system

Abstract

Provide a system. A user terminal includes a means for collecting browsing history information of a plurality of web pages; A means for analyzing the collected web page browsing history information and identifying the user's interests and concerns; A means for encrypting the analysis results and transmitting them to a server; a means for the server to create and update a user profile using the received analysis results; means for the server to generate personalized advertisements based on the user's profile and the context of the current web page; A means for the server to deliver the generated advertisement to a user's terminal; A means for displaying the delivered advertisement on a web page in a user's terminal; A system including:

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] Many modern internet advertisements suffer from a lack of user interest and context, resulting in poor advertising effectiveness. Furthermore, the lack of personalized advertising can lead to a poor user experience. Furthermore, many systems fail to adequately protect user privacy. By addressing these issues, it is necessary to provide useful and relevant advertisements to users. [Means for solving the problem]

[0005] The present invention provides a means for a user's device to collect browsing history information and identify the user's interests by analyzing the collected information. It also includes a means for encrypting and transmitting the analysis results to a server. The server then creates and updates a user profile based on the received analysis results, and generates personalized advertisements based on the user's profile and the context of the current web page. This process involves the use of natural language processing technology. The server then delivers the generated advertisements to the user's device, which then displays the advertisements on the web page. It also includes a means for collecting user ad click information and transmitting it to the server. This achieves a system that provides advertisements that match the user's interests, increasing advertising effectiveness while also improving the user experience.

[0006] A "user terminal" is a device such as a computer, smartphone, or tablet that a user uses to view web pages.

[0007] "Browser history information" is data relating to web pages that a user has accessed in the past, and includes information such as the URL, the time spent on the page, and the links clicked.

[0008] "Analyze" or "analysis" refers to the process of analyzing collected data to find specific patterns or trends.

[0009] "Interests" refers to information that indicates the degree of interest or preference a user has in a particular topic or category.

[0010] "Encryption" is the process of transforming transmitted data using special algorithms to make it unreadable to third parties.

[0011] A "server" is a powerful computer used to provide services to other computers over a network.

[0012] A "profile" is a set of information that summarizes a user's characteristics, such as their interests and behavioral history.

[0013] "Context" refers to the content and circumstances under which a particular web page is displayed.

[0014] "Personalized advertising" refers to advertising that is customized based on a user's interests and current browsing behavior.

[0015] "Natural language processing technology" refers to the technology that processes, understands, and generates human language using a computer.

[0016] "Generating an ad" is the process of creating new ad content based on the user's profile and the web page context.

[0017] "Distributing" refers to the process of sending particular data or content over a network to other devices.

[0018] "Display" refers to visually showing specific content or information on the user's terminal.

[0019] "Advertisement click information" is data generated when a user clicks on a specific advertisement. [Brief explanation of the drawings]

[0020] [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 illustrating 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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The system of the present invention personalizes advertisements based on the user's interests and the context of the web page being viewed by linking the server on the advertisement distribution platform side with the user's terminal. This system is comprised of the following steps.

[0042] Collecting data on user behavior

[0043] When a user browses a web page, their device collects browsing history information, including the URL of the page visited, the time spent on the page, the links clicked, the scrolling depth, and the search queries. This data is collected using browser cookies and local storage.

[0044] User behavior data analysis

[0045] The device analyzes the user's interests based on the collected data. This analysis may involve the use of machine learning algorithms to identify interests in specific categories (e.g., travel, cooking, technology, etc.) by analyzing the pages the user frequently visits and search queries. Data such as the ads the user clicks on and the duration of their visit may also be used for analysis.

[0046] Sending data

[0047] The device then encrypts the analysis results and sends them to the server. This encryption uses a standard encryption algorithm such as AES (Advanced Encryption Standard). The encrypted data is securely sent to the server, preventing eavesdropping or tampering by third parties.

[0048] Receiving Data and Profiling

[0049] The server receives and decrypts the encrypted data sent from the device. At this time, a unique user profile is created. The profile includes the user's areas of interest, past behavioral history, and clicked ad history. This allows the server to understand the user's behavioral patterns and build a foundation for providing appropriate advertisements.

[0050] Ad Generation

[0051] The server generates personalized ads based on the user's profile and contextual information about the current web page. This ad generation process may also use natural language processing (NLP) and image generation techniques. For example, if the user is interested in travel, the server generates promotional ads for flights and hotels. The generated ads are then formatted using HTML5 and JavaScript and converted into a deliverable format.

[0052] Ad delivery and display

[0053] The server delivers the generated advertisements to the user's device in real time, and the device interprets the received advertisements and inserts them into the appropriate positions on the currently displayed web page, allowing the user to see advertisements that are relevant to the page they are viewing.

[0054] Tracking advertising effectiveness

[0055] When a user clicks on an ad, the click information is recorded on the device. The device then sends this click data back to the server, which uses it to track the effectiveness of the ad. The server collects and analyzes data such as ad click rates and conversion rates to evaluate the ad's performance.

[0056] Specific examples

[0057] Example 1: Viewing a cooking recipe site

[0058] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. The device analyzes this data and determines that the user is interested in cooking. The analysis results are encrypted and sent to a server, which updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent back from the device to the server, and the effectiveness of the advertisement is tracked.

[0059] This allows users to receive more relevant ads and also allows advertisers to deliver more targeted ads.

[0060] The system of the present invention provides advertisements that adapt to a user's interests and context in real time, improving the user experience and maximizing the effectiveness of advertisements.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[0064] Step 2:

[0065] The device analyzes user behavior based on the collected browsing history information. For example, it uses machine learning algorithms to analyze the patterns of frequently visited web pages and search queries to identify user interests.

[0066] Step 3:

[0067] The device compiles the analysis results and encrypts them using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0068] Step 4:

[0069] The device then sends the encrypted analysis results to the ad distribution platform's server, using a protocol to protect the user's privacy.

[0070] Step 5:

[0071] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a user profile. The profile includes the user's areas of interest, past behavior, and clicked ads.

[0072] Step 6:

[0073] The server generates personalized ads based on the user's profile and contextual information about the current web page, optionally using natural language processing (NLP) and image generation techniques.

[0074] Step 7:

[0075] The server then formats the generated advertisements in HTML5 or JavaScript format and delivers them to the user's device. The advertisement data also includes tags for tracking purposes.

[0076] Step 8:

[0077] The device interprets the received advertisement and displays it in an appropriate location on the currently viewed web page, without disrupting the user's browsing experience.

[0078] Step 9:

[0079] When a user clicks on a displayed advertisement, the click information is recorded on the terminal. The terminal then sends this click data back to the server, where it is used to evaluate the effectiveness of the advertisement.

