioral patterns obtained from the analysis of behavioral data.

[0104] "Generative AI model" refers to an artificial intelligence model that analyzes collected behavioral data and automatically generates creative elements for advertisements based on user characteristics.

[0105] "Means for automatically generating advertisements" refers to the ability to automatically generate elements such as ad copy, design, and placement using a generative AI model.

[0106] "Means for delivering at the appropriate time" refers to a function for delivering advertisements at the optimal time based on the user's behavioral patterns and usage time period.

[0107] "Means for monitoring the effectiveness of advertising" refers to the function of monitoring the effectiveness of delivered advertisements, such as click rates and conversion rates, in real time.

[0108] "Means for optimization" refers to a function that effectively improves the generation and distribution of advertisements from the next time onwards based on the monitored advertisement effectiveness data.

[0109] "AB testing" refers to a testing method that uses multiple advertising patterns to compare and verify their effectiveness and identify the most effective pattern.

[0110] The present invention provides a system that automatically generates personalized advertisements based on user behavior data and delivers them effectively. This system includes collection of behavioral data by user devices, data analysis by a server, advertisement generation, advertisement delivery, and monitoring and optimization of advertisement effectiveness.

[0111] Data collection

[0112] Device:

[0113] When a user uses a web browser or application, the device collects behavioral data such as search history, browsing history, purchase history, and browsing history on review sites. This includes when a user searches for a specific product, such as the latest smartwatch, and browses products on multiple online shops or reads reviews of smartwatches on review sites.

[0114] server:

[0115] The collected behavioral data is transferred from the device to a server, where it is stored in a secure database such as Amazon RDS, MySQL, or PostgreSQL. This database stores large amounts of big data, which is used for analysis, as described below.

[0116] Data analysis

[0117] server:

[0118] The server uses a generative AI model (e.g., GPT-3, BERT, etc.) to analyze the collected behavioral data. The server retrieves the behavioral data from the database and inputs it into the generative AI. The generative AI analyzes the data in detail and extracts user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.). For example, it may identify that a user is interested in technology gadgets, and generate a user persona.

[0119] Ad Generation

[0120] server:

[0121] Based on the personas created, the generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.). For example, the generative AI creates an ad for a new smartphone that the user is interested in. This ad includes different taglines and design variations. The generated ad set is then configured for A / B testing.

[0122] Ad serving

[0123] server:

[0124] Ad delivery engines (such as Google Ads or Facebook Ads) operate to determine the optimal timing and location for delivery based on user behavior data. For example, an ad for a new smartphone may be delivered within a social networking app at a time when the user is most likely to use that app.

[0125] Device:

[0126] Advertisements are displayed on users' smartphones and PCs. This allows ads to be exposed at the time when users are most likely to respond. For example, an advertisement for a new smartphone is displayed on a user's smartphone during their commute.

[0127] Measurement and optimization

[0128] server:

[0129] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of the AB test are analyzed by the generation AI to identify the most effective ad pattern. Based on these results, the generation AI further optimizes ad generation and delivery for future ads. For example, it will adopt an ad variation with a high click-through rate and reflect that in the next ad generation.

[0130] Prompt Sentence Examples

[0131] You can generate an ad by inputting prompts like the following into the generative AI model:

[0132] Example 1:

[0133] Generate an ad related to the latest smartwatch that User A recently searched for and purchased. The ad should include the following elements: tagline, product image, feature list, and purchase link, reflecting User A's particular interest in technology gadgets.

[0134] Example 2:

[0135] Generate a great ad for a sports-related site that User B frequently visits. The ad should include the following elements: a punchy tagline, action images, discount information, and a link to buy. Consider that User B is interested in fitness equipment.

[0136] The present invention makes it possible to realize effective advertisement delivery based on user characteristics, and significantly improve advertisers' ROI.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1: Data collection

[0139] Device: When a user uses a web browser or application, the device collects search history, browsing history, purchase history, and review site browsing history. This data is saved as a log file. The input is user behavior data (search keywords, URLs of visited web pages, browsing time, etc.). The output is a log file of user behavior data.

[0140] Specific actions: For example, a user may search for "latest smartwatches," browse products on multiple online shops, and purchase one. It may also record actions such as browsing reviews of smartwatches on a review site.

[0141] Step 2: Data Transfer and Storage

[0142] Device: The collected behavioral data is transferred to the server. The device divides the collected data into packets and transmits them using a secure communication protocol (e.g., HTTPS).

[0143] Server: Stores the received behavioral data in a secure database. The input is user behavioral data sent from the device. The output is user behavioral data stored in a database (e.g., Amazon RDS, MySQL, PostgreSQL).

[0144] Specific operation: For example, the user's search history and browsing history are sent to the server and stored in a database.

[0145] Step 3: Data analysis

[0146] Server: A generative AI model (e.g., GPT-3, BERT) is used to analyze the collected behavioral data. The server extracts user behavior data from the database and inputs it into the AI ​​model. The input is the user behavior data in the database. The output is the analyzed user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.).

[0147] How it works: For example, a generative AI might analyze a user's search and purchase history to determine that they're interested in technology gadgets.

[0148] Step 4: Generate Ads

[0149] Server: Based on the generated user characteristics, the generative AI automatically generates the creative elements of the ad. The input includes user characteristic data. The output is the generated ad set (copy, design, placement, etc.).

[0150] What it does: For example, if a user is interested in technology gadgets, the AI ​​will automatically generate ads for new smartphones, with different taglines and design variations.

[0151] Step 5: Ad serving

[0152] Server: The ad distribution engine (e.g., Google Ads, Facebook Ads) runs and determines the optimal timing and location of distribution based on user behavior data. The inputs are user behavior pattern data and the generated ad set. The output is the distribution schedule and the ads to be distributed.

[0153] Device: Advertisements are displayed on the user's smartphone or PC. The input is advertising data sent from the server. The output is the advertisement displayed on the user's device screen.

[0154] Specific behavior: For example, advertisements for new smartphones are displayed within a social media app at the time the user is using the app.

[0155] Step 6: Measure and optimize

[0156] Server: Monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. The input is ad delivery result data. The output is identification of the optimal ad pattern and data for optimizing the next ad based on that.

[0157] What it does: For example, analyze the results of an A / B test to identify the ad variation with the highest click-through rate. Use this information to improve ad generation and delivery for future ads.

[0158] (Application example 1)

[0159] 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."

[0160] Conventional ad delivery systems have limited ad personalization based on user behavior data, making it difficult to reflect detailed real-time behavioral information such as user gaze data. Furthermore, there is a problem in that advanced ad delivery using smart display devices cannot be effectively performed. The present invention aims to solve these problems and realize more accurate ad delivery based on user characteristics.

[0161] 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.

[0162] In this invention, the server includes means for collecting user behavioral data, means for analyzing the collected behavioral data and grasping user characteristics, means for automatically generating advertisements based on the user characteristics, means for delivering the generated advertisements to users, means for monitoring and optimizing the effectiveness of the advertisements, means for collecting user gaze data, means for analyzing behavioral data including the gaze data and grasping user characteristics with high accuracy, and means for displaying advertisements on the smart display device. This enables highly accurate advertisement delivery that reflects detailed user behavioral characteristics in real time.

[0163] "User behavioral data" refers to information such as a user's online search history, browsing history, purchase history, and review site browsing history.

[0164] "Means for analyzing data" refers to the technical methods and algorithms used to analyze collected behavioral data and extract user characteristics.

[0165] "User characteristics" refers to characteristic information such as a user's interests, purchasing tendencies, and behavioral patterns obtained by analyzing behavioral data.

[0166] "Means for automatically generating advertisements" refers to algorithms or systems that automatically generate advertising content (copy, design, placement, etc.) based on user characteristics.

[0167] "Means for delivering advertisements to users" refers to technologies and systems for providing generated advertisements to users at appropriate times and places.

[0168] "Means for monitoring and optimizing advertising effectiveness" refers to technologies and systems that monitor the performance of delivered ads (click-through rate, conversion rate, etc.) in real time and identify the most effective advertising variations.

[0169] "Gaze data" refers to information that indicates the movement of a user's gaze, captured using a camera built into a smart display device or the like.

[0170] "Means for collecting gaze data" refers to technologies and systems that record a user's gaze direction and point of gaze in real time.

[0171] "Smart display device" refers to an advanced display device that can display information to users in real time.

[0172] The system of the present invention collects and analyzes user behavioral data and gaze data, and generates and delivers personalized advertisements based on the collected data. Specific embodiments are described below.

[0173] 1. Hardware Configuration

[0174] This system uses the following main hardware:

[0175] Smart display devices: Devices with built-in cameras and eye-tracking technology.

[0176] Server: A computer used to analyze data and generate advertisements.

[0177] User devices: Mainly smartphones and PCs.

[0178] 2. Software Configuration

[0179] This system uses the following main software:

[0180] OpenCV: A library for collecting and processing gaze data.

[0181] DBSCAN: A clustering algorithm.

[0182] LangChain's OpenAIServer: A server that generates ads using generative AI models.

[0183] Python: A programming language for general data processing.

[0184] 3. Data Collection

[0185] The server collects behavioral data (search history, browsing history, purchase history, etc.) recorded while the user is using a web browser or application. In addition, it uses the built-in camera of the smart display device to capture gaze data in real time and record gaze direction and gaze point.

[0186] 4. Data Analysis

[0187] The collected behavioral and gaze data is transferred to a server and stored in a database. The server uses OpenCV and DBSCAN to analyze the gaze data and extract detailed user behavioral characteristics. This data analysis allows for an understanding of areas of interest, purchasing trends, and behavioral patterns by time of day.

[0188] 5. Ad Generation

[0189] The server determines the creative elements of the ad to be generated based on the data analysis, and generates personalized ad content using LangChain's OpenAIServer, which uses the following prompt text as input to the generative AI model:

[0190] Example prompt sentence:

[0191] User Interests: Cafe, Coffee, Drinks

[0192] Good advertising: Smart Cafe's new drink menu

[0193] 6. Advertisement Delivery

[0194] Based on user behavior data, the generated advertisements are delivered to smart display devices and other user devices at the most appropriate time, allowing for effective advertisements to be provided at the time when users are likely to be interested.

[0195] 7. Measurement and optimization

[0196] After an ad is delivered, its effectiveness (click-through rate, conversion rate, etc.) is monitored in real time. Based on this data, the server identifies the optimal ad variation and optimizes subsequent ad generation and delivery.

[0197] Specific examples

[0198] Suppose a user is walking down the street and their gaze falls on a cafe sign that catches their eye. Their gaze data is captured and analyzed, revealing that they are interested in the cafe. Based on the user's interests, the generative AI model generates an advertisement for a limited-time menu item at a nearby cafe and displays it in real time on a smart display device. It is expected that the user will see this advertisement and visit the specific cafe.

[0199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0200] Step 1:

[0201] The device collects user behavior data (search history, browsing history, purchase history). When a user uses a web browser or application, various data obtained from the operation is saved as a log. This data is transferred to the server.

[0202] Input: User search history, browsing history, purchase history

[0203] Output: Collected behavioral data

[0204] Step 2:

[0205] The server receives the behavioral data transferred from the device, stores it in a secure database, and then prepares the data for analysis.

[0206] Input: Collected behavioral data

[0207] Output: Behavioral data stored in a database

[0208] Step 3:

[0209] The device uses the built-in camera of the smart display device to capture the user's gaze data in real time, recording the gaze direction and gaze point, and this data is also sent to the server.

[0210] Input: User gaze data

[0211] Output: Gaze data captured and sent to the server

[0212] Step 4:

[0213] The server receives the transmitted gaze data and extracts gaze maps and gaze points using OpenCV, while clustering the gaze data using the DBSCAN algorithm to identify the user's gaze patterns.

[0214] Input: User gaze data

[0215] Output: Clustered gaze pattern data

[0216] Step 5:

[0217] The server integrates the collected behavioral data with the clustered gaze patterns to extract more detailed user characteristics, such as areas of interest, purchasing tendencies, and behavioral patterns by time of day.

[0218] Input: Stored behavioral data, clustered gaze pattern data

[0219] Output: Extracted user characteristics

[0220] Step 6:

[0221] The server generates personalized ads using a generative AI model (LangChain's OpenAIServer) based on the extracted user characteristics, and generates appropriate ad content using the prompt sentence as input.

[0222] Input: Prompt sentence (e.g., user interests: cafe, coffee, drinks)

[0223] Output: Generated ad content

[0224] Step 7:

[0225] The server delivers the generated advertising content to users at a time and place appropriate for them, for example, displaying the advertisement when the user is using a social networking app or when the user is in a specific location, and also instructs the smart display device to display the advertisement.

[0226] Input: Generated advertising content, user behavior patterns

[0227] Output: Ads delivered to user devices and smart display devices

[0228] Step 8:

[0229] The server monitors the performance of the delivered ads in real time, analyzing click-through rates, conversion rates, etc. Based on the obtained data, it identifies the optimal ad variation and optimizes subsequent ad generation and delivery.

[0230] Input: Performance data of delivered ads

[0231] Output: Optimized ad generation and delivery patterns

[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] This invention is a system that collects and analyzes not only user behavioral data but also user emotional data, and generates and delivers personalized advertisements. This system includes processes that collect behavioral data and emotional data from the user's device, analyze the data on the server, and generate, deliver, measure the effectiveness of, and optimize advertisements.

[0234] System program processing explanation

[0235] Data collection

[0236] Device:

[0237] When a user uses a web browser or application, behavioral data such as search history, browsing history, purchase history, and review site browsing history are recorded. The device also uses cameras and sensors to collect emotional data based on the user's facial expressions and voice data. This emotional data reflects the user's emotional state (e.g., joy, sadness, excitement, etc.).

[0238] server:

[0239] The collected behavioral and emotional data is transferred to a server and stored in a secure database, which also includes historical data.

[0240] Data analysis

[0241] server:

[0242] The server runs a generation AI and emotion engine to analyze behavioral and emotional data. The generation AI analyzes the behavioral data in detail to extract the user's characteristics. The emotion engine analyzes the emotional data and grasps the user's emotional state in real time. This allows a more specific persona to be formed for each user.

[0243] Ad Generation

[0244] server:

[0245] Based on the generated persona and real-time emotional state, the generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.), including different variations of the ad pattern and configuring them for A / B testing. It can also change the content of the ad depending on the user's emotional state.

[0246] Ad serving

[0247] server:

[0248] The ad delivery engine works to determine the optimal timing and location of delivery based on the user's behavioral patterns and emotional state, for example, delivering positive advertising messages when the user is happy.

[0249] Device:

[0250] Advertisements are displayed on users' smartphones and PCs, allowing them to be exposed to ads at the time when they are most likely to respond to them.

[0251] Measurement and optimization

[0252] server:

[0253] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of AB tests are analyzed to identify the most effective ad pattern. Also, how the user's emotional state affects ad response is analyzed. Based on the identified effective ad pattern, subsequent ad generation and delivery are optimized.

[0254] Specific examples

[0255] For User B

[0256] 1. Data Collection:

[0257] Device: User B searches for "the latest smartphone" on his smartphone, browses products on multiple online shops, and purchases one. User B also browses smartphone reviews on a review site. Furthermore, User B's facial expressions and voice data are collected via the device's camera and microphone, and emotion data is obtained.

[0258] 2. Data Analysis:

[0259] Server: The server analyzes User B's search history, purchase history, review site browsing history, as well as emotional data, to determine that User B has a high interest in technology gadgets and was excited about purchasing them. A persona for User B is created to reflect his interest in new gadgets and reviews, as well as his emotional state.

[0260] 3. Ad generation:

[0261] Server: Automatically generate new smartphone ads based on User B's persona and emotional state. Multiple ad variations with different taglines and designs are created and set up for A / B testing. Include ads with more dynamic designs that take into account the user's excitement state.

[0262] 4. Advertisement Delivery:

[0263] Server: Identify the time and emotional state when User B is most likely to respond to ads, and deliver the optimal ad variation at that time. For example, deliver a video ad with energetic music when User B is in an excited state.

[0264] Device: User B's smartphone receives a video ad for a new smartphone during rush hour.

[0265] 5. Measurement and optimization:

[0266] Server: Monitors the click-through rate and conversion rate of delivered ads in real time to identify the most effective ad pattern. Based on the results, optimizes future ad delivery. Also, analyzes how the user's emotional state affected the effectiveness of the ad and reflects this in future ad generation.

[0267] This invention makes it possible to realize detailed ad delivery based on user characteristics and emotional state, thereby significantly improving advertisers' ROI.

[0268] The processing flow will be explained below.

[0269] Step 1: Collect behavioral and emotional data

[0270] Device: When a user uses a web browser or application, the device records search history, browsing history, purchase history, and browsing history on review sites. It also uses cameras and sensors to collect facial expressions and voice data, obtaining emotional data in real time.

[0271] Step 2: Send and store data

[0272] Terminal: Sends collected behavioral and emotional data to the server.

[0273] Server: Stores the received data in a secure database, including historical behavioral and emotional data.

[0274] Step 3: Data analysis

[0275] Server: Runs the generative AI and emotion engine to analyze the stored behavioral and emotional data.

[0276] Server: The generative AI analyzes the behavioral data and extracts user characteristics such as user interests and purchasing tendencies.

[0277] Server: The emotion engine analyzes the emotion data and identifies the user's emotional state in real time.

[0278] Server: Create a persona for each user, reflecting their emotional state.

[0279] Step 4: Generate Ads

[0280] Server: Automatically generate the creative elements of the ad (copy, design, placement, etc.) based on the generated persona and emotional state.

[0281] Server: Generates different ad variations and sets them up for A / B testing.

[0282] Server: Adjust the content and design of the ad depending on the user's emotional state (for example, use an energetic design if the user is excited).

[0283] Step 5: Decide the timing of ad delivery

[0284] Server: Analyzes user behavior patterns (time of use, frequency, etc.) and emotional state to determine the optimal timing for delivering advertisements.

[0285] Step 6: Ad serving

[0286] Server: Delivers the appropriate ad variation to the user's device at the determined time and place.

[0287] Device: Advertisements are displayed on users' smartphones or PCs. For example, specific ads are delivered during lunch breaks when users have positive emotions.

[0288] Step 7: Measure your results

[0289] Server: Collects real-time performance data such as click rates and conversion rates of delivered ads.

[0290] Server: Analyzes the results of A / B tests to identify the most effective ad variations and evaluates how emotional states affect ad effectiveness.

[0291] Step 8: Optimize your ads

[0292] Server: Optimize your ad generation and delivery strategy based on the results of your measurement. This involves strengthening what works and correcting areas that need improvement.

[0293] Server: Reflects the optimized advertising strategy in subsequent ad generation and delivery.

[0294] By repeating these steps, we can deliver ads in real time that are best suited to each user's individual characteristics and emotional state, maximizing advertisers' ROI.

[0295] Example 2

[0296] 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."

[0297] Current ad delivery systems generate and deliver ads based on user behavior data, but do not take the user's emotional state into account. This results in ads that are not appropriate for the user's emotions and are therefore less effective. Furthermore, if the content or timing of an ad does not match the user's current emotional state, it can negatively impact the user experience and lead to lower click-through rates and conversion rates.

[0298] 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.

[0299] In this invention, the server includes means for collecting user behavioral data, means for collecting user emotional data, means for transferring the collected behavioral data and emotional data to the server, means for analyzing the transferred behavioral data and emotional data to grasp the user's characteristics and emotional state, means for automatically generating advertisements based on the user's characteristics and emotional state, means for delivering the generated advertisements based on the user's behavioral patterns and emotional state, and means for monitoring and optimizing the effectiveness of the advertisements. This makes it possible to generate and deliver highly accurate advertisements tailored to the user's characteristics and real-time emotional state, thereby maximizing the effectiveness of the advertisements.

[0300] "Behavioral data" refers to data such as search history, browsing history, purchase history, and review viewing history that is generated when a user uses a web browser or application.

[0301] "Emotion data" is data obtained from the user's facial expressions and voice, and indicates the user's emotional state, such as joy, sadness, or excitement.

[0302] The "server" is a computer system that analyzes collected behavioral and emotional data and generates and delivers advertisements based on that data.

[0303] "User characteristics" are characteristics such as user interests and purchasing tendencies that can be obtained by analyzing behavioral data.

[0304] "Emotional state" refers to a real-time understanding of the user's emotions based on an analysis of collected emotional data.

[0305] "Ad generation" is the process of automatically generating the creative elements of an ad (copy, design, placement, etc.) based on user characteristics and emotional state.

[0306] "Ad serving" is the process of displaying generated advertisements at appropriate times and places based on the user's behavioral patterns and emotional state.

[0307] "Effectiveness measurement" is the process of monitoring the effectiveness of delivered advertisements, such as click rates and conversion rates, in real time.

