System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting historical user behavior; means for analyzing the historical user behavior and generating advertising content; means for optimizing the generated advertising content based on a user profile; and means for delivering the optimized advertising content.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] In recent years, the inclinations and interests of Internet users have become more diverse, making it necessary to frequently review the content of advertisements, which increases the effort and cost involved. Furthermore, conventional advertising systems have difficulty generating and delivering advertisements based on a user's latest interests and behavioral history, making effective targeting difficult. The present invention aims to solve these problems and maximize the effectiveness of advertisements. [Means for solving the problem]

[0005] The present invention provides a system including a means for collecting user behavior history, a generating means for analyzing the user behavior history and generating advertising content, a targeting means for optimizing the advertising content generated by the generating means based on a user profile, and a means for delivering the advertising content optimized by the targeting means, which can efficiently generate and deliver advertisements based on the user's latest preferences and maximize conversion rates.

[0006] "User behavior history" refers to data such as visited pages, clicks, search queries, and viewed content that is recorded when a user uses a website or app.

[0007] The "generation means" refers to the process and technology for analyzing the user behavior history and generating advertising content such as advertising phrases, images, videos, and music.

[0008] "Targeting methods" refers to the processes and technologies that optimize generated advertising content based on user profiles and customize the timing and content of delivery.

[0009] "Delivery Method" refers to the process and technology that displays optimized advertising content on websites and apps used by users.

[0010] A "user profile" is information that indicates the characteristics and tendencies of an advertising target, created based on data such as the user's behavioral history, interests, and preferences.

[0011] "Advertising Content" refers to information in various forms, including advertising phrases, images, videos, and music, delivered to users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0020] [First embodiment]

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

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

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

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

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

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

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

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

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0033] This invention shows an embodiment of a system that collects user behavior history and generates, optimizes, and delivers advertising content based on that data. The specific processing of the program is explained below, along with specific examples.

[0034] Program processing overview

[0035] 1. Collecting user behavior history

[0036] When a device uses a website or app, page visits, clicks, search queries, and content viewed are tracked in real time. This behavioral data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0037] 2. Generating advertising content using the generative AI module

[0038] The server extracts the latest user behavior history data from the database and inputs it into the generation AI module, which performs the following processes:

[0039] Analyzing data to identify user preferences and interests.

[0040] Based on the identified preferences, advertising phrases, images, videos, and music are generated.

[0041] For example, if a user frequently visits sports-related websites and reads many articles about sporting goods, the generative AI module will generate sporting goods ads for that user.

[0042] 3. Optimization with AGI targeting module

[0043] The server receives the ad content output from the Generative AI module and sends it to the AGI Targeting module, which performs the following tasks:

[0044] Conduct deeper analysis of user behavioral history to create detailed user profiles.

[0045] Optimize the content and timing of generated ads based on user profiles.

[0046] For example, if a particular user consumes a lot of sports-related content at night, the AGI targeting module will adjust ads to appear at that time.

[0047] 4. Delivery of advertisements

[0048] The server sends the final generated ad content to the ad delivery server, which does the following:

[0049] Upload the optimized ads to each distribution platform.

[0050] Set ad delivery schedules based on user activity times and platform characteristics.

[0051] The device receives advertisements from the ad distribution system and displays them on websites and within apps that the user is using.

[0052] Specific examples

[0053] A user has a habit of buying sports equipment. He frequently browses sports-related websites and videos. One evening, he browses reviews of running shoes. The device records this behavior and sends it to the server. The server stores the data in a database.

[0054] The server then sends this data to a generative AI module, which generates a promotional video with the phrase "30% off the latest running shoes!" The server then sends the information to an AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0055] The server sends the ad to the ad delivery server, which then sets it up so that the ad is displayed on social media overnight. That evening, when the user checks social media, an optimized ad for running shoes appears, and the user clicks on it and purchases the product.

[0056] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the latest behavioral history of a user.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] While the device is using websites and apps, it tracks user activity in real time, including page visits, clicks, search queries, and content viewed, and this data is sent in encrypted form to a server.

[0060] Step 2:

[0061] The server stores the received user behavior data in a database, including user IDs, page access history, click history, search queries, and viewed content.

[0062] Step 3:

[0063] The server periodically extracts user behavior history from the database and sends it to the generation AI module. This process can be done in batch or real-time.

[0064] Step 4:

[0065] The generative AI module analyzes the received user behavior history and performs data mining to identify user preferences and interests, then generates advertising phrases, images, videos, and music based on the identified preferences.

[0066] Step 5:

[0067] The server stores the generated advertising content in temporary storage, which includes specific advertising phrases, images, video, and music files.

[0068] Step 6:

[0069] The server retrieves the ad content from temporary storage and sends it to the AGI targeting module, along with user behavior data and the generated ad content.

[0070] Step 7:

[0071] The AGI targeting module creates a detailed user profile based on user behavior history and generated ads, including user interests, behavioral patterns, and time of day.

[0072] Step 8:

[0073] The AGI targeting module optimizes ads based on user profiles, determining when ads are delivered, customizing their content, and on which platforms they appear.

[0074] Step 9:

[0075] The server sends the optimized advertisement content to the advertisement delivery server, which then uploads these advertisements to each distribution platform.

[0076] Step 10:

[0077] The ad delivery server sets an ad delivery schedule based on the user's activity time and the platform's characteristics. For example, if a user is more active at night, the server can set the ad delivery schedule to be delivered during that time.

[0078] Step 11:

[0079] The device receives advertisements sent from the ad distribution system and displays them on websites and apps that the user uses. The advertisements are customized based on the user's behavioral history and an optimized user profile.

[0080] Step 12:

[0081] The device again tracks the user's response to the ad (clicks, viewing time, purchases, etc.) and sends this data to a server to evaluate the effectiveness of the ad.

[0082] Step 13:

[0083] The server analyzes the received response data and evaluates the effectiveness of the advertisement. The results of this evaluation are fed back to the generation AI module and AGI targeting module, and are used for the next advertisement generation and targeting.

[0084] Example 1

[0085] 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."

[0086] Conventional ad delivery systems have had difficulty generating and optimizing ad content by effectively utilizing user behavioral history. In addition, there was a lack of means to optimize the timing and content of ad delivery based on user profiles, making it difficult to achieve effective ad delivery.

[0087] 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.

[0088] In this invention, the server includes a means for collecting user behavior history, a generation means for analyzing the user behavior history and generating advertising content, and a targeting means for optimizing the advertising content generated by the generation means based on a user profile, thereby enabling the generation, optimization, and distribution of effective advertisements based on the user behavior history.

[0089] "User behavior history" is data that records the actions a user takes while using a website or app, such as page visits, clicks, search queries, and content viewed.

[0090] The "generation means" is a means for analyzing user behavior history and generating advertising content based on the analysis.

[0091] The "targeting means" is a means for optimizing the advertising content generated by the generating means based on a user profile.

[0092] "Delivery means" refers to a means for delivering advertising content optimized by targeting means to users.

[0093] A "generative AI model" is a module that uses artificial intelligence technology to analyze data and generate content such as advertising phrases, images, videos, and music.

[0094] A "user profile" is a detailed profile formed based on a user's behavioral history that identifies the user's preferences and interests.

[0095] This invention relates to an embodiment of a system that collects user behavior history and generates, optimizes, and delivers advertising content based on that data. Specific implementation methods of this invention are described below.

[0096] The system uses a server, terminals, and database as hardware, and a generative AI model, a data analysis module, an AGI targeting module, and an ad delivery module as software.

[0097] Collection of user behavior history

[0098] When a device uses a website or app, page accesses, clicks, search queries, viewed content, and other activities are tracked in real time. The tracked behavioral data is sent to a server in encrypted form. The server stores the received data in a database and retains it for subsequent analysis. Specifically, the device uses a JavaScript tracking script to collect user behavioral data and sends it to the server as an encrypted HTTP request.

[0099] Advertising content generation

[0100] The server extracts the latest user behavior history data from the database and inputs it into a generative AI model. The generative AI model analyzes the data and identifies user preferences and interests. Based on the identified preferences, it generates advertising phrases, images, videos, and music. Specifically, the server uses a Python script to extract data using SQL queries and sends an API request to a generative AI model (e.g., OpenAI's GPT-3) to generate advertising content.

[0101] Ad content optimization

[0102] The server receives the advertising content output by the generative AI model and sends it to the AGI targeting module, which then performs a deeper analysis of the user's behavioral history to create a detailed user profile. Based on this profile, the content and timing of the generated ads are optimized. Specifically, the server performs data analysis using Python analytical libraries (e.g., Pandas and Scikit-learn), and the AGI targeting module configures ad delivery based on the user's online time.

[0103] Ad serving

[0104] The server finally sends the generated and optimized ad content to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform and sets the ad delivery schedule based on the user's activity time and platform characteristics. For example, the server uses a REST API to send ad data to the ad delivery server, which then uploads it to Google Ads or social media platforms (e.g., Facebook Ads). The device displays the ads received from the ad delivery system on the website or app the user is using.

[0105] Specific examples

[0106] A user has a habit of buying sports equipment. He frequently browses sports-related websites and videos. One evening, he browses reviews of running shoes. The device records this behavior and sends it to the server, which stores the data in a database.

[0107] The server then sends this data to a generative AI model, which generates a promotional video with the phrase "30% off the latest running shoes!" The server then sends this information to an AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0108] The server then sends the ad to the ad delivery server, which then configures it to be displayed on social media overnight. When the user checks social media that evening, the optimized running shoe ad appears, and the user clicks on it to purchase the product.

[0109] Prompt Sentence Examples

[0110] Examples of prompts to input to a generative AI model might include:

[0111] "Generate ads for users who have recently visited a sports-related website."

[0112] "Create a promotional video for 30% off running shoes."

[0113] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the latest behavioral history of a user.

[0114] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0115] Step 1:

[0116] The device tracks real-time behavioral data when a user uses a website or app, including page visits, clicks, search queries, and content viewed. Specifically, a JavaScript tracking script running on the device collects this behavioral data and sends it to a server as an encrypted HTTP request.

[0117] Input: User behavior data (page visits, clicks, search queries, etc.)

[0118] Output: Data in the form of an encrypted HTTP request

[0119] Step 2:

[0120] The server receives the encrypted data sent from the terminal and stores it in a database. Specifically, the server receives the encrypted data via the HTTPS protocol and executes an SQL query to store it in the database.

[0121] Input: Behavioral data in the form of encrypted HTTP requests

[0122] Output: User behavior data stored in a database

[0123] Step 3:

[0124] The server extracts the latest user behavior history data from the database and sends it to the generative AI model. Specifically, the server executes SQL queries using Python scripts and sends API requests to the generative AI model (e.g., OpenAI's GPT-3).

[0125] Input: User behavior history data stored in a database

[0126] Output: API request data to the generative AI model

[0127] Step 4:

[0128] The generative AI model analyzes the received user behavior history data to identify user preferences and interests. Based on the results, it generates advertising phrases, images, videos, and music. Specifically, the generative AI model generates advertising content using data analysis algorithms.

[0129] Input: API request data to the generative AI model

[0130] Output: Generated advertising content (advertising phrase, images, videos, music)

[0131] Step 5:

[0132] The server receives the ad content output by the generative AI model and sends it to the AGI targeting module, which then performs further analysis of the user's behavioral history data to create a detailed user profile. Specifically, the server uses Python analytical libraries (such as Pandas and Scikit-learn) to analyze the data and optimize the content and timing of ads.

[0133] Input: Generated ad content

[0134] Output: Detailed user profile and optimized ad delivery settings

[0135] Step 6:

[0136] The server sends the final generated and optimized ad content from the generative AI model and AGI targeting module to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform and sets the ad delivery schedule based on user activity times and platform characteristics. Specifically, the server sends data to the ad delivery server using a REST API.

[0137] Input: Final generated and optimized ad content and delivery settings

[0138] Output: Uploaded ads to distribution platforms

[0139] Step 7:

[0140] The device receives the ads from the ad distribution system and displays them on the website or app the user is using. Specifically, the device renders the ads based on the data it receives and displays them in the appropriate location.

[0141] Input: Advertising data received from the distribution platform

[0142] Output: Ads displayed on websites and in apps

[0143] This is the process flow of this system. At each step, the input data is processed and analyzed, and ultimately optimized advertising content is displayed, achieving effective ad delivery based on the user's behavioral history.

[0144] (Application example 1)

[0145] 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."

[0146] In conventional ad distribution systems, ads are generated and displayed based on a user's behavioral history, but because they are distributed uniformly without considering the detailed profiles of each individual user, the effectiveness of the ads is limited and targeting is insufficient.In addition, because the display device and timing of ads are not optimized, it is difficult to attract users' attention.

[0147] 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.

[0148] In this invention, the server includes means for collecting user behavior history, means for analyzing the user behavior history and generating advertising content, means for optimizing the advertising content generated by the generation means based on a user profile, means for delivering the advertising content optimized by the targeting means, means for delivering the generated advertising content to a display device based on the behavior history of a specific user, and means for displaying the delivered advertising content on the user's operating terminal and receiving responses. This makes it possible to optimize the timing and content of advertising display based on the detailed profile of each individual user, and to deliver advertising according to the display device.

[0149] "User behavior history" is a record of a user's actions and operations on the Internet, including page accesses, clicks, search queries, and viewed content.

[0150] "Generation means" refers to a combination of equipment and software for analyzing user behavior history and generating advertising content.

[0151] "Targeting tools" are any combination of devices and software used to optimize advertising content generated based on user profiles.

[0152] "Delivery means" refers to the combination of equipment and software used to actually deliver advertising content optimized by targeting means to users.

[0153] "Display device" refers to the user's operating terminal on which advertising content is displayed, and specifically includes electronic devices such as smartphones, tablets, and personal computers.

[0154] An "operation terminal" is an electronic device that is actually operated by a user and on which advertising content is displayed.

[0155] "Generation Module" means a specific software module for generating advertising phrases, images, videos, and music.

[0156] A "user profile" is a collection of data detailing a user's attributes and preferences, created based on user behavior history and other related information.

[0157] This invention describes a system that collects "user behavior history" and generates, optimizes, and delivers advertising content based on that data. To implement this invention, server, terminal, and user components, as well as related hardware and software, are required.

[0158] Program processing overview

[0159] 1. Collecting user behavior history

[0160] When a user uses a website or application, their device tracks real-time behavioral data, such as page visits, clicks, search queries, and content viewed. This behavioral data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0161] 2. Generating advertising content using the generative AI module

[0162] The server extracts the latest user behavior history data from the database and inputs it into the generative AI module, which analyzes the data to identify user preferences and interests, and then generates advertising phrases, images, videos, and music based on the identified preferences.

[0163] 3. Optimization with AGI targeting module

[0164] The server receives the advertising content output by the generation AI module and sends it to the AGI targeting module, which then performs a deeper analysis of the user's behavioral history to create a detailed user profile. Based on the user profile, the content and timing of the generated advertisements are optimized.

[0165] 4. Delivery of advertisements

[0166] The server sends the final generated advertisement content to the advertisement delivery server, which delivers the optimized advertisement to the specified display device. The advertisement is displayed on the user's operating device and records how the user responded to the advertisement.

[0167] Hardware and software used

[0168] Server: Carries out a series of processes including data collection, storage, analysis, content generation, optimization, and distribution. Specifically, it includes a database server that encrypts and stores user behavior history data, and a processing server that runs the generative AI module and AGI targeting module.

[0169] Generative AI module: Uses the Generative AI class to generate advertisements based on user behavior history data.

[0170] AGI Targeting Module: Uses the AGIModule class to optimize ads based on detailed user profiles.

[0171] User device: The device on which the ad is displayed and the user's interactions are recorded. This includes electronic devices such as smartphones, tablets, and computers.

[0172] Specific examples

[0173] Here's a concrete example: Consider a user who frequently buys sports equipment and frequently visits sports-related websites. One day, the user browses reviews of running shoes. The device records this behavior and sends it to the server. The server stores the data in a database and feeds it into the generative AI module.

[0174] The generative AI module generates the phrase "30% off the latest running shoes!" and a promotional video. The server then sends that information to the AGI targeting module, which analyzes the user's detailed profile. For example, it discovers that the user tends to be online at night. The server sends the ad to the ad delivery server and sets it up to be displayed on social media at night. When the user checks social media that evening, an optimized ad for running shoes appears. The user clicks on the ad and purchases the product.

[0175] Prompt Sentence Examples

[0176] Prompt: User behavior: Searched for "latest running shoes." Generate appropriate ad content.

[0177] Thus, a specific system for implementing the present invention and its operation will be described.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] Collection of user behavior history

[0181] The input used is behavioral data (page accesses, clicks, search queries, viewed content, etc.) from the user's operating device (e.g., smartphone or PC). When the device is using a website or application, the behavioral data is tracked in real time and sent in encrypted form to a server. The server stores the received data in a database and keeps it for later analysis. In this process, it is important to accurately collect user behavior patterns.

[0182] Step 2:

[0183] Generative AI module generates advertising content

[0184] The server extracts the latest user behavior history data from the database and sends it as input to the generation AI module. The generation AI module analyzes this behavior history data to identify the user's preferences and interests. It then generates advertising phrases, images, videos, and music based on the user's identified preferences. For example, the generated advertising phrase might be "30% off the latest running shoes!" The output of this step is the generated advertising content.

[0185] Step 3:

[0186] Optimization with AGI targeting module

[0187] The server receives the ad content output from the generation AI module and sends it as input to the AGI targeting module, which then performs a more detailed analysis of the user's behavioral history to create a detailed user profile. Based on this profile, the content and timing of the generated ads are optimized. For example, if a particular user tends to be online at night, ads can be displayed at that time. The output of this step is optimized ad content.

[0188] Step 4:

[0189] Ad serving

[0190] The server sends the optimized advertising content using the AGI targeting module to the advertising delivery server. The advertising delivery server delivers the optimized advertisements to the user's operating device (for example, a display device such as a smartphone or PC). The advertisements are displayed within the application or website the user is using. In addition, data on users' clicks and responses to the advertisements is collected and sent to the server. This makes it possible to evaluate the effectiveness of the advertisements based on user responses.

[0191] Through this series of processes, advertisements generated based on the user's behavioral history are delivered to each individual user at the optimal time and in the optimal way.

[0192] 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.