[0080] Step 10:

[0081] The server analyzes the click data received and evaluates the ad's click rate, conversion rate, etc. This feedback is used to generate future ads.

[0082] Example 1

[0083] 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."

[0084] Conventional ad delivery systems were unable to fully utilize user behavioral data and had low accuracy in delivering personalized ads, making it difficult to provide ads based on users' interests. Furthermore, tracking of advertising effectiveness was also insufficient, making it difficult to accurately measure effectiveness for advertisers. This made it difficult to improve the user experience and maximize the effectiveness of advertising.

[0085] 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.

[0086] In this invention, the server includes means for creating and updating a user profile, means for generating personalized advertisements, and means for delivering the generated advertisements to the user's terminal, thereby enabling personalized advertisements based on the user's interests to be provided in real time.

[0087] A "user terminal" is an electronic device that a user uses to browse web pages and that has the functionality to display personalized advertisements.

[0088] "Web page browsing history information" is data that includes information such as the URLs of web pages accessed by a user, the length of time spent on those pages, the links clicked, and search queries.

[0089] "Analysis" is the process of using collected data to identify user behavior patterns and interests.

[0090] "Encryption" is the act of converting data using a specific algorithm to protect it from eavesdropping or tampering by third parties.

[0091] A "server" is a computer system that receives data sent from a user's terminal and creates and updates a user's profile based on the analysis results.

[0092] A "profile" is a data set that compiles user-specific information, including the user's interests, past behavioral history, and history of clicked ads.

[0093] "Personalized ads" are ads that are optimally generated for an individual user based on the user's profile and contextual information of the current web page.

[0094] "Delivery" is the process of transmitting the generated advertisement to the user's terminal via the Internet.

[0095] "Tracking" is the process of recording users' responses and actions to advertisements and measuring the effectiveness of the advertisements.

[0096] A "machine learning algorithm" is a program or method that allows a computer to analyze large amounts of data and learn based on that data.

[0097] "Natural language processing technology" is a computer technology for understanding and generating human language.

[0098] "Image generation technology" is technology for generating images using a computer.

[0099] The system of this invention connects an advertising distribution platform server with a user's device to personalize advertisements based on the user's interests and the context of the web page they are viewing. This system includes a series of processes: collecting data on user behavior, analyzing the data, generating advertisements, and delivering and tracking the advertisements.

[0100] When a user browses a web page, the device collects the user's browsing history information. This information includes the URL of the web page, the time spent on the page, the links clicked, and the search queries. Specifically, this data is collected by using browser cookies and local storage. For this purpose, the user's PC, smartphone, and web browser (e.g., GOOGLE CHROME (registered trademark), Mozilla Firefox) are used.

[0101] The device then analyzes the collected data to identify the user's interests. This analysis is performed using machine learning algorithms. Specific software used includes Tensorflow (registered trademark) and Scikit-learn. For example, if the user visits many travel-related pages, the device will determine that the user is interested in travel.

[0102] The analysis results are encrypted and sent to the server using the AES (Advanced Encryption Standard) algorithm. The encrypted data is then securely sent to the server via the Internet.

[0103] The server then decrypts the encrypted data and creates and updates a unique user profile, which includes information about the user's interests, past behavior, and the ads they have clicked.

[0104] The server then generates ads based on the user's profile and the context of the currently viewed webpage. This ad generation process uses natural language processing (NLP) and image generation techniques, including libraries such as BERT and GANs. For example, if a user's profile indicates an interest in travel, the server generates promotional ads for flights and hotels.

[0105] The generated ads are formatted in HTML5 or JavaScript format and delivered in real time from the server to the device. The device then inserts the received ads into the appropriate location on the web page, allowing the user to see ads that are highly relevant to the page they are currently viewing.

[0106] Finally, when a user clicks on an ad, the click information is again recorded on the device, encrypted, and sent to the server. The server analyzes this information and uses it to track the effectiveness of the ad. Specifically, data such as click-through rates and conversion rates are collected and used to evaluate the performance of the ad.

[0107] Specific examples

[0108] Viewing recipe sites

[0109] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. The device analyzes this data and determines that the user is interested in cooking. The results of this analysis are encrypted and sent to a server, which updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent back from the device to the server, and the effectiveness of the advertisement is tracked.

[0110] Example prompts for generative AI models

[0111] "Create a program that generates personalized advertisements for cooking utensils for users browsing a cooking recipe website. Explain the entire process, from collecting user behavior data to tracking the effectiveness of the advertisements."

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] The device collects the user's browsing history information.

[0115] Input: User's web page visits, links clicked, search queries, and time spent.

[0116] Data processing: Record information in browser cookies or local storage. For example, if a user enters the search query "pasta recipes," that information is stored in a cookie.

[0117] Output: Browsing history information (e.g. URLs visited, time spent, links clicked).

[0118] Step 2:

[0119] Analyze the data collected by the device.

[0120] Input: Browsing history information collected in step 1.

[0121] Data Computing: Identifying user interests using machine learning algorithms. Performing data analysis using TensorFlow and Scikit-learn.

[0122] Specific operation: If the user visits many travel-related pages, the device will analyze that the user is interested in travel.

[0123] Output: Interest analysis results (e.g. "User is interested in travel").

[0124] Step 3:

[0125] The device encrypts the analysis results and sends them to the server.

[0126] Input: Analysis results obtained in step 2.

[0127] Data processing: Encrypt data using AES (Advanced Encryption Standard).

[0128] Specific operation: The analysis result "The user is interested in traveling" is encrypted as "encrypted_data".

[0129] Output: The encrypted data.

[0130] Step 4:

[0131] The server receives and decrypts the encrypted data to create and update the user profile.

[0132] Input: The encrypted data sent in step 3.

[0133] Data operations: Decrypt AES encryption and decode user interests. Update user profile.

[0134] Specific behavior: The decrypted data "user is interested in travel" is added to the profile.

[0135] Output: The updated user profile.

[0136] Step 5:

[0137] A server generates advertisements based on the user's profile and contextual information.

[0138] Input: Updated user profile, context information for the current web page.

[0139] Data Computation: Generate ads using natural language processing (NLP) and image generation, using BERT and GANs.

[0140] Specific Operation: If the user's profile indicates that they are "interested in travel," the server generates promotional ads for flights and hotels.

[0141] Output: The generated ad (in HTML5 and JavaScript formats).

[0142] Step 6:

[0143] The server distributes the generated advertisement to the user's terminal.

[0144] Input: The ad generated in step 5.

[0145] Data transmission: Sending advertisements over the internet in real time.

[0146] Specific operation: The generated advertisement is sent to the terminal and inserted into the current web page.

[0147] Output: The ad that was delivered.