[0308] "Optimization" is the process of adjusting the ad generation and delivery process based on the results of effectiveness measurement and identifying the most effective advertising pattern.

[0309] "AB testing" is a testing method in which multiple advertising patterns are delivered simultaneously and their effectiveness is compared and analyzed.

[0310] This invention is a system that collects and analyzes user behavioral and emotional data, and generates and delivers personalized advertisements. This system is mainly composed of a user terminal and a server.

[0311] Data collection

[0312] A user's device has a web browser and applications installed. When the user uses them, the device collects the following behavioral data:

[0313] Search history (e.g. keywords entered into search engines)

[0314] Browsing history (e.g., URLs and categories of accessed web pages)

[0315] Purchase history (e.g. details of products purchased on an online shopping site)

[0316] Review viewing history (e.g., the content and ratings of reviews you have read)

[0317] Furthermore, the device is equipped with a camera and microphone, which can analyze the user's facial expressions and voice in real time to collect emotional data, which indicates the user's emotional state, such as joy, sadness, or excitement.

[0318] Data Transfer

[0319] The device transfers the collected behavioral and emotional data to a server using a secure protocol (e.g., HTTPS). This transfer occurs periodically to ensure the security of the communication.

[0320] Data analysis

[0321] The server receives the transferred behavioral and emotional data and stores it in a secure database, which also stores the user's past data.

[0322] The server runs a generative AI model and an emotion engine. The generative AI model analyzes behavioral data and extracts user characteristics (e.g., interest in technology gadgets). Meanwhile, the emotion engine analyzes emotional data and can grasp the user's emotional state in real time. This allows a detailed persona to be formed for each user.

[0323] Ad Generation

[0324] The server automatically generates the creative elements of the ad (copy, design, placement, etc.) based on the generated persona and real-time emotional state. Different ad variations can also be configured for A / B testing using the generative AI model. The content of the ad dynamically changes depending on the user's emotional state.

[0325] Ad serving

[0326] The server's ad delivery engine determines the optimal timing and location for delivery based on the user's behavioral patterns and emotional state, for example, delivering a positive, energetic advertising message when the user is excited.

[0327] Advertisements are displayed on users' devices (smartphones and PCs) at the time when users are most likely to respond to them.

[0328] Measurement and optimization

[0329] The server monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. It analyzes the results of AB tests to identify the most effective ad pattern. It also analyzes how the user's emotional state affects the effectiveness of ads, thereby optimizing ad generation and delivery for future ads.

[0330] Specific examples

[0331] As a concrete example, consider the case of User B. User B searches for the "latest smartphone" on his / her smartphone, browses products on multiple online shops, and purchases one of them. While browsing smartphone reviews on a review site, the device's camera captures User B's facial expressions and acquires emotion data.

[0332] The server analyzes User B's search history, purchase history, review site browsing history, and emotional data to determine that User B has a high interest in technology gadgets and was excited about them while making purchases. Using a generative AI model, it creates a persona for User B to reflect his interest in new gadgets and reviews, as well as his emotional state.

[0333] Based on User B's persona and emotional state, a generative AI model automatically generates a new smartphone ad. Multiple ad variations with different taglines and designs are created and set up for A / B testing. Include a more dynamic ad design that takes into account the user's excitement level.

[0334] The server identifies the time period and emotional state when User B is most likely to respond to an advertisement, and delivers the optimal advertisement variation at that timing. For example, if User B is in an excited state, it delivers a video advertisement with energetic music.

[0335] A new smartphone video ad is delivered to User B's smartphone during commuting hours. The server monitors the click-through rate and conversion rate of the delivered ad in real time to identify the most effective ad pattern. Based on the results, it optimizes ad delivery from the next time onwards. It also analyzes how the user's emotional state affected the effectiveness of the ad and reflects this in future ad generation.

[0336] Prompt Sentence Examples

[0337] Based on the behavioral history and emotional data of users searching for "latest smartphones," please generate ads with the following conditions:

[0338] An energetic design that matches the user's excitement

[0339] Multiple copy and design variations for A / B testing

[0340] Delivered during the user's commute time

[0341] The embodiments of the present invention have been described in detail above.

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1: Data collection

[0344] Device:

[0345] When a user uses a web browser or application, behavioral data such as search history, browsing history, purchase history, and review browsing history is collected. Furthermore, facial expression and voice data is collected using the device's camera and microphone, and emotional data is obtained. The input is the user's operational behavior and the device's sensor data. The output is the collected behavioral data and emotional data.

[0346] Specifically, when a user searches for "the latest smartphone" on a smartphone browser, browses multiple online shops, and reads reviews, the system collects their browsing history. The system also captures emotion data from the user's facial expressions captured by a camera while they are reading the reviews.

[0347] Step 2: Data Transfer

[0348] Device:

[0349] The collected behavioral and emotional data is sent to a server using a secure protocol (e.g., HTTPS). As input, the data collected in step 1 is used. As output, the data sent to the server is obtained.

[0350] Specifically, the device generates data packets at regular intervals, encrypts them, and sends them to the server, along with emotion data.

[0351] Step 3: Data analysis

[0352] server:

[0353] The received behavioral and emotional data is stored in a database on the server. Then, this data is analyzed using a generative AI model and an emotion engine. As input, the data sent in step 2 is used. As output, analysis results are obtained to understand the user's characteristics and emotional state.

[0354] Specifically, the generative AI model analyzes the user's search and purchase history to extract characteristics such as "this user tends to get excited about new gadgets." The emotion engine also analyzes the user's emotional data and determines in real time that "the user was in a highly excited state when looking at product reviews."

[0355] Step 4: Generate Ads

[0356] server:

[0357] Based on the generated personas and real-time emotional states, the generative AI model automatically generates the creative elements of the ad (copy, design, placement, etc.). It generates different ad variations for AB testing. It uses the user characteristics and emotional states obtained in step 3 as input. The generated ad variations are obtained as output.

[0358] For example, when generating an advertisement for the latest smartphone, the system creates several variations of the advertisement with dynamic effects to stimulate users' excitement. For A / B testing, it generates ad variations with different catchphrases and designs.

[0359] Step 5: Ad serving

[0360] server:

[0361] The ad serving engine determines the optimal timing and location of delivery based on the user's behavioral patterns and emotional state. As input, it uses the ad variations generated in step 4 and previous user behavior and emotional state data. The output is the ad to be delivered and its timing.

[0362] Device:

[0363] The generated advertisement is delivered to the user's terminal at a time and place determined by the server.

[0364] Specifically, while User B is reading the news on his smartphone during his commute, a video advertisement for the "latest smartphone" with dynamic effects and energetic music is displayed.

[0365] Step 6: Measure and optimize

[0366] server:

[0367] It monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. It analyzes the results of AB tests to identify the most effective ad pattern. It also analyzes how the user's emotional state affected the effectiveness of the ad. As input, it uses performance data of the ad being delivered and real-time user emotional data. As output, it obtains an optimized ad generation and delivery strategy.

[0368] Specifically, the system analyzes in real time the number of clicks on ads and the number of times they lead to subsequent purchases, and gains insights such as "advertisements delivered when users are excited have a higher click rate." Based on these findings, it optimizes ad generation and delivery for future ads.

[0369] The above is the specific processing flow of the system program.

[0370] (Application example 2)

[0371] 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."

[0372] Conventional ad delivery systems generate and deliver ads based on user behavior data, but do not take the user's emotional state into account, limiting the effectiveness of the ads. Furthermore, they lack targeting accuracy and real-time optimization, making it difficult to attract user attention.

[0373] 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.

[0374] In this invention, the server includes means for analyzing user behavioral data and emotional data to grasp user characteristics, means for automatically generating advertisements based on the analyzed user characteristics and emotional state, and means for delivering optimal advertisements based on the user's behavioral patterns and emotional state, thereby enabling targeting that takes into account the user's emotional state and real-time advertisement optimization.

[0375] "User behavior data" refers to information such as search history, browsing history, and purchase history when a user uses a web browser or application.

[0376] "Emotion data" is data obtained from the user's facial expressions, voice, etc., and reflects the user's emotional state (joy, sadness, excitement, etc.).

[0377] "User characteristics" refer to the user's interests, preferences, and behavioral patterns extracted based on the analyzed user's behavioral data and emotional data.

[0378] "Automatic ad generation" refers to the automatic generation of creative elements of an ad (copy, design, placement, etc.) using generative AI based on user characteristics and emotional state.

[0379] "Multiple ad variations" are ad variations with different copy and design, used to conduct A / B testing.

[0380] "AB testing" is a method of comparing and verifying the effectiveness of multiple advertising patterns to identify the most effective one.

[0381] "Advertisement delivery" refers to delivering the generated advertisement at the optimal timing based on the user's behavioral patterns and emotional state.

[0382] "Monitoring" means monitoring the effectiveness of delivered advertisements (click-through rate, conversion rate, etc.) in real time.

[0383] "Optimization" is the process of adjusting advertising content and delivery timing based on monitoring results to maximize advertising effectiveness.

[0384] The present invention relates to a system that collects and analyzes user behavioral data and emotional data, and generates and distributes personalized advertisements. A specific embodiment of this system is described below.

[0385] System Configuration

[0386] 1. Data Collection

[0387] Device: A user's smartphone records search history, browsing history, and purchase history when using a web browser or application. The device also uses a camera and microphone to collect facial and voice data and obtain emotional data. To do this, the device uses camera sensors and voice recognition software (e.g., Google APIs for Computer Vision and Emotion AI).

[0388] Server: Collected behavioral and emotional data is transferred to the server and stored in a secure database (e.g., AWS RDS, Google Cloud SQL).

[0389] 2. Data Analysis

[0390] Server: The server analyzes the collected behavioral and emotional data as follows:

[0391] Generative AI (e.g., OpenAI GPT-4) is used to perform detailed analysis of behavioral data and extract user characteristics.

[0392] Using an emotion engine (e.g., IBM Watson Tone Analyzer), emotion data is analyzed to understand the user's real-time emotional state.

[0393] 3. Ad generation

[0394] Server: The server automatically generates the creative elements of the ad (copy, design, placement, etc.) based on the generated user characteristics and emotional state. Using generative AI, multiple ad variations are created and prepared for A / B testing.

[0395] 4. Advertisement Delivery

[0396] Server: The server uses an ad serving engine (e.g., AdRoll, Google Ads API) to deliver ads at the optimal time and place based on the user's behavioral patterns and emotional state.

[0397] Device: Real-time personalized ads are displayed on the user's smartphone, taking into account the user's emotional state, for example, delivering energetic ads when they are excited.

[0398] 5. Measurement and optimization

[0399] Server: The server monitors and analyzes the effectiveness of the delivered advertisements in real time. For example, it measures click-through rates and conversion rates and identifies the most effective advertisement patterns. It optimizes the content and timing of advertisements based on the monitoring results.

[0400] Specific examples

[0401] For user C:

[0402] Data collection: User C searches for "new running shoes" on their smartphone and browses multiple running-related blog articles and reviews. The smartphone camera also collects User C's facial expressions and voice data, recording their emotional data.

[0403] Data analysis: The server analyzes user C's search history, browsing history, and emotional data and finds out that user C has a high interest in fitness products, especially running, and has positive emotions.

[0404] Ad generation: Based on the generated persona and emotional state of User C, an advertisement for a new running shoe is automatically generated. Multiple advertisement variations with different catchphrases and designs are created and prepared for A / B testing.

[0405] Advertisement delivery: When User C is excited after his morning jog, a video advertisement with energetic music is delivered to his smartphone.

[0406] Effectiveness measurement and optimization: Monitor click-through rates and conversion rates of delivered ads in real time, identify the most effective ad patterns, and reflect them in future ad deliveries.

[0407] Prompt Sentence Examples

[0408] For example, input the following prompt into your generative AI model:

[0409] User C has a strong interest in fitness, especially running. His emotional data shows that he feels excited after jogging. Based on this data, we generate ad copy and design variations that emphasize the appeal of running shoes. For example, we could create an ad that includes the following content:

[0410] 1. "New running shoes that offer the best running experience!"

[0411] 2. "Break your personal best in your next race! These running shoes make it possible."

[0412] Also consider what will excite User C even more.

[0413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0414] Step 1:

[0415] The device collects user behavior data (search history, browsing history, purchase history, etc.). This includes the user's actions when using a web browser or application, and the product browsing history in an online shop. The collected data is stored in an initial database on the smartphone, ready to be transferred to the server.

[0416] Step 2:

[0417] The device collects the user's emotional data. It uses a camera and microphone to capture the user's facial expressions and voice data, and analyzes their emotional state (happiness, sadness, excitement, etc.) based on this data. For this purpose, it uses, for example, Google APIs for Computer Vision and Emotion AI. The collected emotional data is also temporarily stored on the smartphone.

[0418] Step 3:

[0419] The device transfers behavioral and emotional data to the server via secure communication. The data is protected using security protocols such as Transport Layer Security (TLS). The server receives the data and stores it in a secure database (e.g., AWS RDS or Google Cloud SQL).

[0420] Step 4:

[0421] The server uses generative AI (e.g., OpenAI GPT-4) to analyze user behavioral data and extract user characteristics. Here, the server identifies the user's interests, preferences, and behavioral patterns from past search history, browsing history, and purchase history. Based on the input behavioral data, it performs text analysis and pattern recognition.

[0422] Step 5:

[0423] The server analyzes the user's emotional data using an emotion engine (e.g., IBM Watson Tone Analyzer) to understand the user's current emotional state. It identifies the user's real-time emotional state through facial expression analysis and voice analysis and records it along with the user's characteristics.

[0424] Step 6:

[0425] The server uses generative AI to automatically generate ads based on analyzed user characteristics and emotional states. The generative AI creates multiple ad variations, including different copy and designs. The generated ads are then set up for A / B testing.

[0426] Step 7:

[0427] The server uses an ad distribution engine (e.g., AdRoll, Google Ads API) to distribute ads at optimal times based on the user's behavioral patterns and emotional state. For example, it adjusts the distribution of energetic ads when the user is in an excited state.

[0428] Step 8:

[0429] The device displays the delivered advertisements to the user in real time, allowing the user to interact with the advertisement at the time when they are most likely to respond.The advertisements are displayed using the smartphone's notification function and in-app banner ads.

[0430] Step 9:

[0431] The server monitors the click-through rate and conversion rate of the delivered ads in real time. Here, data analysis tools (e.g., Google Analytics) are used to evaluate the effectiveness of the ads. The results of the AB tests are analyzed to identify the most effective ad patterns.

[0432] Step 10:

[0433] The server optimizes the ad content and delivery timing based on the monitoring results, giving priority to highly effective ad patterns and reflecting this in the next ad generation and delivery, thereby maximizing advertising ROI.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Second embodiment]

[0438] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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."

[0450] The present invention provides a system that automatically generates personalized advertisements based on user behavior data and delivers them effectively. This system includes collection of behavioral data by user devices, data analysis by a server, advertisement generation, advertisement delivery, and monitoring and optimization of advertisement effectiveness.

[0451] System program processing explanation

[0452] Data collection

[0453] Device:

[0454] When a user uses a web browser or application, their device collects behavioral data such as their search history, browsing history, purchase history, and review site browsing history. This data is used as the basis for reflecting the user's interests and preferences.

[0455] server:

[0456] The collected behavioral data is transferred to a server and stored in a secure database, which stores huge amounts of big data and is used for analysis.

[0457] Data analysis

[0458] server:

[0459] The server runs a generation AI to analyze the behavioral data. The generation AI analyzes the collected behavioral data in detail and extracts user characteristics. The extracted user characteristics include areas of interest, purchasing tendencies, and behavioral patterns by time of day. This creates a persona for each user.

[0460] Ad Generation

[0461] server:

[0462] Based on the personas, generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.), including an ad set with multiple variations of the ad. The generated ads are then configured for A / B testing, with settings set up to measure the effectiveness of each variation.

[0463] Ad serving

[0464] server:

[0465] The ad distribution engine then runs and determines the optimal timing and location for distribution based on user behavior data. For example, it can deliver a specified ad during times when users are most likely to use a social networking app.

[0466] Device:

[0467] Advertisements are displayed on users' smartphones and PCs, allowing them to interact with the ads at the time when they are most likely to respond.

[0468] Measurement and optimization

[0469] server:

[0470] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of AB tests are analyzed to identify the optimal ad variations. Subsequent ad generation and delivery are optimized based on the identified effective ad variations.

[0471] Specific examples

[0472] For User A

[0473] 1. Data Collection:

[0474] Device: User A searches for "latest smartwatches" on their smartphone, browses products on multiple online shops, and purchases one. User A also browses reviews of smartwatches on a review site.

[0475] 2. Data Analysis:

[0476] Server: The server analyzes User A's search history, purchase history, and browsing history on review sites to determine that User A has a high interest in technology gadgets. A persona is created for User A to reflect his interest in new gadgets and reviews.

[0477] 3. Ad generation:

[0478] Server: Automatically generate a new smartphone ad based on User A's persona. Multiple ad variations (different copy and design) are created and set up for A / B testing.

[0479] 4. Advertisement Delivery:

[0480] Server: Identify the time of day when User A uses the SNS app and deliver the optimal ad variation at that time.

[0481] Device: User A's smartphone receives an advertisement for a new smartphone during commuting hours.

[0482] 5. Measurement and optimization:

[0483] Server: Monitors the click-through rate and conversion rate of delivered ads in real time, identifies the most effective ad pattern, and optimizes future ad delivery based on the results.

[0484] The present invention makes it possible to realize effective advertisement delivery based on user characteristics, and significantly improve advertisers' ROI.

[0485] The processing flow will be explained below.

[0486] Step 1: Data collection

[0487] Device: When a user uses a web browser or application, their search history, browsing history, purchase history, and review site browsing history are recorded.

[0488] Device: Periodically transmits collected behavioral data to the server.

[0489] Step 2: Save data

[0490] Server: Receives behavioral data sent from the device and stores it in a secure database, including historical data.

[0491] Step 3: Data analysis

[0492] Server: Runs generative AI algorithms to analyze stored behavioral data.

[0493] Server: Generative AI extracts user characteristics (interests, concerns, purchasing tendencies, etc.) based on behavioral data and creates a persona for each user.

[0494] Step 4: Generate Ads

[0495] Server: Based on the generated personas, the generative AI creates the creative elements of the ad (copy, design, placement, etc.).

[0496] Server: Generates different variations of ads (e.g., multiple copy and designs) and sets them up for A / B testing.

[0497] Step 5: Decide the timing of ad delivery

[0498] Server: Analyzes user behavior patterns (such as the time of day and frequency of device use) to determine the optimal timing for delivering advertisements.

[0499] Step 6: Ad serving

[0500] Server: At the specified time, deliver the optimal ad variation to the user's device.

[0501] Device: Advertisements are displayed on users' smartphones or PCs. For example, specific ads may be displayed within social media apps during the morning commute.

[0502] Step 7: Measure your results

[0503] Server: Monitors user responses to delivered ads (click-through rate, conversion rate, etc.) in real time.

[0504] Server: Analyzes the monitoring results and identifies the most effective advertising patterns.

[0505] Step 8: Optimize your ads

[0506] Server: Optimize ad generation and distribution strategies based on the results of effectiveness measurement. This includes strengthening effective elements and improving weak areas.

[0507] Server: Reflects the optimized advertising strategy in subsequent ad generation and delivery.

[0508] By repeating these steps, you can maximize the effectiveness of your ads and significantly increase your ROI.

[0509] Example 1

[0510] 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."

[0511] In today's online advertising market, there is a demand for personalized ad delivery based on user interests and behavior. However, conventional methods lack sufficient accuracy in analyzing user behavior data and optimizing ad generation and delivery, making effective ad delivery difficult. Furthermore, while there is a demand for optimizing the timing and location of ad delivery, as well as real-time monitoring of ad effectiveness and rapid optimization based on that monitoring, these methods also have limitations. Therefore, there is a need for a system that can effectively utilize user behavior data to generate and deliver ads that are optimal for each individual user, thereby maximizing advertising effectiveness.

[0512] 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.

[0513] In this invention, the server includes means for collecting user behavioral data, means for analyzing the collected behavioral data and grasping user characteristics, means for using a generative AI model to automatically generate advertisements based on the user characteristics, means for delivering the generated advertisements to users at appropriate times, and means for monitoring and optimizing the effectiveness of the advertisements. This makes it possible to generate and deliver personalized advertisements based on the user's interests and behavior, thereby maximizing the effectiveness of the advertisements.

[0514] "User behavior data" refers to data such as search history, browsing history, purchase history, and review site browsing history that is recorded when a user uses a web browser or application.

[0515] "Means for collection" refers to the function for recording user behavior data on the terminal and transferring it to the server.