[0193] This invention shows an embodiment of a system that generates, optimizes, and delivers advertising content by combining user behavior history and user emotion data. The specific processing of the program is explained below, along with specific examples.

[0194] Program processing overview

[0195] 1. Collecting user behavior history

[0196] While a device is using a website or app, it tracks user activity in real time, including page visits, clicks, search queries, and content viewed. This data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0197] 2. Acquiring emotional data using the emotion engine

[0198] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and sends them to the emotion engine. It also analyzes emotions from the text the user types. This emotion data is sent to a server and integrated with the user's behavioral data.

[0199] 3. Generating advertising content using the generative AI module

[0200] The server extracts the latest user behavior history and emotion data from the database and inputs it into the generative AI module, which performs the following processes:

[0201] Analyze the data to identify user preferences, interests, and emotional states.

[0202] Generate advertising phrases, images, videos, and music based on identified preferences and emotional states.

[0203] For example, if a user frequently visits sports-related websites and also displays positive emotions while watching fitness videos, the generative AI module will generate ads for sports equipment for that user.

[0204] 4. Optimization with AGI targeting module

[0205] The server receives the ad content output from the Generative AI module and sends it to the AGI Targeting module, which performs the following tasks:

[0206] Deeper analysis of user behavioral history and sentiment data creates detailed user profiles.

[0207] Optimize the content and timing of generated ads based on user profiles.

[0208] For example, if a particular user consumes a lot of fitness-related content at night and exhibits positive emotions, the AGI targeting module will adjust ads to appear at those times.

[0209] 5. Delivery of advertisements

[0210] The server sends the final generated ad content to the ad delivery server, which does the following:

[0211] Upload the optimized ads to each distribution platform.

[0212] Set ad delivery schedules based on user activity times and platform characteristics.

[0213] The device receives ads from the ad serving system and displays them within the websites and apps the user is using. The ads are customized based on the user's behavioral history, emotional data, and optimized user profile.

[0214] Specific examples

[0215] Example 1: User purchases sporting goods

[0216] A user frequently purchases sports equipment and frequently browses sports-related websites and videos. One evening, the user browses a review of running shoes and the camera captures a smile that expresses positive emotions. The device records this behavior and emotion and sends it to the server. The server stores the data in a database and then sends it to the generative AI module.

[0217] The generative AI module generates an advertising slogan and promotional video for the user, such as "Get 30% off the latest running shoes!" It also selects music that emphasizes enjoyment based on the user's emotional data. The server sends this information to the AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0218] The server sends the advertisement to the ad delivery server and configures it to be displayed on social media overnight. When the user checks social media that evening, an optimized ad for running shoes appears, the user clicks on the ad, and purchases the product.

[0219] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the user's behavior history and emotion data.

[0220] The processing flow will be explained below.

[0221] Step 1:

[0222] The device tracks real-time behavioral data such as page visits, clicks, search queries, and content viewed while users are using websites and apps, and this data is sent in encrypted form to a server.

[0223] Step 2:

[0224] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends the data to the emotion engine, which analyzes the user's emotions from this data and generates emotion data.If there is text input, it extracts emotion data from the text.

[0225] Step 3:

[0226] The server stores the received user behavior history data and emotion data in a database, including user IDs, page access history, click history, search queries, viewed content, and emotion data.

[0227] Step 4:

[0228] The server periodically extracts user behavior history and emotion data from the database and inputs it into the generative AI module. This process can be done in batch or real-time.

[0229] Step 5:

[0230] The generative AI module analyzes the received user behavior history and emotional data, performs data mining to identify the user's preferences and emotional state, and generates advertising phrases, images, videos, and music based on the analysis results.

[0231] Step 6:

[0232] The server stores the generated advertising content in temporary storage, which includes specific advertising phrases, images, video, and music files.

[0233] Step 7:

[0234] The server retrieves the ad content from temporary storage and sends it to the AGI targeting module, along with user behavior data, sentiment data, and the generated ad content.

[0235] Step 8:

[0236] The AGI targeting module uses user behavioral history and emotional data to create a detailed user profile, which includes the user's interests, behavioral patterns, active times, and emotional state.

[0237] Step 9:

[0238] The AGI targeting module optimizes ads based on user profiles, determining when ads are delivered, customizing their content, and on which platforms they appear.

[0239] Step 10:

[0240] The server sends the optimized advertisement content to the advertisement delivery server, which then uploads these advertisements to each distribution platform.

[0241] Step 11:

[0242] The ad delivery server sets an ad delivery schedule based on the user's activity time and the platform's characteristics. For example, if a user is more active at night, the server can set the ad delivery schedule to be delivered during that time.

[0243] Step 12:

[0244] The device receives the advertisements sent from the ad distribution system and displays them on the websites and apps the user is using. The advertisements are customized based on the user's behavioral history, emotional data, and optimized user profile.

[0245] Step 13:

[0246] The device again tracks the user's response to the ad (clicks, viewing time, purchases, etc.) and sends this data to a server to evaluate the effectiveness of the ad.

[0247] Step 14:

[0248] The server analyzes the received response data and evaluates the effectiveness of the advertisement. The results of this evaluation are fed back to the generation AI module and AGI targeting module, and are used for the next advertisement generation and targeting.

[0249] Example 2

[0250] 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."

[0251] Conventional ad delivery systems generate and deliver advertising content based on user behavior history, but they have difficulty reflecting the user's real-time emotional state, resulting in insufficient advertising effectiveness. Furthermore, they are unable to quickly respond to changes in user preferences and interests, resulting in insufficient ad optimization. Therefore, a new system is needed that can deliver ads effectively and attract user attention.

[0252] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting user behavior history, a means for acquiring user emotion data and integrating it with the user behavior history, a generation means for analyzing the user behavior history and emotion data and generating advertising content, a targeting means for optimizing the generated advertising content based on a user profile, and a means for delivering the optimized advertising content. This makes it possible to integrate the user behavior history and emotion data and deliver more effective and adaptive advertising.

[0253] "User behavior history" refers to the series of activities a user performs when using a website or app, such as page visits, clicks, search queries, and content viewed.

[0254] "Emotional data" refers to data that indicates a user's emotional state, such as data obtained from the user's facial expressions, voice, and input text.

[0255] "Generation means" refers to a module that has the function of analyzing user behavior history and emotional data and generating advertising content (e.g., advertising phrases, images, videos, music).

[0256] "Targeting means" refers to a module that has the function of optimizing the content and timing of display of generated advertising content based on user profiles.

[0257] "Delivery Means" refers to a module that has the function of delivering optimized advertising content to the platform used by the user.

[0258] "User Profile" refers to a data set that specifies a user's preferences and interests, generated from the user's behavioral history, emotional data, and other related data.

[0259] This invention shows an embodiment of a system that generates, optimizes, and delivers advertising content by combining user behavior history and user emotion data. The specific processing of the system is explained below, along with specific examples.

[0260] Collection of user behavior history

[0261] The device tracks behavioral data such as page accesses, clicks, search queries, and viewed content in real time while the user is using a website or app. To do this, a tracking code or SDK must be embedded, allowing detailed collection of user activity. The device encrypts the collected data and sends it to a server using a secure communication protocol such as HTTPS. The server stores the received data in a database, where it is used for analysis.

[0262] Acquiring emotion data

[0263] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It then uses natural language processing technology to analyze the text entered by the user and determine their emotions. The emotional data is anonymized and securely sent to the engine. The emotion engine receives and analyzes this data, and sends it to the server. The server then integrates this emotional data with the user's behavioral history and stores it in a database.

[0264] Advertising content generation

[0265] The server retrieves the latest user behavior history and emotional data from the database and inputs it into the generative AI module, which analyzes the data using deep learning models and other methods to identify the user's preferences, interests, and emotional state. It then uses GANs (generative artificial network) and language models to generate advertising phrases, images, videos, and music.

[0266] For example, if a user frequently visits a sports-related website and displays positive emotions while watching fitness videos, the generative AI module will generate a promotional video for that user, stating, "Get 30% off the latest running shoes!", along with music that emphasizes the fun factor.

[0267] Ad content optimization

[0268] The server sends the advertising content output by the generation AI module to the AGI targeting module, which then performs detailed analysis of the user's behavioral history and emotional data to generate a user profile. Based on this profile, the content and timing of advertising displays are optimized. For example, if a user often consumes fitness-related content at night, ads can be displayed at that time.

[0269] Ad serving

[0270] The server finally sends the generated and optimized ad content to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform (such as social media or video streaming services) and schedules and executes ad delivery based on the user's activity time and platform characteristics. The device receives the ads from the ad delivery system and displays them on the website or app the user is using.

[0271] Specific examples

[0272] Example 1: User purchases sporting goods

[0273] A user frequently purchases sports equipment and frequently browses sports-related websites and videos. One evening, the user looks at a review of running shoes and the camera captures a smile that expresses positive emotions. The device records this behavior and emotion and sends it to the server. The server stores the data in a database and sends it to the generative AI module. The generative AI module generates an advertising phrase such as "30% off the latest running shoes!" and a promotional video. It also selects music that emphasizes enjoyment based on the emotional data. The server sends this information to the AGI targeting module, which analyzes the user's detailed profile and discovers that the user tends to be online at night. The server sends the advertisement to the ad delivery server, which configures the advertisement to be displayed on social media at night. That evening, when the user checks social media, an optimized ad for running shoes appears, the user clicks on the ad, and purchases the product.

[0274] Examples of prompt statements

[0275] Examples of prompts for situations using generative AI models:

[0276] Please provide a list of URLs of websites that the user has visited frequently recently, along with their content types (e.g., sports, fitness), and the user's recent emotional state (e.g., positive, negative).

[0277] The above is a description of a specific embodiment of the system of the present invention, which enables effective advertisement delivery based on the user's behavior history and emotion data.

[0278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0279] Step 1: Collect user behavior history

[0280] The device tracks behavioral data such as page visits, clicks, search queries, and content viewed in real time while the user is using websites and apps.

[0281] The behavioral data collected by the device is encrypted and sent to a server using a secure communication protocol such as HTTPS. For example, the device collects data such as "a user read a specific sports article."

[0282] The server stores the received user behavior data in a database for use in subsequent processing steps.

[0283] Input: Data about specific user behavior on your website or app.

[0284] Output: The encrypted behavioral data is sent to the server and stored in a database.

[0285] Step 2: Obtaining emotion data

[0286] The device uses a camera and microphone to capture the user's facial expressions and voice in real time, for example, capturing the moment when the user smiles.

[0287] The device sends the captured data to the emotion engine for sentiment analysis, and uses natural language processing to analyze the sentiment of the text entered by the user.

[0288] The emotion engine analyzes the user's emotional state, encrypts the results, and sends them to the server.

[0289] The server integrates the received emotion data with behavioral data and stores it in a database.

[0290] Input: Data obtained from the user's facial expressions, voice, and text.

[0291] Output: The analyzed emotion data is sent to the server and stored in a database.

[0292] Step 3: Generate advertising content

[0293] The server retrieves the latest user behavior history and emotional data from the database and inputs it into the generative AI module.

[0294] The generative AI module uses deep learning models to analyze the user's preferences, interests, and emotional state.

[0295] The generative AI module generates advertising phrases, images, videos, and music based on the analysis. For example, if the user frequently views sports-related content and expresses positive emotions,

[0296] The generative AI module generates the advertising phrase "Get the latest running shoes 30% off!", a promotional video, and music that emphasizes fun.

[0297] Input: User behavior history and emotion data.

[0298] Output: User-optimized advertising content (phrases, images, videos, music).

[0299] Step 4: Optimize your ad content

[0300] The server sends the advertising content output from the generation AI module to the AGI targeting module.

[0301] The AGI targeting module performs detailed analysis of user behavioral history and emotional data to create a user profile.

[0302] The AGI targeting module optimizes ad content and timing based on user profiles. For example, if a user tends to consume more fitness-related content at night, it will adjust ad delivery schedules to accommodate those times.

[0303] Input: Generated ad content, user behavior history, and emotion data.

[0304] Output: A detailed user profile and optimized advertising content.

[0305] Step 5: Serving Ads

[0306] The server finally sends the generated and optimized ad content to the ad delivery server.

[0307] The ad delivery server uploads the optimized ads to each distribution platform (such as social media or video streaming services).

[0308] The ad delivery server sets and executes an ad delivery schedule based on the user's activity time and platform characteristics. For example, ads are displayed at the optimal time for users who access the internet at night.

[0309] The device receives advertisements from the ad distribution system and displays them on websites and within apps that the user is using.

[0310] Input: Optimized ad content.

[0311] Output: An ad that is displayed on the user's device, and the user is expected to take action based on the ad they receive.

[0312] (Application example 2)

[0313] 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."

[0314] Conventional ad delivery systems generate and deliver ads based solely on user behavioral history, making it difficult to deliver ads effectively based on user emotions. Furthermore, they are unable to display ads at the right time or provide personalized ads that reflect the user's momentary emotional state, limiting the effectiveness of ads.

[0315] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavior history, means for analyzing the user behavior history and generating advertising content, means for optimizing the advertising content generated by the generation means based on a user profile, means for delivering the advertising content optimized by the targeting means, means for acquiring emotional data of the user, means for providing the emotional data to the generation means and using it in generating advertising content, and means for determining the optimal timing for delivering the advertising content generated by the generation means. This makes it possible to generate and deliver personalized advertisements that combine user behavior history and emotional data, and is expected to improve advertising effectiveness.

[0316] "User behavior history" refers to behavioral information such as access history, clicks, search queries, and viewed content when a user uses a website or application.

[0317] "Advertising content" refers to promotional information such as advertising phrases, images, videos, music, etc. delivered to users.

[0318] "Generation means" refers to a method or device for analyzing a user's behavioral history and emotional data and generating advertising content based on the analysis.

[0319] "Targeting means" refers to a method or device for optimizing generated advertising content based on a user profile and effectively delivering it to a specific user at a specific time.

[0320] "Emotional data" refers to the emotional state extracted from a user's facial expression, voice, or text, such as data indicating emotions such as joy, excitement, or sadness.

[0321] "User profile" refers to detailed user information including user behavioral history, emotional data, interests and preferences.

[0322] "Delivery means" refers to a method or device for delivering optimized advertising content to users at an appropriate time and in an appropriate manner.

[0323] "Optimal timing" refers to the time and situation in which a user is predicted to be most likely to respond to advertising content, based on the user's behavioral history and emotional data.

[0324] The system for implementing this invention collects user behavior history, acquires emotion data, and generates, optimizes, and distributes advertising content based on that data. This system is composed of the following main elements:

[0325] First, the device collects user behavior in real time. Specifically, it tracks information such as the pages visited, clicks, search queries, and content viewed while the user is using websites and apps. This data is sent in encrypted form to a server and stored in a database.

[0326] The device then uses the camera and microphone to capture the user's emotional data. It captures facial expressions and voice in real time, and also analyzes emotions from text entered by the user. This emotional data is then sent to a server and integrated with the user's behavioral data.

[0327] The server contains a generation AI module that extracts and analyzes the latest user behavior and emotional data from the database. It then uses prompts to generate advertising phrases, images, videos, and music based on the user's preferences, interests, and emotional state. For example, a prompt might look like this: "User behavior: frequently visits sports-related websites and reads running shoe reviews," "User emotion: smiling (positive emotion)," and "Generate ad: promotional ad for fitness products (e.g., 30% off the latest running shoes)."

[0328] The server also includes an AGI targeting module as a targeting method. This module analyzes user profiles in detail and optimizes the content and timing of generated ads. In doing so, it determines the optimal timing when users are predicted to be most likely to respond to ads based on user behavioral history and emotional data.

[0329] Finally, the advertising content generated and optimized by the generative AI module and AGI targeting module is delivered from the server to the device via the ad delivery server. Specifically, customized ads are displayed within the apps and websites the user is using. These ads are personalized based on the user's behavioral history and emotional data, so they can be expected to be highly effective.

[0330] This makes it possible to generate and deliver personalized advertisements that combine user behavioral history and emotional data, significantly increasing the effectiveness of advertising.

[0331] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0332] Step 1:

[0333] The device collects user behavior data, tracking it in real time as users browse websites, click within apps, and enter search queries. This data is sent in encrypted form to a server and stored in a database.

[0334] Input: Data about your website and app behavior.

[0335] Output: Encrypted behavioral data is stored in a database.

[0336] Step 2:

[0337] The device collects the user's emotional data. It uses a camera and microphone to capture the user's facial expressions and voice in real time. It also analyzes emotions from text entered by the user. This emotional data is sent to a server and integrated with the user's behavioral data.

[0338] Input: User's facial expression data, voice data, and text data.

[0339] Output: The analyzed emotion data is sent to the server and stored in a database.

[0340] Step 3:

[0341] The server extracts the user's behavioral history and emotional data from the database. This data is then input into the generative AI module, which analyzes the user's behavioral and emotional patterns to understand the user's preferences, interests, and emotional state.

[0342] Input: User behavior history and emotion data stored in a database.

[0343] Output: Analyzed data about the user's preferences, interests, and emotional state.

[0344] Step 4:

[0345] The generative AI module generates advertising content based on the analysis data. For example, if a user frequently visits sports-related websites and smiles while reading reviews of running shoes, it will generate an advertising phrase or promotional video such as "30% off the latest running shoes!"

[0346] Input: Parsed preference, interest, and emotional state data.

[0347] Output: Generated advertising content (advertising phrase, images, videos, music).

[0348] Step 5:

[0349] The server then sends the generated ad content to the AGI targeting module, which performs detailed analysis of user profiles to optimize the timing and content of ad displays. For example, if a particular user consumes fitness-related content at night and displays positive emotions, the module will adjust the ads to be displayed at that time.

[0350] Input: Generated advertising content, user profile.

[0351] Output: Optimized ad delivery schedule.

[0352] Step 6:

[0353] The server sends the optimized ad content to the ad delivery server, which then delivers the ad to the user's device at the appropriate time. Users can then view personalized ads on the websites and apps they use.

[0354] Input: Optimized ad content, delivery schedule.

[0355] Output: Personalized ads delivered to the user's device.

[0356] Through these steps, personalized advertisements can be generated and delivered based on user behavior history and emotional data.

[0357] 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.

[0358] 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.

[0359] 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.

[0360] [Second embodiment]

[0361] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0362] 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.

[0363] 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).

[0364] 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.

[0365] 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.

[0366] 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).

[0367] 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.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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."