[0148] Step 7:

[0149] A user clicks on a delivered ad.

[0150] Input: Personalized ads displayed on web pages.

[0151] Specific operation: When a user clicks on an advertisement, the click information is recorded on the device.

[0152] Output: Click data (e.g. clicked ad ID, click timestamp).

[0153] Step 8:

[0154] The device sends click information to a server to track advertising effectiveness.

[0155] Input: Click data recorded in step 7.

[0156] Data transmission: Click data is encrypted and sent to the server.

[0157] Specific operation: The device encrypts the click information using AES and sends it to the server.

[0158] Output: Encrypted click data, ad effectiveness tracking data.

[0159] (Application example 1)

[0160] 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."

[0161] Conventional ad delivery systems only perform basic analysis of user behavior data, limiting their ability to provide effective personalized ads. Furthermore, they lacked the functionality to collect ad click information and track ad effectiveness, preventing ad performance optimization. This resulted in ads that were not relevant to users, making it difficult to increase purchasing motivation and for advertisers to deliver ads efficiently.

[0162] 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.

[0163] In this invention, the server includes means for creating and updating a user profile using the received analysis results, means for generating personalized advertisements based on the user profile and the context of the current web page, and means for delivering the generated advertisements to the user's terminal, thereby enabling effective generation and delivery of personalized advertisements based on user behavior data and tracking of advertising effectiveness.

[0164] A "user device" is a computing device used to browse web pages, collect and analyze data, and deliver and display personalized advertisements.

[0165] "Web page browsing history information" refers to data such as the URLs of the web pages visited by the user, the time spent on the page, the links clicked, the scrolling depth, and the search queries.

[0166] "Analysis results" are data that indicate areas of interest and behavioral patterns generated using machine learning algorithms, etc., based on browsing history information collected to identify user interests.

[0167] "Encryption" is the process of transforming digital data using an encryption algorithm such as AES to protect the data from eavesdropping or tampering by third parties.

[0168] "Server" refers to a central computing device that receives data sent from a user's device, creates and updates the user's profile based on the analysis results, and generates and delivers personalized advertisements.

[0169] A "user profile" is a collection of information that compiles a user's areas of interest, past behavioral history, clicked ad history, etc., and is a data set used for ad personalization.

[0170] "Current web page context" refers to the content, theme, text information, metadata, etc. of the web page currently being viewed by the user, and is information that can be used to generate personalized ads.

[0171] "Personalized advertising" refers to advertising that is tailored to a specific user based on the user's interests or the content of the web page currently being viewed.

[0172] "Advertisement click information" is data when a user clicks on a displayed advertisement, and includes the type of advertisement clicked, the timing of the click, location information, and the like.

[0173] "Advertising effectiveness tracking" is the process of collecting and analyzing data such as the number of times an ad is displayed, click rate, and conversion rate in order to measure how effective the ad was.

[0174] The present invention is a system for generating and delivering personalized advertisements based on a user's web browsing history information, and specifically comprises the following steps.

[0175] First, the user's device collects browsing history information for multiple web pages visited by the user, including the URLs of the pages visited, the duration of stay, the links clicked, the scrolling depth, and the search queries. This data collection is done using browser cookies and local storage.

[0176] The collected web page browsing history information is analyzed on the user's device. A machine learning algorithm is used for the analysis. The user's interests in specific categories (e.g., travel, cooking, technology, etc.) are identified through this analysis. Data such as the ads the user clicked and the length of time they spent on the site is also used for the analysis. The results of this analysis are encrypted and sent to the server. Standard encryption algorithms such as AES are used for encryption.

[0177] The server receives and decrypts the encrypted data sent from the device. At this time, a unique user profile is created. The profile includes the user's areas of interest, past behavioral history, and clicked ad history. The server understands the user's behavioral patterns and builds a foundation for providing appropriate advertisements.

[0178] The server then generates personalized ads based on the user's profile and contextual information about the current web page. This ad generation process may also use natural language processing (NLP) and image generation techniques. For example, if the user is interested in cooking, the server might generate ads for cooking equipment and ingredients. The ads are then formatted in HTML5 and JavaScript and converted into a deliverable format.

[0179] The generated advertisements are delivered to the user's device in real time, where they are interpreted and inserted into the currently displayed web page at the appropriate location, ensuring that the user sees relevant advertisements on the page they are viewing.

[0180] Finally, when a user clicks on an ad, the click information is recorded on the user's device. The user's device then sends this click data back to the server to track the effectiveness of the ad. The server then collects and analyzes data such as ad click rates and conversion rates to evaluate the ad's performance.

[0181] To illustrate this in a concrete example, if a user frequently visits gardening-related web pages, personalized ads such as "Discount sales on gardening tools" or "Special fertilizer for home gardens" can be generated. The prompts for the AI ​​model to generate these ads are as follows:

[0182] Example prompt:

[0183] The user's interest category is "Gardening". His recent search queries include terms like "How to grow roses" and "Vegetable garden". Generate the best ads for this user.

[0184] To implement this invention, the user's terminal and the server each require a web browser, software for data collection and analysis, and a program with encryption and decryption functions, and the server requires an advertisement generation system incorporating machine learning models and natural language processing technology.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] The user's device collects web page browsing history information. The input is the web pages the user visited, the time spent there, the links clicked, the scrolling depth, and the search queries. The output is a dataset that compiles and records this information. The data collection is done using browser extensions, cookies, local storage, etc.

[0188] Step 2:

[0189] The user's device analyzes the collected web page browsing history information. The input is the browsing history information collected in step 1. The output is the analyzed user's areas of interest. The analysis uses a machine learning algorithm to identify the user's interest in specific categories (e.g., travel, cooking, technology, etc.). Data such as the ads the user clicked and the length of time they stayed on the site are also used for the analysis.

[0190] Step 3:

[0191] The user's device encrypts the analysis results and sends them to the server. The input is the analysis results obtained in step 2. The output is the encrypted data. A standard encryption algorithm such as AES is used for encryption.

[0192] Step 4:

[0193] The server receives and decrypts the encrypted data. The input is the encrypted data sent from the user's device. The output is the decrypted analysis result. The server creates and updates the user's profile based on the decrypted data.

[0194] Step 5:

[0195] The server generates personalized ads based on the user's profile and the context of the current web page. The input is the user's profile and the context of the web page. The output is a personalized ad. Natural language processing (NLP) and image generation techniques may be used to generate the ads.

[0196] Step 6:

[0197] The server delivers the generated advertisement to the user's device in real time. The input is the advertisement generated in step 5. The output is the delivered advertisement. The advertisement is formatted in HTML5 or JavaScript format and sent to the user's device.