[0516] "Analysis means" refers to the function that performs processing to extract user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.) based on collected behavioral data.

[0517] "Target characteristics" refer to characteristics such as user interests, preferences, purchasing tendencies, and behavioral patterns obtained from the analysis of behavioral data.

[0518] "Generative AI model" refers to an artificial intelligence model that analyzes collected behavioral data and automatically generates creative elements for advertisements based on user characteristics.

[0519] "Means for automatically generating advertisements" refers to the ability to automatically generate elements such as ad copy, design, and placement using a generative AI model.

[0520] "Means for delivering at the appropriate time" refers to a function for delivering advertisements at the optimal time based on the user's behavioral patterns and usage time period.

[0521] "Means for monitoring the effectiveness of advertising" refers to the function of monitoring the effectiveness of delivered advertisements, such as click rates and conversion rates, in real time.

[0522] "Means for optimization" refers to a function that effectively improves the generation and distribution of advertisements from the next time onwards based on the monitored advertisement effectiveness data.

[0523] "AB testing" refers to a testing method that uses multiple advertising patterns to compare and verify their effectiveness and identify the most effective pattern.

[0524] The present invention provides a system that automatically generates personalized advertisements based on user behavior data and delivers them effectively. This system includes collection of behavioral data by user devices, data analysis by a server, advertisement generation, advertisement delivery, and monitoring and optimization of advertisement effectiveness.

[0525] Data collection

[0526] Device:

[0527] When a user uses a web browser or application, the device collects behavioral data such as search history, browsing history, purchase history, and browsing history on review sites. This includes when a user searches for a specific product, such as the latest smartwatch, and browses products on multiple online shops or reads reviews of smartwatches on review sites.

[0528] server:

[0529] The collected behavioral data is transferred from the device to a server, where it is stored in a secure database such as Amazon RDS, MySQL, or PostgreSQL. This database stores large amounts of big data, which is used for analysis, as described below.

[0530] Data analysis

[0531] server:

[0532] The server uses a generative AI model (e.g., GPT-3, BERT, etc.) to analyze the collected behavioral data. The server retrieves the behavioral data from the database and inputs it into the generative AI. The generative AI analyzes the data in detail and extracts user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.). For example, it may identify that a user is interested in technology gadgets, and generate a user persona.

[0533] Ad Generation

[0534] server:

[0535] Based on the personas created, the generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.). For example, the generative AI creates an ad for a new smartphone that the user is interested in. This ad includes different taglines and design variations. The generated ad set is then configured for A / B testing.

[0536] Ad serving

[0537] server:

[0538] Ad delivery engines (such as Google Ads or Facebook Ads) operate to determine the optimal timing and location for delivery based on user behavior data. For example, an ad for a new smartphone may be delivered within a social networking app at a time when the user is most likely to use that app.

[0539] Device:

[0540] Advertisements are displayed on users' smartphones and PCs. This allows ads to be exposed at the time when users are most likely to respond. For example, an advertisement for a new smartphone is displayed on a user's smartphone during their commute.

[0541] Measurement and optimization

[0542] server:

[0543] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of the AB test are analyzed by the generation AI to identify the most effective ad pattern. Based on these results, the generation AI further optimizes ad generation and delivery for future ads. For example, it will adopt an ad variation with a high click-through rate and reflect that in the next ad generation.

[0544] Prompt Sentence Examples

[0545] You can generate an ad by inputting prompts like the following into the generative AI model:

[0546] Example 1:

[0547] Generate an ad related to the latest smartwatch that User A recently searched for and purchased. The ad should include the following elements: tagline, product image, feature list, and purchase link, reflecting User A's particular interest in technology gadgets.

[0548] Example 2:

[0549] Generate a great ad for a sports-related site that User B frequently visits. The ad should include the following elements: a punchy tagline, action images, discount information, and a link to buy. Consider that User B is interested in fitness equipment.

[0550] The present invention makes it possible to realize effective advertisement delivery based on user characteristics, and significantly improve advertisers' ROI.

[0551] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0552] Step 1: Data collection

[0553] Device: When a user uses a web browser or application, the device collects search history, browsing history, purchase history, and review site browsing history. This data is saved as a log file. The input is user behavior data (search keywords, URLs of visited web pages, browsing time, etc.). The output is a log file of user behavior data.

[0554] Specific actions: For example, a user may search for "latest smartwatches," browse products on multiple online shops, and purchase one. It may also record actions such as browsing reviews of smartwatches on a review site.

[0555] Step 2: Data Transfer and Storage

[0556] Device: The collected behavioral data is transferred to the server. The device divides the collected data into packets and transmits them using a secure communication protocol (e.g., HTTPS).

[0557] Server: Stores the received behavioral data in a secure database. The input is user behavioral data sent from the device. The output is user behavioral data stored in a database (e.g., Amazon RDS, MySQL, PostgreSQL).

[0558] Specific operation: For example, the user's search history and browsing history are sent to the server and stored in a database.

[0559] Step 3: Data analysis

[0560] Server: A generative AI model (e.g., GPT-3, BERT) is used to analyze the collected behavioral data. The server extracts user behavior data from the database and inputs it into the AI ​​model. The input is the user behavior data in the database. The output is the analyzed user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.).

[0561] How it works: For example, a generative AI might analyze a user's search and purchase history to determine that they're interested in technology gadgets.

[0562] Step 4: Generate Ads

[0563] Server: Based on the generated user characteristics, the generative AI automatically generates the creative elements of the ad. The input includes user characteristic data. The output is the generated ad set (copy, design, placement, etc.).

[0564] What it does: For example, if a user is interested in technology gadgets, the AI ​​will automatically generate ads for new smartphones, with different taglines and design variations.

[0565] Step 5: Ad serving

[0566] Server: The ad distribution engine (e.g., Google Ads, Facebook Ads) runs and determines the optimal timing and location of distribution based on user behavior data. The inputs are user behavior pattern data and the generated ad set. The output is the distribution schedule and the ads to be distributed.

[0567] Device: Advertisements are displayed on the user's smartphone or PC. The input is advertising data sent from the server. The output is the advertisement displayed on the user's device screen.

[0568] Specific behavior: For example, advertisements for new smartphones are displayed within a social media app at the time the user is using the app.

[0569] Step 6: Measure and optimize

[0570] Server: Monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. The input is ad delivery result data. The output is identification of the optimal ad pattern and data for optimizing the next ad based on that.

[0571] What it does: For example, analyze the results of an A / B test to identify the ad variation with the highest click-through rate. Use this information to improve ad generation and delivery for future ads.

[0572] (Application example 1)

[0573] 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."

[0574] Conventional ad delivery systems have limited ad personalization based on user behavior data, making it difficult to reflect detailed real-time behavioral information such as user gaze data. Furthermore, there is a problem in that advanced ad delivery using smart display devices cannot be effectively performed. The present invention aims to solve these problems and realize more accurate ad delivery based on user characteristics.

[0575] 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.

[0576] In this invention, the server includes means for collecting user behavioral data, means for analyzing the collected behavioral data and grasping user characteristics, means for automatically generating advertisements based on the user characteristics, means for delivering the generated advertisements to users, means for monitoring and optimizing the effectiveness of the advertisements, means for collecting user gaze data, means for analyzing behavioral data including the gaze data and grasping user characteristics with high accuracy, and means for displaying advertisements on the smart display device. This enables highly accurate advertisement delivery that reflects detailed user behavioral characteristics in real time.

[0577] "User behavioral data" refers to information such as a user's online search history, browsing history, purchase history, and review site browsing history.

[0578] "Means for analyzing data" refers to the technical methods and algorithms used to analyze collected behavioral data and extract user characteristics.

[0579] "User characteristics" refers to characteristic information such as a user's interests, purchasing tendencies, and behavioral patterns obtained by analyzing behavioral data.

[0580] "Means for automatically generating advertisements" refers to algorithms or systems that automatically generate advertising content (copy, design, placement, etc.) based on user characteristics.

[0581] "Means for delivering advertisements to users" refers to technologies and systems for providing generated advertisements to users at appropriate times and places.

[0582] "Means for monitoring and optimizing advertising effectiveness" refers to technologies and systems that monitor the performance of delivered ads (click-through rate, conversion rate, etc.) in real time and identify the most effective advertising variations.

[0583] "Gaze data" refers to information that indicates the movement of a user's gaze, captured using a camera built into a smart display device or the like.

[0584] "Means for collecting gaze data" refers to technologies and systems that record a user's gaze direction and point of gaze in real time.

[0585] "Smart display device" refers to an advanced display device that can display information to users in real time.

[0586] The system of the present invention collects and analyzes user behavioral data and gaze data, and generates and delivers personalized advertisements based on the collected data. Specific embodiments are described below.

[0587] 1. Hardware Configuration

[0588] This system uses the following main hardware:

[0589] Smart display devices: Devices with built-in cameras and eye-tracking technology.

[0590] Server: A computer used to analyze data and generate advertisements.

[0591] User devices: Mainly smartphones and PCs.

[0592] 2. Software Configuration

[0593] This system uses the following main software:

[0594] OpenCV: A library for collecting and processing gaze data.

[0595] DBSCAN: A clustering algorithm.

[0596] LangChain's OpenAIServer: A server that generates ads using generative AI models.

[0597] Python: A programming language for general data processing.

[0598] 3. Data Collection

[0599] The server collects behavioral data (search history, browsing history, purchase history, etc.) recorded while the user is using a web browser or application. In addition, it uses the built-in camera of the smart display device to capture gaze data in real time and record gaze direction and gaze point.

[0600] 4. Data Analysis

[0601] The collected behavioral and gaze data is transferred to a server and stored in a database. The server uses OpenCV and DBSCAN to analyze the gaze data and extract detailed user behavioral characteristics. This data analysis allows for an understanding of areas of interest, purchasing trends, and behavioral patterns by time of day.

[0602] 5. Ad Generation

[0603] The server determines the creative elements of the ad to be generated based on the data analysis, and generates personalized ad content using LangChain's OpenAIServer, which uses the following prompt text as input to the generative AI model:

[0604] Example prompt sentence:

[0605] User Interests: Cafe, Coffee, Drinks

[0606] Good advertising: Smart Cafe's new drink menu

[0607] 6. Advertisement Delivery

[0608] Based on user behavior data, the generated advertisements are delivered to smart display devices and other user devices at the most appropriate time, allowing for effective advertisements to be provided at the time when users are likely to be interested.

[0609] 7. Measurement and optimization

[0610] After an ad is delivered, its effectiveness (click-through rate, conversion rate, etc.) is monitored in real time. Based on this data, the server identifies the optimal ad variation and optimizes subsequent ad generation and delivery.

[0611] Specific examples

[0612] Suppose a user is walking down the street and their gaze falls on a cafe sign that catches their eye. Their gaze data is captured and analyzed, revealing that they are interested in the cafe. Based on the user's interests, the generative AI model generates an advertisement for a limited-time menu item at a nearby cafe and displays it in real time on a smart display device. It is expected that the user will see this advertisement and visit the specific cafe.

[0613] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0614] Step 1:

[0615] The device collects user behavior data (search history, browsing history, purchase history). When a user uses a web browser or application, various data obtained from the operation is saved as a log. This data is transferred to the server.

[0616] Input: User search history, browsing history, purchase history

[0617] Output: Collected behavioral data

[0618] Step 2:

[0619] The server receives the behavioral data transferred from the device, stores it in a secure database, and then prepares the data for analysis.

[0620] Input: Collected behavioral data

[0621] Output: Behavioral data stored in a database

[0622] Step 3:

[0623] The device uses the built-in camera of the smart display device to capture the user's gaze data in real time, recording the gaze direction and gaze point, and this data is also sent to the server.

[0624] Input: User gaze data

[0625] Output: Gaze data captured and sent to the server

[0626] Step 4:

[0627] The server receives the transmitted gaze data and extracts gaze maps and gaze points using OpenCV, while clustering the gaze data using the DBSCAN algorithm to identify the user's gaze patterns.

[0628] Input: User gaze data

[0629] Output: Clustered gaze pattern data

[0630] Step 5:

[0631] The server integrates the collected behavioral data with the clustered gaze patterns to extract more detailed user characteristics, such as areas of interest, purchasing tendencies, and behavioral patterns by time of day.

[0632] Input: Stored behavioral data, clustered gaze pattern data

[0633] Output: Extracted user characteristics

[0634] Step 6:

[0635] The server generates personalized ads using a generative AI model (LangChain's OpenAIServer) based on the extracted user characteristics, and generates appropriate ad content using prompt sentences as input.

[0636] Input: Prompt sentence (e.g., user interests: cafe, coffee, drinks)

[0637] Output: Generated ad content

[0638] Step 7:

[0639] The server then delivers the generated advertising content to the user at a time and place appropriate for the user, for example, displaying the advertisement when the user is using a social networking app or when the user is in a specific location, and also instructs the smart display device to display the advertisement.

[0640] Input: Generated advertising content, user behavior patterns

[0641] Output: Ads delivered to user devices and smart display devices

[0642] Step 8:

[0643] The server monitors the performance of the delivered ads in real time, analyzing click-through rates, conversion rates, etc. Based on the obtained data, it identifies the optimal ad variation and optimizes subsequent ad generation and delivery.

[0644] Input: Performance data of delivered ads

[0645] Output: Optimized ad generation and delivery patterns

[0646] 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.

[0647] This invention is a system that collects and analyzes not only user behavioral data but also user emotional data, and generates and delivers personalized advertisements. This system includes processes that collect behavioral data and emotional data from the user's device, analyze the data on the server, and generate, deliver, measure the effectiveness of, and optimize advertisements.

[0648] System program processing explanation

[0649] Data collection

[0650] Device:

[0651] When a user uses a web browser or application, behavioral data such as search history, browsing history, purchase history, and review site browsing history are recorded. The device also uses cameras and sensors to collect emotional data based on the user's facial expressions and voice data. This emotional data reflects the user's emotional state (e.g., joy, sadness, excitement, etc.).

[0652] server:

[0653] The collected behavioral and emotional data is transferred to a server and stored in a secure database, which also includes historical data.

[0654] Data analysis

[0655] server:

[0656] The server runs a generation AI and emotion engine to analyze behavioral and emotional data. The generation AI analyzes the behavioral data in detail to extract the user's characteristics. The emotion engine analyzes the emotional data and grasps the user's emotional state in real time. This allows a more specific persona to be formed for each user.

[0657] Ad Generation

[0658] server:

[0659] Based on the generated persona and real-time emotional state, the generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.), including different variations of the ad pattern and configuring them for A / B testing. It can also change the content of the ad depending on the user's emotional state.

[0660] Ad serving

[0661] server:

[0662] The ad delivery engine works to determine the optimal timing and location of delivery based on the user's behavioral patterns and emotional state, for example, delivering positive advertising messages when the user is happy.

[0663] Device:

[0664] Advertisements are displayed on users' smartphones and PCs, allowing them to be exposed to ads at the time when they are most likely to respond to them.

[0665] Measurement and optimization

[0666] server:

[0667] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of AB tests are analyzed to identify the most effective ad pattern. Also, how the user's emotional state affects ad response is analyzed. Based on the identified effective ad pattern, subsequent ad generation and delivery are optimized.

[0668] Specific examples

[0669] For User B

[0670] 1. Data Collection:

[0671] Device: User B searches for "the latest smartphone" on his smartphone, browses products on multiple online shops, and purchases one. User B also browses smartphone reviews on a review site. Furthermore, User B's facial expressions and voice data are collected via the device's camera and microphone, and emotion data is obtained.

[0672] 2. Data Analysis:

[0673] Server: The server analyzes User B's search history, purchase history, review site browsing history, as well as emotional data, to determine that User B has a high interest in technology gadgets and was excited about purchasing them. A persona for User B is created to reflect his interest in new gadgets and reviews, as well as his emotional state.

[0674] 3. Ad generation:

[0675] Server: Automatically generate new smartphone ads based on User B's persona and emotional state. Multiple ad variations with different taglines and designs are created and set up for A / B testing. Include ads with more dynamic designs that take into account the user's excitement state.

[0676] 4. Advertisement Delivery:

[0677] Server: Identify the time and emotional state when User B is most likely to respond to ads, and deliver the optimal ad variation at that time. For example, deliver a video ad with energetic music when User B is in an excited state.

[0678] Device: User B's smartphone receives a video ad for a new smartphone during rush hour.

[0679] 5. Measurement and optimization:

[0680] Server: Monitors the click-through rate and conversion rate of delivered ads in real time to identify the most effective ad pattern. Based on the results, optimizes future ad delivery. Also, analyzes how the user's emotional state affected the effectiveness of the ad and reflects this in future ad generation.

[0681] This invention makes it possible to realize detailed ad delivery based on user characteristics and emotional state, thereby significantly improving advertisers' ROI.

[0682] The processing flow will be explained below.

[0683] Step 1: Collect behavioral and emotional data

[0684] Device: When a user uses a web browser or application, the device records search history, browsing history, purchase history, and browsing history on review sites. It also uses cameras and sensors to collect facial expressions and voice data, obtaining emotional data in real time.

[0685] Step 2: Send and store data

[0686] Terminal: Sends collected behavioral and emotional data to the server.

[0687] Server: Stores the received data in a secure database, including historical behavioral and emotional data.

[0688] Step 3: Data analysis

[0689] Server: Runs the generative AI and emotion engine to analyze the stored behavioral and emotional data.

[0690] Server: The generative AI analyzes the behavioral data and extracts user characteristics such as user interests and purchasing tendencies.

[0691] Server: The emotion engine analyzes the emotion data and identifies the user's emotional state in real time.

[0692] Server: Create a persona for each user, reflecting their emotional state.

[0693] Step 4: Generate Ads

[0694] Server: Automatically generate the creative elements of the ad (copy, design, placement, etc.) based on the generated persona and emotional state.

[0695] Server: Generates different ad variations and sets them up for A / B testing.

[0696] Server: Adjust the content and design of the ad depending on the user's emotional state (for example, use an energetic design if the user is excited).

[0697] Step 5: Decide the timing of ad delivery

[0698] Server: Analyzes user behavior patterns (time of use, frequency, etc.) and emotional state to determine the optimal timing for delivering advertisements.

[0699] Step 6: Ad serving

[0700] Server: Delivers the appropriate ad variation to the user's device at the determined time and place.

[0701] Device: Advertisements are displayed on users' smartphones or PCs. For example, specific ads are delivered during lunch breaks when users have positive emotions.

[0702] Step 7: Measure your results

[0703] Server: Collects real-time performance data such as click rates and conversion rates of delivered ads.

[0704] Server: Analyzes the results of A / B tests to identify the most effective ad variations and evaluates how emotional states affect ad effectiveness.

[0705] Step 8: Optimize your ads

[0706] Server: Optimize your ad generation and delivery strategy based on the results of your measurement. This involves strengthening what works and correcting areas that need improvement.

[0707] Server: Reflects the optimized advertising strategy in subsequent ad generation and delivery.

[0708] By repeating these steps, we can deliver ads in real time that are best suited to each user's individual characteristics and emotional state, maximizing advertisers' ROI.

[0709] Example 2

[0710] 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."

[0711] Current ad delivery systems generate and deliver ads based on user behavior data, but do not take the user's emotional state into account. This results in ads that are not appropriate for the user's emotions and are therefore less effective. Furthermore, if the content or timing of an ad does not match the user's current emotional state, it can negatively impact the user experience and lead to lower click-through rates and conversion rates.

[0712] 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.

[0713] In this invention, the server includes means for collecting user behavioral data, means for collecting user emotional data, means for transferring the collected behavioral data and emotional data to the server, means for analyzing the transferred behavioral data and emotional data to grasp the user's characteristics and emotional state, means for automatically generating advertisements based on the user's characteristics and emotional state, means for delivering the generated advertisements based on the user's behavioral patterns and emotional state, and means for monitoring and optimizing the effectiveness of the advertisements. This makes it possible to generate and deliver highly accurate advertisements tailored to the user's characteristics and real-time emotional state, thereby maximizing the effectiveness of the advertisements.

[0714] "Behavioral data" refers to data such as search history, browsing history, purchase history, and review viewing history that is generated when a user uses a web browser or application.

[0715] "Emotion data" is data obtained from the user's facial expressions and voice, and indicates the user's emotional state, such as joy, sadness, or excitement.

[0716] The "server" is a computer system that analyzes collected behavioral and emotional data and generates and delivers advertisements based on that data.

[0717] "User characteristics" are characteristics such as user interests and purchasing tendencies that can be obtained by analyzing behavioral data.

[0718] "Emotional state" refers to a real-time understanding of the user's emotions based on an analysis of collected emotional data.

[0719] "Ad generation" is the process of automatically generating the creative elements of an ad (copy, design, placement, etc.) based on user characteristics and emotional state.