[0373] This invention shows an embodiment of a system that collects user behavior history and generates, optimizes, and delivers advertising content based on that data. The specific processing of the program is explained below, along with specific examples.

[0374] Program processing overview

[0375] 1. Collecting user behavior history

[0376] When a device uses a website or app, page visits, clicks, search queries, and content viewed are tracked in real time. This behavioral data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0377] 2. Generating advertising content using the generative AI module

[0378] The server extracts the latest user behavior history data from the database and inputs it into the generation AI module, which performs the following processes:

[0379] Analyzing data to identify user preferences and interests.

[0380] Based on the identified preferences, advertising phrases, images, videos, and music are generated.

[0381] For example, if a user frequently visits sports-related websites and reads many articles about sporting goods, the generative AI module will generate sporting goods ads for that user.

[0382] 3. Optimization with AGI targeting module

[0383] The server receives the ad content output from the Generative AI module and sends it to the AGI Targeting module, which performs the following tasks:

[0384] Conduct deeper analysis of user behavioral history to create detailed user profiles.

[0385] Optimize the content and timing of generated ads based on user profiles.

[0386] For example, if a particular user consumes a lot of sports-related content at night, the AGI targeting module will adjust ads to appear at that time.

[0387] 4. Delivery of advertisements

[0388] The server sends the final generated ad content to the ad delivery server, which does the following:

[0389] Upload the optimized ads to each distribution platform.

[0390] Set ad delivery schedules based on user activity times and platform characteristics.

[0391] The device receives advertisements from the ad distribution system and displays them on websites and within apps that the user is using.

[0392] Specific examples

[0393] A user has a habit of buying sports equipment. He frequently browses sports-related websites and videos. One evening, he browses reviews of running shoes. The device records this behavior and sends it to the server. The server stores the data in a database.

[0394] The server then sends this data to a generative AI module, which generates a promotional video with the phrase "30% off the latest running shoes!" The server then sends the information to an AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0395] The server sends the ad to the ad delivery server, which then sets it up so that the ad is displayed on social media overnight. That evening, when the user checks social media, an optimized ad for running shoes appears, and the user clicks on it and purchases the product.

[0396] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the latest behavioral history of a user.

[0397] The processing flow will be explained below.

[0398] Step 1:

[0399] While the device is using websites and apps, it tracks user activity in real time, including page visits, clicks, search queries, and content viewed, and this data is sent in encrypted form to a server.

[0400] Step 2:

[0401] The server stores the received user behavior data in a database, including user IDs, page access history, click history, search queries, and viewed content.

[0402] Step 3:

[0403] The server periodically extracts user behavior history from the database and sends it to the generation AI module. This process can be done in batch or real-time.

[0404] Step 4:

[0405] The generative AI module analyzes the received user behavior history and performs data mining to identify user preferences and interests, then generates advertising phrases, images, videos, and music based on the identified preferences.

[0406] Step 5:

[0407] The server stores the generated advertising content in temporary storage, which includes specific advertising phrases, images, video, and music files.

[0408] Step 6:

[0409] The server retrieves the ad content from temporary storage and sends it to the AGI targeting module, along with user behavior data and the generated ad content.

[0410] Step 7:

[0411] The AGI targeting module creates a detailed user profile based on user behavior history and generated ads, including user interests, behavioral patterns, and time of day.

[0412] Step 8:

[0413] The AGI targeting module optimizes ads based on user profiles, determining when ads are delivered, customizing their content, and on which platforms they appear.

[0414] Step 9:

[0415] The server sends the optimized advertisement content to the advertisement delivery server, which then uploads these advertisements to each distribution platform.

[0416] Step 10:

[0417] The ad delivery server sets an ad delivery schedule based on the user's activity time and the platform's characteristics. For example, if a user is more active at night, the server can set the ad delivery schedule to be delivered during that time.

[0418] Step 11:

[0419] The device receives advertisements sent from the ad distribution system and displays them on websites and apps that the user uses. The advertisements are customized based on the user's behavioral history and an optimized user profile.

[0420] Step 12:

[0421] The device again tracks the user's response to the ad (clicks, viewing time, purchases, etc.) and sends this data to a server to evaluate the effectiveness of the ad.

[0422] Step 13:

[0423] The server analyzes the received response data and evaluates the effectiveness of the advertisement. The results of this evaluation are fed back to the generation AI module and AGI targeting module, and are used for the next advertisement generation and targeting.

[0424] Example 1

[0425] 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."

[0426] Conventional ad delivery systems have had difficulty generating and optimizing ad content by effectively utilizing user behavioral history. In addition, there was a lack of means to optimize the timing and content of ad delivery based on user profiles, making it difficult to achieve effective ad delivery.

[0427] 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.

[0428] In this invention, the server includes a means for collecting user behavior history, a generation means for analyzing the user behavior history and generating advertising content, and a targeting means for optimizing the advertising content generated by the generation means based on a user profile, thereby enabling the generation, optimization, and distribution of effective advertisements based on the user behavior history.

[0429] "User behavior history" is data that records the actions a user takes while using a website or app, such as page visits, clicks, search queries, and content viewed.

[0430] The "generation means" is a means for analyzing user behavior history and generating advertising content based on the analysis.

[0431] The "targeting means" is a means for optimizing the advertising content generated by the generating means based on a user profile.

[0432] "Delivery means" refers to a means for delivering advertising content optimized by targeting means to users.

[0433] A "generative AI model" is a module that uses artificial intelligence technology to analyze data and generate content such as advertising phrases, images, videos, and music.

[0434] A "user profile" is a detailed profile formed based on a user's behavioral history that identifies the user's preferences and interests.

[0435] This invention relates to an embodiment of a system that collects user behavior history and generates, optimizes, and delivers advertising content based on that data. Specific implementation methods of this invention are described below.

[0436] The system uses a server, terminals, and database as hardware, and a generative AI model, a data analysis module, an AGI targeting module, and an ad delivery module as software.

[0437] Collection of user behavior history

[0438] When a device uses a website or app, page accesses, clicks, search queries, viewed content, and other activities are tracked in real time. The tracked behavioral data is sent to a server in encrypted form. The server stores the received data in a database and retains it for subsequent analysis. Specifically, the device uses a JavaScript tracking script to collect user behavioral data and sends it to the server as an encrypted HTTP request.

[0439] Advertising content generation

[0440] The server extracts the latest user behavior history data from the database and inputs it into a generative AI model. The generative AI model analyzes the data and identifies user preferences and interests. Based on the identified preferences, it generates advertising phrases, images, videos, and music. Specifically, the server uses a Python script to extract data using SQL queries and sends an API request to a generative AI model (e.g., OpenAI's GPT-3) to generate advertising content.

[0441] Ad content optimization

[0442] The server receives the advertising content output by the generative AI model and sends it to the AGI targeting module, which then performs a deeper analysis of the user's behavioral history to create a detailed user profile. Based on this profile, the content and timing of the generated ads are optimized. Specifically, the server performs data analysis using Python analytical libraries (e.g., Pandas and Scikit-learn), and the AGI targeting module configures ad delivery based on the user's online time.

[0443] Ad serving

[0444] The server finally sends the generated and optimized ad content to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform and sets the ad delivery schedule based on the user's activity time and platform characteristics. For example, the server uses a REST API to send ad data to the ad delivery server, which then uploads it to Google Ads or social media platforms (e.g., Facebook Ads). The device displays the ads received from the ad delivery system on the website or app the user is using.

[0445] Specific examples

[0446] A user has a habit of buying sports equipment. He frequently browses sports-related websites and videos. One evening, he browses reviews of running shoes. The device records this behavior and sends it to the server, which stores the data in a database.

[0447] The server then sends this data to a generative AI model, which generates a promotional video with the phrase "30% off the latest running shoes!" The server then sends this information to an AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0448] The server then sends the ad to the ad delivery server, which then configures it to be displayed on social media overnight. When the user checks social media that evening, the optimized running shoe ad appears, and the user clicks on it to purchase the product.

[0449] Prompt Sentence Examples

[0450] Examples of prompts to input to a generative AI model might include:

[0451] "Generate ads for users who have recently visited a sports-related website."

[0452] "Create a promotional video for 30% off running shoes."

[0453] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the latest behavioral history of a user.

[0454] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0455] Step 1:

[0456] The device tracks real-time behavioral data when a user uses a website or app, including page visits, clicks, search queries, and content viewed. Specifically, a JavaScript tracking script running on the device collects this behavioral data and sends it to a server as an encrypted HTTP request.

[0457] Input: User behavior data (page visits, clicks, search queries, etc.)

[0458] Output: Data in the form of an encrypted HTTP request

[0459] Step 2:

[0460] The server receives the encrypted data sent from the terminal and stores it in a database. Specifically, the server receives the encrypted data via the HTTPS protocol and executes an SQL query to store it in the database.

[0461] Input: Behavioral data in the form of encrypted HTTP requests

[0462] Output: User behavior data stored in a database

[0463] Step 3:

[0464] The server extracts the latest user behavior history data from the database and sends it to the generative AI model. Specifically, the server executes SQL queries using Python scripts and sends API requests to the generative AI model (e.g., OpenAI's GPT-3).

[0465] Input: User behavior history data stored in a database

[0466] Output: API request data to the generative AI model

[0467] Step 4:

[0468] The generative AI model analyzes the received user behavior history data to identify user preferences and interests. Based on the results, it generates advertising phrases, images, videos, and music. Specifically, the generative AI model generates advertising content using data analysis algorithms.

[0469] Input: API request data to the generative AI model

[0470] Output: Generated advertising content (advertising phrase, images, videos, music)

[0471] Step 5:

[0472] The server receives the ad content output by the generative AI model and sends it to the AGI targeting module, which then performs further analysis of the user's behavioral history data to create a detailed user profile. Specifically, the server uses Python analytical libraries (such as Pandas and Scikit-learn) to analyze the data and optimize the content and timing of ads.

[0473] Input: Generated ad content

[0474] Output: Detailed user profile and optimized ad delivery settings

[0475] Step 6:

[0476] The server sends the final generated and optimized ad content from the generative AI model and AGI targeting module to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform and sets the ad delivery schedule based on user activity times and platform characteristics. Specifically, the server sends data to the ad delivery server using a REST API.

[0477] Input: Final generated and optimized ad content and delivery settings

[0478] Output: Uploaded ads to distribution platforms

[0479] Step 7:

[0480] The device receives the ads from the ad distribution system and displays them on the website or app the user is using. Specifically, the device renders the ads based on the data it receives and displays them in the appropriate location.

[0481] Input: Advertising data received from the distribution platform

[0482] Output: Ads displayed on websites and in apps

[0483] This is the process flow of this system. At each step, the input data is processed and analyzed, and ultimately optimized advertising content is displayed, achieving effective ad delivery based on the user's behavioral history.

[0484] (Application example 1)

[0485] 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."

[0486] In conventional ad distribution systems, ads are generated and displayed based on a user's behavioral history, but because they are distributed uniformly without considering the detailed profiles of each individual user, the effectiveness of the ads is limited and targeting is insufficient.In addition, because the display device and timing of ads are not optimized, it is difficult to attract users' attention.

[0487] 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.

[0488] In this invention, the server includes means for collecting user behavior history, means for analyzing the user behavior history and generating advertising content, means for optimizing the advertising content generated by the generation means based on a user profile, means for delivering the advertising content optimized by the targeting means, means for delivering the generated advertising content to a display device based on the behavior history of a specific user, and means for displaying the delivered advertising content on the user's operating terminal and receiving responses. This makes it possible to optimize the timing and content of advertising display based on the detailed profile of each individual user, and to deliver advertising according to the display device.

[0489] "User behavior history" is a record of a user's actions and operations on the Internet, including page accesses, clicks, search queries, and viewed content.

[0490] "Generation means" refers to a combination of equipment and software for analyzing user behavior history and generating advertising content.

[0491] "Targeting tools" are any combination of devices and software used to optimize advertising content generated based on user profiles.

[0492] "Delivery means" refers to the combination of equipment and software used to actually deliver advertising content optimized by targeting means to users.

[0493] "Display device" refers to the user's operating terminal on which advertising content is displayed, and specifically includes electronic devices such as smartphones, tablets, and personal computers.

[0494] An "operation terminal" is an electronic device that is actually operated by a user and on which advertising content is displayed.

[0495] "Generation Module" means a specific software module for generating advertising phrases, images, videos, and music.

[0496] A "user profile" is a collection of data detailing a user's attributes and preferences, created based on user behavior history and other related information.

[0497] This invention describes a system that collects "user behavior history" and generates, optimizes, and delivers advertising content based on that data. To implement this invention, server, terminal, and user components, as well as related hardware and software, are required.

[0498] Program processing overview

[0499] 1. Collecting user behavior history

[0500] When a user uses a website or application, their device tracks real-time behavioral data, such as page visits, clicks, search queries, and content viewed. This behavioral data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0501] 2. Generating advertising content using the generative AI module

[0502] The server extracts the latest user behavior history data from the database and inputs it into the generative AI module, which analyzes the data to identify user preferences and interests, and then generates advertising phrases, images, videos, and music based on the identified preferences.

[0503] 3. Optimization with AGI targeting module

[0504] The server receives the advertising content output by the generation AI module and sends it to the AGI targeting module, which then performs a deeper analysis of the user's behavioral history to create a detailed user profile. Based on the user profile, the content and timing of the generated advertisements are optimized.

[0505] 4. Delivery of advertisements

[0506] The server sends the final generated advertisement content to the advertisement delivery server, which delivers the optimized advertisement to the specified display device. The advertisement is displayed on the user's operating device and records how the user responded to the advertisement.

[0507] Hardware and software used

[0508] Server: Carries out a series of processes including data collection, storage, analysis, content generation, optimization, and distribution. Specifically, it includes a database server that encrypts and stores user behavior history data, and a processing server that runs the generative AI module and AGI targeting module.

[0509] Generative AI module: Uses the Generative AI class to generate advertisements based on user behavior history data.

[0510] AGI Targeting Module: Uses the AGIModule class to optimize ads based on detailed user profiles.

[0511] User device: The device on which the ad is displayed and the user's interactions are recorded. This includes electronic devices such as smartphones, tablets, and computers.

[0512] Specific examples

[0513] Here's a concrete example: Consider a user who frequently buys sports equipment and frequently visits sports-related websites. One day, the user browses reviews of running shoes. The device records this behavior and sends it to the server. The server stores the data in a database and feeds it into the generative AI module.

[0514] The generative AI module generates the phrase "30% off the latest running shoes!" and a promotional video. The server then sends that information to the AGI targeting module, which analyzes the user's detailed profile. For example, it discovers that the user tends to be online at night. The server sends the ad to the ad delivery server and sets it up to be displayed on social media at night. When the user checks social media that evening, an optimized ad for running shoes appears. The user clicks on the ad and purchases the product.

[0515] Prompt Sentence Examples

[0516] Prompt: User behavior: Searched for "latest running shoes." Generate appropriate ad content.

[0517] Thus, a specific system for implementing the present invention and its operation will be described.

[0518] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0519] Step 1:

[0520] Collection of user behavior history

[0521] The input used is behavioral data (page accesses, clicks, search queries, viewed content, etc.) from the user's operating device (e.g., smartphone or PC). When the device is using a website or application, the behavioral data is tracked in real time and sent in encrypted form to a server. The server stores the received data in a database and keeps it for later analysis. In this process, it is important to accurately collect user behavior patterns.

[0522] Step 2:

[0523] Generative AI module generates advertising content

[0524] The server extracts the latest user behavior history data from the database and sends it as input to the generation AI module. The generation AI module analyzes this behavior history data to identify the user's preferences and interests. It then generates advertising phrases, images, videos, and music based on the user's identified preferences. For example, the generated advertising phrase might be "30% off the latest running shoes!" The output of this step is the generated advertising content.

[0525] Step 3:

[0526] Optimization with AGI targeting module

[0527] The server receives the ad content output from the generation AI module and sends it as input to the AGI targeting module, which then performs a more detailed analysis of the user's behavioral history to create a detailed user profile. Based on this profile, the content and timing of the generated ads are optimized. For example, if a particular user tends to be online at night, ads can be displayed at that time. The output of this step is optimized ad content.

[0528] Step 4:

[0529] Ad serving

[0530] The server sends the optimized advertising content using the AGI targeting module to the advertising delivery server. The advertising delivery server delivers the optimized advertisements to the user's operating device (for example, a display device such as a smartphone or PC). The advertisements are displayed within the application or website the user is using. In addition, data on users' clicks and responses to the advertisements is collected and sent to the server. This makes it possible to evaluate the effectiveness of the advertisements based on user responses.

[0531] Through this series of processes, advertisements generated based on the user's behavioral history are delivered to each individual user at the optimal time and in the optimal way.

[0532] 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.

[0533] This invention shows an embodiment of a system that generates, optimizes, and delivers advertising content by combining user behavior history and user emotion data. The specific processing of the program is explained below, along with specific examples.

[0534] Program processing overview

[0535] 1. Collecting user behavior history

[0536] While a device is using a website or app, it tracks user activity in real time, including page visits, clicks, search queries, and content viewed. This data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0537] 2. Acquiring emotional data using the emotion engine

[0538] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and sends them to the emotion engine. It also analyzes emotions from the text the user types. This emotion data is sent to a server and integrated with the user's behavioral data.

[0539] 3. Generating advertising content using the generative AI module

[0540] The server extracts the latest user behavior history and emotion data from the database and inputs it into the generative AI module, which performs the following processes:

[0541] Analyze the data to identify user preferences, interests, and emotional states.

[0542] Generate advertising phrases, images, videos, and music based on identified preferences and emotional states.

[0543] For example, if a user frequently visits sports-related websites and also displays positive emotions while watching fitness videos, the generative AI module will generate ads for sports equipment for that user.

[0544] 4. Optimization with AGI targeting module

[0545] The server receives the ad content output from the Generative AI module and sends it to the AGI Targeting module, which performs the following tasks:

[0546] Deeper analysis of user behavioral history and sentiment data creates detailed user profiles.

[0547] Optimize the content and timing of generated ads based on user profiles.

[0548] For example, if a particular user consumes a lot of fitness-related content at night and exhibits positive emotions, the AGI targeting module will adjust ads to appear at those times.

[0549] 5. Delivery of advertisements

[0550] The server sends the final generated ad content to the ad delivery server, which does the following:

[0551] Upload the optimized ads to each distribution platform.