[0198] Step 7:

[0199] The user's device interprets the received advertisement and inserts it into the appropriate location on the currently displayed web page. The input is the advertisement delivered in step 6. The output is the advertisement displayed within the web page. This allows the user to see advertisements based on their interests.

[0200] Step 8:

[0201] When a user clicks on an ad, the click information is recorded on the user's device. The input is the information about the ad the user clicked on. The output is the recorded click information. The device then sends this click data back to the server and uses it to track the effectiveness of the ad.

[0202] 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.

[0203] The system of the present invention generates and delivers personalized advertisements by linking the server of an advertisement distribution platform with the user's device, taking into consideration not only the user's interests and the context of the web page being viewed, but also the user's emotional state. The present invention is composed of the following steps.

[0204] Collecting data on user behavior

[0205] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling. In addition, more detailed data can be obtained by collecting text information entered by the user and rich media (voice, facial expressions, etc.).

[0206] User behavior and sentiment data analysis

[0207] The device analyzes the user's behavior and emotions based on the collected browsing history information and input text information. This analysis uses machine learning algorithms and natural language processing (NLP) technology. It also uses an emotion engine to identify the user's emotional state from the user's text information and rich media. For example, it can recognize emotional states such as joy, sadness, and anger from the comments and reactions entered by the user.

[0208] Sending data

[0209] The device compiles the results of the behavioral and emotional analysis and encrypts them using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0210] Receiving Data and Profiling

[0211] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a user profile. The profile includes the user's areas of interest, past behavior, clicked ads, emotional state, and more.

[0212] Ad Generation

[0213] The server generates personalized ads based on the user's profile, contextual information about the current web page, and the user's emotional state. This process also uses natural language processing and image generation techniques. For example, if a user is interested in travel and is in a positive emotional state, ads for travel promotions and special offers will be generated.

[0214] Ad delivery and display

[0215] The server formats the generated advertisement in HTML5 or JavaScript format and delivers it to the user's device. The advertisement data also includes tags for tracking. The device interprets the received advertisement and displays it in the appropriate position on the currently viewed web page. This allows the user to see advertisements that are highly relevant to the page they are viewing.

[0216] Tracking advertising effectiveness

[0217] When a user clicks on an ad, the click information is recorded on the device. The device then sends this click data back to the server, where it is used to evaluate the effectiveness of the ad. The server then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[0218] Specific examples

[0219] Example 1: Viewing a cooking recipe site

[0220] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. If the user posts a positive comment about a recipe, the device also analyzes the user's emotions. The device analyzes this data to determine that the user is interested in cooking and is in a positive emotional state. The analysis results are encrypted and sent to a server. The server updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent again from the device to the server, and the effectiveness of the advertisement is tracked.

[0221] The system of the present invention delivers advertisements in real time that are adaptive to the user's interests, context, and emotions, improving the user experience while maximizing advertising effectiveness.

[0222] The processing flow will be explained below.

[0223] Step 1:

[0224] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling, as well as text information and rich media (voice, facial expressions, etc.) entered by the user.

[0225] Step 2:

[0226] The device analyzes the collected browsing history information and input text information, and uses machine learning algorithms and natural language processing (NLP) technology to identify the user's behavior and emotions. For example, if a user inputs a positive comment, the content is analyzed to recognize the user's emotional state.

[0227] Step 3:

[0228] The emotion engine analyzes the user's emotional state from text information and rich media to detect specific emotions such as joy, sadness, anger, etc. The device then adds this emotional information to the analysis results.

[0229] Step 4:

[0230] The device encrypts the results of behavioral and emotional analysis using encryption algorithms such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0231] Step 5:

[0232] The device then sends the encrypted analysis results to a server, using security protocols to keep the data safe.

[0233] Step 6:

[0234] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a profile of the user, including their interests, past behavior, and emotional state.

[0235] Step 7:

[0236] The server generates personalized advertisements based on the user's profile, contextual information about the current web page, and the user's emotional state. For example, if a user is interested in cooking and is in a positive emotional state, the server generates advertisements for cooking-related products and services.

[0237] Step 8:

[0238] The server then formats the generated advertisements in HTML5 or JavaScript format and delivers them to the user's device. The advertisement data also includes tags for later tracking the effectiveness of the advertisements.

[0239] Step 9:

[0240] The device interprets the received advertisement and displays it in the appropriate location on the currently viewed web page, allowing the user to see the most relevant advertisement on the page they are viewing.

[0241] Step 10:

[0242] When a user clicks on an ad, the click information is recorded on the device, which then sends the click data back to the server and uses it to track the effectiveness of the ad.

[0243] Step 11:

[0244] The server analyzes the click rate and conversion rate of the advertisement based on the received click data, thereby evaluating the performance of the advertisement and helping to generate subsequent advertisements.

[0245] Example 2

[0246] 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."

[0247] Conventional ad delivery systems deliver ads based solely on a user's interests and browsing history, which limits the effectiveness of ads because they are unable to take into account changes in the user's interests or emotional state. It is also difficult to properly analyze user behavior data and click information to generate and deliver personalized ads in real time. This has created a need for systems that can simultaneously improve user experience and maximize advertising effectiveness.

[0248] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0249] In this invention, the server includes means for analyzing user behavioral data and emotional state, means for creating and updating a user profile based on the analysis results, means for generating personalized advertisements based on the user profile and context information of the current web page, and means for tracking advertisement effectiveness, thereby enabling highly personalized advertisement delivery based on the user's behavior and emotions, maximizing the effectiveness of advertisements and improving the user experience.

[0250] "User's terminal" refers to an information processing device that allows a user to access the Internet, view web pages, and input and send data.

[0251] "Web page browsing history information" is data related to the web pages accessed by the user, and includes information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[0252] "Text information" refers to character data entered or displayed by a user on a web page, including comments, reviews, search queries, etc.

[0253] "Rich media" refers to a variety of data formats other than text, including audio, video, images, and user facial expressions.

[0254] "Emotional state" refers to the emotion a user feels from specific text information or rich media, and identifies psychological states such as joy, sadness, or anger.

[0255] A "machine learning algorithm" is a technology used to process data, extract patterns and relationships, and build models to predict user behavior and identify interests.

[0256] "Natural Language Processing (NLP)" is a technology for understanding, interpreting, and generating human language, and is used to analyze user text information.

[0257] "AES (Advanced Encryption Standard)" is an encryption method for protecting data and is used to securely transmit analysis results to the server.

[0258] A "user profile" is a data set that includes information such as a user's interests, past behavioral history, clicked ads, and emotional state.

[0259] "Current web page context" refers to information about the content or subject of the web page most recently accessed by the user.

[0260] A "personalized ad" is an ad that is optimized for a particular user, generated based on the user's profile and the context of the current web page.