[0720] "Ad serving" is the process of displaying generated advertisements at appropriate times and places based on the user's behavioral patterns and emotional state.

[0721] "Effectiveness measurement" is the process of monitoring the effectiveness of delivered advertisements, such as click rates and conversion rates, in real time.

[0722] "Optimization" is the process of adjusting the ad generation and delivery process based on the results of effectiveness measurement and identifying the most effective advertising pattern.

[0723] "AB testing" is a testing method in which multiple advertising patterns are delivered simultaneously and their effectiveness is compared and analyzed.

[0724] This invention is a system that collects and analyzes user behavioral and emotional data, and generates and delivers personalized advertisements. This system is mainly composed of a user terminal and a server.

[0725] Data collection

[0726] A user's device has a web browser and applications installed. When the user uses them, the device collects the following behavioral data:

[0727] Search history (e.g. keywords entered into search engines)

[0728] Browsing history (e.g., URLs and categories of accessed web pages)

[0729] Purchase history (e.g. details of products purchased on an online shopping site)

[0730] Review viewing history (e.g., the content and ratings of reviews you have read)

[0731] Furthermore, the device is equipped with a camera and microphone, which can analyze the user's facial expressions and voice in real time to collect emotional data, which indicates the user's emotional state, such as joy, sadness, or excitement.

[0732] Data Transfer

[0733] The device transfers the collected behavioral and emotional data to a server using a secure protocol (e.g., HTTPS). This transfer occurs periodically to ensure the security of the communication.

[0734] Data analysis

[0735] The server receives the transferred behavioral and emotional data and stores it in a secure database, which also stores the user's past data.

[0736] The server runs a generative AI model and an emotion engine. The generative AI model analyzes behavioral data and extracts user characteristics (e.g., interest in technology gadgets). Meanwhile, the emotion engine analyzes emotional data and can grasp the user's emotional state in real time. This allows a detailed persona to be formed for each user.

[0737] Ad Generation

[0738] The server automatically generates the creative elements of the ad (copy, design, placement, etc.) based on the generated persona and real-time emotional state. Different ad variations can also be configured for A / B testing using the generative AI model. The content of the ad dynamically changes depending on the user's emotional state.

[0739] Ad serving

[0740] The server's ad delivery engine determines the optimal timing and location for delivery based on the user's behavioral patterns and emotional state, for example, delivering a positive, energetic advertising message when the user is excited.

[0741] Advertisements are displayed on users' devices (smartphones and PCs) at the time when users are most likely to respond to them.

[0742] Measurement and optimization

[0743] The server monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. It analyzes the results of AB tests to identify the most effective ad pattern. It also analyzes how the user's emotional state affects the effectiveness of ads, thereby optimizing ad generation and delivery for future ads.

[0744] Specific examples

[0745] As a concrete example, consider the case of User B. User B searches for the "latest smartphone" on his / her smartphone, browses products on multiple online shops, and purchases one of them. While browsing smartphone reviews on a review site, the device's camera captures User B's facial expressions and acquires emotion data.

[0746] The server analyzes User B's search history, purchase history, review site browsing history, and emotional data to determine that User B has a high interest in technology gadgets and was excited about them while making purchases. Using a generative AI model, it creates a persona for User B to reflect his interest in new gadgets and reviews, as well as his emotional state.

[0747] Based on User B's persona and emotional state, a generative AI model automatically generates a new smartphone ad. Multiple ad variations with different taglines and designs are created and set up for A / B testing. Include a more dynamic ad design that takes into account the user's excitement level.

[0748] The server identifies the time period and emotional state when User B is most likely to respond to an advertisement, and delivers the optimal advertisement variation at that timing. For example, if User B is in an excited state, it delivers a video advertisement with energetic music.

[0749] A new smartphone video ad is delivered to User B's smartphone during commuting hours. The server monitors the click-through rate and conversion rate of the delivered ad in real time to identify the most effective ad pattern. Based on the results, it optimizes ad delivery from the next time onwards. It also analyzes how the user's emotional state affected the effectiveness of the ad and reflects this in future ad generation.

[0750] Prompt Sentence Examples

[0751] Based on the behavioral history and emotional data of users searching for "latest smartphones," please generate ads with the following conditions:

[0752] An energetic design that matches the user's excitement

[0753] Multiple copy and design variations for A / B testing

[0754] Delivered during the user's commute time

[0755] The embodiments of the present invention have been described in detail above.

[0756] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0757] Step 1: Data collection

[0758] Device:

[0759] When a user uses a web browser or application, behavioral data such as search history, browsing history, purchase history, and review browsing history is collected. Furthermore, facial expression and voice data is collected using the device's camera and microphone, and emotional data is obtained. The input is the user's operational behavior and the device's sensor data. The output is the collected behavioral data and emotional data.

[0760] Specifically, when a user searches for "the latest smartphone" on a smartphone browser, browses multiple online shops, and reads reviews, the system collects their browsing history. The system also captures emotion data from the user's facial expressions captured by a camera while they are reading the reviews.

[0761] Step 2: Data Transfer

[0762] Device:

[0763] The collected behavioral and emotional data is sent to a server using a secure protocol (e.g., HTTPS). As input, the data collected in step 1 is used. As output, the data sent to the server is obtained.

[0764] Specifically, the device generates data packets at regular intervals, encrypts them, and sends them to the server, along with emotion data.

[0765] Step 3: Data analysis

[0766] server:

[0767] The received behavioral and emotional data is stored in a database on the server. Then, this data is analyzed using a generative AI model and an emotion engine. As input, the data sent in step 2 is used. As output, analysis results are obtained to understand the user's characteristics and emotional state.

[0768] Specifically, the generative AI model analyzes the user's search and purchase history to extract characteristics such as "this user tends to get excited about new gadgets." The emotion engine also analyzes the user's emotional data and determines in real time that "the user was in a highly excited state when looking at product reviews."

[0769] Step 4: Generate Ads

[0770] server:

[0771] Based on the generated personas and real-time emotional states, the generative AI model automatically generates the creative elements of the ad (copy, design, placement, etc.). It generates different ad variations for AB testing. It uses the user characteristics and emotional states obtained in step 3 as input. The generated ad variations are obtained as output.

[0772] For example, when generating an advertisement for the latest smartphone, the system creates several variations of the advertisement with dynamic effects to stimulate users' excitement. For A / B testing, it generates ad variations with different catchphrases and designs.

[0773] Step 5: Ad serving

[0774] server:

[0775] The ad serving engine determines the optimal timing and location of delivery based on the user's behavioral patterns and emotional state. As input, it uses the ad variations generated in step 4 and previous user behavior and emotional state data. The output is the ad to be delivered and its timing.

[0776] Device:

[0777] The generated advertisement is delivered to the user's terminal at a time and place determined by the server.

[0778] Specifically, while User B is reading the news on his smartphone during his commute, a video advertisement for the "latest smartphone" with dynamic effects and energetic music is displayed.

[0779] Step 6: Measure and optimize

[0780] server:

[0781] It monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. It analyzes the results of AB tests to identify the most effective ad pattern. It also analyzes how the user's emotional state affected the effectiveness of the ad. As input, it uses performance data of the ad being delivered and real-time user emotional data. As output, it obtains an optimized ad generation and delivery strategy.

[0782] Specifically, the system analyzes in real time the number of clicks on ads and the number of times they lead to subsequent purchases, and gains insights such as "advertisements delivered when users are excited have a higher click rate." Based on these findings, it optimizes ad generation and delivery for future ads.

[0783] The above is the specific processing flow of the system program.

[0784] (Application example 2)

[0785] 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."

[0786] Conventional ad delivery systems generate and deliver ads based on user behavior data, but do not take the user's emotional state into account, limiting the effectiveness of the ads. Furthermore, they lack targeting accuracy and real-time optimization, making it difficult to attract user attention.

[0787] 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.

[0788] In this invention, the server includes means for analyzing user behavioral data and emotional data to grasp user characteristics, means for automatically generating advertisements based on the analyzed user characteristics and emotional state, and means for delivering optimal advertisements based on the user's behavioral patterns and emotional state, thereby enabling targeting that takes into account the user's emotional state and real-time advertisement optimization.

[0789] "User behavior data" refers to information such as search history, browsing history, and purchase history when a user uses a web browser or application.

[0790] "Emotion data" is data obtained from the user's facial expressions, voice, etc., and reflects the user's emotional state (joy, sadness, excitement, etc.).

[0791] "User characteristics" refer to the user's interests, preferences, and behavioral patterns extracted based on the analyzed user's behavioral data and emotional data.

[0792] "Automatic ad generation" refers to the automatic generation of creative elements of an ad (copy, design, placement, etc.) using generative AI based on user characteristics and emotional state.

[0793] "Multiple ad variations" are ad variations with different copy and design, used to conduct A / B testing.

[0794] "AB testing" is a method of comparing and verifying the effectiveness of multiple advertising patterns to identify the most effective one.

[0795] "Advertisement delivery" refers to delivering the generated advertisement at the optimal timing based on the user's behavioral patterns and emotional state.

[0796] "Monitoring" means monitoring the effectiveness of delivered advertisements (click-through rate, conversion rate, etc.) in real time.

[0797] "Optimization" is the process of adjusting advertising content and delivery timing based on monitoring results to maximize advertising effectiveness.

[0798] The present invention relates to a system that collects and analyzes user behavioral data and emotional data, and generates and distributes personalized advertisements. A specific embodiment of this system is described below.

[0799] System Configuration

[0800] 1. Data Collection

[0801] Device: A user's smartphone records search history, browsing history, and purchase history when using a web browser or application. The device also uses a camera and microphone to collect facial and voice data and obtain emotional data. To do this, the device uses camera sensors and voice recognition software (e.g., Google APIs for Computer Vision and Emotion AI).

[0802] Server: Collected behavioral and emotional data is transferred to the server and stored in a secure database (e.g., AWS RDS, Google Cloud SQL).

[0803] 2. Data Analysis

[0804] Server: The server analyzes the collected behavioral and emotional data as follows:

[0805] Generative AI (e.g., OpenAI GPT-4) is used to perform detailed analysis of behavioral data and extract user characteristics.

[0806] Using an emotion engine (e.g., IBM Watson Tone Analyzer), emotion data is analyzed to understand the user's real-time emotional state.

[0807] 3. Ad generation

[0808] Server: The server automatically generates the creative elements of the ad (copy, design, placement, etc.) based on the generated user characteristics and emotional state. Using generative AI, multiple ad variations are created and prepared for A / B testing.

[0809] 4. Advertisement Delivery

[0810] Server: The server uses an ad serving engine (e.g., AdRoll, Google Ads API) to deliver ads at the optimal time and place based on the user's behavioral patterns and emotional state.

[0811] Device: Real-time personalized ads are displayed on the user's smartphone, taking into account the user's emotional state, for example, delivering energetic ads when they are excited.

[0812] 5. Measurement and optimization

[0813] Server: The server monitors and analyzes the effectiveness of the delivered advertisements in real time. For example, it measures click-through rates and conversion rates and identifies the most effective advertisement patterns. It optimizes the content and timing of advertisements based on the monitoring results.

[0814] Specific examples

[0815] For user C:

[0816] Data collection: User C searches for "new running shoes" on their smartphone and browses multiple running-related blog articles and reviews. The smartphone camera also collects User C's facial expressions and voice data, recording their emotional data.

[0817] Data analysis: The server analyzes user C's search history, browsing history, and emotional data and finds out that user C has a high interest in fitness products, especially running, and has positive emotions.

[0818] Ad generation: Based on the generated persona and emotional state of User C, an advertisement for a new running shoe is automatically generated. Multiple advertisement variations with different catchphrases and designs are created and prepared for A / B testing.

[0819] Advertisement delivery: When User C is excited after his morning jog, a video advertisement with energetic music is delivered to his smartphone.

[0820] Effectiveness measurement and optimization: Monitor click-through rates and conversion rates of delivered ads in real time, identify the most effective ad patterns, and reflect them in future ad deliveries.

[0821] Prompt Sentence Examples

[0822] For example, input the following prompt into your generative AI model:

[0823] User C has a strong interest in fitness, especially running. His emotional data shows that he feels excited after jogging. Based on this data, we generate ad copy and design variations that emphasize the appeal of running shoes. For example, we could create an ad that includes the following content:

[0824] 1. "New running shoes that offer the best running experience!"

[0825] 2. "Break your personal best in your next race! These running shoes make it possible."

[0826] Also consider what will excite User C even more.

[0827] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0828] Step 1:

[0829] The device collects user behavior data (search history, browsing history, purchase history, etc.). This includes the user's actions when using a web browser or application, and the product browsing history in an online shop. The collected data is stored in an initial database on the smartphone, ready to be transferred to the server.

[0830] Step 2:

[0831] The device collects the user's emotional data. It uses a camera and microphone to capture the user's facial expressions and voice data, and analyzes their emotional state (happiness, sadness, excitement, etc.) based on this data. For this purpose, it uses, for example, Google APIs for Computer Vision and Emotion AI. The collected emotional data is also temporarily stored on the smartphone.

[0832] Step 3:

[0833] The device transfers behavioral and emotional data to the server via secure communication. The data is protected using security protocols such as Transport Layer Security (TLS). The server receives the data and stores it in a secure database (e.g., AWS RDS or Google Cloud SQL).

[0834] Step 4:

[0835] The server uses generative AI (e.g., OpenAI GPT-4) to analyze user behavioral data and extract user characteristics. Here, the server identifies the user's interests, preferences, and behavioral patterns from past search history, browsing history, and purchase history. Based on the input behavioral data, it performs text analysis and pattern recognition.

[0836] Step 5:

[0837] The server analyzes the user's emotional data using an emotion engine (e.g., IBM Watson Tone Analyzer) to understand the user's current emotional state. It identifies the user's real-time emotional state through facial expression analysis and voice analysis and records it along with the user's characteristics.

[0838] Step 6:

[0839] The server uses generative AI to automatically generate ads based on analyzed user characteristics and emotional states. The generative AI creates multiple ad variations, including different copy and designs. The generated ads are then set up for A / B testing.

[0840] Step 7:

[0841] The server uses an ad distribution engine (e.g., AdRoll, Google Ads API) to distribute ads at optimal times based on the user's behavioral patterns and emotional state. For example, it adjusts the distribution of energetic ads when the user is in an excited state.

[0842] Step 8:

[0843] The device displays the delivered advertisements to the user in real time, allowing the user to interact with the advertisement at the time when they are most likely to respond.The advertisements are displayed using the smartphone's notification function and in-app banner ads.

[0844] Step 9:

[0845] The server monitors the click-through rate and conversion rate of the delivered ads in real time. Here, data analysis tools (e.g., Google Analytics) are used to evaluate the effectiveness of the ads. The results of the AB tests are analyzed to identify the most effective ad patterns.

[0846] Step 10:

[0847] The server optimizes the ad content and delivery timing based on the monitoring results, giving priority to highly effective ad patterns and reflecting this in the next ad generation and delivery, thereby maximizing advertising ROI.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] [Third embodiment]

[0852] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0853] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0854] 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).

[0855] 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.

[0856] 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.

[0857] 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).

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] The present invention provides a system that automatically generates personalized advertisements based on user behavior data and delivers them effectively. This system includes collection of behavioral data by user devices, data analysis by a server, advertisement generation, advertisement delivery, and monitoring and optimization of advertisement effectiveness.

[0865] System program processing explanation

[0866] Data collection

[0867] Device:

[0868] When a user uses a web browser or application, their device collects behavioral data such as their search history, browsing history, purchase history, and review site browsing history. This data is used as the basis for reflecting the user's interests and preferences.

[0869] server:

[0870] The collected behavioral data is transferred to a server and stored in a secure database, which stores huge amounts of big data and is used for analysis.

[0871] Data analysis

[0872] server:

[0873] The server runs a generation AI to analyze the behavioral data. The generation AI analyzes the collected behavioral data in detail and extracts user characteristics. The extracted user characteristics include areas of interest, purchasing tendencies, and behavioral patterns by time of day. This creates a persona for each user.

[0874] Ad Generation

[0875] server:

[0876] Based on the personas, generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.), including an ad set with multiple variations of the ad. The generated ads are then configured for A / B testing, with settings set up to measure the effectiveness of each variation.

[0877] Ad serving

[0878] server:

[0879] The ad distribution engine then runs and determines the optimal timing and location for distribution based on user behavior data. For example, it can deliver a specified ad during times when users are most likely to use a social networking app.

[0880] Device:

[0881] Advertisements are displayed on users' smartphones and PCs, allowing them to interact with the ads at the time when they are most likely to respond.

[0882] Measurement and optimization

[0883] server:

[0884] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of AB tests are analyzed to identify the optimal ad variations. Subsequent ad generation and delivery are optimized based on the identified effective ad variations.

[0885] Specific examples

[0886] For User A

[0887] 1. Data Collection:

[0888] Device: User A searches for "latest smartwatches" on their smartphone, browses products on multiple online shops, and purchases one. User A also browses reviews of smartwatches on a review site.

[0889] 2. Data Analysis:

[0890] Server: The server analyzes User A's search history, purchase history, and browsing history on review sites to determine that User A has a high interest in technology gadgets. A persona is created for User A to reflect his interest in new gadgets and reviews.

[0891] 3. Ad generation:

[0892] Server: Automatically generate a new smartphone ad based on User A's persona. Multiple ad variations (different copy and design) are created and set up for A / B testing.

[0893] 4. Advertisement Delivery:

[0894] Server: Identify the time of day when User A uses the SNS app and deliver the optimal ad variation at that time.

[0895] Device: User A's smartphone receives an advertisement for a new smartphone during commuting hours.

[0896] 5. Measurement and optimization:

[0897] Server: Monitors the click-through rate and conversion rate of delivered ads in real time, identifies the most effective ad pattern, and optimizes future ad delivery based on the results.

[0898] The present invention makes it possible to realize effective advertisement delivery based on user characteristics, and significantly improve advertisers' ROI.

[0899] The processing flow will be explained below.

[0900] Step 1: Data collection

[0901] Device: When a user uses a web browser or application, their search history, browsing history, purchase history, and review site browsing history are recorded.

[0902] Device: Periodically transmits collected behavioral data to the server.

[0903] Step 2: Save data

[0904] Server: Receives behavioral data sent from the device and stores it in a secure database, including historical data.

[0905] Step 3: Data analysis

[0906] Server: Runs generative AI algorithms to analyze stored behavioral data.

[0907] Server: Generative AI extracts user characteristics (interests, concerns, purchasing tendencies, etc.) based on behavioral data and creates a persona for each user.

[0908] Step 4: Generate Ads

[0909] Server: Based on the generated personas, the generative AI creates the creative elements of the ad (copy, design, placement, etc.).

[0910] Server: Generates different variations of ads (e.g., multiple copy and designs) and sets them up for A / B testing.

[0911] Step 5: Decide the timing of ad delivery

[0912] Server: Analyzes user behavior patterns (such as the time of day and frequency of device use) to determine the optimal timing for delivering advertisements.

[0913] Step 6: Ad serving

[0914] Server: At the specified time, deliver the optimal ad variation to the user's device.

[0915] Device: Advertisements are displayed on users' smartphones or PCs. For example, specific ads may be displayed within social media apps during the morning commute.

[0916] Step 7: Measure your results

[0917] Server: Monitors user responses to delivered ads (click-through rate, conversion rate, etc.) in real time.

[0918] Server: Analyzes the monitoring results and identifies the most effective advertising patterns.

[0919] Step 8: Optimize your ads

[0920] Server: Optimize ad generation and distribution strategies based on the results of effectiveness measurement. This includes strengthening effective elements and improving weak areas.

[0921] Server: Reflects the optimized advertising strategy in subsequent ad generation and delivery.

[0922] By repeating these steps, you can maximize the effectiveness of your ads and significantly increase your ROI.

[0923] Example 1

[0924] 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."

[0925] In today's online advertising market, there is a demand for personalized ad delivery based on user interests and behavior. However, conventional methods lack sufficient accuracy in analyzing user behavior data and optimizing ad generation and delivery, making effective ad delivery difficult. Furthermore, while there is a demand for optimizing the timing and location of ad delivery, as well as real-time monitoring of ad effectiveness and rapid optimization based on that monitoring, these methods also have limitations. Therefore, there is a need for a system that can effectively utilize user behavior data to generate and deliver ads that are optimal for each individual user, thereby maximizing advertising effectiveness.

[0926] 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.

[0927] In this invention, the server includes means for collecting user behavioral data, means for analyzing the collected behavioral data and grasping user characteristics, means for using a generative AI model to automatically generate advertisements based on the user characteristics, means for delivering the generated advertisements to users at appropriate times, and means for monitoring and optimizing the effectiveness of the advertisements. This makes it possible to generate and deliver personalized advertisements based on the user's interests and behavior, thereby maximizing the effectiveness of the advertisements.