[0552] Set ad delivery schedules based on user activity times and platform characteristics.

[0553] The device receives ads from the ad serving system and displays them within the websites and apps the user is using. The ads are customized based on the user's behavioral history, emotional data, and optimized user profile.

[0554] Specific examples

[0555] Example 1: User purchases sporting goods

[0556] A user frequently purchases sports equipment and frequently browses sports-related websites and videos. One evening, the user browses a review of running shoes and the camera captures a smile that expresses positive emotions. The device records this behavior and emotion and sends it to the server. The server stores the data in a database and then sends it to the generative AI module.

[0557] The generative AI module generates an advertising slogan and promotional video for the user, such as "Get 30% off the latest running shoes!" It also selects music that emphasizes enjoyment based on the user's emotional data. The server sends this information to the AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0558] The server sends the advertisement to the ad delivery server and configures it to be displayed on social media overnight. When the user checks social media that evening, an optimized ad for running shoes appears, the user clicks on the ad, and purchases the product.

[0559] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the user's behavior history and emotion data.

[0560] The processing flow will be explained below.

[0561] Step 1:

[0562] The device tracks real-time behavioral data such as page visits, clicks, search queries, and content viewed while users are using websites and apps, and this data is sent in encrypted form to a server.

[0563] Step 2:

[0564] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends the data to the emotion engine, which analyzes the user's emotions from this data and generates emotion data.If there is text input, it extracts emotion data from the text.

[0565] Step 3:

[0566] The server stores the received user behavior history data and emotion data in a database, including user IDs, page access history, click history, search queries, viewed content, and emotion data.

[0567] Step 4:

[0568] The server periodically extracts user behavior history and emotion data from the database and inputs it into the generative AI module. This process can be done in batch or real-time.

[0569] Step 5:

[0570] The generative AI module analyzes the received user behavior history and emotional data, performs data mining to identify the user's preferences and emotional state, and generates advertising phrases, images, videos, and music based on the analysis results.

[0571] Step 6:

[0572] The server stores the generated advertising content in temporary storage, which includes specific advertising phrases, images, video, and music files.

[0573] Step 7:

[0574] The server retrieves the ad content from temporary storage and sends it to the AGI targeting module, along with user behavior data, sentiment data, and the generated ad content.

[0575] Step 8:

[0576] The AGI targeting module uses user behavioral history and emotional data to create a detailed user profile, which includes the user's interests, behavioral patterns, active times, and emotional state.

[0577] Step 9:

[0578] The AGI targeting module optimizes ads based on user profiles, determining when ads are delivered, customizing their content, and on which platforms they appear.

[0579] Step 10:

[0580] The server sends the optimized advertisement content to the advertisement delivery server, which then uploads these advertisements to each distribution platform.

[0581] Step 11:

[0582] The ad delivery server sets an ad delivery schedule based on the user's activity time and the platform's characteristics. For example, if a user is more active at night, the server can set the ad delivery schedule to be delivered during that time.

[0583] Step 12:

[0584] The device receives the advertisements sent from the ad distribution system and displays them on the websites and apps the user is using. The advertisements are customized based on the user's behavioral history, emotional data, and optimized user profile.

[0585] Step 13:

[0586] The device again tracks the user's response to the ad (clicks, viewing time, purchases, etc.) and sends this data to a server to evaluate the effectiveness of the ad.

[0587] Step 14:

[0588] The server analyzes the received response data and evaluates the effectiveness of the advertisement. The results of this evaluation are fed back to the generation AI module and AGI targeting module, and are used for the next advertisement generation and targeting.

[0589] Example 2

[0590] 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."

[0591] Conventional ad delivery systems generate and deliver advertising content based on user behavior history, but they have difficulty reflecting the user's real-time emotional state, resulting in insufficient advertising effectiveness. Furthermore, they are unable to quickly respond to changes in user preferences and interests, resulting in insufficient ad optimization. Therefore, a new system is needed that can deliver ads effectively and attract user attention.

[0592] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting user behavior history, a means for acquiring user emotion data and integrating it with the user behavior history, a generation means for analyzing the user behavior history and emotion data and generating advertising content, a targeting means for optimizing the generated advertising content based on a user profile, and a means for delivering the optimized advertising content. This makes it possible to integrate the user behavior history and emotion data and deliver more effective and adaptive advertising.

[0593] "User behavior history" refers to the series of activities a user performs when using a website or app, such as page visits, clicks, search queries, and content viewed.

[0594] "Emotional data" refers to data that indicates a user's emotional state, such as data obtained from the user's facial expressions, voice, and input text.

[0595] "Generation means" refers to a module that has the function of analyzing user behavior history and emotional data and generating advertising content (e.g., advertising phrases, images, videos, music).

[0596] "Targeting means" refers to a module that has the function of optimizing the content and timing of display of generated advertising content based on user profiles.

[0597] "Delivery Means" refers to a module that has the function of delivering optimized advertising content to the platform used by the user.

[0598] "User Profile" refers to a data set that specifies a user's preferences and interests, generated from the user's behavioral history, emotional data, and other related data.

[0599] This invention shows an embodiment of a system that generates, optimizes, and delivers advertising content by combining user behavior history and user emotion data. The specific processing of the system is explained below, along with specific examples.

[0600] Collection of user behavior history

[0601] The device tracks behavioral data such as page accesses, clicks, search queries, and viewed content in real time while the user is using a website or app. To do this, a tracking code or SDK must be embedded, allowing detailed collection of user activity. The device encrypts the collected data and sends it to a server using a secure communication protocol such as HTTPS. The server stores the received data in a database, where it is used for analysis.

[0602] Acquiring emotion data

[0603] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It then uses natural language processing technology to analyze the text entered by the user and determine their emotions. The emotional data is anonymized and securely sent to the engine. The emotion engine receives and analyzes this data, and sends it to the server. The server then integrates this emotional data with the user's behavioral history and stores it in a database.

[0604] Advertising content generation

[0605] The server retrieves the latest user behavior history and emotional data from the database and inputs it into the generative AI module, which analyzes the data using deep learning models and other methods to identify the user's preferences, interests, and emotional state. It then uses GANs (generative artificial network) and language models to generate advertising phrases, images, videos, and music.

[0606] For example, if a user frequently visits a sports-related website and displays positive emotions while watching fitness videos, the generative AI module will generate a promotional video for that user, stating, "Get 30% off the latest running shoes!", along with music that emphasizes the fun factor.

[0607] Ad content optimization

[0608] The server sends the advertising content output by the generation AI module to the AGI targeting module, which then performs detailed analysis of the user's behavioral history and emotional data to generate a user profile. Based on this profile, the content and timing of advertising displays are optimized. For example, if a user often consumes fitness-related content at night, ads can be displayed at that time.

[0609] Ad serving

[0610] The server finally sends the generated and optimized ad content to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform (such as social media or video streaming services) and schedules and executes ad delivery based on the user's activity time and platform characteristics. The device receives the ads from the ad delivery system and displays them on the website or app the user is using.

[0611] Specific examples

[0612] Example 1: User purchases sporting goods

[0613] A user frequently purchases sports equipment and frequently browses sports-related websites and videos. One evening, the user looks at a review of running shoes and the camera captures a smile that expresses positive emotions. The device records this behavior and emotion and sends it to the server. The server stores the data in a database and sends it to the generative AI module. The generative AI module generates an advertising phrase such as "30% off the latest running shoes!" and a promotional video. It also selects music that emphasizes enjoyment based on the emotional data. The server sends this information to the AGI targeting module, which analyzes the user's detailed profile and discovers that the user tends to be online at night. The server sends the advertisement to the ad delivery server, which configures the advertisement to be displayed on social media at night. That evening, when the user checks social media, an optimized ad for running shoes appears, the user clicks on the ad, and purchases the product.

[0614] Examples of prompt statements

[0615] Examples of prompts for situations using generative AI models:

[0616] Please provide a list of URLs of websites that the user has visited frequently recently, along with their content types (e.g., sports, fitness), and the user's recent emotional state (e.g., positive, negative).

[0617] The above is a description of a specific embodiment of the system of the present invention, which enables effective advertisement delivery based on the user's behavior history and emotion data.

[0618] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0619] Step 1: Collect user behavior history

[0620] The device tracks behavioral data such as page visits, clicks, search queries, and content viewed in real time while the user is using websites and apps.

[0621] The behavioral data collected by the device is encrypted and sent to a server using a secure communication protocol such as HTTPS. For example, the device collects data such as "a user read a specific sports article."

[0622] The server stores the received user behavior data in a database for use in subsequent processing steps.

[0623] Input: Data about specific user behavior on your website or app.

[0624] Output: The encrypted behavioral data is sent to the server and stored in a database.

[0625] Step 2: Obtaining emotion data

[0626] The device uses a camera and microphone to capture the user's facial expressions and voice in real time, for example, capturing the moment when the user smiles.

[0627] The device sends the captured data to the emotion engine for sentiment analysis, and uses natural language processing to analyze the sentiment of the text entered by the user.

[0628] The emotion engine analyzes the user's emotional state, encrypts the results, and sends them to the server.

[0629] The server integrates the received emotion data with behavioral data and stores it in a database.

[0630] Input: Data obtained from the user's facial expressions, voice, and text.

[0631] Output: The analyzed emotion data is sent to the server and stored in a database.

[0632] Step 3: Generate advertising content

[0633] The server retrieves the latest user behavior history and emotional data from the database and inputs it into the generative AI module.

[0634] The generative AI module uses deep learning models to analyze the user's preferences, interests, and emotional state.

[0635] The generative AI module generates advertising phrases, images, videos, and music based on the analysis. For example, if the user frequently views sports-related content and expresses positive emotions,

[0636] The generative AI module generates the advertising phrase "Get the latest running shoes 30% off!", a promotional video, and music that emphasizes fun.

[0637] Input: User behavior history and emotion data.

[0638] Output: User-optimized advertising content (phrases, images, videos, music).

[0639] Step 4: Optimize your ad content

[0640] The server sends the advertising content output from the generation AI module to the AGI targeting module.

[0641] The AGI targeting module performs detailed analysis of user behavioral history and emotional data to create a user profile.

[0642] The AGI targeting module optimizes ad content and timing based on user profiles. For example, if a user tends to consume more fitness-related content at night, it will adjust ad delivery schedules to accommodate those times.

[0643] Input: Generated ad content, user behavior history, and emotion data.

[0644] Output: A detailed user profile and optimized advertising content.

[0645] Step 5: Serving Ads

[0646] The server finally sends the generated and optimized ad content to the ad delivery server.

[0647] The ad delivery server uploads the optimized ads to each distribution platform (such as social media or video streaming services).

[0648] The ad delivery server sets and executes an ad delivery schedule based on the user's activity time and platform characteristics. For example, ads are displayed at the optimal time for users who access the internet at night.

[0649] The device receives advertisements from the ad distribution system and displays them on websites and within apps that the user is using.

[0650] Input: Optimized ad content.

[0651] Output: An ad that is displayed on the user's device, and the user is expected to take action based on the ad they receive.

[0652] (Application example 2)

[0653] 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."

[0654] Conventional ad delivery systems generate and deliver ads based solely on user behavioral history, making it difficult to deliver ads effectively based on user emotions. Furthermore, they are unable to display ads at the right time or provide personalized ads that reflect the user's momentary emotional state, limiting the effectiveness of ads.

[0655] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavior history, means for analyzing the user behavior history and generating advertising content, means for optimizing the advertising content generated by the generation means based on a user profile, means for delivering the advertising content optimized by the targeting means, means for acquiring emotional data of the user, means for providing the emotional data to the generation means and using it in generating advertising content, and means for determining the optimal timing for delivering the advertising content generated by the generation means. This makes it possible to generate and deliver personalized advertisements that combine user behavior history and emotional data, and is expected to improve advertising effectiveness.

[0656] "User behavior history" refers to behavioral information such as access history, clicks, search queries, and viewed content when a user uses a website or application.

[0657] "Advertising content" refers to promotional information such as advertising phrases, images, videos, music, etc. delivered to users.

[0658] "Generation means" refers to a method or device for analyzing a user's behavioral history and emotional data and generating advertising content based on the analysis.

[0659] "Targeting means" refers to a method or device for optimizing generated advertising content based on a user profile and effectively delivering it to a specific user at a specific time.

[0660] "Emotional data" refers to the emotional state extracted from a user's facial expression, voice, or text, such as data indicating emotions such as joy, excitement, or sadness.

[0661] "User profile" refers to detailed user information including user behavioral history, emotional data, interests and preferences.

[0662] "Delivery means" refers to a method or device for delivering optimized advertising content to users at an appropriate time and in an appropriate manner.

[0663] "Optimal timing" refers to the time and situation in which a user is predicted to be most likely to respond to advertising content, based on the user's behavioral history and emotional data.

[0664] The system for implementing this invention collects user behavior history, acquires emotion data, and generates, optimizes, and distributes advertising content based on that data. This system is composed of the following main elements:

[0665] First, the device collects user behavior in real time. Specifically, it tracks information such as the pages visited, clicks, search queries, and content viewed while the user is using websites and apps. This data is sent in encrypted form to a server and stored in a database.

[0666] The device then uses the camera and microphone to capture the user's emotional data. It captures facial expressions and voice in real time, and also analyzes emotions from text entered by the user. This emotional data is then sent to a server and integrated with the user's behavioral data.

[0667] The server contains a generation AI module that extracts and analyzes the latest user behavior and emotional data from the database. It then uses prompts to generate advertising phrases, images, videos, and music based on the user's preferences, interests, and emotional state. For example, a prompt might look like this: "User behavior: frequently visits sports-related websites and reads running shoe reviews," "User emotion: smiling (positive emotion)," and "Generate ad: promotional ad for fitness products (e.g., 30% off the latest running shoes)."

[0668] The server also includes an AGI targeting module as a targeting method. This module analyzes user profiles in detail and optimizes the content and timing of generated ads. In doing so, it determines the optimal timing when users are predicted to be most likely to respond to ads based on user behavioral history and emotional data.

[0669] Finally, the advertising content generated and optimized by the generative AI module and AGI targeting module is delivered from the server to the device via the ad delivery server. Specifically, customized ads are displayed within the apps and websites the user is using. These ads are personalized based on the user's behavioral history and emotional data, so they can be expected to be highly effective.

[0670] This makes it possible to generate and deliver personalized advertisements that combine user behavioral history and emotional data, significantly increasing the effectiveness of advertising.

[0671] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0672] Step 1:

[0673] The device collects user behavior data, tracking it in real time as users browse websites, click within apps, and enter search queries. This data is sent in encrypted form to a server and stored in a database.

[0674] Input: Data about your website and app behavior.

[0675] Output: Encrypted behavioral data is stored in a database.

[0676] Step 2:

[0677] The device collects the user's emotional data. It uses a camera and microphone to capture the user's facial expressions and voice in real time. It also analyzes emotions from text entered by the user. This emotional data is sent to a server and integrated with the user's behavioral data.

[0678] Input: User's facial expression data, voice data, and text data.

[0679] Output: The analyzed emotion data is sent to the server and stored in a database.

[0680] Step 3:

[0681] The server extracts the user's behavioral history and emotional data from the database. This data is then input into the generative AI module, which analyzes the user's behavioral and emotional patterns to understand the user's preferences, interests, and emotional state.

[0682] Input: User behavior history and emotion data stored in a database.

[0683] Output: Analyzed data about the user's preferences, interests, and emotional state.

[0684] Step 4:

[0685] The generative AI module generates advertising content based on the analysis data. For example, if a user frequently visits sports-related websites and smiles while reading reviews of running shoes, it will generate an advertising phrase or promotional video such as "30% off the latest running shoes!"

[0686] Input: Parsed preference, interest, and emotional state data.

[0687] Output: Generated advertising content (advertising phrase, images, videos, music).

[0688] Step 5:

[0689] The server then sends the generated ad content to the AGI targeting module, which performs detailed analysis of user profiles to optimize the timing and content of ad displays. For example, if a particular user consumes fitness-related content at night and displays positive emotions, the module will adjust the ads to be displayed at that time.

[0690] Input: Generated advertising content, user profile.

[0691] Output: Optimized ad delivery schedule.

[0692] Step 6:

[0693] The server sends the optimized ad content to the ad delivery server, which then delivers the ad to the user's device at the appropriate time. Users can then view personalized ads on the websites and apps they use.

[0694] Input: Optimized ad content, delivery schedule.

[0695] Output: Personalized ads delivered to the user's device.

[0696] Through these steps, personalized advertisements can be generated and delivered based on user behavior history and emotional data.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] [Third embodiment]

[0701] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0702] 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.

[0703] 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).

[0704] 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.

[0705] 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.

[0706] 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).

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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."

[0713] This invention shows an embodiment of a system that collects user behavior history and generates, optimizes, and delivers advertising content based on that data. The specific processing of the program is explained below, along with specific examples.

[0714] Program processing overview

[0715] 1. Collecting user behavior history

[0716] When a device uses a website or app, page visits, clicks, search queries, and content viewed are tracked in real time. This behavioral data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0717] 2. Generating advertising content using the generative AI module

[0718] The server extracts the latest user behavior history data from the database and inputs it into the generation AI module, which performs the following processes:

[0719] Analyzing data to identify user preferences and interests.

[0720] Based on the identified preferences, advertising phrases, images, videos, and music are generated.

[0721] For example, if a user frequently visits sports-related websites and reads many articles about sporting goods, the generative AI module will generate sporting goods ads for that user.

[0722] 3. Optimization with AGI targeting module

[0723] The server receives the ad content output from the Generative AI module and sends it to the AGI Targeting module, which performs the following tasks:

[0724] Conduct deeper analysis of user behavioral history to create detailed user profiles.

[0725] Optimize the content and timing of generated ads based on user profiles.

[0726] For example, if a particular user consumes a lot of sports-related content at night, the AGI targeting module will adjust ads to appear at that time.

[0727] 4. Delivery of advertisements

[0728] The server sends the final generated ad content to the ad delivery server, which does the following:

[0729] Upload the optimized ads to each distribution platform.

[0730] Set ad delivery schedules based on user activity times and platform characteristics.

[0731] The device receives advertisements from the ad distribution system and displays them on websites and within apps that the user is using.

[0732] Specific examples

[0733] A user has a habit of buying sports equipment. He frequently browses sports-related websites and videos. One evening, he browses reviews of running shoes. The device records this behavior and sends it to the server. The server stores the data in a database.