[0261] "Image generation technology" means technology for generating or editing digital images used to create the visual elements of an advertisement.

[0262] "Advertising tracking" is the process of measuring how an ad impacts users, and refers to the collection and analysis of data such as click-through rates and conversion rates.

[0263] "Click information" refers to data when a user clicks on an advertisement, and is used to analyze the time of the click and the relevance of the advertisement.

[0264] The system of the present invention works in cooperation with the advertising distribution platform server and the user's device to generate and deliver personalized advertisements that take into account the user's interests, the context of the web page being viewed, and even the user's emotional state.

[0265] Hardware and software used

[0266] Hardware used

[0267] User's device: Information processing device such as computer, smartphone, tablet, etc.

[0268] Servers: Cloud servers, physical servers, servers in data centers

[0269] Software used

[0270] Web browser: Google® Chrome, Mozilla Firefox, Apple Safari, MICROSOFT® EDGE®, etc.

[0271] Machine learning algorithms: Scikit-learn, TensorFlow, PyTorch, etc.

[0272] Natural Language Processing (NLP): NLTK, spaCy, BERT, etc.

[0273] Encryption algorithm: AES (Advanced Encryption Standard)

[0274] Data analysis tools: Google Analytics, Apache (registered trademark) Hadoop, etc.

[0275] Specific examples of program processing

[0276] Collecting data on user behavior

[0277] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling. Furthermore, text information and rich media (voice, facial expressions, etc.) entered by the user are also collected. For example, if a user posts a comment on a blog post, the content of the comment and facial expression data are collected.

[0278] User behavior and sentiment data analysis

[0279] The device uses machine learning algorithms to analyze user behavior and identify their interests based on the collected browsing history and input text information. It also uses natural language processing technology and an emotion engine to recognize the user's emotional state from their comments and reactions. For example, if a user comments, "This book is really great!", it identifies that positive emotion.

[0280] Sending data

[0281] The device compiles the results of the behavioral and emotional analysis and encrypts them using the Advanced Encryption Standard (AES). The encrypted data is then sent to a server using HTTPS to prevent unauthorized access by third parties.

[0282] Receiving Data and Profiling

[0283] The server decrypts the received data and creates and updates a user profile, which includes information such as the user's interests, past behavior, clicked ads, and emotional state.

[0284] Ad generation and delivery

[0285] The server generates personalized ads based on the user's profile and the context of the current web page using generative AI models, natural language processing, and image generation technologies. The ads are formatted in HTML5 or JavaScript and delivered to the user's device with tracking tags. The device interprets the received ads and displays them in the appropriate location on the currently viewed web page.

[0286] Tracking advertising effectiveness

[0287] When a user clicks on an ad, the click information is recorded on the device and sent back to the server, which then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[0288] Specific examples

[0289] Example 1: Viewing a cooking recipe site

[0290] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. If the user posts a positive comment about a recipe, the device also analyzes the user's sentiment. The device analyzes this data and determines that the user is interested in cooking and is in a positive emotional state. The analysis results are encrypted and sent to a server. The server updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent again from the device to the server, and the effectiveness of the advertisement is tracked.

[0291] Prompt Sentence Examples

[0292] "If a user is browsing a cooking recipe site, how would you optimize your ads?"

[0293] "How can we personalize ads based on user behavioral data and emotional state?"

[0294] This system enables the generation and delivery of advertisements based on user behavior, emotions, and context, improving the user experience and maximizing advertising effectiveness.

[0295] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0296] Step 1: Collecting data on user behavior

[0297] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling, as well as text information and rich media (voice, facial expressions, etc.) entered by the user.

[0298] Input: Information about web pages you visit, text and rich media you enter

[0299] Data processing: storing information in browser cookies and local storage

[0300] Output: Browsing history information, text information, rich media data

[0301] Step 2: Analyzing user behavior and sentiment data

[0302] The device uses machine learning algorithms to analyze user behavior and identify their interests based on collected browsing history and input text information, and uses natural language processing technology and an emotion engine to recognize the user's emotional state from their comments and reactions.

[0303] Input: browsing history information, text information, rich media data

[0304] Data Computing: Behavioral analysis using machine learning algorithms, emotion analysis using natural language processing technology and emotion engines

[0305] Output: User interests, emotional state

[0306] Step 3: Sending data

[0307] The device compiles the behavioral and emotional analysis results and encrypts them using AES (Advanced Encryption Standard), which is then sent to a server using HTTPS.

[0308] Input: User interests, emotional state

[0309] Data processing: AES encryption

[0310] Output: Encrypted analysis results

[0311] Step 4: Receiving data and creating a profile

[0312] The server receives the data sent from the device and decrypts the AES-encrypted data. Based on the decrypted data, it creates and updates a user profile. The profile includes the user's areas of interest, past behavioral history, clicked ad history, emotional state, etc.

[0313] Input: Encrypted analysis results

[0314] Data calculation: AES decryption, profile creation and updating

[0315] Output: User profile

[0316] Step 5: Generate Ads

[0317] The server generates personalized ads based on the user's profile and contextual information from the current web page using generative AI models, natural language processing techniques, and image generation techniques.

[0318] Input: User profile, current web page context

[0319] Data Computing: Ad Generation with Generative AI Models and Natural Language Processing Techniques

[0320] Output: Personalized ads

[0321] Step 6: Serving and displaying ads

[0322] The server then formats the generated ad in HTML5 or JavaScript format, includes a tracking tag, and delivers it to the user's device, where it is interpreted and displayed in the appropriate location on the currently viewed web page.

[0323] Input: Personalized ads (HTML5 and JavaScript formats)

[0324] Data processing: Formatting into HTML5 or JavaScript format

[0325] Output: Advertisement displayed on a web page

[0326] Step 7: Track your advertising success

[0327] When a user clicks on an ad, the click information is recorded on the device and sent back to the server, which then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[0328] Input: Ad click information

[0329] Data processing: Record click data and send it to the server

[0330] Output: Ad performance data (click-through rate, conversion rate)

[0331] (Application example 2)

[0332] 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."

[0333] Conventional ad delivery systems only consider users' interests and concerns, but are unable to provide personalized ads that reflect the user's emotional state. As a result, improving the user experience and maximizing advertising effectiveness have not been fully achieved. Furthermore, because emotion analysis technology has not been integrated into the ad generation system, it has been difficult to display ads that are optimized for the user's current psychological state.

[0334] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a personalized advertisement based on the user profile and the context of the current web page, means for creating and updating the user profile using the received analysis results, and means for delivering the generated advertisement to the user's terminal. This makes it possible to provide optimal personalized advertisements that reflect the user's interests and real-time emotional state.