[0928] "User behavior data" refers to data such as search history, browsing history, purchase history, and review site browsing history that is recorded when a user uses a web browser or application.

[0929] "Means for collection" refers to the function for recording user behavior data on the terminal and transferring it to the server.

[0930] "Analysis means" refers to the function that performs processing to extract user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.) based on collected behavioral data.

[0931] "Target characteristics" refer to characteristics such as user interests, preferences, purchasing tendencies, and behavioral patterns obtained from the analysis of behavioral data.

[0932] "Generative AI model" refers to an artificial intelligence model that analyzes collected behavioral data and automatically generates creative elements for advertisements based on user characteristics.

[0933] "Means for automatically generating advertisements" refers to the ability to automatically generate elements such as ad copy, design, and placement using a generative AI model.

[0934] "Means for delivering at the appropriate time" refers to a function for delivering advertisements at the optimal time based on the user's behavioral patterns and usage time period.

[0935] "Means for monitoring the effectiveness of advertising" refers to the function of monitoring the effectiveness of delivered advertisements, such as click rates and conversion rates, in real time.

[0936] "Means for optimization" refers to a function that effectively improves the generation and distribution of advertisements from the next time onwards based on the monitored advertisement effectiveness data.

[0937] "AB testing" refers to a testing method that uses multiple advertising patterns to compare and verify their effectiveness and identify the most effective pattern.

[0938] The present invention provides a system that automatically generates and effectively delivers personalized advertisements based on user behavior data. This system includes collection of behavioral data by user devices, data analysis by a server, advertisement generation, advertisement delivery, and monitoring and optimization of advertisement effectiveness.

[0939] Data collection

[0940] Device:

[0941] When a user uses a web browser or application, the device collects behavioral data such as search history, browsing history, purchase history, and browsing history on review sites. This includes when a user searches for a specific product, such as the latest smartwatch, and browses products on multiple online shops or reads reviews of smartwatches on review sites.

[0942] server:

[0943] The collected behavioral data is transferred from the device to a server, where it is stored in a secure database such as Amazon RDS, MySQL, or PostgreSQL. This database stores large amounts of big data, which is used for analysis, as described below.

[0944] Data analysis

[0945] server:

[0946] The server uses a generative AI model (e.g., GPT-3, BERT, etc.) to analyze the collected behavioral data. The server retrieves the behavioral data from the database and inputs it into the generative AI. The generative AI analyzes the data in detail and extracts user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.). For example, it may identify that a user is interested in technology gadgets, and generate a user persona.

[0947] Ad Generation

[0948] server:

[0949] Based on the personas created, the generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.). For example, the generative AI creates an ad for a new smartphone that the user is interested in. This ad includes different taglines and design variations. The generated ad set is then configured for A / B testing.

[0950] Ad serving

[0951] server:

[0952] Ad delivery engines (such as Google Ads or Facebook Ads) operate to determine the optimal timing and location for delivery based on user behavior data. For example, an ad for a new smartphone may be delivered within a social networking app at a time when the user is most likely to use that app.

[0953] Device:

[0954] Advertisements are displayed on users' smartphones and PCs. This allows ads to be exposed at the time when users are most likely to respond. For example, an advertisement for a new smartphone is displayed on a user's smartphone during their commute.

[0955] Measurement and optimization

[0956] server:

[0957] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of the AB test are analyzed by the generation AI to identify the most effective ad pattern. Based on these results, the generation AI further optimizes ad generation and delivery for future ads. For example, it will adopt an ad variation with a high click-through rate and reflect that in the next ad generation.

[0958] Prompt Sentence Examples

[0959] You can generate an ad by inputting prompts like the following into the generative AI model:

[0960] Example 1:

[0961] Generate an ad related to the latest smartwatch that User A recently searched for and purchased. The ad should include the following elements: tagline, product image, feature list, and purchase link, reflecting User A's particular interest in technology gadgets.

[0962] Example 2:

[0963] Generate a great ad for a sports-related site that User B frequently visits. The ad should include the following elements: a punchy tagline, action images, discount information, and a link to buy. Consider that User B is interested in fitness equipment.

[0964] The present invention makes it possible to realize effective advertisement delivery based on user characteristics, and significantly improve advertisers' ROI.

[0965] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0966] Step 1: Data collection

[0967] Device: When a user uses a web browser or application, the device collects search history, browsing history, purchase history, and review site browsing history. This data is saved as a log file. The input is user behavior data (search keywords, URLs of visited web pages, browsing time, etc.). The output is a log file of user behavior data.

[0968] Specific actions: For example, a user may search for "latest smartwatches," browse products on multiple online shops, and purchase one. It may also record actions such as browsing reviews of smartwatches on a review site.

[0969] Step 2: Data Transfer and Storage

[0970] Device: The collected behavioral data is transferred to the server. The device divides the collected data into packets and transmits them using a secure communication protocol (e.g., HTTPS).

[0971] Server: Stores the received behavioral data in a secure database. The input is user behavioral data sent from the device. The output is user behavioral data stored in a database (e.g., Amazon RDS, MySQL, PostgreSQL).

[0972] Specific operation: For example, the user's search history and browsing history are sent to the server and stored in a database.

[0973] Step 3: Data analysis

[0974] Server: A generative AI model (e.g., GPT-3, BERT) is used to analyze the collected behavioral data. The server extracts user behavior data from the database and inputs it into the AI ​​model. The input is the user behavior data in the database. The output is the analyzed user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.).

[0975] How it works: For example, a generative AI might analyze a user's search and purchase history to determine that they're interested in technology gadgets.

[0976] Step 4: Generate Ads

[0977] Server: Based on the generated user characteristics, the generative AI automatically generates the creative elements of the ad. The input includes user characteristic data. The output is the generated ad set (copy, design, placement, etc.).

[0978] What it does: For example, if a user is interested in technology gadgets, the AI ​​will automatically generate ads for new smartphones, with different taglines and design variations.

[0979] Step 5: Ad serving

[0980] Server: The ad distribution engine (e.g., Google Ads, Facebook Ads) runs and determines the optimal timing and location of distribution based on user behavior data. The inputs are user behavior pattern data and the generated ad set. The output is the distribution schedule and the ads to be distributed.

[0981] Device: Advertisements are displayed on the user's smartphone or PC. The input is advertising data sent from the server. The output is the advertisement displayed on the user's device screen.

[0982] Specific behavior: For example, advertisements for new smartphones are displayed within a social media app at the time the user is using the app.

[0983] Step 6: Measure and optimize

[0984] Server: Monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. The input is ad delivery result data. The output is identification of the optimal ad pattern and data for optimizing the next ad based on that.

[0985] What it does: For example, analyze the results of an A / B test to identify the ad variation with the highest click-through rate. Use this information to improve ad generation and delivery for future ads.

[0986] (Application example 1)

[0987] 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."

[0988] Conventional ad delivery systems have limited ad personalization based on user behavior data, making it difficult to reflect detailed real-time behavioral information such as user gaze data. Furthermore, there is a problem in that advanced ad delivery using smart display devices cannot be effectively performed. The present invention aims to solve these problems and realize more accurate ad delivery based on user characteristics.

[0989] 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.

[0990] In this invention, the server includes means for collecting user behavioral data, means for analyzing the collected behavioral data and grasping user characteristics, means for automatically generating advertisements based on the user characteristics, means for delivering the generated advertisements to users, means for monitoring and optimizing the effectiveness of the advertisements, means for collecting user gaze data, means for analyzing behavioral data including the gaze data and grasping user characteristics with high accuracy, and means for displaying advertisements on the smart display device. This enables highly accurate advertisement delivery that reflects detailed user behavioral characteristics in real time.

[0991] "User behavioral data" refers to information such as a user's online search history, browsing history, purchase history, and review site browsing history.

[0992] "Means for analyzing data" refers to the technical methods and algorithms used to analyze collected behavioral data and extract user characteristics.

[0993] "User characteristics" refers to characteristic information such as a user's interests, purchasing tendencies, and behavioral patterns obtained by analyzing behavioral data.

[0994] "Means for automatically generating advertisements" refers to algorithms or systems that automatically generate advertising content (copy, design, placement, etc.) based on user characteristics.

[0995] "Means for delivering advertisements to users" refers to technologies and systems for providing generated advertisements to users at appropriate times and places.

[0996] "Means for monitoring and optimizing advertising effectiveness" refers to technologies and systems that monitor the performance of delivered ads (click-through rate, conversion rate, etc.) in real time and identify the most effective advertising variations.

[0997] "Gaze data" refers to information that indicates the movement of a user's gaze, captured using a camera built into a smart display device or the like.

[0998] "Means for collecting gaze data" refers to technologies and systems that record a user's gaze direction and point of gaze in real time.

[0999] "Smart display device" refers to an advanced display device that can display information to users in real time.

[1000] The system of the present invention collects and analyzes user behavioral data and gaze data, and generates and delivers personalized advertisements based on the collected data. Specific embodiments are described below.

[1001] 1. Hardware Configuration

[1002] This system uses the following main hardware:

[1003] Smart display devices: Devices with built-in cameras and eye-tracking technology.

[1004] Server: A computer used to analyze data and generate advertisements.

[1005] User devices: Mainly smartphones and PCs.

[1006] 2. Software Configuration

[1007] This system uses the following main software:

[1008] OpenCV: A library for collecting and processing gaze data.

[1009] DBSCAN: A clustering algorithm.

[1010] LangChain's OpenAIServer: A server that generates ads using generative AI models.

[1011] Python: A programming language for general data processing.

[1012] 3. Data Collection

[1013] The server collects behavioral data (search history, browsing history, purchase history, etc.) recorded while the user is using a web browser or application. In addition, it uses the built-in camera of the smart display device to capture gaze data in real time and record gaze direction and gaze point.

[1014] 4. Data Analysis

[1015] The collected behavioral and gaze data is transferred to a server and stored in a database. The server uses OpenCV and DBSCAN to analyze the gaze data and extract detailed user behavioral characteristics. This data analysis allows for an understanding of areas of interest, purchasing trends, and behavioral patterns by time of day.

[1016] 5. Ad Generation

[1017] The server determines the creative elements of the ad to be generated based on the data analysis, and generates personalized ad content using LangChain's OpenAIServer, which uses the following prompt text as input to the generative AI model:

[1018] Example prompt sentence:

[1019] User Interests: Cafe, Coffee, Drinks

[1020] Good advertising: Smart Cafe's new drink menu

[1021] 6. Advertisement Delivery

[1022] Based on user behavior data, the generated advertisements are delivered to smart display devices and other user devices at the most appropriate time, allowing for effective advertisements to be provided at the time when users are likely to be interested.

[1023] 7. Measurement and optimization

[1024] After an ad is delivered, its effectiveness (click-through rate, conversion rate, etc.) is monitored in real time. Based on this data, the server identifies the optimal ad variation and optimizes subsequent ad generation and delivery.

[1025] Specific examples

[1026] Suppose a user is walking down the street and their gaze falls on a cafe sign that catches their eye. Their gaze data is captured and analyzed, revealing that they are interested in the cafe. Based on the user's interests, the generative AI model generates an advertisement for a limited-time menu item at a nearby cafe and displays it in real time on a smart display device. It is expected that the user will see this advertisement and visit the specific cafe.

[1027] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1028] Step 1:

[1029] The device collects user behavior data (search history, browsing history, purchase history). When a user uses a web browser or application, various data obtained from the operation is saved as a log. This data is transferred to the server.

[1030] Input: User search history, browsing history, purchase history

[1031] Output: Collected behavioral data

[1032] Step 2:

[1033] The server receives the behavioral data transferred from the device, stores it in a secure database, and then prepares the data for analysis.

[1034] Input: Collected behavioral data

[1035] Output: Behavioral data stored in a database

[1036] Step 3:

[1037] The device uses the built-in camera of the smart display device to capture the user's gaze data in real time, recording the gaze direction and gaze point, and this data is also sent to the server.

[1038] Input: User gaze data

[1039] Output: Gaze data captured and sent to the server

[1040] Step 4:

[1041] The server receives the transmitted gaze data and extracts gaze maps and gaze points using OpenCV, while clustering the gaze data using the DBSCAN algorithm to identify the user's gaze patterns.

[1042] Input: User gaze data

[1043] Output: Clustered gaze pattern data

[1044] Step 5:

[1045] The server integrates the collected behavioral data with the clustered gaze patterns to extract more detailed user characteristics, such as areas of interest, purchasing tendencies, and behavioral patterns by time of day.

[1046] Input: Stored behavioral data, clustered gaze pattern data

[1047] Output: Extracted user characteristics

[1048] Step 6:

[1049] The server generates personalized ads using a generative AI model (LangChain's OpenAIServer) based on the extracted user characteristics, and generates appropriate ad content using the prompt sentence as input.

[1050] Input: Prompt sentence (e.g., user interests: cafe, coffee, drinks)

[1051] Output: Generated ad content

[1052] Step 7:

[1053] The server delivers the generated advertising content to users at a time and place appropriate for them, for example, displaying the advertisement when the user is using a social networking app or when the user is in a specific location, and also instructs the smart display device to display the advertisement.

[1054] Input: Generated advertising content, user behavior patterns

[1055] Output: Ads delivered to user devices and smart display devices

[1056] Step 8:

[1057] The server monitors the performance of the delivered ads in real time, analyzing click-through rates, conversion rates, etc. Based on the obtained data, it identifies the optimal ad variation and optimizes subsequent ad generation and delivery.

[1058] Input: Performance data of delivered ads

[1059] Output: Optimized ad generation and delivery patterns

[1060] 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.

[1061] This invention is a system that collects and analyzes not only user behavioral data but also user emotional data, and generates and delivers personalized advertisements. This system includes processes that collect behavioral data and emotional data from the user's device, analyze the data on the server, and generate, deliver, measure the effectiveness of, and optimize advertisements.

[1062] System program processing explanation

[1063] Data collection

[1064] Device:

[1065] When a user uses a web browser or application, behavioral data such as search history, browsing history, purchase history, and review site browsing history are recorded. The device also uses cameras and sensors to collect emotional data based on the user's facial expressions and voice data. This emotional data reflects the user's emotional state (e.g., joy, sadness, excitement, etc.).

[1066] server:

[1067] The collected behavioral and emotional data is transferred to a server and stored in a secure database, which also includes historical data.

[1068] Data analysis

[1069] server:

[1070] The server runs a generation AI and emotion engine to analyze behavioral and emotional data. The generation AI analyzes the behavioral data in detail to extract the user's characteristics. The emotion engine analyzes the emotional data and grasps the user's emotional state in real time. This allows a more specific persona to be formed for each user.

[1071] Ad Generation

[1072] server:

[1073] Based on the generated persona and real-time emotional state, the generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.), including different variations of the ad pattern and configuring them for A / B testing. It can also change the content of the ad depending on the user's emotional state.

[1074] Ad serving

[1075] server:

[1076] The ad delivery engine works to determine the optimal timing and location of delivery based on the user's behavioral patterns and emotional state, for example, delivering positive advertising messages when the user is happy.

[1077] Device:

[1078] Advertisements are displayed on users' smartphones and PCs, allowing them to be exposed to ads at the time when they are most likely to respond to them.

[1079] Measurement and optimization

[1080] server:

[1081] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of AB tests are analyzed to identify the most effective ad pattern. Also, how the user's emotional state affects ad response is analyzed. Based on the identified effective ad pattern, subsequent ad generation and delivery are optimized.

[1082] Specific examples

[1083] For User B

[1084] 1. Data Collection:

[1085] Device: User B searches for "the latest smartphone" on his smartphone, browses products on multiple online shops, and purchases one. User B also browses smartphone reviews on a review site. Furthermore, User B's facial expressions and voice data are collected via the device's camera and microphone, and emotion data is obtained.

[1086] 2. Data Analysis:

[1087] Server: The server analyzes User B's search history, purchase history, review site browsing history, as well as emotional data, to determine that User B has a high interest in technology gadgets and was excited about purchasing them. A persona for User B is created to reflect his interest in new gadgets and reviews, as well as his emotional state.

[1088] 3. Ad generation:

[1089] Server: Automatically generate new smartphone ads based on User B's persona and emotional state. Multiple ad variations with different taglines and designs are created and set up for A / B testing. Include ads with more dynamic designs that take into account the user's excitement state.

[1090] 4. Advertisement Delivery:

[1091] Server: Identify the time and emotional state when User B is most likely to respond to ads, and deliver the optimal ad variation at that time. For example, deliver a video ad with energetic music when User B is in an excited state.

[1092] Device: User B's smartphone receives a video ad for a new smartphone during rush hour.

[1093] 5. Measurement and optimization:

[1094] Server: Monitors the click-through rate and conversion rate of delivered ads in real time to identify the most effective ad pattern. Based on the results, optimizes future ad delivery. Also, analyzes how the user's emotional state affected the effectiveness of the ad and reflects this in future ad generation.

[1095] This invention makes it possible to realize detailed ad delivery based on user characteristics and emotional state, thereby significantly improving advertisers' ROI.

[1096] The processing flow will be explained below.

[1097] Step 1: Collect behavioral and emotional data

[1098] Device: When a user uses a web browser or application, the device records search history, browsing history, purchase history, and browsing history on review sites. It also uses cameras and sensors to collect facial expressions and voice data, obtaining emotional data in real time.

[1099] Step 2: Send and store data

[1100] Terminal: Sends collected behavioral and emotional data to the server.

[1101] Server: Stores the received data in a secure database, including historical behavioral and emotional data.

[1102] Step 3: Data analysis

[1103] Server: Runs the generative AI and emotion engine to analyze the stored behavioral and emotional data.

[1104] Server: The generative AI analyzes the behavioral data and extracts user characteristics such as user interests and purchasing tendencies.

[1105] Server: The emotion engine analyzes the emotion data and identifies the user's emotional state in real time.

[1106] Server: Create a persona for each user, reflecting their emotional state.

[1107] Step 4: Generate Ads

[1108] Server: Automatically generate the creative elements of the ad (copy, design, placement, etc.) based on the generated persona and emotional state.

[1109] Server: Generates different ad variations and sets them up for A / B testing.

[1110] Server: Adjust the content and design of the ad depending on the user's emotional state (for example, use an energetic design if the user is excited).

[1111] Step 5: Decide the timing of ad delivery

[1112] Server: Analyzes user behavior patterns (time of use, frequency, etc.) and emotional state to determine the optimal timing for delivering advertisements.

[1113] Step 6: Ad serving

[1114] Server: Delivers the appropriate ad variation to the user's device at the determined time and place.

[1115] Device: Advertisements are displayed on users' smartphones or PCs. For example, specific ads are delivered during lunch breaks when users have positive emotions.

[1116] Step 7: Measure your results

[1117] Server: Collects real-time performance data such as click rates and conversion rates of delivered ads.

[1118] Server: Analyzes the results of A / B tests to identify the most effective ad variations and evaluates how emotional states affect ad effectiveness.

[1119] Step 8: Optimize your ads

[1120] Server: Optimize your ad generation and delivery strategy based on the results of your measurement. This involves strengthening what works and correcting areas that need improvement.

[1121] Server: Reflects the optimized advertising strategy in subsequent ad generation and delivery.

[1122] By repeating these steps, we can deliver ads in real time that are best suited to each user's individual characteristics and emotional state, maximizing advertisers' ROI.

[1123] Example 2

[1124] 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."

[1125] Current ad delivery systems generate and deliver ads based on user behavior data, but do not take the user's emotional state into account. This results in ads that are not appropriate for the user's emotions and are therefore less effective. Furthermore, if the content or timing of an ad does not match the user's current emotional state, it can negatively impact the user experience and lead to lower click-through rates and conversion rates.

[1126] 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.

[1127] In this invention, the server includes means for collecting user behavioral data, means for collecting user emotional data, means for transferring the collected behavioral data and emotional data to the server, means for analyzing the transferred behavioral data and emotional data to grasp the user's characteristics and emotional state, means for automatically generating advertisements based on the user's characteristics and emotional state, means for delivering the generated advertisements based on the user's behavioral patterns and emotional state, and means for monitoring and optimizing the effectiveness of the advertisements. This makes it possible to generate and deliver highly accurate advertisements tailored to the user's characteristics and real-time emotional state, thereby maximizing the effectiveness of the advertisements.

[1128] "Behavioral data" refers to data such as search history, browsing history, purchase history, and review viewing history that is generated when a user uses a web browser or application.

[1129] "Emotion data" is data obtained from the user's facial expressions and voice, and indicates the user's emotional state, such as joy, sadness, or excitement.

[1130] The "server" is a computer system that analyzes collected behavioral and emotional data and generates and delivers advertisements based on that data.