[0734] The server then sends this data to a generative AI module, which generates a promotional video with the phrase "30% off the latest running shoes!" The server then sends the information to an AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0735] The server sends the ad to the ad delivery server, which then sets it up so that the ad is displayed on social media overnight. That evening, when the user checks social media, an optimized ad for running shoes appears, and the user clicks on it and purchases the product.

[0736] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the latest behavioral history of a user.

[0737] The processing flow will be explained below.

[0738] Step 1:

[0739] While the device is using websites and apps, it tracks user activity in real time, including page visits, clicks, search queries, and content viewed, and this data is sent in encrypted form to a server.

[0740] Step 2:

[0741] The server stores the received user behavior data in a database, including user IDs, page access history, click history, search queries, and viewed content.

[0742] Step 3:

[0743] The server periodically extracts user behavior history from the database and sends it to the generation AI module. This process can be done in batch or real-time.

[0744] Step 4:

[0745] The generative AI module analyzes the received user behavior history and performs data mining to identify user preferences and interests, then generates advertising phrases, images, videos, and music based on the identified preferences.

[0746] Step 5:

[0747] The server stores the generated advertising content in temporary storage, which includes specific advertising phrases, images, video, and music files.

[0748] Step 6:

[0749] The server retrieves the ad content from temporary storage and sends it to the AGI targeting module, along with user behavior data and the generated ad content.

[0750] Step 7:

[0751] The AGI targeting module creates a detailed user profile based on user behavior history and generated ads, including user interests, behavioral patterns, and time of day.

[0752] Step 8:

[0753] The AGI targeting module optimizes ads based on user profiles, determining when ads are delivered, customizing their content, and on which platforms they appear.

[0754] Step 9:

[0755] The server sends the optimized advertisement content to the advertisement delivery server, which then uploads these advertisements to each distribution platform.

[0756] Step 10:

[0757] The ad delivery server sets an ad delivery schedule based on the user's activity time and the platform's characteristics. For example, if a user is more active at night, the server can set the ad delivery schedule to be delivered during that time.

[0758] Step 11:

[0759] The device receives advertisements sent from the ad distribution system and displays them on websites and apps that the user uses. The advertisements are customized based on the user's behavioral history and an optimized user profile.

[0760] Step 12:

[0761] The device again tracks the user's response to the ad (clicks, viewing time, purchases, etc.) and sends this data to a server to evaluate the effectiveness of the ad.

[0762] Step 13:

[0763] The server analyzes the received response data and evaluates the effectiveness of the advertisement. The results of this evaluation are fed back to the generation AI module and AGI targeting module, and are used for the next advertisement generation and targeting.

[0764] Example 1

[0765] 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."

[0766] Conventional ad delivery systems have had difficulty generating and optimizing ad content by effectively utilizing user behavioral history. In addition, there was a lack of means to optimize the timing and content of ad delivery based on user profiles, making it difficult to achieve effective ad delivery.

[0767] 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.

[0768] In this invention, the server includes a means for collecting user behavior history, a generation means for analyzing the user behavior history and generating advertising content, and a targeting means for optimizing the advertising content generated by the generation means based on a user profile, thereby enabling the generation, optimization, and distribution of effective advertisements based on the user behavior history.

[0769] "User behavior history" is data that records the actions a user takes while using a website or app, such as page visits, clicks, search queries, and content viewed.

[0770] The "generation means" is a means for analyzing user behavior history and generating advertising content based on the analysis.

[0771] The "targeting means" is a means for optimizing the advertising content generated by the generating means based on a user profile.

[0772] "Delivery means" refers to a means for delivering advertising content optimized by targeting means to users.

[0773] A "generative AI model" is a module that uses artificial intelligence technology to analyze data and generate content such as advertising phrases, images, videos, and music.

[0774] A "user profile" is a detailed profile formed based on a user's behavioral history that identifies the user's preferences and interests.

[0775] This invention relates to an embodiment of a system that collects user behavior history and generates, optimizes, and delivers advertising content based on that data. Specific implementation methods of this invention are described below.

[0776] The system uses a server, terminals, and database as hardware, and a generative AI model, a data analysis module, an AGI targeting module, and an ad delivery module as software.

[0777] Collection of user behavior history

[0778] When a device uses a website or app, page accesses, clicks, search queries, viewed content, and other activities are tracked in real time. The tracked behavioral data is sent to a server in encrypted form. The server stores the received data in a database and retains it for subsequent analysis. Specifically, the device uses a JavaScript tracking script to collect user behavioral data and sends it to the server as an encrypted HTTP request.

[0779] Advertising content generation

[0780] The server extracts the latest user behavior history data from the database and inputs it into a generative AI model. The generative AI model analyzes the data and identifies user preferences and interests. Based on the identified preferences, it generates advertising phrases, images, videos, and music. Specifically, the server uses a Python script to extract data using SQL queries and sends an API request to a generative AI model (e.g., OpenAI's GPT-3) to generate advertising content.

[0781] Ad content optimization

[0782] The server receives the advertising content output by the generative AI model and sends it to the AGI targeting module, which then performs a deeper analysis of the user's behavioral history to create a detailed user profile. Based on this profile, the content and timing of the generated ads are optimized. Specifically, the server performs data analysis using Python analytical libraries (e.g., Pandas and Scikit-learn), and the AGI targeting module configures ad delivery based on the user's online time.

[0783] Ad serving

[0784] The server finally sends the generated and optimized ad content to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform and sets the ad delivery schedule based on the user's activity time and platform characteristics. For example, the server uses a REST API to send ad data to the ad delivery server, which then uploads it to Google Ads or social media platforms (e.g., Facebook Ads). The device displays the ads received from the ad delivery system on the website or app the user is using.

[0785] Specific examples

[0786] A user has a habit of buying sports equipment. He frequently browses sports-related websites and videos. One evening, he browses reviews of running shoes. The device records this behavior and sends it to the server, which stores the data in a database.

[0787] The server then sends this data to a generative AI model, which generates a promotional video with the phrase "30% off the latest running shoes!" The server then sends this information to an AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0788] The server then sends the ad to the ad delivery server, which then configures it to be displayed on social media overnight. When the user checks social media that evening, the optimized running shoe ad appears, and the user clicks on it to purchase the product.

[0789] Prompt Sentence Examples

[0790] Examples of prompts to input to a generative AI model might include:

[0791] "Generate ads for users who have recently visited a sports-related website."

[0792] "Create a promotional video for 30% off running shoes."

[0793] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the latest behavioral history of a user.

[0794] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0795] Step 1:

[0796] The device tracks real-time behavioral data when a user uses a website or app, including page visits, clicks, search queries, and content viewed. Specifically, a JavaScript tracking script running on the device collects this behavioral data and sends it to a server as an encrypted HTTP request.

[0797] Input: User behavior data (page visits, clicks, search queries, etc.)

[0798] Output: Data in the form of an encrypted HTTP request

[0799] Step 2:

[0800] The server receives the encrypted data sent from the terminal and stores it in a database. Specifically, the server receives the encrypted data via the HTTPS protocol and executes an SQL query to store it in the database.

[0801] Input: Behavioral data in the form of encrypted HTTP requests

[0802] Output: User behavior data stored in a database

[0803] Step 3:

[0804] The server extracts the latest user behavior history data from the database and sends it to the generative AI model. Specifically, the server executes SQL queries using Python scripts and sends API requests to the generative AI model (e.g., OpenAI's GPT-3).

[0805] Input: User behavior history data stored in a database

[0806] Output: API request data to the generative AI model

[0807] Step 4:

[0808] The generative AI model analyzes the received user behavior history data to identify user preferences and interests. Based on the results, it generates advertising phrases, images, videos, and music. Specifically, the generative AI model generates advertising content using data analysis algorithms.

[0809] Input: API request data to the generative AI model

[0810] Output: Generated advertising content (advertising phrase, images, videos, music)

[0811] Step 5:

[0812] The server receives the ad content output by the generative AI model and sends it to the AGI targeting module, which then performs further analysis of the user's behavioral history data to create a detailed user profile. Specifically, the server uses Python analytical libraries (such as Pandas and Scikit-learn) to analyze the data and optimize the content and timing of ads.

[0813] Input: Generated ad content

[0814] Output: Detailed user profile and optimized ad delivery settings

[0815] Step 6:

[0816] The server sends the final generated and optimized ad content from the generative AI model and AGI targeting module to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform and sets the ad delivery schedule based on user activity times and platform characteristics. Specifically, the server sends data to the ad delivery server using a REST API.

[0817] Input: Final generated and optimized ad content and delivery settings

[0818] Output: Uploaded ads to distribution platforms

[0819] Step 7:

[0820] The device receives the ads from the ad distribution system and displays them on the website or app the user is using. Specifically, the device renders the ads based on the data it receives and displays them in the appropriate location.

[0821] Input: Advertising data received from the distribution platform

[0822] Output: Ads displayed on websites and in apps

[0823] This is the process flow of this system. At each step, the input data is processed and analyzed, and ultimately optimized advertising content is displayed, achieving effective ad delivery based on the user's behavioral history.

[0824] (Application example 1)

[0825] 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."

[0826] In conventional ad distribution systems, ads are generated and displayed based on a user's behavioral history, but because they are distributed uniformly without considering the detailed profiles of each individual user, the effectiveness of the ads is limited and targeting is insufficient.In addition, because the display device and timing of ads are not optimized, it is difficult to attract users' attention.

[0827] 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.

[0828] In this invention, the server includes means for collecting user behavior history, means for analyzing the user behavior history and generating advertising content, means for optimizing the advertising content generated by the generation means based on a user profile, means for delivering the advertising content optimized by the targeting means, means for delivering the generated advertising content to a display device based on the behavior history of a specific user, and means for displaying the delivered advertising content on the user's operating terminal and receiving responses. This makes it possible to optimize the timing and content of advertising display based on the detailed profile of each individual user, and to deliver advertising according to the display device.

[0829] "User behavior history" is a record of a user's actions and operations on the Internet, including page accesses, clicks, search queries, and viewed content.

[0830] "Generation means" refers to a combination of equipment and software for analyzing user behavior history and generating advertising content.

[0831] "Targeting tools" are any combination of devices and software used to optimize advertising content generated based on user profiles.

[0832] "Delivery means" refers to the combination of equipment and software used to actually deliver advertising content optimized by targeting means to users.

[0833] "Display device" refers to the user's operating terminal on which advertising content is displayed, and specifically includes electronic devices such as smartphones, tablets, and personal computers.

[0834] An "operation terminal" is an electronic device that is actually operated by a user and on which advertising content is displayed.

[0835] "Generation Module" means a specific software module for generating advertising phrases, images, videos, and music.

[0836] A "user profile" is a collection of data detailing a user's attributes and preferences, created based on user behavior history and other related information.

[0837] This invention describes a system that collects "user behavior history" and generates, optimizes, and delivers advertising content based on that data. To implement this invention, server, terminal, and user components, as well as related hardware and software, are required.

[0838] Program processing overview

[0839] 1. Collecting user behavior history

[0840] When a user uses a website or application, their device tracks real-time behavioral data, such as page visits, clicks, search queries, and content viewed. This behavioral data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0841] 2. Generating advertising content using the generative AI module

[0842] The server extracts the latest user behavior history data from the database and inputs it into the generative AI module, which analyzes the data to identify user preferences and interests, and then generates advertising phrases, images, videos, and music based on the identified preferences.

[0843] 3. Optimization with AGI targeting module

[0844] The server receives the advertising content output by the generation AI module and sends it to the AGI targeting module, which then performs a deeper analysis of the user's behavioral history to create a detailed user profile. Based on the user profile, the content and timing of the generated advertisements are optimized.

[0845] 4. Delivery of advertisements

[0846] The server sends the final generated advertisement content to the advertisement delivery server, which delivers the optimized advertisement to the specified display device. The advertisement is displayed on the user's operating device and records how the user responded to the advertisement.

[0847] Hardware and software used

[0848] Server: Carries out a series of processes including data collection, storage, analysis, content generation, optimization, and distribution. Specifically, it includes a database server that encrypts and stores user behavior history data, and a processing server that runs the generative AI module and AGI targeting module.

[0849] Generative AI module: Uses the Generative AI class to generate advertisements based on user behavior history data.

[0850] AGI Targeting Module: Uses the AGIModule class to optimize ads based on detailed user profiles.

[0851] User device: The device on which the ad is displayed and the user's interactions are recorded. This includes electronic devices such as smartphones, tablets, and computers.

[0852] Specific examples

[0853] Here's a concrete example: Consider a user who frequently buys sports equipment and frequently visits sports-related websites. One day, the user browses reviews of running shoes. The device records this behavior and sends it to the server. The server stores the data in a database and feeds it into the generative AI module.

[0854] The generative AI module generates the phrase "30% off the latest running shoes!" and a promotional video. The server then sends that information to the AGI targeting module, which analyzes the user's detailed profile. For example, it discovers that the user tends to be online at night. The server sends the ad to the ad delivery server and sets it up to be displayed on social media at night. When the user checks social media that evening, an optimized ad for running shoes appears. The user clicks on the ad and purchases the product.

[0855] Prompt Sentence Examples

[0856] Prompt: User behavior: Searched for "latest running shoes." Generate appropriate ad content.

[0857] Thus, a specific system for implementing the present invention and its operation will be described.

[0858] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0859] Step 1:

[0860] Collection of user behavior history

[0861] The input used is behavioral data (page accesses, clicks, search queries, viewed content, etc.) from the user's operating device (e.g., smartphone or PC). When the device is using a website or application, the behavioral data is tracked in real time and sent in encrypted form to a server. The server stores the received data in a database and keeps it for later analysis. In this process, it is important to accurately collect user behavior patterns.

[0862] Step 2:

[0863] Generative AI module generates advertising content

[0864] The server extracts the latest user behavior history data from the database and sends it as input to the generation AI module. The generation AI module analyzes this behavior history data to identify the user's preferences and interests. It then generates advertising phrases, images, videos, and music based on the user's identified preferences. For example, the generated advertising phrase might be "30% off the latest running shoes!" The output of this step is the generated advertising content.

[0865] Step 3:

[0866] Optimization with AGI targeting module

[0867] The server receives the ad content output from the generation AI module and sends it as input to the AGI targeting module, which then performs a more detailed analysis of the user's behavioral history to create a detailed user profile. Based on this profile, the content and timing of the generated ads are optimized. For example, if a particular user tends to be online at night, ads can be displayed at that time. The output of this step is optimized ad content.

[0868] Step 4:

[0869] Ad serving

[0870] The server sends the optimized advertising content using the AGI targeting module to the advertising delivery server. The advertising delivery server delivers the optimized advertisements to the user's operating device (for example, a display device such as a smartphone or PC). The advertisements are displayed within the application or website the user is using. In addition, data on users' clicks and responses to the advertisements is collected and sent to the server. This makes it possible to evaluate the effectiveness of the advertisements based on user responses.

[0871] Through this series of processes, advertisements generated based on the user's behavioral history are delivered to each individual user at the optimal time and in the optimal way.

[0872] 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.

[0873] This invention shows an embodiment of a system that generates, optimizes, and delivers advertising content by combining user behavior history and user emotion data. The specific processing of the program is explained below, along with specific examples.

[0874] Program processing overview

[0875] 1. Collecting user behavior history

[0876] While a device is using a website or app, it tracks user activity in real time, including page visits, clicks, search queries, and content viewed. This data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[0877] 2. Acquiring emotional data using the emotion engine

[0878] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and sends them to the emotion engine. It also analyzes emotions from the text the user types. This emotion data is sent to a server and integrated with the user's behavioral data.

[0879] 3. Generating advertising content using the generative AI module

[0880] The server extracts the latest user behavior history and emotion data from the database and inputs it into the generative AI module, which performs the following processes:

[0881] Analyze the data to identify user preferences, interests, and emotional states.

[0882] Generate advertising phrases, images, videos, and music based on identified preferences and emotional states.

[0883] For example, if a user frequently visits sports-related websites and also displays positive emotions while watching fitness videos, the generative AI module will generate ads for sports equipment for that user.

[0884] 4. Optimization with AGI targeting module

[0885] The server receives the ad content output from the Generative AI module and sends it to the AGI Targeting module, which performs the following tasks:

[0886] Deeper analysis of user behavioral history and sentiment data creates detailed user profiles.

[0887] Optimize the content and timing of generated ads based on user profiles.

[0888] For example, if a particular user consumes a lot of fitness-related content at night and exhibits positive emotions, the AGI targeting module will adjust ads to appear at those times.

[0889] 5. Delivery of advertisements

[0890] The server sends the final generated ad content to the ad delivery server, which does the following:

[0891] Upload the optimized ads to each distribution platform.

[0892] Set ad delivery schedules based on user activity times and platform characteristics.

[0893] The device receives ads from the ad serving system and displays them within the websites and apps the user is using. The ads are customized based on the user's behavioral history, emotional data, and optimized user profile.

[0894] Specific examples

[0895] Example 1: User purchases sporting goods

[0896] A user frequently purchases sports equipment and frequently browses sports-related websites and videos. One evening, the user browses a review of running shoes and the camera captures a smile that expresses positive emotions. The device records this behavior and emotion and sends it to the server. The server stores the data in a database and then sends it to the generative AI module.

[0897] The generative AI module generates an advertising slogan and promotional video for the user, such as "Get 30% off the latest running shoes!" It also selects music that emphasizes enjoyment based on the user's emotional data. The server sends this information to the AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[0898] The server sends the advertisement to the ad delivery server and configures it to be displayed on social media overnight. When the user checks social media that evening, an optimized ad for running shoes appears, the user clicks on the ad, and purchases the product.

[0899] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the user's behavior history and emotion data.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] The device tracks real-time behavioral data such as page visits, clicks, search queries, and content viewed while users are using websites and apps, and this data is sent in encrypted form to a server.

[0903] Step 2:

[0904] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends the data to the emotion engine, which analyzes the user's emotions from this data and generates emotion data.If there is text input, it extracts emotion data from the text.

[0905] Step 3:

[0906] The server stores the received user behavior history data and emotion data in a database, including user IDs, page access history, click history, search queries, viewed content, and emotion data.

[0907] Step 4:

[0908] The server periodically extracts user behavior history and emotion data from the database and inputs it into the generative AI module. This process can be done in batch or real-time.