[0335] "Web pages" refers to multiple different websites or web pages that a user accesses on the Internet.

[0336] "Browser history information" is information that is left behind when a user browses a web page, and includes data such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[0337] "Analyzing emotions" refers to identifying a user's emotional state (e.g., joy, sadness, anger, etc.) from collected data using machine learning algorithms and natural language processing techniques.

[0338] "Identifying user interests" refers to analyzing the collected browsing history information and emotional information to identify what themes and products the user is interested in.

[0339] "Encrypting the analysis results and sending them to the server" refers to encrypting the analyzed information to protect data security and sending it to the server via the Internet.

[0340] "Server" refers to a central processing unit that receives, analyzes, stores, generates and distributes data, and is the core of this system.

[0341] A "user profile" is a data set that compiles various data about a user (interests, browsing history, emotional state, etc.) to provide customized information to each individual user.

[0342] "Current web page context" refers to the content, theme, and related information of the web page that the user is currently viewing.

[0343] "Personalized advertising" refers to advertising that is individually optimized based on a user's profile and the context of the current web page.

[0344] "Generating an ad" refers to creating ad content, such as text, images, or video, based on the user's profile and contextual information about the current web page.

[0345] "Distributing the generated advertisement" refers to sending the advertisement generated by the server to the user's terminal and sending the data necessary for displaying it.

[0346] "Displaying an advertisement on a web page" refers to positioning and visually presenting an advertisement within the web page that a user is viewing.

[0347] This invention is a system that generates and delivers personalized advertisements using web page browsing history information and emotional data collected by the user's device. This system analyzes the user's browsing behavior and emotional state in real time and displays appropriate advertisements based on that analysis.

[0348] Hardware and Software Used

[0349] Hardware: Smartphone (including camera, microphone, and sensors)

[0350] Software: Python programming language, TextBlob (for sentiment analysis), Scikit-learn (for machine learning), AES (for encryption), Requests (for data transmission)

[0351] Data processing and calculation

[0352] 1. Data Collection

[0353] The user's device collects browsing history information for each web page the user visits. The collected information includes the URL of the visited page, the time spent on the page, the links clicked, the scrolling depth, and emotional data such as the user's comments and facial expressions. This allows for comprehensive collection of user behavioral and emotional data.

[0354] 2. Emotion analysis

[0355] The device analyzes collected comments and rich media data and uses machine learning algorithms and natural language processing techniques to identify the user's emotional state. Specifically, it uses the TextBlob library to distinguish between positive, negative, and neutral sentiment from comments. In addition, it uses Scikit-learn to cluster the data and classify the user's interests.

[0356] 3. Data Encryption and Transmission

[0357] The analysis results are encrypted using the AES encryption algorithm, and then the encrypted data is sent to the server, ensuring secure communication.

[0358] 4. Profile Creation and Updates

[0359] The server then decrypts the encrypted data and creates or updates a user profile, which includes information about the user's interests, browsing history, ad clicks, emotional state, and more.

[0360] 5. Ad Generation

[0361] The server generates personalized advertisements using natural language processing and sentiment analysis techniques based on the user's profile and contextual information of the current web page. For example, if the user is interested in travel and is in a positive emotional state, advertisements for travel promotions and special offers will be generated.

[0362] 6. Advertisement Delivery and Display

[0363] The generated advertisements are delivered from the server to the user's device, and are then displayed at appropriate positions on the web page on the smartphone, allowing the user to view advertisements related to the content they are viewing.

[0364] Specific examples

[0365] If a user views a "chocolate cake" recipe on a cooking recipe site and posts a positive comment, their profile will be updated with that information, and ads for suitable kitchen supplies and ingredients will be displayed.

[0366] Prompt Sentence Examples

[0367] "Write a Python program that analyzes the URLs of pages visited by users, the time spent on them, the links clicked, the scroll depth, and their emotional state, and then suggests ads based on that."

[0368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0369] Step 1:

[0370] Input: URLs of web pages visited by users, time spent on the page, links clicked, scroll depth, and emotional data such as user comments and facial expressions.

[0371] Processing: The user's device collects this browsing history information and emotional data using browser cookies and local storage.

[0372] Output: Collected web page browsing history information and user emotion data

[0373] While visiting a web page, the user's device collects information such as the actions the user takes and the time spent on the page, as well as emotional data from the user's comments and facial expressions.

[0374] Step 2:

[0375] Input: Collected browsing history information and emotional data

[0376] Processing: The device performs sentiment analysis on the collected data using the TextBlob library, and uses Scikit-learn to cluster page content. It also identifies emotional states (positive, negative, neutral) and classifies interests based on user comments and media data.

[0377] Output: Parsed emotional state and clustering results

[0378] The device analyzes user comments to understand their interests and focuses on specific areas of interest, and then categorizes browsing data into specific clusters.

[0379] Step 3:

[0380] Input: Parsed emotional states and clustering results

[0381] Processing: The analysis results are encrypted using the AES encryption algorithm.

[0382] Output: Encrypted analysis results

[0383] The device ensures data security by encrypting the results of sentiment analysis and clustering.

[0384] Step 4:

[0385] Input: Encrypted analysis results

[0386] Action: Send the encrypted data to the server.

[0387] Output: The server where the analysis results are sent

[0388] The server receives the encrypted data and prepares it for decryption.

[0389] Step 5:

[0390] Input: Encrypted analysis results

[0391] Processing: The server decrypts the received data and creates and updates a profile based on the user's interests, browsing history, and emotional state.

[0392] Output: Updated user profile

[0393] The server uses the decrypted data to create and update a detailed profile of the user for further processing.

[0394] Step 6:

[0395] Input: User profile and current web page context information

[0396] Processing: The server generates personalized advertisements using natural language processing and sentiment analysis techniques.

[0397] Output: Generated personalized ad

[0398] The server uses the user's profile and the context of the current web page to generate appropriate ads that match the user's interests and emotional state.

[0399] Step 7:

[0400] Input: Generated personalized ad

[0401] Processing: The server delivers the generated advertisement to the user's device and sends data to display it on the web page.

[0402] Output: Personalized ads delivered

[0403] The server delivers the generated advertisement to the user's terminal, which then displays it appropriately on a web page.

[0404] Step 8:

[0405] Input: Personalized ads delivered

[0406] Processing: The user's device displays the delivered advertisement on the web page.

[0407] Output: The ad shown to the user

[0408] The user's device displays ads that match the content of the web page, allowing the user to see ads that are directly related to the web page they are viewing.

[0409] 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.

[0410] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0411] 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.

[0412] [Second embodiment]

[0413] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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."