[1131] "User characteristics" are characteristics such as user interests and purchasing tendencies that can be obtained by analyzing behavioral data.

[1132] "Emotional state" refers to a real-time understanding of the user's emotions based on an analysis of collected emotional data.

[1133] "Ad generation" is the process of automatically generating the creative elements of an ad (copy, design, placement, etc.) based on user characteristics and emotional state.

[1134] "Ad serving" is the process of displaying generated advertisements at appropriate times and places based on the user's behavioral patterns and emotional state.

[1135] "Effectiveness measurement" is the process of monitoring the effectiveness of delivered advertisements, such as click rates and conversion rates, in real time.

[1136] "Optimization" is the process of adjusting the ad generation and delivery process based on the results of effectiveness measurement and identifying the most effective advertising pattern.

[1137] "AB testing" is a testing method in which multiple advertising patterns are delivered simultaneously and their effectiveness is compared and analyzed.

[1138] This invention is a system that collects and analyzes user behavioral and emotional data, and generates and delivers personalized advertisements. This system is mainly composed of a user terminal and a server.

[1139] Data collection

[1140] A user's device has a web browser and applications installed. When the user uses them, the device collects the following behavioral data:

[1141] Search history (e.g. keywords entered into search engines)

[1142] Browsing history (e.g., URLs and categories of accessed web pages)

[1143] Purchase history (e.g. details of products purchased on an online shopping site)

[1144] Review viewing history (e.g., the content and ratings of reviews you have read)

[1145] Furthermore, the device is equipped with a camera and microphone, which can analyze the user's facial expressions and voice in real time to collect emotional data, which indicates the user's emotional state, such as joy, sadness, or excitement.

[1146] Data Transfer

[1147] The device transfers the collected behavioral and emotional data to a server using a secure protocol (e.g., HTTPS). This transfer occurs periodically to ensure the security of the communication.

[1148] Data analysis

[1149] The server receives the transferred behavioral and emotional data and stores it in a secure database, which also stores the user's past data.

[1150] The server runs a generative AI model and an emotion engine. The generative AI model analyzes behavioral data and extracts user characteristics (e.g., interest in technology gadgets). Meanwhile, the emotion engine analyzes emotional data and can grasp the user's emotional state in real time. This allows a detailed persona to be formed for each user.

[1151] Ad Generation

[1152] The server automatically generates the creative elements of the ad (copy, design, placement, etc.) based on the generated persona and real-time emotional state. Different ad variations can also be configured for A / B testing using the generative AI model. The content of the ad dynamically changes depending on the user's emotional state.

[1153] Ad serving

[1154] The server's ad delivery engine determines the optimal timing and location for delivery based on the user's behavioral patterns and emotional state, for example, delivering a positive, energetic advertising message when the user is excited.

[1155] Advertisements are displayed on users' devices (smartphones and PCs) at the time when users are most likely to respond to them.

[1156] Measurement and optimization

[1157] The server monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. It analyzes the results of AB tests to identify the most effective ad pattern. It also analyzes how the user's emotional state affects the effectiveness of ads, thereby optimizing ad generation and delivery for future ads.

[1158] Specific examples

[1159] As a concrete example, consider the case of User B. User B searches for the "latest smartphone" on his / her smartphone, browses products on multiple online shops, and purchases one of them. While browsing smartphone reviews on a review site, the device's camera captures User B's facial expressions and acquires emotion data.

[1160] The server analyzes User B's search history, purchase history, review site browsing history, and emotional data to determine that User B has a high interest in technology gadgets and was excited about them while making purchases. Using a generative AI model, it creates a persona for User B to reflect his interest in new gadgets and reviews, as well as his emotional state.

[1161] Based on User B's persona and emotional state, a generative AI model automatically generates a new smartphone ad. Multiple ad variations with different taglines and designs are created and set up for A / B testing. Include a more dynamic ad design that takes into account the user's excitement level.

[1162] The server identifies the time period and emotional state when User B is most likely to respond to an advertisement, and delivers the optimal advertisement variation at that timing. For example, if User B is in an excited state, it delivers a video advertisement with energetic music.

[1163] A new smartphone video ad is delivered to User B's smartphone during commuting hours. The server monitors the click-through rate and conversion rate of the delivered ad in real time to identify the most effective ad pattern. Based on the results, it optimizes ad delivery from the next time onwards. It also analyzes how the user's emotional state affected the effectiveness of the ad and reflects this in future ad generation.

[1164] Prompt Sentence Examples

[1165] Based on the behavioral history and emotional data of users searching for "latest smartphones," please generate ads with the following conditions:

[1166] An energetic design that matches the user's excitement

[1167] Multiple copy and design variations for A / B testing

[1168] Delivered during the user's commute time

[1169] The embodiments of the present invention have been described in detail above.

[1170] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1171] Step 1: Data collection

[1172] Device:

[1173] When a user uses a web browser or application, behavioral data such as search history, browsing history, purchase history, and review browsing history is collected. Furthermore, facial expression and voice data is collected using the device's camera and microphone, and emotional data is obtained. The input is the user's operational behavior and the device's sensor data. The output is the collected behavioral data and emotional data.

[1174] Specifically, when a user searches for "the latest smartphone" on a smartphone browser, browses multiple online shops, and reads reviews, the system collects their browsing history. The system also captures emotion data from the user's facial expressions captured by a camera while they are reading the reviews.

[1175] Step 2: Data Transfer

[1176] Device:

[1177] The collected behavioral and emotional data is sent to a server using a secure protocol (e.g., HTTPS). As input, the data collected in step 1 is used. As output, the data sent to the server is obtained.

[1178] Specifically, the device generates data packets at regular intervals, encrypts them, and sends them to the server, along with emotion data.

[1179] Step 3: Data analysis

[1180] server:

[1181] The received behavioral and emotional data is stored in a database on the server. Then, this data is analyzed using a generative AI model and an emotion engine. As input, the data sent in step 2 is used. As output, analysis results are obtained to understand the user's characteristics and emotional state.

[1182] Specifically, the generative AI model analyzes the user's search and purchase history to extract characteristics such as "this user tends to get excited about new gadgets." The emotion engine also analyzes the user's emotional data and determines in real time that "the user was in a highly excited state when looking at product reviews."

[1183] Step 4: Generate Ads

[1184] server:

[1185] Based on the generated personas and real-time emotional states, the generative AI model automatically generates the creative elements of the ad (copy, design, placement, etc.). It generates different ad variations for AB testing. It uses the user characteristics and emotional states obtained in step 3 as input. The generated ad variations are obtained as output.

[1186] For example, when generating an advertisement for the latest smartphone, the system creates several variations of the advertisement with dynamic effects to stimulate users' excitement. For A / B testing, it generates ad variations with different catchphrases and designs.

[1187] Step 5: Ad serving

[1188] server:

[1189] The ad serving engine determines the optimal timing and location of delivery based on the user's behavioral patterns and emotional state. As input, it uses the ad variations generated in step 4 and previous user behavior and emotional state data. The output is the ad to be delivered and its timing.

[1190] Device:

[1191] The generated advertisement is delivered to the user's terminal at a time and place determined by the server.

[1192] Specifically, while User B is reading the news on his smartphone during his commute, a video advertisement for the "latest smartphone" with dynamic effects and energetic music is displayed.

[1193] Step 6: Measure and optimize

[1194] server:

[1195] It monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. It analyzes the results of AB tests to identify the most effective ad pattern. It also analyzes how the user's emotional state affected the effectiveness of the ad. As input, it uses performance data of the ad being delivered and real-time user emotional data. As output, it obtains an optimized ad generation and delivery strategy.

[1196] Specifically, the system analyzes in real time the number of clicks on ads and the number of times they lead to subsequent purchases, and gains insights such as "advertisements delivered when users are excited have a higher click rate." Based on these findings, it optimizes ad generation and delivery for future ads.

[1197] The above is the specific processing flow of the system program.

[1198] (Application example 2)

[1199] 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."

[1200] Conventional ad delivery systems generate and deliver ads based on user behavior data, but do not take the user's emotional state into account, limiting the effectiveness of the ads. Furthermore, they lack targeting accuracy and real-time optimization, making it difficult to attract user attention.

[1201] 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.

[1202] In this invention, the server includes means for analyzing user behavioral data and emotional data to grasp user characteristics, means for automatically generating advertisements based on the analyzed user characteristics and emotional state, and means for delivering optimal advertisements based on the user's behavioral patterns and emotional state, thereby enabling targeting that takes into account the user's emotional state and real-time advertisement optimization.

[1203] "User behavior data" refers to information such as search history, browsing history, and purchase history when a user uses a web browser or application.

[1204] "Emotion data" is data obtained from the user's facial expressions, voice, etc., and reflects the user's emotional state (joy, sadness, excitement, etc.).

[1205] "User characteristics" refer to the user's interests, preferences, and behavioral patterns extracted based on the analyzed user's behavioral data and emotional data.

[1206] "Automatic ad generation" refers to the automatic generation of creative elements of an ad (copy, design, placement, etc.) using generative AI based on user characteristics and emotional state.

[1207] "Multiple ad variations" are ad variations with different copy and design, used to conduct A / B testing.

[1208] "AB testing" is a method of comparing and verifying the effectiveness of multiple advertising patterns to identify the most effective one.

[1209] "Advertisement delivery" refers to delivering the generated advertisement at the optimal timing based on the user's behavioral patterns and emotional state.

[1210] "Monitoring" means monitoring the effectiveness of delivered advertisements (click-through rate, conversion rate, etc.) in real time.

[1211] "Optimization" is the process of adjusting advertising content and delivery timing based on monitoring results to maximize advertising effectiveness.

[1212] The present invention relates to a system that collects and analyzes user behavioral data and emotional data, and generates and distributes personalized advertisements. A specific embodiment of this system is described below.

[1213] System Configuration

[1214] 1. Data Collection

[1215] Device: A user's smartphone records search history, browsing history, and purchase history when using a web browser or application. The device also uses a camera and microphone to collect facial and voice data and obtain emotional data. To do this, the device uses camera sensors and voice recognition software (e.g., Google APIs for Computer Vision and Emotion AI).

[1216] Server: Collected behavioral and emotional data is transferred to the server and stored in a secure database (e.g., AWS RDS, Google Cloud SQL).

[1217] 2. Data Analysis

[1218] Server: The server analyzes the collected behavioral and emotional data as follows:

[1219] Generative AI (e.g., OpenAI GPT-4) is used to perform detailed analysis of behavioral data and extract user characteristics.

[1220] Using an emotion engine (e.g., IBM Watson Tone Analyzer), emotion data is analyzed to understand the user's real-time emotional state.

[1221] 3. Ad generation

[1222] Server: The server automatically generates the creative elements of the ad (copy, design, placement, etc.) based on the generated user characteristics and emotional state. Using generative AI, multiple ad variations are created and prepared for A / B testing.

[1223] 4. Advertisement Delivery

[1224] Server: The server uses an ad serving engine (e.g., AdRoll, Google Ads API) to deliver ads at the optimal time and place based on the user's behavioral patterns and emotional state.

[1225] Device: Real-time personalized ads are displayed on the user's smartphone, taking into account the user's emotional state, for example, delivering energetic ads when they are excited.

[1226] 5. Measurement and optimization

[1227] Server: The server monitors and analyzes the effectiveness of the delivered advertisements in real time. For example, it measures click-through rates and conversion rates and identifies the most effective advertisement patterns. It optimizes the content and timing of advertisements based on the monitoring results.

[1228] Specific examples

[1229] For user C:

[1230] Data collection: User C searches for "new running shoes" on their smartphone and browses multiple running-related blog articles and reviews. The smartphone camera also collects User C's facial expressions and voice data, recording their emotional data.

[1231] Data analysis: The server analyzes user C's search history, browsing history, and emotional data and finds out that user C has a high interest in fitness products, especially running, and has positive emotions.

[1232] Ad generation: Based on the generated persona and emotional state of User C, an advertisement for a new running shoe is automatically generated. Multiple advertisement variations with different catchphrases and designs are created and prepared for A / B testing.

[1233] Advertisement delivery: When User C is excited after his morning jog, a video advertisement with energetic music is delivered to his smartphone.

[1234] Effectiveness measurement and optimization: Monitor click-through rates and conversion rates of delivered ads in real time, identify the most effective ad patterns, and reflect them in future ad deliveries.

[1235] Prompt Sentence Examples

[1236] For example, input the following prompt into your generative AI model:

[1237] User C has a strong interest in fitness, especially running. His emotional data shows that he feels excited after jogging. Based on this data, we generate ad copy and design variations that emphasize the appeal of running shoes. For example, we could create an ad that includes the following content:

[1238] 1. "New running shoes that offer the best running experience!"

[1239] 2. "Break your personal best in your next race! These running shoes make it possible."

[1240] Also consider what will excite User C even more.

[1241] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1242] Step 1:

[1243] The device collects user behavior data (search history, browsing history, purchase history, etc.). This includes the user's actions when using a web browser or application, and the product browsing history in an online shop. The collected data is stored in an initial database on the smartphone, ready to be transferred to the server.

[1244] Step 2:

[1245] The device collects the user's emotional data. It uses a camera and microphone to capture the user's facial expressions and voice data, and analyzes their emotional state (happiness, sadness, excitement, etc.) based on this data. For this purpose, it uses, for example, Google APIs for Computer Vision and Emotion AI. The collected emotional data is also temporarily stored on the smartphone.

[1246] Step 3:

[1247] The device transfers behavioral and emotional data to the server via secure communication. The data is protected using security protocols such as Transport Layer Security (TLS). The server receives the data and stores it in a secure database (e.g., AWS RDS or Google Cloud SQL).

[1248] Step 4:

[1249] The server uses generative AI (e.g., OpenAI GPT-4) to analyze user behavioral data and extract user characteristics. Here, the server identifies the user's interests, preferences, and behavioral patterns from past search history, browsing history, and purchase history. Based on the input behavioral data, it performs text analysis and pattern recognition.

[1250] Step 5:

[1251] The server analyzes the user's emotional data using an emotion engine (e.g., IBM Watson Tone Analyzer) to understand the user's current emotional state. It identifies the user's real-time emotional state through facial expression analysis and voice analysis and records it along with the user's characteristics.

[1252] Step 6:

[1253] The server uses generative AI to automatically generate ads based on analyzed user characteristics and emotional states. The generative AI creates multiple ad variations, including different copy and designs. The generated ads are then set up for A / B testing.

[1254] Step 7:

[1255] The server uses an ad distribution engine (e.g., AdRoll, Google Ads API) to distribute ads at optimal times based on the user's behavioral patterns and emotional state. For example, it adjusts the distribution of energetic ads when the user is in an excited state.

[1256] Step 8:

[1257] The device displays the delivered advertisements to the user in real time, allowing the user to interact with the advertisement at the time when they are most likely to respond.The advertisements are displayed using the smartphone's notification function and in-app banner ads.

[1258] Step 9:

[1259] The server monitors the click-through rate and conversion rate of the delivered ads in real time. Here, data analysis tools (e.g., Google Analytics) are used to evaluate the effectiveness of the ads. The results of the AB tests are analyzed to identify the most effective ad patterns.

[1260] Step 10:

[1261] The server optimizes the ad content and delivery timing based on the monitoring results, giving priority to highly effective ad patterns and reflecting this in the next ad generation and delivery, thereby maximizing advertising ROI.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] [Fourth embodiment]

[1266] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1267] 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.

[1268] 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).

[1269] 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.

[1270] 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.

[1271] 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).

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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."

[1279] The present invention provides a system that automatically generates personalized advertisements based on user behavior data and delivers them effectively. This system includes collection of behavioral data by user devices, data analysis by a server, advertisement generation, advertisement delivery, and monitoring and optimization of advertisement effectiveness.

[1280] System program processing explanation

[1281] Data collection

[1282] Device:

[1283] When a user uses a web browser or application, their device collects behavioral data such as their search history, browsing history, purchase history, and review site browsing history. This data is used as the basis for reflecting the user's interests and preferences.

[1284] server:

[1285] The collected behavioral data is transferred to a server and stored in a secure database, which stores huge amounts of big data and is used for analysis.

[1286] Data analysis

[1287] server:

[1288] The server runs a generation AI to analyze the behavioral data. The generation AI analyzes the collected behavioral data in detail and extracts user characteristics. The extracted user characteristics include areas of interest, purchasing tendencies, and behavioral patterns by time of day. This creates a persona for each user.

[1289] Ad Generation

[1290] server:

[1291] Based on the personas, generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.), including an ad set with multiple variations of the ad. The generated ads are then configured for A / B testing, with settings set up to measure the effectiveness of each variation.

[1292] Ad serving

[1293] server:

[1294] The ad distribution engine then runs and determines the optimal timing and location for distribution based on user behavior data. For example, it can deliver a specified ad during times when users are most likely to use a social networking app.

[1295] Device:

[1296] Advertisements are displayed on users' smartphones and PCs, allowing them to interact with the ads at the time when they are most likely to respond.

[1297] Measurement and optimization

[1298] server:

[1299] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of AB tests are analyzed to identify the optimal ad variations. Subsequent ad generation and delivery are optimized based on the identified effective ad variations.

[1300] Specific examples

[1301] For User A

[1302] 1. Data Collection:

[1303] Device: User A searches for "latest smartwatches" on their smartphone, browses products on multiple online shops, and purchases one. User A also browses reviews of smartwatches on a review site.

[1304] 2. Data Analysis:

[1305] Server: The server analyzes User A's search history, purchase history, and browsing history on review sites to determine that User A has a high interest in technology gadgets. A persona is created for User A to reflect his interest in new gadgets and reviews.

[1306] 3. Ad generation:

[1307] Server: Automatically generate a new smartphone ad based on User A's persona. Multiple ad variations (different copy and design) are created and set up for A / B testing.

[1308] 4. Advertisement Delivery:

[1309] Server: Identify the time of day when User A uses the SNS app and deliver the optimal ad variation at that time.

[1310] Device: User A's smartphone receives an advertisement for a new smartphone during commuting hours.

[1311] 5. Measurement and optimization:

[1312] Server: Monitors the click-through rate and conversion rate of delivered ads in real time, identifies the most effective ad pattern, and optimizes future ad delivery based on the results.

[1313] The present invention makes it possible to realize effective advertisement delivery based on user characteristics, and significantly improve advertisers' ROI.

[1314] The processing flow will be explained below.

[1315] Step 1: Data collection

[1316] Device: When a user uses a web browser or application, their search history, browsing history, purchase history, and review site browsing history are recorded.

[1317] Device: Periodically transmits collected behavioral data to the server.

[1318] Step 2: Save data

[1319] Server: Receives behavioral data sent from the device and stores it in a secure database, including historical data.

[1320] Step 3: Data analysis

[1321] Server: Runs generative AI algorithms to analyze stored behavioral data.

[1322] Server: Generative AI extracts user characteristics (interests, concerns, purchasing tendencies, etc.) based on behavioral data and creates a persona for each user.

[1323] Step 4: Generate Ads

[1324] Server: Based on the generated personas, the generative AI creates the creative elements of the ad (copy, design, placement, etc.).

[1325] Server: Generates different variations of ads (e.g., multiple copy and designs) and sets them up for A / B testing.

[1326] Step 5: Decide the timing of ad delivery

[1327] Server: Analyzes user behavior patterns (such as the time of day and frequency of device use) to determine the optimal timing for delivering advertisements.

[1328] Step 6: Ad serving

[1329] Server: At the specified time, deliver the optimal ad variation to the user's device.

[1330] Device: Advertisements are displayed on users' smartphones or PCs. For example, specific ads may be displayed within social media apps during the morning commute.

[1331] Step 7: Measure your results

[1332] Server: Monitors user responses to delivered ads (click-through rate, conversion rate, etc.) in real time.

[1333] Server: Analyzes the monitoring results and identifies the most effective advertising patterns.

[1334] Step 8: Optimize your ads

[1335] Server: Optimize ad generation and distribution strategies based on the results of effectiveness measurement. This includes strengthening effective elements and improving weak areas.

[1336] Server: Reflects the optimized advertising strategy in subsequent ad generation and delivery.

[1337] By repeating these steps, you can maximize the effectiveness of your ads and significantly increase your ROI.

[1338] Example 1

[1339] 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."

[1340] In today's online advertising market, there is a demand for personalized ad delivery based on user interests and behavior. However, conventional methods lack sufficient accuracy in analyzing user behavior data and optimizing ad generation and delivery, making effective ad delivery difficult. Furthermore, while there is a demand for optimizing the timing and location of ad delivery, as well as real-time monitoring of ad effectiveness and rapid optimization based on that monitoring, these methods also have limitations. Therefore, there is a need for a system that can effectively utilize user behavior data to generate and deliver ads that are optimal for each individual user, thereby maximizing advertising effectiveness.

[1341] 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.