[0909] Step 5:

[0910] The generative AI module analyzes the received user behavior history and emotional data, performs data mining to identify the user's preferences and emotional state, and generates advertising phrases, images, videos, and music based on the analysis results.

[0911] Step 6:

[0912] The server stores the generated advertising content in temporary storage, which includes specific advertising phrases, images, video, and music files.

[0913] Step 7:

[0914] The server retrieves the ad content from temporary storage and sends it to the AGI targeting module, along with user behavior data, sentiment data, and the generated ad content.

[0915] Step 8:

[0916] The AGI targeting module uses user behavioral history and emotional data to create a detailed user profile, which includes the user's interests, behavioral patterns, active times, and emotional state.

[0917] Step 9:

[0918] The AGI targeting module optimizes ads based on user profiles, determining when ads are delivered, customizing their content, and on which platforms they appear.

[0919] Step 10:

[0920] The server sends the optimized advertisement content to the advertisement delivery server, which then uploads these advertisements to each distribution platform.

[0921] Step 11:

[0922] The ad delivery server sets an ad delivery schedule based on the user's activity time and the platform's characteristics. For example, if a user is more active at night, the server can set the ad delivery schedule to be delivered during that time.

[0923] Step 12:

[0924] The device receives the advertisements sent from the ad distribution system and displays them on the websites and apps the user is using. The advertisements are customized based on the user's behavioral history, emotional data, and optimized user profile.

[0925] Step 13:

[0926] The device again tracks the user's response to the ad (clicks, viewing time, purchases, etc.) and sends this data to a server to evaluate the effectiveness of the ad.

[0927] Step 14:

[0928] The server analyzes the received response data and evaluates the effectiveness of the advertisement. The results of this evaluation are fed back to the generation AI module and AGI targeting module, and are used for the next advertisement generation and targeting.

[0929] Example 2

[0930] 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."

[0931] Conventional ad delivery systems generate and deliver advertising content based on user behavior history, but they have difficulty reflecting the user's real-time emotional state, resulting in insufficient advertising effectiveness. Furthermore, they are unable to quickly respond to changes in user preferences and interests, resulting in insufficient ad optimization. Therefore, a new system is needed that can deliver ads effectively and attract user attention.

[0932] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting user behavior history, a means for acquiring user emotion data and integrating it with the user behavior history, a generation means for analyzing the user behavior history and emotion data and generating advertising content, a targeting means for optimizing the generated advertising content based on a user profile, and a means for delivering the optimized advertising content. This makes it possible to integrate the user behavior history and emotion data and deliver more effective and adaptive advertising.

[0933] "User behavior history" refers to the series of activities a user performs when using a website or app, such as page visits, clicks, search queries, and content viewed.

[0934] "Emotional data" refers to data that indicates a user's emotional state, such as data obtained from the user's facial expressions, voice, and input text.

[0935] "Generation means" refers to a module that has the function of analyzing user behavior history and emotional data and generating advertising content (e.g., advertising phrases, images, videos, music).

[0936] "Targeting means" refers to a module that has the function of optimizing the content and timing of display of generated advertising content based on user profiles.

[0937] "Delivery Means" refers to a module that has the function of delivering optimized advertising content to the platform used by the user.

[0938] "User Profile" refers to a data set that specifies a user's preferences and interests, generated from the user's behavioral history, emotional data, and other related data.

[0939] This invention shows an embodiment of a system that generates, optimizes, and delivers advertising content by combining user behavior history and user emotion data. The specific processing of the system is explained below, along with specific examples.

[0940] Collection of user behavior history

[0941] The device tracks behavioral data such as page accesses, clicks, search queries, and viewed content in real time while the user is using a website or app. To do this, a tracking code or SDK must be embedded, allowing detailed collection of user activity. The device encrypts the collected data and sends it to a server using a secure communication protocol such as HTTPS. The server stores the received data in a database, where it is used for analysis.

[0942] Acquiring emotion data

[0943] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It then uses natural language processing technology to analyze the text entered by the user and determine their emotions. The emotional data is anonymized and securely sent to the engine. The emotion engine receives and analyzes this data, and sends it to the server. The server then integrates this emotional data with the user's behavioral history and stores it in a database.

[0944] Advertising content generation

[0945] The server retrieves the latest user behavior history and emotional data from the database and inputs it into the generative AI module, which analyzes the data using deep learning models and other methods to identify the user's preferences, interests, and emotional state. It then uses GANs (generative artificial network) and language models to generate advertising phrases, images, videos, and music.

[0946] For example, if a user frequently visits a sports-related website and displays positive emotions while watching fitness videos, the generative AI module will generate a promotional video for that user, stating, "Get 30% off the latest running shoes!", along with music that emphasizes the fun factor.

[0947] Ad content optimization

[0948] The server sends the advertising content output by the generation AI module to the AGI targeting module, which then performs detailed analysis of the user's behavioral history and emotional data to generate a user profile. Based on this profile, the content and timing of advertising displays are optimized. For example, if a user often consumes fitness-related content at night, ads can be displayed at that time.

[0949] Ad serving

[0950] The server finally sends the generated and optimized ad content to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform (such as social media or video streaming services) and schedules and executes ad delivery based on the user's activity time and platform characteristics. The device receives the ads from the ad delivery system and displays them on the website or app the user is using.

[0951] Specific examples

[0952] Example 1: User purchases sporting goods

[0953] A user frequently purchases sports equipment and frequently browses sports-related websites and videos. One evening, the user looks at a review of running shoes and the camera captures a smile that expresses positive emotions. The device records this behavior and emotion and sends it to the server. The server stores the data in a database and sends it to the generative AI module. The generative AI module generates an advertising phrase such as "30% off the latest running shoes!" and a promotional video. It also selects music that emphasizes enjoyment based on the emotional data. The server sends this information to the AGI targeting module, which analyzes the user's detailed profile and discovers that the user tends to be online at night. The server sends the advertisement to the ad delivery server, which configures the advertisement to be displayed on social media at night. That evening, when the user checks social media, an optimized ad for running shoes appears, the user clicks on the ad, and purchases the product.

[0954] Examples of prompt statements

[0955] Examples of prompts for situations using generative AI models:

[0956] Please provide a list of URLs of websites that the user has visited frequently recently, along with their content types (e.g., sports, fitness), and the user's recent emotional state (e.g., positive, negative).

[0957] The above is a description of a specific embodiment of the system of the present invention, which enables effective advertisement delivery based on the user's behavior history and emotion data.

[0958] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0959] Step 1: Collect user behavior history

[0960] The device tracks behavioral data such as page visits, clicks, search queries, and content viewed in real time while the user is using websites and apps.

[0961] The behavioral data collected by the device is encrypted and sent to a server using a secure communication protocol such as HTTPS. For example, the device collects data such as "a user read a specific sports article."

[0962] The server stores the received user behavior data in a database for use in subsequent processing steps.

[0963] Input: Data about specific user behavior on your website or app.

[0964] Output: The encrypted behavioral data is sent to the server and stored in a database.

[0965] Step 2: Obtaining emotion data

[0966] The device uses a camera and microphone to capture the user's facial expressions and voice in real time, for example, capturing the moment when the user smiles.

[0967] The device sends the captured data to the emotion engine for sentiment analysis, and uses natural language processing to analyze the sentiment of the text entered by the user.

[0968] The emotion engine analyzes the user's emotional state, encrypts the results, and sends them to the server.

[0969] The server integrates the received emotion data with behavioral data and stores it in a database.

[0970] Input: Data obtained from the user's facial expressions, voice, and text.

[0971] Output: The analyzed emotion data is sent to the server and stored in a database.

[0972] Step 3: Generate advertising content

[0973] The server retrieves the latest user behavior history and emotional data from the database and inputs it into the generative AI module.

[0974] The generative AI module uses deep learning models to analyze the user's preferences, interests, and emotional state.

[0975] The generative AI module generates advertising phrases, images, videos, and music based on the analysis. For example, if the user frequently views sports-related content and expresses positive emotions,

[0976] The generative AI module generates the advertising phrase "Get the latest running shoes 30% off!", a promotional video, and music that emphasizes fun.

[0977] Input: User behavior history and emotion data.

[0978] Output: User-optimized advertising content (phrases, images, videos, music).

[0979] Step 4: Optimize your ad content

[0980] The server sends the advertising content output from the generation AI module to the AGI targeting module.

[0981] The AGI targeting module performs detailed analysis of user behavioral history and emotional data to create a user profile.

[0982] The AGI targeting module optimizes ad content and timing based on user profiles. For example, if a user tends to consume more fitness-related content at night, it will adjust ad delivery schedules to accommodate those times.

[0983] Input: Generated ad content, user behavior history, and emotion data.

[0984] Output: A detailed user profile and optimized advertising content.

[0985] Step 5: Serving Ads

[0986] The server finally sends the generated and optimized ad content to the ad delivery server.

[0987] The ad delivery server uploads the optimized ads to each distribution platform (such as social media or video streaming services).

[0988] The ad delivery server sets and executes an ad delivery schedule based on the user's activity time and platform characteristics. For example, ads are displayed at the optimal time for users who access the internet at night.

[0989] The device receives advertisements from the ad distribution system and displays them on websites and within apps that the user is using.

[0990] Input: Optimized ad content.

[0991] Output: An ad that is displayed on the user's device, and the user is expected to take action based on the ad they receive.

[0992] (Application example 2)

[0993] 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."

[0994] Conventional ad delivery systems generate and deliver ads based solely on user behavioral history, making it difficult to deliver ads effectively based on user emotions. Furthermore, they are unable to display ads at the right time or provide personalized ads that reflect the user's momentary emotional state, limiting the effectiveness of ads.

[0995] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavior history, means for analyzing the user behavior history and generating advertising content, means for optimizing the advertising content generated by the generation means based on a user profile, means for delivering the advertising content optimized by the targeting means, means for acquiring emotional data of the user, means for providing the emotional data to the generation means and using it in generating advertising content, and means for determining the optimal timing for delivering the advertising content generated by the generation means. This makes it possible to generate and deliver personalized advertisements that combine user behavior history and emotional data, and is expected to improve advertising effectiveness.

[0996] "User behavior history" refers to behavioral information such as access history, clicks, search queries, and viewed content when a user uses a website or application.

[0997] "Advertising content" refers to promotional information such as advertising phrases, images, videos, music, etc. delivered to users.

[0998] "Generation means" refers to a method or device for analyzing a user's behavioral history and emotional data and generating advertising content based on the analysis.

[0999] "Targeting means" refers to a method or device for optimizing generated advertising content based on a user profile and effectively delivering it to a specific user at a specific time.

[1000] "Emotional data" refers to the emotional state extracted from a user's facial expression, voice, or text, such as data indicating emotions such as joy, excitement, or sadness.

[1001] "User profile" refers to detailed user information including user behavioral history, emotional data, interests and preferences.

[1002] "Delivery means" refers to a method or device for delivering optimized advertising content to users at an appropriate time and in an appropriate manner.

[1003] "Optimal timing" refers to the time and situation in which a user is predicted to be most likely to respond to advertising content, based on the user's behavioral history and emotional data.

[1004] The system for implementing this invention collects user behavior history, acquires emotion data, and generates, optimizes, and distributes advertising content based on that data. This system is composed of the following main elements:

[1005] First, the device collects user behavior in real time. Specifically, it tracks information such as the pages visited, clicks, search queries, and content viewed while the user is using websites and apps. This data is sent in encrypted form to a server and stored in a database.

[1006] The device then uses the camera and microphone to capture the user's emotional data. It captures facial expressions and voice in real time, and also analyzes emotions from text entered by the user. This emotional data is then sent to a server and integrated with the user's behavioral data.

[1007] The server contains a generation AI module that extracts and analyzes the latest user behavior and emotional data from the database. It then uses prompts to generate advertising phrases, images, videos, and music based on the user's preferences, interests, and emotional state. For example, a prompt might look like this: "User behavior: frequently visits sports-related websites and reads running shoe reviews," "User emotion: smiling (positive emotion)," and "Generate ad: promotional ad for fitness products (e.g., 30% off the latest running shoes)."

[1008] The server also includes an AGI targeting module as a targeting method. This module analyzes user profiles in detail and optimizes the content and timing of generated ads. In doing so, it determines the optimal timing when users are predicted to be most likely to respond to ads based on user behavioral history and emotional data.

[1009] Finally, the advertising content generated and optimized by the generative AI module and AGI targeting module is delivered from the server to the device via the ad delivery server. Specifically, customized ads are displayed within the apps and websites the user is using. These ads are personalized based on the user's behavioral history and emotional data, so they can be expected to be highly effective.

[1010] This makes it possible to generate and deliver personalized advertisements that combine user behavioral history and emotional data, significantly increasing the effectiveness of advertising.

[1011] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1012] Step 1:

[1013] The device collects user behavior data, tracking it in real time as users browse websites, click within apps, and enter search queries. This data is sent in encrypted form to a server and stored in a database.

[1014] Input: Data about your website and app behavior.

[1015] Output: Encrypted behavioral data is stored in a database.

[1016] Step 2:

[1017] The device collects the user's emotional data. It uses a camera and microphone to capture the user's facial expressions and voice in real time. It also analyzes emotions from text entered by the user. This emotional data is sent to a server and integrated with the user's behavioral data.

[1018] Input: User's facial expression data, voice data, and text data.

[1019] Output: The analyzed emotion data is sent to the server and stored in a database.

[1020] Step 3:

[1021] The server extracts the user's behavioral history and emotional data from the database. This data is then input into the generative AI module, which analyzes the user's behavioral and emotional patterns to understand the user's preferences, interests, and emotional state.

[1022] Input: User behavior history and emotion data stored in a database.

[1023] Output: Analyzed data about the user's preferences, interests, and emotional state.

[1024] Step 4:

[1025] The generative AI module generates advertising content based on the analysis data. For example, if a user frequently visits sports-related websites and smiles while reading reviews of running shoes, it will generate an advertising phrase or promotional video such as "30% off the latest running shoes!"

[1026] Input: Parsed preference, interest, and emotional state data.

[1027] Output: Generated advertising content (advertising phrase, images, videos, music).

[1028] Step 5:

[1029] The server then sends the generated ad content to the AGI targeting module, which performs detailed analysis of user profiles to optimize the timing and content of ad displays. For example, if a particular user consumes fitness-related content at night and displays positive emotions, the module will adjust the ads to be displayed at that time.

[1030] Input: Generated advertising content, user profile.

[1031] Output: Optimized ad delivery schedule.

[1032] Step 6:

[1033] The server sends the optimized ad content to the ad delivery server, which then delivers the ad to the user's device at the appropriate time. Users can then view personalized ads on the websites and apps they use.

[1034] Input: Optimized ad content, delivery schedule.

[1035] Output: Personalized ads delivered to the user's device.

[1036] Through these steps, personalized advertisements can be generated and delivered based on user behavior history and emotional data.

[1037] 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.

[1038] 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.

[1039] 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.

[1040] [Fourth embodiment]

[1041] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1042] 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.

[1043] 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).

[1044] 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.

[1045] 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.

[1046] 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).

[1047] 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.

[1048] 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.

[1049] 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.

[1050] 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.

[1051] 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.

[1052] 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.

[1053] 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."

[1054] This invention shows an embodiment of a system that collects user behavior history and generates, optimizes, and delivers advertising content based on that data. The specific processing of the program is explained below, along with specific examples.

[1055] Program processing overview

[1056] 1. Collecting user behavior history

[1057] When a device uses a website or app, page visits, clicks, search queries, and content viewed are tracked in real time. This behavioral data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[1058] 2. Generating advertising content using the generative AI module

[1059] The server extracts the latest user behavior history data from the database and inputs it into the generation AI module, which performs the following processes:

[1060] Analyzing data to identify user preferences and interests.

[1061] Based on the identified preferences, advertising phrases, images, videos, and music are generated.

[1062] For example, if a user frequently visits sports-related websites and reads many articles about sporting goods, the generative AI module will generate sporting goods ads for that user.

[1063] 3. Optimization with AGI targeting module

[1064] The server receives the ad content output from the Generative AI module and sends it to the AGI Targeting module, which performs the following tasks:

[1065] Conduct deeper analysis of user behavioral history to create detailed user profiles.

[1066] Optimize the content and timing of generated ads based on user profiles.

[1067] For example, if a particular user consumes a lot of sports-related content at night, the AGI targeting module will adjust ads to appear at that time.

[1068] 4. Delivery of advertisements

[1069] The server sends the final generated ad content to the ad delivery server, which does the following:

[1070] Upload the optimized ads to each distribution platform.

[1071] Set ad delivery schedules based on user activity times and platform characteristics.

[1072] The device receives advertisements from the ad distribution system and displays them on websites and within apps that the user is using.

[1073] Specific examples

[1074] A user has a habit of buying sports equipment. He frequently browses sports-related websites and videos. One evening, he browses reviews of running shoes. The device records this behavior and sends it to the server. The server stores the data in a database.

[1075] The server then sends this data to a generative AI module, which generates a promotional video with the phrase "30% off the latest running shoes!" The server then sends the information to an AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[1076] The server sends the ad to the ad delivery server, which then sets it up so that the ad is displayed on social media overnight. That evening, when the user checks social media, an optimized ad for running shoes appears, and the user clicks on it and purchases the product.

[1077] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the latest behavioral history of a user.

[1078] The processing flow will be explained below.

[1079] Step 1:

[1080] While the device is using websites and apps, it tracks user activity in real time, including page visits, clicks, search queries, and content viewed, and this data is sent in encrypted form to a server.

[1081] Step 2:

[1082] The server stores the received user behavior data in a database, including user IDs, page access history, click history, search queries, and viewed content.

[1083] Step 3:

[1084] The server periodically extracts user behavior history from the database and sends it to the generation AI module. This process can be done in batch or real-time.

[1085] Step 4:

[1086] The generative AI module analyzes the received user behavior history and performs data mining to identify user preferences and interests, then generates advertising phrases, images, videos, and music based on the identified preferences.

[1087] Step 5:

[1088] The server stores the generated advertising content in temporary storage, which includes specific advertising phrases, images, video, and music files.

[1089] Step 6:

[1090] The server retrieves the ad content from temporary storage and sends it to the AGI targeting module, along with user behavior data and the generated ad content.

[1091] Step 7:

[1092] The AGI targeting module creates a detailed user profile based on user behavior history and generated ads, including user interests, behavioral patterns, and time of day.