[0425] The system of the present invention personalizes advertisements based on the user's interests and the context of the web page being viewed by linking the server on the advertisement distribution platform side with the user's terminal. This system is comprised of the following steps.

[0426] Collecting data on user behavior

[0427] When a user browses a web page, their device collects browsing history information, including the URL of the page visited, the time spent on the page, the links clicked, the scrolling depth, and the search queries. This data is collected using browser cookies and local storage.

[0428] User behavior data analysis

[0429] The device analyzes the user's interests based on the collected data. This analysis may involve the use of machine learning algorithms to identify interests in specific categories (e.g., travel, cooking, technology, etc.) by analyzing the pages the user frequently visits and search queries. Data such as the ads the user clicks on and the duration of their visit may also be used for analysis.

[0430] Sending data

[0431] The device then encrypts the analysis results and sends them to the server. This encryption uses a standard encryption algorithm such as AES (Advanced Encryption Standard). The encrypted data is securely sent to the server, preventing eavesdropping or tampering by third parties.

[0432] Receiving Data and Profiling

[0433] The server receives and decrypts the encrypted data sent from the device. At this time, a unique user profile is created. The profile includes the user's areas of interest, past behavioral history, and clicked ad history. This allows the server to understand the user's behavioral patterns and build a foundation for providing appropriate advertisements.

[0434] Ad Generation

[0435] The server generates personalized ads based on the user's profile and contextual information about the current web page. This ad generation process may also use natural language processing (NLP) and image generation techniques. For example, if the user is interested in travel, the server generates promotional ads for flights and hotels. The generated ads are then formatted in HTML5 and JavaScript and converted into a deliverable format.

[0436] Ad delivery and display

[0437] The server delivers the generated advertisements to the user's device in real time, and the device interprets the received advertisements and inserts them into the appropriate positions on the currently displayed web page, allowing the user to see advertisements that are relevant to the page they are viewing.

[0438] Tracking advertising effectiveness

[0439] When a user clicks on an ad, the click information is recorded on the device. The device then sends this click data back to the server, which uses it to track the effectiveness of the ad. The server collects and analyzes data such as ad click rates and conversion rates to evaluate the ad's performance.

[0440] Specific examples

[0441] Example 1: Viewing a cooking recipe site

[0442] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. The device analyzes this data and determines that the user is interested in cooking. The analysis results are encrypted and sent to a server, which updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent back from the device to the server, and the effectiveness of the advertisement is tracked.

[0443] This allows users to receive more relevant ads and also allows advertisers to deliver more targeted ads.

[0444] The system of the present invention provides advertisements that adapt to a user's interests and context in real time, improving the user experience and maximizing the effectiveness of advertisements.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[0448] Step 2:

[0449] The device analyzes user behavior based on the collected browsing history information. For example, it uses machine learning algorithms to analyze the patterns of frequently visited web pages and search queries to identify user interests.

[0450] Step 3:

[0451] The device compiles the analysis results and encrypts them using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0452] Step 4:

[0453] The device then sends the encrypted analysis results to the ad distribution platform's server, using a protocol to protect the user's privacy.

[0454] Step 5:

[0455] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a user profile. The profile includes the user's areas of interest, past behavior, and clicked ads.

[0456] Step 6:

[0457] The server generates personalized ads based on the user's profile and contextual information about the current web page, optionally using natural language processing (NLP) and image generation techniques.

[0458] Step 7:

[0459] The server then formats the generated advertisements in HTML5 or JavaScript format and delivers them to the user's device. The advertisement data also includes tags for tracking purposes.

[0460] Step 8:

[0461] The device interprets the received advertisement and displays it in an appropriate location on the currently viewed web page, without disrupting the user's browsing experience.

[0462] Step 9:

[0463] When a user clicks on a displayed advertisement, the click information is recorded on the terminal. The terminal then sends this click data back to the server, where it is used to evaluate the effectiveness of the advertisement.

[0464] Step 10:

[0465] The server analyzes the click data received and evaluates the ad's click rate, conversion rate, etc. This feedback is used to generate future ads.

[0466] Example 1

[0467] 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."

[0468] Conventional ad delivery systems were unable to fully utilize user behavioral data and had low accuracy in delivering personalized ads, making it difficult to provide ads based on users' interests. Furthermore, tracking of advertising effectiveness was also insufficient, making it difficult to accurately measure effectiveness for advertisers. This made it difficult to improve the user experience and maximize the effectiveness of advertising.

[0469] 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.

[0470] In this invention, the server includes means for creating and updating a user profile, means for generating personalized advertisements, and means for delivering the generated advertisements to the user's terminal, thereby enabling personalized advertisements based on the user's interests to be provided in real time.

[0471] A "user terminal" is an electronic device that a user uses to browse web pages and that has the functionality to display personalized advertisements.

[0472] "Web page browsing history information" is data that includes information such as the URLs of web pages accessed by a user, the length of time spent on those pages, the links clicked, and search queries.

[0473] "Analysis" is the process of using collected data to identify user behavior patterns and interests.

[0474] "Encryption" is the act of converting data using a specific algorithm to protect it from eavesdropping or tampering by third parties.

[0475] A "server" is a computer system that receives data sent from a user's terminal and creates and updates a user's profile based on the analysis results.

[0476] A "profile" is a data set that compiles user-specific information, including the user's interests, past behavioral history, and history of clicked ads.

[0477] "Personalized ads" are ads that are optimally generated for an individual user based on the user's profile and contextual information of the current web page.

[0478] "Delivery" is the process of transmitting the generated advertisement to the user's terminal via the Internet.

[0479] "Tracking" is the process of recording users' responses and actions to advertisements and measuring the effectiveness of the advertisements.

[0480] A "machine learning algorithm" is a program or method that allows a computer to analyze large amounts of data and learn based on that data.

[0481] "Natural language processing technology" is a computer technology for understanding and generating human language.

[0482] "Image generation technology" is technology for generating images using a computer.

[0483] The system of this invention connects an advertising distribution platform server with a user's device to personalize advertisements based on the user's interests and the context of the web page they are viewing. This system includes a series of processes: collecting data on user behavior, analyzing the data, generating advertisements, and delivering and tracking the advertisements.

[0484] When a user browses a web page, their device collects their browsing history information. This information includes the URL of the web page, the time spent on the page, the links clicked, and the search queries. Specifically, this data is collected by using browser cookies and local storage. For this purpose, the user's PC, smartphone, and web browser (e.g., Google Chrome, Mozilla Firefox) are used.

[0485] The device then analyzes the collected data to identify the user's interests. This analysis is performed using machine learning algorithms, specifically software such as TensorFlow and Scikit-learn. For example, if the user visits many travel-related pages, the device will determine that the user is interested in travel.