[1342] In this invention, the server includes means for collecting user behavioral data, means for analyzing the collected behavioral data and grasping user characteristics, means for using a generative AI model to automatically generate advertisements based on the user characteristics, means for delivering the generated advertisements to users at appropriate times, and means for monitoring and optimizing the effectiveness of the advertisements. This makes it possible to generate and deliver personalized advertisements based on the user's interests and behavior, thereby maximizing the effectiveness of the advertisements.

[1343] "User behavior data" refers to data such as search history, browsing history, purchase history, and review site browsing history that is recorded when a user uses a web browser or application.

[1344] "Means for collection" refers to the function for recording user behavior data on the terminal and transferring it to the server.

[1345] "Analysis means" refers to the function that performs processing to extract user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.) based on collected behavioral data.

[1346] "Target characteristics" refer to characteristics such as user interests, preferences, purchasing tendencies, and behavioral patterns obtained from the analysis of behavioral data.

[1347] "Generative AI model" refers to an artificial intelligence model that analyzes collected behavioral data and automatically generates creative elements for advertisements based on user characteristics.

[1348] "Means for automatically generating advertisements" refers to the ability to automatically generate elements such as ad copy, design, and placement using a generative AI model.

[1349] "Means for delivering at the appropriate time" refers to a function for delivering advertisements at the optimal time based on the user's behavioral patterns and usage time period.

[1350] "Means for monitoring the effectiveness of advertising" refers to the function of monitoring the effectiveness of delivered advertisements, such as click rates and conversion rates, in real time.

[1351] "Means for optimization" refers to a function that effectively improves the generation and distribution of advertisements from the next time onwards based on the monitored advertisement effectiveness data.

[1352] "AB testing" refers to a testing method that uses multiple advertising patterns to compare and verify their effectiveness and identify the most effective pattern.

[1353] The present invention provides a system that automatically generates personalized advertisements based on user behavior data and delivers them effectively. This system includes collection of behavioral data by user devices, data analysis by a server, advertisement generation, advertisement delivery, and monitoring and optimization of advertisement effectiveness.

[1354] Data collection

[1355] Device:

[1356] When a user uses a web browser or application, the device collects behavioral data such as search history, browsing history, purchase history, and browsing history on review sites. This includes when a user searches for a specific product, such as the latest smartwatch, and browses products on multiple online shops or reads reviews of smartwatches on review sites.

[1357] server:

[1358] The collected behavioral data is transferred from the device to a server, where it is stored in a secure database such as Amazon RDS, MySQL, or PostgreSQL. This database stores large amounts of big data, which is used for analysis, as described below.

[1359] Data analysis

[1360] server:

[1361] The server uses a generative AI model (e.g., GPT-3, BERT, etc.) to analyze the collected behavioral data. The server retrieves the behavioral data from the database and inputs it into the generative AI. The generative AI analyzes the data in detail and extracts user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.). For example, it may identify that a user is interested in technology gadgets, and generate a user persona.

[1362] Ad Generation

[1363] server:

[1364] Based on the personas created, the generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.). For example, the generative AI creates an ad for a new smartphone that the user is interested in. This ad includes different taglines and design variations. The generated ad set is then configured for A / B testing.

[1365] Ad serving

[1366] server:

[1367] Ad delivery engines (such as Google Ads or Facebook Ads) operate to determine the optimal timing and location for delivery based on user behavior data. For example, an ad for a new smartphone may be delivered within a social networking app at a time when the user is most likely to use that app.

[1368] Device:

[1369] Advertisements are displayed on users' smartphones and PCs. This allows ads to be exposed at the time when users are most likely to respond. For example, an advertisement for a new smartphone is displayed on a user's smartphone during their commute.

[1370] Measurement and optimization

[1371] server:

[1372] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of the AB test are analyzed by the generation AI to identify the most effective ad pattern. Based on these results, the generation AI further optimizes ad generation and delivery for future ads. For example, it will adopt an ad variation with a high click-through rate and reflect that in the next ad generation.

[1373] Prompt Sentence Examples

[1374] You can generate an ad by inputting prompts like the following into the generative AI model:

[1375] Example 1:

[1376] Generate an ad related to the latest smartwatch that User A recently searched for and purchased. The ad should include the following elements: tagline, product image, feature list, and purchase link, reflecting User A's particular interest in technology gadgets.

[1377] Example 2:

[1378] Generate a great ad for a sports-related site that User B frequently visits. The ad should include the following elements: a punchy tagline, action images, discount information, and a link to buy. Consider that User B is interested in fitness equipment.

[1379] The present invention makes it possible to realize effective advertisement delivery based on user characteristics, and significantly improve advertisers' ROI.

[1380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1381] Step 1: Data collection

[1382] Device: When a user uses a web browser or application, the device collects search history, browsing history, purchase history, and review site browsing history. This data is saved as a log file. The input is user behavior data (search keywords, URLs of visited web pages, browsing time, etc.). The output is a log file of user behavior data.

[1383] Specific actions: For example, a user may search for "latest smartwatches," browse products on multiple online shops, and purchase one. It may also record actions such as browsing reviews of smartwatches on a review site.

[1384] Step 2: Data Transfer and Storage

[1385] Device: The collected behavioral data is transferred to the server. The device divides the collected data into packets and transmits them using a secure communication protocol (e.g., HTTPS).

[1386] Server: Stores the received behavioral data in a secure database. The input is user behavioral data sent from the device. The output is user behavioral data stored in a database (e.g., Amazon RDS, MySQL, PostgreSQL).

[1387] Specific operation: For example, the user's search history and browsing history are sent to the server and stored in a database.

[1388] Step 3: Data analysis

[1389] Server: A generative AI model (e.g., GPT-3, BERT) is used to analyze the collected behavioral data. The server extracts user behavior data from the database and inputs it into the AI ​​model. The input is the user behavior data in the database. The output is the analyzed user characteristics (areas of interest, purchasing tendencies, behavioral patterns by time of day, etc.).

[1390] How it works: For example, a generative AI might analyze a user's search and purchase history to determine that they're interested in technology gadgets.

[1391] Step 4: Generate Ads

[1392] Server: Based on the generated user characteristics, the generative AI automatically generates the creative elements of the ad. The input includes user characteristic data. The output is the generated ad set (copy, design, placement, etc.).

[1393] What it does: For example, if a user is interested in technology gadgets, the AI ​​will automatically generate ads for new smartphones, with different taglines and design variations.

[1394] Step 5: Ad serving

[1395] Server: The ad distribution engine (e.g., Google Ads, Facebook Ads) runs and determines the optimal timing and location of distribution based on user behavior data. The inputs are user behavior pattern data and the generated ad set. The output is the distribution schedule and the ads to be distributed.

[1396] Device: Advertisements are displayed on the user's smartphone or PC. The input is advertising data sent from the server. The output is the advertisement displayed on the user's device screen.

[1397] Specific behavior: For example, advertisements for new smartphones are displayed within a social media app at the time the user is using the app.

[1398] Step 6: Measure and optimize

[1399] Server: Monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. The input is ad delivery result data. The output is identification of the optimal ad pattern and data for optimizing the next ad based on that.

[1400] What it does: For example, analyze the results of an A / B test to identify the ad variation with the highest click-through rate. Use this information to improve ad generation and delivery for future ads.

[1401] (Application example 1)

[1402] 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."

[1403] Conventional ad delivery systems have limited ad personalization based on user behavior data, making it difficult to reflect detailed real-time behavioral information such as user gaze data. Furthermore, there is a problem in that advanced ad delivery using smart display devices cannot be effectively performed. The present invention aims to solve these problems and realize more accurate ad delivery based on user characteristics.

[1404] 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.

[1405] In this invention, the server includes means for collecting user behavioral data, means for analyzing the collected behavioral data and grasping user characteristics, means for automatically generating advertisements based on the user characteristics, means for delivering the generated advertisements to users, means for monitoring and optimizing the effectiveness of the advertisements, means for collecting user gaze data, means for analyzing behavioral data including the gaze data and grasping user characteristics with high accuracy, and means for displaying advertisements on the smart display device. This enables highly accurate advertisement delivery that reflects detailed user behavioral characteristics in real time.

[1406] "User behavioral data" refers to information such as a user's online search history, browsing history, purchase history, and review site browsing history.

[1407] "Means for analyzing data" refers to the technical methods and algorithms used to analyze collected behavioral data and extract user characteristics.

[1408] "User characteristics" refers to characteristic information such as a user's interests, purchasing tendencies, and behavioral patterns obtained by analyzing behavioral data.

[1409] "Means for automatically generating advertisements" refers to algorithms or systems that automatically generate advertising content (copy, design, placement, etc.) based on user characteristics.

[1410] "Means for delivering advertisements to users" refers to technologies and systems for providing generated advertisements to users at appropriate times and places.

[1411] "Means for monitoring and optimizing advertising effectiveness" refers to technologies and systems that monitor the performance of delivered ads (click-through rate, conversion rate, etc.) in real time and identify the most effective advertising variations.

[1412] "Gaze data" refers to information that indicates the movement of a user's gaze, captured using a camera built into a smart display device or the like.

[1413] "Means for collecting gaze data" refers to technologies and systems that record a user's gaze direction and point of gaze in real time.

[1414] "Smart display device" refers to an advanced display device that can display information to users in real time.

[1415] The system of the present invention collects and analyzes user behavioral data and gaze data, and generates and delivers personalized advertisements based on the collected data. Specific embodiments are described below.

[1416] 1. Hardware Configuration

[1417] This system uses the following main hardware:

[1418] Smart display devices: Devices with built-in cameras and eye-tracking technology.

[1419] Server: A computer used to analyze data and generate advertisements.

[1420] User devices: Mainly smartphones and PCs.

[1421] 2. Software Configuration

[1422] This system uses the following main software:

[1423] OpenCV: A library for collecting and processing gaze data.

[1424] DBSCAN: A clustering algorithm.

[1425] LangChain's OpenAIServer: A server that generates ads using generative AI models.

[1426] Python: A programming language for general data processing.

[1427] 3. Data Collection

[1428] The server collects behavioral data (search history, browsing history, purchase history, etc.) recorded while the user is using a web browser or application. In addition, it uses the built-in camera of the smart display device to capture gaze data in real time and record gaze direction and gaze point.

[1429] 4. Data Analysis

[1430] The collected behavioral and gaze data is transferred to a server and stored in a database. The server uses OpenCV and DBSCAN to analyze the gaze data and extract detailed user behavioral characteristics. This data analysis allows for an understanding of areas of interest, purchasing trends, and behavioral patterns by time of day.

[1431] 5. Ad Generation

[1432] The server determines the creative elements of the ad to be generated based on the data analysis, and generates personalized ad content using LangChain's OpenAIServer, which uses the following prompt text as input to the generative AI model:

[1433] Example prompt sentence:

[1434] User Interests: Cafe, Coffee, Drinks

[1435] Good advertising: Smart Cafe's new drink menu

[1436] 6. Advertisement Delivery

[1437] Based on user behavior data, the generated advertisements are delivered to smart display devices and other user devices at the most appropriate time, allowing for effective advertisements to be provided at the time when users are likely to be interested.

[1438] 7. Measurement and optimization

[1439] After an ad is delivered, its effectiveness (click-through rate, conversion rate, etc.) is monitored in real time. Based on this data, the server identifies the optimal ad variation and optimizes subsequent ad generation and delivery.

[1440] Specific examples

[1441] Suppose a user is walking down the street and their gaze falls on a cafe sign that catches their eye. Their gaze data is captured and analyzed, revealing that they are interested in the cafe. Based on the user's interests, the generative AI model generates an advertisement for a limited-time menu item at a nearby cafe and displays it in real time on a smart display device. It is expected that the user will see this advertisement and visit the specific cafe.

[1442] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1443] Step 1:

[1444] The device collects user behavior data (search history, browsing history, purchase history). When a user uses a web browser or application, various data obtained from the operation is saved as a log. This data is transferred to the server.

[1445] Input: User search history, browsing history, purchase history

[1446] Output: Collected behavioral data

[1447] Step 2:

[1448] The server receives the behavioral data transferred from the device, stores it in a secure database, and then prepares the data for analysis.

[1449] Input: Collected behavioral data

[1450] Output: Behavioral data stored in a database

[1451] Step 3:

[1452] The device uses the built-in camera of the smart display device to capture the user's gaze data in real time, recording the gaze direction and gaze point, and this data is also sent to the server.

[1453] Input: User gaze data

[1454] Output: Gaze data captured and sent to the server

[1455] Step 4:

[1456] The server receives the transmitted gaze data and extracts gaze maps and gaze points using OpenCV, while clustering the gaze data using the DBSCAN algorithm to identify the user's gaze patterns.

[1457] Input: User gaze data

[1458] Output: Clustered gaze pattern data

[1459] Step 5:

[1460] The server integrates the collected behavioral data with the clustered gaze patterns to extract more detailed user characteristics, such as areas of interest, purchasing tendencies, and behavioral patterns by time of day.

[1461] Input: Stored behavioral data, clustered gaze pattern data

[1462] Output: Extracted user characteristics

[1463] Step 6:

[1464] The server generates personalized ads using a generative AI model (LangChain's OpenAIServer) based on the extracted user characteristics, and generates appropriate ad content using the prompt sentence as input.

[1465] Input: Prompt sentence (e.g., user interests: cafe, coffee, drinks)

[1466] Output: Generated ad content

[1467] Step 7:

[1468] The server delivers the generated advertising content to users at a time and place appropriate for them, for example, displaying the advertisement when the user is using a social networking app or when the user is in a specific location, and also instructs the smart display device to display the advertisement.

[1469] Input: Generated advertising content, user behavior patterns

[1470] Output: Ads delivered to user devices and smart display devices

[1471] Step 8:

[1472] The server monitors the performance of the delivered ads in real time, analyzing click-through rates, conversion rates, etc. Based on the obtained data, it identifies the optimal ad variation and optimizes subsequent ad generation and delivery.

[1473] Input: Performance data of delivered ads

[1474] Output: Optimized ad generation and delivery patterns

[1475] 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.

[1476] This invention is a system that collects and analyzes not only user behavioral data but also user emotional data, and generates and delivers personalized advertisements. This system includes processes that collect behavioral data and emotional data from the user's device, analyze the data on the server, and generate, deliver, measure the effectiveness of, and optimize advertisements.

[1477] System program processing explanation

[1478] Data collection

[1479] Device:

[1480] When a user uses a web browser or application, behavioral data such as search history, browsing history, purchase history, and review site browsing history are recorded. The device also uses cameras and sensors to collect emotional data based on the user's facial expressions and voice data. This emotional data reflects the user's emotional state (e.g., joy, sadness, excitement, etc.).

[1481] server:

[1482] The collected behavioral and emotional data is transferred to a server and stored in a secure database, which also includes historical data.

[1483] Data analysis

[1484] server:

[1485] The server runs a generation AI and emotion engine to analyze behavioral and emotional data. The generation AI analyzes the behavioral data in detail to extract the user's characteristics. The emotion engine analyzes the emotional data and grasps the user's emotional state in real time. This allows a more specific persona to be formed for each user.

[1486] Ad Generation

[1487] server:

[1488] Based on the generated persona and real-time emotional state, the generative AI automatically generates the creative elements of the ad (copy, design, placement, etc.), including different variations of the ad pattern and configuring them for A / B testing. It can also change the content of the ad depending on the user's emotional state.

[1489] Ad serving

[1490] server:

[1491] The ad delivery engine works to determine the optimal timing and location of delivery based on the user's behavioral patterns and emotional state, for example, delivering positive advertising messages when the user is happy.

[1492] Device:

[1493] Advertisements are displayed on users' smartphones and PCs, allowing them to be exposed to ads at the time when they are most likely to respond to them.

[1494] Measurement and optimization

[1495] server:

[1496] The effectiveness of delivered ads (click-through rate, conversion rate, etc.) is monitored in real time. The results of AB tests are analyzed to identify the most effective ad pattern. Also, how the user's emotional state affects ad response is analyzed. Based on the identified effective ad pattern, subsequent ad generation and delivery are optimized.

[1497] Specific examples

[1498] For User B

[1499] 1. Data Collection:

[1500] Device: User B searches for "the latest smartphone" on his smartphone, browses products on multiple online shops, and purchases one. User B also browses smartphone reviews on a review site. Furthermore, User B's facial expressions and voice data are collected via the device's camera and microphone, and emotion data is obtained.

[1501] 2. Data Analysis:

[1502] Server: The server analyzes User B's search history, purchase history, review site browsing history, as well as emotional data, to determine that User B has a high interest in technology gadgets and was excited about purchasing them. A persona for User B is created to reflect his interest in new gadgets and reviews, as well as his emotional state.

[1503] 3. Ad generation:

[1504] Server: Automatically generate new smartphone ads based on User B's persona and emotional state. Multiple ad variations with different taglines and designs are created and set up for A / B testing. Include ads with more dynamic designs that take into account the user's excitement state.

[1505] 4. Advertisement Delivery:

[1506] Server: Identify the time and emotional state when User B is most likely to respond to ads, and deliver the optimal ad variation at that time. For example, deliver a video ad with energetic music when User B is in an excited state.

[1507] Device: User B's smartphone receives a video ad for a new smartphone during rush hour.

[1508] 5. Measurement and optimization:

[1509] Server: Monitors the click-through rate and conversion rate of delivered ads in real time to identify the most effective ad pattern. Based on the results, optimizes future ad delivery. Also, analyzes how the user's emotional state affected the effectiveness of the ad and reflects this in future ad generation.

[1510] This invention makes it possible to realize detailed ad delivery based on user characteristics and emotional state, thereby significantly improving advertisers' ROI.

[1511] The processing flow will be explained below.

[1512] Step 1: Collect behavioral and emotional data

[1513] Device: When a user uses a web browser or application, the device records search history, browsing history, purchase history, and browsing history on review sites. It also uses cameras and sensors to collect facial expressions and voice data, obtaining emotional data in real time.

[1514] Step 2: Send and store data

[1515] Terminal: Sends collected behavioral and emotional data to the server.

[1516] Server: Stores the received data in a secure database, including historical behavioral and emotional data.

[1517] Step 3: Data analysis

[1518] Server: Runs the generative AI and emotion engine to analyze the stored behavioral and emotional data.

[1519] Server: The generative AI analyzes the behavioral data and extracts user characteristics such as user interests and purchasing tendencies.

[1520] Server: The emotion engine analyzes the emotion data and identifies the user's emotional state in real time.

[1521] Server: Create a persona for each user, reflecting their emotional state.

[1522] Step 4: Generate Ads

[1523] Server: Automatically generate the creative elements of the ad (copy, design, placement, etc.) based on the generated persona and emotional state.

[1524] Server: Generates different ad variations and sets them up for A / B testing.

[1525] Server: Adjust the content and design of the ad depending on the user's emotional state (for example, use an energetic design if the user is excited).

[1526] Step 5: Decide the timing of ad delivery

[1527] Server: Analyzes user behavior patterns (time of use, frequency, etc.) and emotional state to determine the optimal timing for delivering advertisements.

[1528] Step 6: Ad serving

[1529] Server: Delivers the appropriate ad variation to the user's device at the determined time and place.

[1530] Device: Advertisements are displayed on users' smartphones or PCs. For example, specific ads are delivered during lunch breaks when users have positive emotions.

[1531] Step 7: Measure your results

[1532] Server: Collects real-time performance data such as click rates and conversion rates of delivered ads.

[1533] Server: Analyzes the results of A / B tests to identify the most effective ad variations and evaluates how emotional states affect ad effectiveness.

[1534] Step 8: Optimize your ads

[1535] Server: Optimize your ad generation and delivery strategy based on the results of your measurement. This involves strengthening what works and correcting areas that need improvement.

[1536] Server: Reflects the optimized advertising strategy in subsequent ad generation and delivery.

[1537] By repeating these steps, we can deliver ads in real time that are best suited to each user's individual characteristics and emotional state, maximizing advertisers' ROI.

[1538] Example 2

[1539] 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."

[1540] Current ad delivery systems generate and deliver ads based on user behavior data, but do not take the user's emotional state into account. This results in ads that are not appropriate for the user's emotions and are therefore less effective. Furthermore, if the content or timing of an ad does not match the user's current emotional state, it can negatively impact the user experience and lead to lower click-through rates and conversion rates.

[1541] 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.

[1542] In this invention, the server includes means for collecting user behavioral data, means for collecting user emotional data, means for transferring the collected behavioral data and emotional data to the server, means for analyzing the transferred behavioral data and emotional data to grasp the user's characteristics and emotional state, means for automatically generating advertisements based on the user's characteristics and emotional state, means for delivering the generated advertisements based on the user's behavioral patterns and emotional state, and means for monitoring and optimizing the effectiveness of the advertisements. This makes it possible to generate and deliver highly accurate advertisements tailored to the user's characteristics and real-time emotional state, thereby maximizing the effectiveness of the advertisements.