[1093] Step 8:

[1094] The AGI targeting module optimizes ads based on user profiles, determining when ads are delivered, customizing their content, and on which platforms they appear.

[1095] Step 9:

[1096] The server sends the optimized advertisement content to the advertisement delivery server, which then uploads these advertisements to each distribution platform.

[1097] Step 10:

[1098] The ad delivery server sets an ad delivery schedule based on the user's activity time and the platform's characteristics. For example, if a user is more active at night, the server can set the ad delivery schedule to be delivered during that time.

[1099] Step 11:

[1100] The device receives advertisements sent from the ad distribution system and displays them on websites and apps that the user uses. The advertisements are customized based on the user's behavioral history and an optimized user profile.

[1101] Step 12:

[1102] The device again tracks the user's response to the ad (clicks, viewing time, purchases, etc.) and sends this data to a server to evaluate the effectiveness of the ad.

[1103] Step 13:

[1104] The server analyzes the received response data and evaluates the effectiveness of the advertisement. The results of this evaluation are fed back to the generation AI module and AGI targeting module, and are used for the next advertisement generation and targeting.

[1105] Example 1

[1106] 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."

[1107] Conventional ad delivery systems have had difficulty generating and optimizing ad content by effectively utilizing user behavioral history. In addition, there was a lack of means to optimize the timing and content of ad delivery based on user profiles, making it difficult to achieve effective ad delivery.

[1108] 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.

[1109] In this invention, the server includes a means for collecting user behavior history, a generation means for analyzing the user behavior history and generating advertising content, and a targeting means for optimizing the advertising content generated by the generation means based on a user profile, thereby enabling the generation, optimization, and distribution of effective advertisements based on the user behavior history.

[1110] "User behavior history" is data that records the actions a user takes while using a website or app, such as page visits, clicks, search queries, and content viewed.

[1111] The "generation means" is a means for analyzing user behavior history and generating advertising content based on the analysis.

[1112] The "targeting means" is a means for optimizing the advertising content generated by the generating means based on a user profile.

[1113] "Delivery means" refers to a means for delivering advertising content optimized by targeting means to users.

[1114] A "generative AI model" is a module that uses artificial intelligence technology to analyze data and generate content such as advertising phrases, images, videos, and music.

[1115] A "user profile" is a detailed profile formed based on a user's behavioral history that identifies the user's preferences and interests.

[1116] This invention relates to an embodiment of a system that collects user behavior history and generates, optimizes, and delivers advertising content based on that data. Specific implementation methods of this invention are described below.

[1117] The system uses a server, terminals, and database as hardware, and a generative AI model, a data analysis module, an AGI targeting module, and an ad delivery module as software.

[1118] Collection of user behavior history

[1119] When a device uses a website or app, page accesses, clicks, search queries, viewed content, and other activities are tracked in real time. The tracked behavioral data is sent to a server in encrypted form. The server stores the received data in a database and retains it for subsequent analysis. Specifically, the device uses a JavaScript tracking script to collect user behavioral data and sends it to the server as an encrypted HTTP request.

[1120] Advertising content generation

[1121] The server extracts the latest user behavior history data from the database and inputs it into a generative AI model. The generative AI model analyzes the data and identifies user preferences and interests. Based on the identified preferences, it generates advertising phrases, images, videos, and music. Specifically, the server uses a Python script to extract data using SQL queries and sends an API request to a generative AI model (e.g., OpenAI's GPT-3) to generate advertising content.

[1122] Ad content optimization

[1123] The server receives the advertising content output by the generative AI model and sends it to the AGI targeting module, which then performs a deeper analysis of the user's behavioral history to create a detailed user profile. Based on this profile, the content and timing of the generated ads are optimized. Specifically, the server performs data analysis using Python analytical libraries (e.g., Pandas and Scikit-learn), and the AGI targeting module configures ad delivery based on the user's online time.

[1124] Ad serving

[1125] The server finally sends the generated and optimized ad content to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform and sets the ad delivery schedule based on the user's activity time and platform characteristics. For example, the server uses a REST API to send ad data to the ad delivery server, which then uploads it to Google Ads or social media platforms (e.g., Facebook Ads). The device displays the ads received from the ad delivery system on the website or app the user is using.

[1126] Specific examples

[1127] A user has a habit of buying sports equipment. He frequently browses sports-related websites and videos. One evening, he browses reviews of running shoes. The device records this behavior and sends it to the server, which stores the data in a database.

[1128] The server then sends this data to a generative AI model, which generates a promotional video with the phrase "30% off the latest running shoes!" The server then sends this information to an AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[1129] The server then sends the ad to the ad delivery server, which then configures it to be displayed on social media overnight. When the user checks social media that evening, the optimized running shoe ad appears, and the user clicks on it to purchase the product.

[1130] Prompt Sentence Examples

[1131] Examples of prompts to input to a generative AI model might include:

[1132] "Generate ads for users who have recently visited a sports-related website."

[1133] "Create a promotional video for 30% off running shoes."

[1134] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the latest behavioral history of a user.

[1135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1136] Step 1:

[1137] The device tracks real-time behavioral data when a user uses a website or app, including page visits, clicks, search queries, and content viewed. Specifically, a JavaScript tracking script running on the device collects this behavioral data and sends it to a server as an encrypted HTTP request.

[1138] Input: User behavior data (page visits, clicks, search queries, etc.)

[1139] Output: Data in the form of an encrypted HTTP request

[1140] Step 2:

[1141] The server receives the encrypted data sent from the terminal and stores it in a database. Specifically, the server receives the encrypted data via the HTTPS protocol and executes an SQL query to store it in the database.

[1142] Input: Behavioral data in the form of encrypted HTTP requests

[1143] Output: User behavior data stored in a database

[1144] Step 3:

[1145] The server extracts the latest user behavior history data from the database and sends it to the generative AI model. Specifically, the server executes SQL queries using Python scripts and sends API requests to the generative AI model (e.g., OpenAI's GPT-3).

[1146] Input: User behavior history data stored in a database

[1147] Output: API request data to the generative AI model

[1148] Step 4:

[1149] The generative AI model analyzes the received user behavior history data to identify user preferences and interests. Based on the results, it generates advertising phrases, images, videos, and music. Specifically, the generative AI model generates advertising content using data analysis algorithms.

[1150] Input: API request data to the generative AI model

[1151] Output: Generated advertising content (advertising phrase, images, videos, music)

[1152] Step 5:

[1153] The server receives the ad content output by the generative AI model and sends it to the AGI targeting module, which then performs further analysis of the user's behavioral history data to create a detailed user profile. Specifically, the server uses Python analytical libraries (such as Pandas and Scikit-learn) to analyze the data and optimize the content and timing of ads.

[1154] Input: Generated ad content

[1155] Output: Detailed user profile and optimized ad delivery settings

[1156] Step 6:

[1157] The server sends the final generated and optimized ad content from the generative AI model and AGI targeting module to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform and sets the ad delivery schedule based on user activity times and platform characteristics. Specifically, the server sends data to the ad delivery server using a REST API.

[1158] Input: Final generated and optimized ad content and delivery settings

[1159] Output: Uploaded ads to distribution platforms

[1160] Step 7:

[1161] The device receives the ads from the ad distribution system and displays them on the website or app the user is using. Specifically, the device renders the ads based on the data it receives and displays them in the appropriate location.

[1162] Input: Advertising data received from the distribution platform

[1163] Output: Ads displayed on websites and in apps

[1164] This is the process flow of this system. At each step, the input data is processed and analyzed, and ultimately optimized advertising content is displayed, achieving effective ad delivery based on the user's behavioral history.

[1165] (Application example 1)

[1166] 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."

[1167] In conventional ad distribution systems, ads are generated and displayed based on a user's behavioral history, but because they are distributed uniformly without considering the detailed profiles of each individual user, the effectiveness of the ads is limited and targeting is insufficient.In addition, because the display device and timing of ads are not optimized, it is difficult to attract users' attention.

[1168] 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.

[1169] In this invention, the server includes means for collecting user behavior history, means for analyzing the user behavior history and generating advertising content, means for optimizing the advertising content generated by the generation means based on a user profile, means for delivering the advertising content optimized by the targeting means, means for delivering the generated advertising content to a display device based on the behavior history of a specific user, and means for displaying the delivered advertising content on the user's operating terminal and receiving responses. This makes it possible to optimize the timing and content of advertising display based on the detailed profile of each individual user, and to deliver advertising according to the display device.

[1170] "User behavior history" is a record of a user's actions and operations on the Internet, including page accesses, clicks, search queries, and viewed content.

[1171] "Generation means" refers to a combination of equipment and software for analyzing user behavior history and generating advertising content.

[1172] "Targeting tools" are any combination of devices and software used to optimize advertising content generated based on user profiles.

[1173] "Delivery means" refers to the combination of equipment and software used to actually deliver advertising content optimized by targeting means to users.

[1174] "Display device" refers to the user's operating terminal on which advertising content is displayed, and specifically includes electronic devices such as smartphones, tablets, and personal computers.

[1175] An "operation terminal" is an electronic device that is actually operated by a user and on which advertising content is displayed.

[1176] "Generation Module" means a specific software module for generating advertising phrases, images, videos, and music.

[1177] A "user profile" is a collection of data detailing a user's attributes and preferences, created based on user behavior history and other related information.

[1178] This invention describes a system that collects "user behavior history" and generates, optimizes, and delivers advertising content based on that data. To implement this invention, server, terminal, and user components, as well as related hardware and software, are required.

[1179] Program processing overview

[1180] 1. Collecting user behavior history

[1181] When a user uses a website or application, their device tracks real-time behavioral data, such as page visits, clicks, search queries, and content viewed. This behavioral data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[1182] 2. Generating advertising content using the generative AI module

[1183] The server extracts the latest user behavior history data from the database and inputs it into the generative AI module, which analyzes the data to identify user preferences and interests, and then generates advertising phrases, images, videos, and music based on the identified preferences.

[1184] 3. Optimization with AGI targeting module

[1185] The server receives the advertising content output by the generation AI module and sends it to the AGI targeting module, which then performs a deeper analysis of the user's behavioral history to create a detailed user profile. Based on the user profile, the content and timing of the generated advertisements are optimized.

[1186] 4. Delivery of advertisements

[1187] The server sends the final generated advertisement content to the advertisement delivery server, which delivers the optimized advertisement to the specified display device. The advertisement is displayed on the user's operating device and records how the user responded to the advertisement.

[1188] Hardware and software used

[1189] Server: Carries out a series of processes including data collection, storage, analysis, content generation, optimization, and distribution. Specifically, it includes a database server that encrypts and stores user behavior history data, and a processing server that runs the generative AI module and AGI targeting module.

[1190] Generative AI module: Uses the Generative AI class to generate advertisements based on user behavior history data.

[1191] AGI Targeting Module: Uses the AGIModule class to optimize ads based on detailed user profiles.

[1192] User device: The device on which the ad is displayed and the user's interactions are recorded. This includes electronic devices such as smartphones, tablets, and computers.

[1193] Specific examples

[1194] Here's a concrete example: Consider a user who frequently buys sports equipment and frequently visits sports-related websites. One day, the user browses reviews of running shoes. The device records this behavior and sends it to the server. The server stores the data in a database and feeds it into the generative AI module.

[1195] The generative AI module generates the phrase "30% off the latest running shoes!" and a promotional video. The server then sends that information to the AGI targeting module, which analyzes the user's detailed profile. For example, it discovers that the user tends to be online at night. The server sends the ad to the ad delivery server and sets it up to be displayed on social media at night. When the user checks social media that evening, an optimized ad for running shoes appears. The user clicks on the ad and purchases the product.

[1196] Prompt Sentence Examples

[1197] Prompt: User behavior: Searched for "latest running shoes." Generate appropriate ad content.

[1198] Thus, a specific system for implementing the present invention and its operation will be described.

[1199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1200] Step 1:

[1201] Collection of user behavior history

[1202] The input used is behavioral data (page accesses, clicks, search queries, viewed content, etc.) from the user's operating device (e.g., smartphone or PC). When the device is using a website or application, the behavioral data is tracked in real time and sent in encrypted form to a server. The server stores the received data in a database and keeps it for later analysis. In this process, it is important to accurately collect user behavior patterns.

[1203] Step 2:

[1204] Generative AI module generates advertising content

[1205] The server extracts the latest user behavior history data from the database and sends it as input to the generation AI module. The generation AI module analyzes this behavior history data to identify the user's preferences and interests. It then generates advertising phrases, images, videos, and music based on the user's identified preferences. For example, the generated advertising phrase might be "30% off the latest running shoes!" The output of this step is the generated advertising content.

[1206] Step 3:

[1207] Optimization with AGI targeting module

[1208] The server receives the ad content output from the generation AI module and sends it as input to the AGI targeting module, which then performs a more detailed analysis of the user's behavioral history to create a detailed user profile. Based on this profile, the content and timing of the generated ads are optimized. For example, if a particular user tends to be online at night, ads can be displayed at that time. The output of this step is optimized ad content.

[1209] Step 4:

[1210] Ad serving

[1211] The server sends the optimized advertising content using the AGI targeting module to the advertising delivery server. The advertising delivery server delivers the optimized advertisements to the user's operating device (for example, a display device such as a smartphone or PC). The advertisements are displayed within the application or website the user is using. In addition, data on users' clicks and responses to the advertisements is collected and sent to the server. This makes it possible to evaluate the effectiveness of the advertisements based on user responses.

[1212] Through this series of processes, advertisements generated based on the user's behavioral history are delivered to each individual user at the optimal time and in the optimal way.

[1213] 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.

[1214] This invention shows an embodiment of a system that generates, optimizes, and delivers advertising content by combining user behavior history and user emotion data. The specific processing of the program is explained below, along with specific examples.

[1215] Program processing overview

[1216] 1. Collecting user behavior history

[1217] While a device is using a website or app, it tracks user activity in real time, including page visits, clicks, search queries, and content viewed. This data is sent in encrypted form to a server, which stores the data in a database and retains it for subsequent analysis.

[1218] 2. Acquiring emotional data using the emotion engine

[1219] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and sends them to the emotion engine. It also analyzes emotions from the text the user types. This emotion data is sent to a server and integrated with the user's behavioral data.

[1220] 3. Generating advertising content using the generative AI module

[1221] The server extracts the latest user behavior history and emotion data from the database and inputs it into the generative AI module, which performs the following processes:

[1222] Analyze the data to identify user preferences, interests, and emotional states.

[1223] Generate advertising phrases, images, videos, and music based on identified preferences and emotional states.

[1224] For example, if a user frequently visits sports-related websites and also displays positive emotions while watching fitness videos, the generative AI module will generate ads for sports equipment for that user.

[1225] 4. Optimization with AGI targeting module

[1226] The server receives the ad content output from the Generative AI module and sends it to the AGI Targeting module, which performs the following tasks:

[1227] Deeper analysis of user behavioral history and sentiment data creates detailed user profiles.

[1228] Optimize the content and timing of generated ads based on user profiles.

[1229] For example, if a particular user consumes a lot of fitness-related content at night and exhibits positive emotions, the AGI targeting module will adjust ads to appear at those times.

[1230] 5. Delivery of advertisements

[1231] The server sends the final generated ad content to the ad delivery server, which does the following:

[1232] Upload the optimized ads to each distribution platform.

[1233] Set ad delivery schedules based on user activity times and platform characteristics.

[1234] The device receives ads from the ad serving system and displays them within the websites and apps the user is using. The ads are customized based on the user's behavioral history, emotional data, and optimized user profile.

[1235] Specific examples

[1236] Example 1: User purchases sporting goods

[1237] A user frequently purchases sports equipment and frequently browses sports-related websites and videos. One evening, the user browses a review of running shoes and the camera captures a smile that expresses positive emotions. The device records this behavior and emotion and sends it to the server. The server stores the data in a database and then sends it to the generative AI module.

[1238] The generative AI module generates an advertising slogan and promotional video for the user, such as "Get 30% off the latest running shoes!" It also selects music that emphasizes enjoyment based on the user's emotional data. The server sends this information to the AGI targeting module, which analyzes the user's detailed profile and discovers that he tends to be online at night.

[1239] The server sends the advertisement to the ad delivery server and configures it to be displayed on social media overnight. When the user checks social media that evening, an optimized ad for running shoes appears, the user clicks on the ad, and purchases the product.

[1240] The above is a description of a specific embodiment of the present invention. This system realizes effective advertisement delivery based on the user's behavior history and emotion data.

[1241] The processing flow will be explained below.

[1242] Step 1:

[1243] The device tracks real-time behavioral data such as page visits, clicks, search queries, and content viewed while users are using websites and apps, and this data is sent in encrypted form to a server.

[1244] Step 2:

[1245] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends the data to the emotion engine, which analyzes the user's emotions from this data and generates emotion data.If there is text input, it extracts emotion data from the text.

[1246] Step 3:

[1247] The server stores the received user behavior history data and emotion data in a database, including user IDs, page access history, click history, search queries, viewed content, and emotion data.

[1248] Step 4:

[1249] The server periodically extracts user behavior history and emotion data from the database and inputs it into the generative AI module. This process can be done in batch or real-time.

[1250] Step 5:

[1251] The generative AI module analyzes the received user behavior history and emotional data, performs data mining to identify the user's preferences and emotional state, and generates advertising phrases, images, videos, and music based on the analysis results.

[1252] Step 6:

[1253] The server stores the generated advertising content in temporary storage, which includes specific advertising phrases, images, video, and music files.

[1254] Step 7:

[1255] The server retrieves the ad content from temporary storage and sends it to the AGI targeting module, along with user behavior data, sentiment data, and the generated ad content.

[1256] Step 8:

[1257] The AGI targeting module uses user behavioral history and emotional data to create a detailed user profile, which includes the user's interests, behavioral patterns, active times, and emotional state.

[1258] Step 9:

[1259] The AGI targeting module optimizes ads based on user profiles, determining when ads are delivered, customizing their content, and on which platforms they appear.

[1260] Step 10:

[1261] The server sends the optimized advertisement content to the advertisement delivery server, which then uploads these advertisements to each distribution platform.

[1262] Step 11:

[1263] The ad delivery server sets an ad delivery schedule based on the user's activity time and the platform's characteristics. For example, if a user is more active at night, the server can set the ad delivery schedule to be delivered during that time.

[1264] Step 12:

[1265] The device receives the advertisements sent from the ad distribution system and displays them on the websites and apps the user is using. The advertisements are customized based on the user's behavioral history, emotional data, and optimized user profile.

[1266] Step 13:

[1267] The device again tracks the user's response to the ad (clicks, viewing time, purchases, etc.) and sends this data to a server to evaluate the effectiveness of the ad.

[1268] Step 14:

[1269] The server analyzes the received response data and evaluates the effectiveness of the advertisement. The results of this evaluation are fed back to the generation AI module and AGI targeting module, and are used for the next advertisement generation and targeting.

[1270] Example 2

[1271] 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."

[1272] Conventional ad delivery systems generate and deliver advertising content based on user behavior history, but they have difficulty reflecting the user's real-time emotional state, resulting in insufficient advertising effectiveness. Furthermore, they are unable to quickly respond to changes in user preferences and interests, resulting in insufficient ad optimization. Therefore, a new system is needed that can deliver ads effectively and attract user attention.

[1273] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting user behavior history, a means for acquiring user emotion data and integrating it with the user behavior history, a generation means for analyzing the user behavior history and emotion data and generating advertising content, a targeting means for optimizing the generated advertising content based on a user profile, and a means for delivering the optimized advertising content. This makes it possible to integrate the user behavior history and emotion data and deliver more effective and adaptive advertising.

[1274] "User behavior history" refers to the series of activities a user performs when using a website or app, such as page visits, clicks, search queries, and content viewed.

[1275] "Emotional data" refers to data that indicates a user's emotional state, such as data obtained from the user's facial expressions, voice, and input text.

[1276] "Generation means" refers to a module that has the function of analyzing user behavior history and emotional data and generating advertising content (e.g., advertising phrases, images, videos, music).

[1277] "Targeting means" refers to a module that has the function of optimizing the content and timing of display of generated advertising content based on user profiles.

[1278] "Delivery Means" refers to a module that has the function of delivering optimized advertising content to the platform used by the user.

[1279] "User Profile" refers to a data set that specifies a user's preferences and interests, generated from the user's behavioral history, emotional data, and other related data.

[1280] This invention shows an embodiment of a system that generates, optimizes, and delivers advertising content by combining user behavior history and user emotion data. The specific processing of the system is explained below, along with specific examples.

[1281] Collection of user behavior history

[1282] The device tracks behavioral data such as page accesses, clicks, search queries, and viewed content in real time while the user is using a website or app. To do this, a tracking code or SDK must be embedded, allowing detailed collection of user activity. The device encrypts the collected data and sends it to a server using a secure communication protocol such as HTTPS. The server stores the received data in a database, where it is used for analysis.

[1283] Acquiring emotion data

[1284] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. It then uses natural language processing technology to analyze the text entered by the user and determine their emotions. The emotional data is anonymized and securely sent to the engine. The emotion engine receives and analyzes this data, and sends it to the server. The server then integrates this emotional data with the user's behavioral history and stores it in a database.

[1285] Advertising content generation

[1286] The server retrieves the latest user behavior history and emotional data from the database and inputs it into the generative AI module, which analyzes the data using deep learning models and other methods to identify the user's preferences, interests, and emotional state. It then uses GANs (generative artificial network) and language models to generate advertising phrases, images, videos, and music.

[1287] For example, if a user frequently visits a sports-related website and displays positive emotions while watching fitness videos, the generative AI module will generate a promotional video for that user, stating, "Get 30% off the latest running shoes!", along with music that emphasizes the fun factor.

[1288] Ad content optimization

[1289] The server sends the advertising content output by the generation AI module to the AGI targeting module, which then performs detailed analysis of the user's behavioral history and emotional data to generate a user profile. Based on this profile, the content and timing of advertising displays are optimized. For example, if a user often consumes fitness-related content at night, ads can be displayed at that time.

[1290] Ad serving

[1291] The server finally sends the generated and optimized ad content to the ad delivery server. The ad delivery server uploads the optimized ads to each distribution platform (such as social media or video streaming services) and schedules and executes ad delivery based on the user's activity time and platform characteristics. The device receives the ads from the ad delivery system and displays them on the website or app the user is using.

[1292] Specific examples

[1293] Example 1: User purchases sporting goods

[1294] A user frequently purchases sports equipment and frequently browses sports-related websites and videos. One evening, the user looks at a review of running shoes and the camera captures a smile that expresses positive emotions. The device records this behavior and emotion and sends it to the server. The server stores the data in a database and sends it to the generative AI module. The generative AI module generates an advertising phrase such as "30% off the latest running shoes!" and a promotional video. It also selects music that emphasizes enjoyment based on the emotional data. The server sends this information to the AGI targeting module, which analyzes the user's detailed profile and discovers that the user tends to be online at night. The server sends the advertisement to the ad delivery server, which configures the advertisement to be displayed on social media at night. That evening, when the user checks social media, an optimized ad for running shoes appears, the user clicks on the ad, and purchases the product.

[1295] Examples of prompt statements

[1296] Examples of prompts for situations using generative AI models:

[1297] Please provide a list of URLs of websites that the user has visited frequently recently, along with their content types (e.g., sports, fitness), and the user's recent emotional state (e.g., positive, negative).

[1298] The above is a description of a specific embodiment of the system of the present invention, which enables effective advertisement delivery based on the user's behavior history and emotion data.

[1299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1300] Step 1: Collect user behavior history

[1301] The device tracks behavioral data such as page visits, clicks, search queries, and content viewed in real time while the user is using websites and apps.

[1302] The behavioral data collected by the device is encrypted and sent to a server using a secure communication protocol such as HTTPS. For example, the device collects data such as "a user read a specific sports article."

[1303] The server stores the received user behavior data in a database for use in subsequent processing steps.

[1304] Input: Data about specific user behavior on your website or app.

[1305] Output: The encrypted behavioral data is sent to the server and stored in a database.

[1306] Step 2: Obtaining emotion data

[1307] The device uses a camera and microphone to capture the user's facial expressions and voice in real time, for example, capturing the moment when the user smiles.

[1308] The device sends the captured data to the emotion engine for sentiment analysis, and uses natural language processing to analyze the sentiment of the text entered by the user.

[1309] The emotion engine analyzes the user's emotional state, encrypts the results, and sends them to the server.

[1310] The server integrates the received emotion data with behavioral data and stores it in a database.

[1311] Input: Data obtained from the user's facial expressions, voice, and text.

[1312] Output: The analyzed emotion data is sent to the server and stored in a database.

[1313] Step 3: Generate advertising content

[1314] The server retrieves the latest user behavior history and emotional data from the database and inputs it into the generative AI module.

[1315] The generative AI module uses deep learning models to analyze the user's preferences, interests, and emotional state.

[1316] The generative AI module generates advertising phrases, images, videos, and music based on the analysis. For example, if the user frequently views sports-related content and expresses positive emotions,

[1317] The generative AI module generates the advertising phrase "Get the latest running shoes 30% off!", a promotional video, and music that emphasizes fun.

[1318] Input: User behavior history and emotion data.

[1319] Output: User-optimized advertising content (phrases, images, videos, music).

[1320] Step 4: Optimize your ad content

[1321] The server sends the advertising content output from the generation AI module to the AGI targeting module.

[1322] The AGI targeting module performs detailed analysis of user behavioral history and emotional data to create a user profile.

[1323] The AGI targeting module optimizes ad content and timing based on user profiles. For example, if a user tends to consume more fitness-related content at night, it will adjust ad delivery schedules to accommodate those times.

[1324] Input: Generated ad content, user behavior history, and emotion data.

[1325] Output: A detailed user profile and optimized advertising content.

[1326] Step 5: Serving Ads

[1327] The server finally sends the generated and optimized ad content to the ad delivery server.

[1328] The ad delivery server uploads the optimized ads to each distribution platform (such as social media or video streaming services).

[1329] The ad delivery server sets and executes an ad delivery schedule based on the user's activity time and platform characteristics. For example, ads are displayed at the optimal time for users who access the internet at night.

[1330] The device receives advertisements from the ad distribution system and displays them on websites and within apps that the user is using.

[1331] Input: Optimized ad content.

[1332] Output: An ad that is displayed on the user's device, and the user is expected to take action based on the ad they receive.

[1333] (Application example 2)

[1334] 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."

[1335] Conventional ad delivery systems generate and deliver ads based solely on user behavioral history, making it difficult to deliver ads effectively based on user emotions. Furthermore, they are unable to display ads at the right time or provide personalized ads that reflect the user's momentary emotional state, limiting the effectiveness of ads.

[1336] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavior history, means for analyzing the user behavior history and generating advertising content, means for optimizing the advertising content generated by the generation means based on a user profile, means for delivering the advertising content optimized by the targeting means, means for acquiring emotional data of the user, means for providing the emotional data to the generation means and using it in generating advertising content, and means for determining the optimal timing for delivering the advertising content generated by the generation means. This makes it possible to generate and deliver personalized advertisements that combine user behavior history and emotional data, and is expected to improve advertising effectiveness.

[1337] "User behavior history" refers to behavioral information such as access history, clicks, search queries, and viewed content when a user uses a website or application.

[1338] "Advertising content" refers to promotional information such as advertising phrases, images, videos, music, etc. delivered to users.

[1339] "Generation means" refers to a method or device for analyzing a user's behavioral history and emotional data and generating advertising content based on the analysis.

[1340] "Targeting means" refers to a method or device for optimizing generated advertising content based on a user profile and effectively delivering it to a specific user at a specific time.

[1341] "Emotional data" refers to the emotional state extracted from a user's facial expression, voice, or text, such as data indicating emotions such as joy, excitement, or sadness.

[1342] "User profile" refers to detailed user information including user behavioral history, emotional data, interests and preferences.

[1343] "Delivery means" refers to a method or device for delivering optimized advertising content to users at an appropriate time and in an appropriate manner.

[1344] "Optimal timing" refers to the time and situation in which a user is predicted to be most likely to respond to advertising content, based on the user's behavioral history and emotional data.

[1345] The system for implementing this invention collects user behavior history, acquires emotion data, and generates, optimizes, and distributes advertising content based on that data. This system is composed of the following main elements:

[1346] First, the device collects user behavior in real time. Specifically, it tracks information such as the pages visited, clicks, search queries, and content viewed while the user is using websites and apps. This data is sent in encrypted form to a server and stored in a database.

[1347] The device then uses the camera and microphone to capture the user's emotional data. It captures facial expressions and voice in real time, and also analyzes emotions from text entered by the user. This emotional data is then sent to a server and integrated with the user's behavioral data.

[1348] The server contains a generation AI module that extracts and analyzes the latest user behavior and emotional data from the database. It then uses prompts to generate advertising phrases, images, videos, and music based on the user's preferences, interests, and emotional state. For example, a prompt might look like this: "User behavior: frequently visits sports-related websites and reads running shoe reviews," "User emotion: smiling (positive emotion)," and "Generate ad: promotional ad for fitness products (e.g., 30% off the latest running shoes)."

[1349] The server also includes an AGI targeting module as a targeting method. This module analyzes user profiles in detail and optimizes the content and timing of generated ads. In doing so, it determines the optimal timing when users are predicted to be most likely to respond to ads based on user behavioral history and emotional data.

[1350] Finally, the advertising content generated and optimized by the generative AI module and AGI targeting module is delivered from the server to the device via the ad delivery server. Specifically, customized ads are displayed within the apps and websites the user is using. These ads are personalized based on the user's behavioral history and emotional data, so they can be expected to be highly effective.

[1351] This makes it possible to generate and deliver personalized advertisements that combine user behavioral history and emotional data, significantly increasing the effectiveness of advertising.

[1352] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1353] Step 1:

[1354] The device collects user behavior data, tracking it in real time as users browse websites, click within apps, and enter search queries. This data is sent in encrypted form to a server and stored in a database.

[1355] Input: Data about your website and app behavior.

[1356] Output: Encrypted behavioral data is stored in a database.

[1357] Step 2:

[1358] The device collects the user's emotional data. It uses a camera and microphone to capture the user's facial expressions and voice in real time. It also analyzes emotions from text entered by the user. This emotional data is sent to a server and integrated with the user's behavioral data.

[1359] Input: User's facial expression data, voice data, and text data.

[1360] Output: The analyzed emotion data is sent to the server and stored in a database.

[1361] Step 3:

[1362] The server extracts the user's behavioral history and emotional data from the database. This data is then input into the generative AI module, which analyzes the user's behavioral and emotional patterns to understand the user's preferences, interests, and emotional state.

[1363] Input: User behavior history and emotion data stored in a database.

[1364] Output: Analyzed data about the user's preferences, interests, and emotional state.

[1365] Step 4:

[1366] The generative AI module generates advertising content based on the analysis data. For example, if a user frequently visits sports-related websites and smiles while reading reviews of running shoes, it will generate an advertising phrase or promotional video such as "30% off the latest running shoes!"

[1367] Input: Parsed preference, interest, and emotional state data.

[1368] Output: Generated advertising content (advertising phrase, images, videos, music).

[1369] Step 5:

[1370] The server then sends the generated ad content to the AGI targeting module, which performs detailed analysis of user profiles to optimize the timing and content of ad displays. For example, if a particular user consumes fitness-related content at night and displays positive emotions, the module will adjust the ads to be displayed at that time.

[1371] Input: Generated advertising content, user profile.

[1372] Output: Optimized ad delivery schedule.

[1373] Step 6:

[1374] The server sends the optimized ad content to the ad delivery server, which then delivers the ad to the user's device at the appropriate time. Users can then view personalized ads on the websites and apps they use.

[1375] Input: Optimized ad content, delivery schedule.

[1376] Output: Personalized ads delivered to the user's device.

[1377] Through these steps, personalized advertisements can be generated and delivered based on user behavior history and emotional data.

[1378] 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.

[1379] 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.

[1380] 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 robot 414.

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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).

[1385] 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.

[1386] 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."

[1387] 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.

[1388] 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).

[1389] 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.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] The following is further disclosed regarding the above embodiment.

[1400] (Claim 1)

[1401] A means of collecting user behavior history;

[1402] a generating means for analyzing the user behavior history and generating advertising content;

[1403] a targeting means for optimizing the advertising content generated by the generating means based on a user profile;

[1404] means for delivering advertising content optimized by the targeting means;

[1405] A system including:

[1406] (Claim 2)

[1407] 10. The system of claim 1, wherein the generating means includes a generating module that generates advertising phrases, images, videos, and music.

[1408] (Claim 3)

[1409] 2. The system according to claim 1, wherein the targeting means includes a module that performs detailed analysis of user profiles and optimizes the timing and content of advertisement display.

[1410] "Example 1"

[1411] (Claim 1)

[1412] A means of collecting user behavior history;

[1413] a generating means for analyzing the user behavior history and generating advertising content;

[1414] a targeting means for optimizing the advertising content generated by the generating means based on a user profile;

[1415] means for delivering advertising content optimized by the targeting means;

[1416] means for transmitting the user behavior history data in an encrypted format;

[1417] The generating means uses a generative AI model to identify user preferences and interests and generate advertising phrases, images, videos, and music;

[1418] The targeting means creates detailed user profiles and optimizes the timing and content of advertisement display;

[1419] A system including:

[1420] (Claim 2)

[1421] 10. The system of claim 1, wherein the generating means includes a generative AI model that generates advertising phrases, images, videos, and music.

[1422] (Claim 3)

[1423] 10. The system of claim 1, wherein the targeting means includes a module for creating detailed user profiles and optimizing the timing and content of advertisement presentations.

[1424] "Application Example 1"

[1425] (Claim 1)

[1426] A means of collecting user behavior history;

[1427] a generating means for analyzing the user behavior history and generating advertising content;

[1428] a targeting means for optimizing the advertising content generated by the generating means based on a user profile;

[1429] means for delivering advertising content optimized by the targeting means;

[1430] A means for delivering the generated advertising content to a display device based on the behavioral history of a specific user;

[1431] A means for displaying the distributed advertising content on a user's operating terminal and receiving a response thereto;

[1432] A system including:

[1433] (Claim 2)

[1434] 10. The system of claim 1, wherein the generating means includes a generating module that generates advertising phrases, images, videos, and music.

[1435] (Claim 3)

[1436] 2. The system according to claim 1, wherein the targeting means includes a module that performs detailed analysis of user profiles and optimizes the timing and content of advertisement display.

[1437] "Example 2: Combining Emotion Engines"

[1438] (Claim 1)

[1439] A means of collecting user behavior history;

[1440] a generating means for analyzing the user behavior history and generating advertising content;

[1441] A means for acquiring user emotion data and integrating it with the user behavior history;

[1442] a targeting means for optimizing the advertising content generated by the generating means based on a user profile;

[1443] means for delivering advertising content optimized by the targeting means;

[1444] A system including:

[1445] (Claim 2)

[1446] 10. The system of claim 1, wherein the generating means includes a generating module that generates advertising phrases, images, videos, and music.

[1447] (Claim 3)

[1448] 2. The system according to claim 1, wherein the targeting means includes a module that performs detailed analysis of user profiles and optimizes the timing and content of advertisement display.

[1449] "Application example 2 when combining emotion engines"

[1450] (Claim 1)

[1451] A means of collecting user behavior history;

[1452] a generating means for analyzing the user behavior history and generating advertising content;

[1453] a targeting means for optimizing the advertising content generated by the generating means based on a user profile;

[1454] means for delivering advertising content optimized by the targeting means;

[1455] means for acquiring emotion data of the user;

[1456] a means for providing the emotion data to a generating means and using the emotion data to generate advertising content;

[1457] means for determining an optimal timing for distributing the advertising content generated by the generating means;

[1458] A system including:

[1459] (Claim 2)

[1460] 10. The system of claim 1, wherein the generating means includes a generating module that generates advertising phrases, images, videos, and music.

[1461] (Claim 3)

[1462] 2. The system according to claim 1, wherein the targeting means includes a module that performs detailed analysis of user profiles and optimizes the timing and content of advertisement display. [Explanation of symbols]

[1463] 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 of collecting user behavior history; a generating means for analyzing the user behavior history and generating advertising content; a targeting means for optimizing the advertising content generated by the generating means based on a user profile; means for delivering advertising content optimized by the targeting means; A system including:

2. The system of claim 1 , wherein the generating means includes a generating module that generates advertising phrases, images, videos, and music.

3. 2. The system according to claim 1, wherein the targeting means includes a module that analyzes user profiles in detail and optimizes the timing and content of advertisement display.

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