[1543] "Behavioral data" refers to data such as search history, browsing history, purchase history, and review viewing history that is generated when a user uses a web browser or application.

[1544] "Emotion data" is data obtained from the user's facial expressions and voice, and indicates the user's emotional state, such as joy, sadness, or excitement.

[1545] The "server" is a computer system that analyzes collected behavioral and emotional data and generates and delivers advertisements based on that data.

[1546] "User characteristics" are characteristics such as user interests and purchasing tendencies that can be obtained by analyzing behavioral data.

[1547] "Emotional state" refers to a real-time understanding of the user's emotions based on an analysis of collected emotional data.

[1548] "Ad generation" is the process of automatically generating the creative elements of an ad (copy, design, placement, etc.) based on user characteristics and emotional state.

[1549] "Ad serving" is the process of displaying generated advertisements at appropriate times and places based on the user's behavioral patterns and emotional state.

[1550] "Effectiveness measurement" is the process of monitoring the effectiveness of delivered advertisements, such as click rates and conversion rates, in real time.

[1551] "Optimization" is the process of adjusting the ad generation and delivery process based on the results of effectiveness measurement and identifying the most effective advertising pattern.

[1552] "AB testing" is a testing method in which multiple advertising patterns are delivered simultaneously and their effectiveness is compared and analyzed.

[1553] This invention is a system that collects and analyzes user behavioral and emotional data, and generates and delivers personalized advertisements. This system is mainly composed of a user terminal and a server.

[1554] Data collection

[1555] A user's device has a web browser and applications installed. When the user uses them, the device collects the following behavioral data:

[1556] Search history (e.g. keywords entered into search engines)

[1557] Browsing history (e.g., URLs and categories of accessed web pages)

[1558] Purchase history (e.g. details of products purchased on an online shopping site)

[1559] Review viewing history (e.g., the content and ratings of reviews you have read)

[1560] Furthermore, the device is equipped with a camera and microphone, which can analyze the user's facial expressions and voice in real time to collect emotional data, which indicates the user's emotional state, such as joy, sadness, or excitement.

[1561] Data Transfer

[1562] The device transfers the collected behavioral and emotional data to a server using a secure protocol (e.g., HTTPS). This transfer occurs periodically to ensure the security of the communication.

[1563] Data analysis

[1564] The server receives the transferred behavioral and emotional data and stores it in a secure database, which also stores the user's past data.

[1565] The server runs a generative AI model and an emotion engine. The generative AI model analyzes behavioral data and extracts user characteristics (e.g., interest in technology gadgets). Meanwhile, the emotion engine analyzes emotional data and can grasp the user's emotional state in real time. This allows a detailed persona to be formed for each user.

[1566] Ad Generation

[1567] The server automatically generates the creative elements of the ad (copy, design, placement, etc.) based on the generated persona and real-time emotional state. Different ad variations can also be configured for A / B testing using the generative AI model. The content of the ad dynamically changes depending on the user's emotional state.

[1568] Ad serving

[1569] The server's ad delivery engine determines the optimal timing and location for delivery based on the user's behavioral patterns and emotional state, for example, delivering a positive, energetic advertising message when the user is excited.

[1570] Advertisements are displayed on users' devices (smartphones and PCs) at the time when users are most likely to respond to them.

[1571] Measurement and optimization

[1572] The server monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. It analyzes the results of AB tests to identify the most effective ad pattern. It also analyzes how the user's emotional state affects the effectiveness of ads, thereby optimizing ad generation and delivery for future ads.

[1573] Specific examples

[1574] As a concrete example, consider the case of User B. User B searches for the "latest smartphone" on his / her smartphone, browses products on multiple online shops, and purchases one of them. While browsing smartphone reviews on a review site, the device's camera captures User B's facial expressions and acquires emotion data.

[1575] The server analyzes User B's search history, purchase history, review site browsing history, and emotional data to determine that User B has a high interest in technology gadgets and was excited about them while making purchases. Using a generative AI model, it creates a persona for User B to reflect his interest in new gadgets and reviews, as well as his emotional state.

[1576] Based on User B's persona and emotional state, a generative AI model automatically generates a new smartphone ad. Multiple ad variations with different taglines and designs are created and set up for A / B testing. Include a more dynamic ad design that takes into account the user's excitement level.

[1577] The server identifies the time period and emotional state when User B is most likely to respond to an advertisement, and delivers the optimal advertisement variation at that timing. For example, if User B is in an excited state, it delivers a video advertisement with energetic music.

[1578] A new smartphone video ad is delivered to User B's smartphone during commuting hours. The server monitors the click-through rate and conversion rate of the delivered ad in real time to identify the most effective ad pattern. Based on the results, it optimizes ad delivery from the next time onwards. It also analyzes how the user's emotional state affected the effectiveness of the ad and reflects this in future ad generation.

[1579] Prompt Sentence Examples

[1580] Based on the behavioral history and emotional data of users searching for "latest smartphones," please generate ads with the following conditions:

[1581] An energetic design that matches the user's excitement

[1582] Multiple copy and design variations for A / B testing

[1583] Delivered during the user's commute time

[1584] The embodiments of the present invention have been described in detail above.

[1585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1586] Step 1: Data collection

[1587] Device:

[1588] When a user uses a web browser or application, behavioral data such as search history, browsing history, purchase history, and review browsing history is collected. Furthermore, facial expression and voice data is collected using the device's camera and microphone, and emotional data is obtained. The input is the user's operational behavior and the device's sensor data. The output is the collected behavioral data and emotional data.

[1589] Specifically, when a user searches for "the latest smartphone" on a smartphone browser, browses multiple online shops, and reads reviews, the system collects their browsing history. The system also captures emotion data from the user's facial expressions captured by a camera while they are reading the reviews.

[1590] Step 2: Data Transfer

[1591] Device:

[1592] The collected behavioral and emotional data is sent to a server using a secure protocol (e.g., HTTPS). As input, the data collected in step 1 is used. As output, the data sent to the server is obtained.

[1593] Specifically, the device generates data packets at regular intervals, encrypts them, and sends them to the server, along with emotion data.

[1594] Step 3: Data analysis

[1595] server:

[1596] The received behavioral and emotional data is stored in a database on the server. Then, this data is analyzed using a generative AI model and an emotion engine. As input, the data sent in step 2 is used. As output, analysis results are obtained to understand the user's characteristics and emotional state.

[1597] Specifically, the generative AI model analyzes the user's search and purchase history to extract characteristics such as "this user tends to get excited about new gadgets." The emotion engine also analyzes the user's emotional data and determines in real time that "the user was in a highly excited state when looking at product reviews."

[1598] Step 4: Generate Ads

[1599] server:

[1600] Based on the generated personas and real-time emotional states, the generative AI model automatically generates the creative elements of the ad (copy, design, placement, etc.). It generates different ad variations for AB testing. It uses the user characteristics and emotional states obtained in step 3 as input. The generated ad variations are obtained as output.

[1601] For example, when generating an advertisement for the latest smartphone, the system creates several variations of the advertisement with dynamic effects to stimulate users' excitement. For A / B testing, it generates ad variations with different catchphrases and designs.

[1602] Step 5: Ad serving

[1603] server:

[1604] The ad serving engine determines the optimal timing and location of delivery based on the user's behavioral patterns and emotional state. As input, it uses the ad variations generated in step 4 and previous user behavior and emotional state data. The output is the ad to be delivered and its timing.

[1605] Device:

[1606] The generated advertisement is delivered to the user's terminal at a time and place determined by the server.

[1607] Specifically, while User B is reading the news on his smartphone during his commute, a video advertisement for the "latest smartphone" with dynamic effects and energetic music is displayed.

[1608] Step 6: Measure and optimize

[1609] server:

[1610] It monitors the effectiveness of delivered ads (click-through rate, conversion rate, etc.) in real time. It analyzes the results of AB tests to identify the most effective ad pattern. It also analyzes how the user's emotional state affected the effectiveness of the ad. As input, it uses performance data of the ad being delivered and real-time user emotional data. As output, it obtains an optimized ad generation and delivery strategy.

[1611] Specifically, the system analyzes in real time the number of clicks on ads and the number of times they lead to subsequent purchases, and gains insights such as "advertisements delivered when users are excited have a higher click rate." Based on these findings, it optimizes ad generation and delivery for future ads.

[1612] The above is the specific processing flow of the system program.

[1613] (Application example 2)

[1614] 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."

[1615] Conventional ad delivery systems generate and deliver ads based on user behavior data, but do not take the user's emotional state into account, limiting the effectiveness of the ads. Furthermore, they lack targeting accuracy and real-time optimization, making it difficult to attract user attention.

[1616] 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.

[1617] In this invention, the server includes means for analyzing user behavioral data and emotional data to grasp user characteristics, means for automatically generating advertisements based on the analyzed user characteristics and emotional state, and means for delivering optimal advertisements based on the user's behavioral patterns and emotional state, thereby enabling targeting that takes into account the user's emotional state and real-time advertisement optimization.

[1618] "User behavior data" refers to information such as search history, browsing history, and purchase history when a user uses a web browser or application.

[1619] "Emotion data" is data obtained from the user's facial expressions, voice, etc., and reflects the user's emotional state (joy, sadness, excitement, etc.).

[1620] "User characteristics" refer to the user's interests, preferences, and behavioral patterns extracted based on the analyzed user's behavioral data and emotional data.

[1621] "Automatic ad generation" refers to the automatic generation of creative elements of an ad (copy, design, placement, etc.) using generative AI based on user characteristics and emotional state.

[1622] "Multiple ad variations" are ad variations with different copy and design, used to conduct A / B testing.

[1623] "AB testing" is a method of comparing and verifying the effectiveness of multiple advertising patterns to identify the most effective one.

[1624] "Advertisement delivery" refers to delivering the generated advertisement at the optimal timing based on the user's behavioral patterns and emotional state.

[1625] "Monitoring" means monitoring the effectiveness of delivered advertisements (click-through rate, conversion rate, etc.) in real time.

[1626] "Optimization" is the process of adjusting advertising content and delivery timing based on monitoring results to maximize advertising effectiveness.

[1627] The present invention relates to a system that collects and analyzes user behavioral data and emotional data, and generates and distributes personalized advertisements. A specific embodiment of this system is described below.

[1628] System Configuration

[1629] 1. Data Collection

[1630] Device: A user's smartphone records search history, browsing history, and purchase history when using a web browser or application. The device also uses a camera and microphone to collect facial and voice data and obtain emotional data. To do this, the device uses camera sensors and voice recognition software (e.g., Google APIs for Computer Vision and Emotion AI).

[1631] Server: Collected behavioral and emotional data is transferred to the server and stored in a secure database (e.g., AWS RDS, Google Cloud SQL).

[1632] 2. Data Analysis

[1633] Server: The server analyzes the collected behavioral and emotional data as follows:

[1634] Generative AI (e.g., OpenAI GPT-4) is used to perform detailed analysis of behavioral data and extract user characteristics.

[1635] Using an emotion engine (e.g., IBM Watson Tone Analyzer), emotion data is analyzed to understand the user's real-time emotional state.

[1636] 3. Ad generation

[1637] Server: The server automatically generates the creative elements of the ad (copy, design, placement, etc.) based on the generated user characteristics and emotional state. Using generative AI, multiple ad variations are created and prepared for A / B testing.

[1638] 4. Advertisement Delivery

[1639] Server: The server uses an ad serving engine (e.g., AdRoll, Google Ads API) to deliver ads at the optimal time and place based on the user's behavioral patterns and emotional state.

[1640] Device: Real-time personalized ads are displayed on the user's smartphone, taking into account the user's emotional state, for example, delivering energetic ads when they are excited.

[1641] 5. Measurement and optimization

[1642] Server: The server monitors and analyzes the effectiveness of the delivered advertisements in real time. For example, it measures click-through rates and conversion rates and identifies the most effective advertisement patterns. It optimizes the content and timing of advertisements based on the monitoring results.

[1643] Specific examples

[1644] For user C:

[1645] Data collection: User C searches for "new running shoes" on their smartphone and browses multiple running-related blog articles and reviews. The smartphone camera also collects User C's facial expressions and voice data, recording their emotional data.

[1646] Data analysis: The server analyzes user C's search history, browsing history, and emotional data and finds out that user C has a high interest in fitness products, especially running, and has positive emotions.

[1647] Ad generation: Based on the generated persona and emotional state of User C, an advertisement for a new running shoe is automatically generated. Multiple advertisement variations with different catchphrases and designs are created and prepared for A / B testing.

[1648] Advertisement delivery: When User C is excited after his morning jog, a video advertisement with energetic music is delivered to his smartphone.

[1649] Effectiveness measurement and optimization: Monitor click-through rates and conversion rates of delivered ads in real time, identify the most effective ad patterns, and reflect them in future ad deliveries.

[1650] Prompt Sentence Examples

[1651] For example, input the following prompt into your generative AI model:

[1652] User C has a strong interest in fitness, especially running. His emotional data shows that he feels excited after jogging. Based on this data, we generate ad copy and design variations that emphasize the appeal of running shoes. For example, we could create an ad that includes the following content:

[1653] 1. "New running shoes that offer the best running experience!"

[1654] 2. "Break your personal best in your next race! These running shoes make it possible."

[1655] Also consider what will excite User C even more.

[1656] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1657] Step 1:

[1658] The device collects user behavior data (search history, browsing history, purchase history, etc.). This includes the user's actions when using a web browser or application, and the product browsing history in an online shop. The collected data is stored in an initial database on the smartphone, ready to be transferred to the server.

[1659] Step 2:

[1660] The device collects the user's emotional data. It uses a camera and microphone to capture the user's facial expressions and voice data, and analyzes their emotional state (happiness, sadness, excitement, etc.) based on this data. For this purpose, it uses, for example, Google APIs for Computer Vision and Emotion AI. The collected emotional data is also temporarily stored on the smartphone.

[1661] Step 3:

[1662] The device transfers behavioral and emotional data to the server via secure communication. The data is protected using security protocols such as Transport Layer Security (TLS). The server receives the data and stores it in a secure database (e.g., AWS RDS or Google Cloud SQL).

[1663] Step 4:

[1664] The server uses generative AI (e.g., OpenAI GPT-4) to analyze user behavioral data and extract user characteristics. Here, the server identifies the user's interests, preferences, and behavioral patterns from past search history, browsing history, and purchase history. Based on the input behavioral data, it performs text analysis and pattern recognition.

[1665] Step 5:

[1666] The server analyzes the user's emotional data using an emotion engine (e.g., IBM Watson Tone Analyzer) to understand the user's current emotional state. It identifies the user's real-time emotional state through facial expression analysis and voice analysis and records it along with the user's characteristics.

[1667] Step 6:

[1668] The server uses generative AI to automatically generate ads based on analyzed user characteristics and emotional states. The generative AI creates multiple ad variations, including different copy and designs. The generated ads are then set up for A / B testing.

[1669] Step 7:

[1670] The server uses an ad distribution engine (e.g., AdRoll, Google Ads API) to distribute ads at optimal times based on the user's behavioral patterns and emotional state. For example, it adjusts the distribution of energetic ads when the user is in an excited state.

[1671] Step 8:

[1672] The device displays the delivered advertisements to the user in real time, allowing the user to interact with the advertisement at the time when they are most likely to respond.The advertisements are displayed using the smartphone's notification function and in-app banner ads.

[1673] Step 9:

[1674] The server monitors the click-through rate and conversion rate of the delivered ads in real time. Here, data analysis tools (e.g., Google Analytics) are used to evaluate the effectiveness of the ads. The results of the AB tests are analyzed to identify the most effective ad patterns.

[1675] Step 10:

[1676] The server optimizes the ad content and delivery timing based on the monitoring results, giving priority to highly effective ad patterns and reflecting this in the next ad generation and delivery, thereby maximizing advertising ROI.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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).

[1684] 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.

[1685] 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."

[1686] 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.

[1687] 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).

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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.

[1698] The following is further disclosed regarding the above embodiment.

[1699] (Claim 1)

[1700] a means for collecting user behavior data;

[1701] A means for analyzing the collected behavioral data and understanding user characteristics;

[1702] means for automatically generating advertisements based on user characteristics;

[1703] means for delivering the generated advertisement to a user;

[1704] A means of monitoring and optimizing the effectiveness of advertising;

[1705] A system including:

[1706] (Claim 2)

[1707] 10. The system according to claim 1, further comprising means for generating a plurality of advertisement patterns based on user characteristics and conducting an AB test with the advertisement patterns.

[1708] (Claim 3)

[1709] 2. The system according to claim 1, further comprising means for determining a timing for delivering the generated advertisement based on a behavioral pattern of the user.

[1710] "Example 1"

[1711] (Claim 1)

[1712] a means for collecting user behavior data;

[1713] A means for analyzing the collected behavioral data and understanding user characteristics;

[1714] A means for using a generative AI model to automatically generate advertisements based on user characteristics;

[1715] means for delivering the generated advertisement to a user at an appropriate time;

[1716] A means of monitoring and optimizing the effectiveness of advertising;

[1717] A system including:

[1718] (Claim 2)

[1719] 10. The system according to claim 1, further comprising means for generating a plurality of advertisement patterns based on user characteristics and conducting an AB test with the advertisement patterns.

[1720] (Claim 3)

[1721] 2. The system according to claim 1, further comprising means for determining a timing for delivering the generated advertisement based on a behavioral pattern of the user.

[1722] "Application Example 1"

[1723] (Claim 1)

[1724] a means for collecting user behavior data;

[1725] A means for analyzing the collected behavioral data and understanding user characteristics;

[1726] means for automatically generating advertisements based on user characteristics;

[1727] means for delivering the generated advertisement to a user;

[1728] A means of monitoring and optimizing the effectiveness of advertising;

[1729] means for collecting user gaze data;

[1730] A means for analyzing behavioral data including gaze data and grasping user characteristics with high accuracy;

[1731] means for displaying advertisements on a smart display device;

[1732] A system including:

[1733] (Claim 2)

[1734] 10. The system according to claim 1, further comprising means for generating a plurality of advertisement patterns based on user characteristics and conducting an AB test with the advertisement patterns.

[1735] (Claim 3)

[1736] 2. The system according to claim 1, further comprising means for determining a timing for delivering the generated advertisement based on a behavioral pattern of the user.

[1737] "Example 2: Combining Emotion Engines"

[1738] (Claim 1)

[1739] a means for collecting user behavior data;

[1740] means for collecting user emotion data;

[1741] means for transferring the collected behavioral data and emotion data to a server;

[1742] means for analyzing the transferred behavioral data and emotional data to grasp the user's characteristics and emotional state;

[1743] means for automatically generating advertisements based on user characteristics and emotional states;

[1744] means for delivering the generated advertisements based on the user's behavioral patterns and emotional state;

[1745] A means of monitoring and optimizing the effectiveness of advertising;

[1746] A system including:

[1747] (Claim 2)

[1748] 2. The system of claim 1, wherein a plurality of advertising patterns are generated based on user characteristics and emotional states, and AB testing is performed on the advertising patterns.

[1749] (Claim 3)

[1750] 10. The system of claim 1, wherein the timing of delivery of the generated advertisement is determined based on the user's behavioral patterns and emotional state.

[1751] "Application example 2 when combining emotion engines"

[1752] (Claim 1)

[1753] a means for collecting user behavior data;

[1754] A means for analyzing the collected behavioral data and emotional data to understand user characteristics;

[1755] means for automatically generating advertisements based on the analyzed user characteristics and emotional state;

[1756] A method for conducting AB tests on the multiple ad patterns generated and identifying the optimal ad;

[1757] means for delivering optimal advertisements based on the user's behavioral patterns and emotional state;

[1758] A means to monitor the effectiveness of advertising in real time and optimize the content and timing of advertising,

[1759] A system including:

[1760] (Claim 2)

[1761] 2. The system according to claim 1, further comprising means for acquiring emotion data by collecting facial expression and voice data of the user.

[1762] (Claim 3)

[1763] 10. The system according to claim 1, further comprising means for determining the timing of advertisement delivery based on emotion data. [Explanation of symbols]

[1764] 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. a means for collecting user behavior data; A means for analyzing the collected behavioral data and understanding user characteristics; means for automatically generating advertisements based on user characteristics; means for delivering the generated advertisement to a user; A means of monitoring and optimizing the effectiveness of advertising; A system including:

2. The system according to claim 1 , further comprising means for generating a plurality of advertisement patterns based on user characteristics and conducting an AB test with the advertisement patterns.

3. 2. The system according to claim 1, further comprising means for determining a distribution timing of the generated advertisement based on a behavioral pattern of the user.

Images