[0486] The analysis results are encrypted and sent to the server using the AES (Advanced Encryption Standard) algorithm. The encrypted data is then securely sent to the server via the Internet.

[0487] The server then decrypts the encrypted data and creates and updates a unique user profile, which includes information about the user's interests, past behavior, and the ads they have clicked.

[0488] The server then generates ads based on the user's profile and the context of the currently viewed webpage. This ad generation process uses natural language processing (NLP) and image generation techniques, including libraries such as BERT and GANs. For example, if a user's profile indicates an interest in travel, the server generates promotional ads for flights and hotels.

[0489] The generated ads are formatted in HTML5 or JavaScript format and delivered in real time from the server to the device. The device then inserts the received ads into the appropriate location on the web page, allowing the user to see ads that are highly relevant to the page they are currently viewing.

[0490] Finally, when a user clicks on an ad, the click information is again recorded on the device, encrypted, and sent to the server. The server analyzes this information and uses it to track the effectiveness of the ad. Specifically, data such as click-through rates and conversion rates are collected and used to evaluate the performance of the ad.

[0491] Specific examples

[0492] Viewing recipe sites

[0493] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. The device analyzes this data and determines that the user is interested in cooking. The results of this analysis are encrypted and sent to a server, which updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent back from the device to the server, and the effectiveness of the advertisement is tracked.

[0494] Example prompts for generative AI models

[0495] "Create a program that generates personalized advertisements for cooking utensils for users browsing a cooking recipe website. Explain the entire process, from collecting user behavior data to tracking the effectiveness of the advertisements."

[0496] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0497] Step 1:

[0498] The device collects the user's browsing history information.

[0499] Input: User's web page visits, links clicked, search queries, and time spent.

[0500] Data processing: Record information in browser cookies or local storage. For example, if a user enters the search query "pasta recipes," that information is stored in a cookie.

[0501] Output: Browsing history information (e.g. URLs visited, time spent, links clicked).

[0502] Step 2:

[0503] Analyze the data collected by the device.

[0504] Input: Browsing history information collected in step 1.

[0505] Data Computing: Identifying user interests using machine learning algorithms. Performing data analysis using TensorFlow and Scikit-learn.

[0506] Specific operation: If the user visits many travel-related pages, the device will analyze that the user is interested in travel.

[0507] Output: Interest analysis results (e.g. "User is interested in travel").

[0508] Step 3:

[0509] The device encrypts the analysis results and sends them to the server.

[0510] Input: Analysis results obtained in step 2.

[0511] Data processing: Encrypt data using AES (Advanced Encryption Standard).

[0512] Specific operation: The analysis result "The user is interested in traveling" is encrypted as "encrypted_data".

[0513] Output: The encrypted data.

[0514] Step 4:

[0515] The server receives and decrypts the encrypted data to create and update the user profile.

[0516] Input: The encrypted data sent in step 3.

[0517] Data operations: Decrypt AES encryption and decode user interests. Update user profile.

[0518] Specific behavior: The decrypted data "user is interested in travel" is added to the profile.

[0519] Output: The updated user profile.

[0520] Step 5:

[0521] A server generates advertisements based on the user's profile and contextual information.

[0522] Input: Updated user profile, context information for the current web page.

[0523] Data Computation: Generate ads using natural language processing (NLP) and image generation, using BERT and GANs.

[0524] Specific Operation: If the user's profile indicates that they are "interested in travel," the server generates promotional ads for flights and hotels.

[0525] Output: The generated ad (in HTML5 and JavaScript formats).

[0526] Step 6:

[0527] The server distributes the generated advertisement to the user's terminal.

[0528] Input: The ad generated in step 5.

[0529] Data transmission: Sending advertisements over the internet in real time.

[0530] Specific operation: The generated advertisement is sent to the terminal and inserted into the current web page.

[0531] Output: The ad that was delivered.

[0532] Step 7:

[0533] A user clicks on a delivered ad.

[0534] Input: Personalized ads displayed on web pages.

[0535] Specific operation: When a user clicks on an advertisement, the click information is recorded on the device.

[0536] Output: Click data (e.g. clicked ad ID, click timestamp).

[0537] Step 8:

[0538] The device sends click information to a server to track advertising effectiveness.

[0539] Input: Click data recorded in step 7.

[0540] Data transmission: Click data is encrypted and sent to the server.

[0541] Specific operation: The device encrypts the click information using AES and sends it to the server.

[0542] Output: Encrypted click data, ad effectiveness tracking data.

[0543] (Application example 1)

[0544] 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."

[0545] Conventional ad delivery systems only perform basic analysis of user behavior data, limiting their ability to provide effective personalized ads. Furthermore, they lacked the functionality to collect ad click information and track ad effectiveness, preventing ad performance optimization. This resulted in ads that were not relevant to users, making it difficult to increase purchasing motivation and for advertisers to deliver ads efficiently.

[0546] 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.

[0547] In this invention, the server includes means for creating and updating a user profile using the received analysis results, means for generating personalized advertisements based on the user profile and the context of the current web page, and means for delivering the generated advertisements to the user's terminal, thereby enabling effective generation and delivery of personalized advertisements based on user behavior data and tracking of advertising effectiveness.

[0548] A "user device" is a computing device used to browse web pages, collect and analyze data, and deliver and display personalized advertisements.

[0549] "Web page browsing history information" refers to data such as the URLs of the web pages visited by the user, the time spent on the page, the links clicked, the scrolling depth, and the search queries.

[0550] "Analysis results" are data that indicate areas of interest and behavioral patterns generated using machine learning algorithms, etc., based on browsing history information collected to identify user interests.

[0551] "Encryption" is the process of transforming digital data using an encryption algorithm such as AES to protect the data from eavesdropping or tampering by third parties.

[0552] "Server" refers to a central computing device that receives data sent from a user's device, creates and updates the user's profile based on the analysis results, and generates and delivers personalized advertisements.

[0553] A "user profile" is a collection of information that compiles a user's areas of interest, past behavioral history, clicked ad history, etc., and is a data set used for ad personalization.

[0554] "Current web page context" refers to the content, theme, text information, metadata, etc. of the web page currently being viewed by the user, and is information that can be used to generate personalized ads.

[0555] "Personalized advertising" refers to advertising that is tailored to a specific user based on the user's interests or the content of the web page currently being viewed.

[0556] "Advertisement click information" is data when a user clicks on a displayed advertisement, and includes the type of advertisement clicked, the timing of the click, location information, and the like.

[0557] "Advertising effectiveness tracking" is the process of collecting and analyzing data such as the number of times an ad is displayed, click rate, and conversion rate in order to measure how effective the ad was.

[0558] The present invention is a system for generating and delivering personalized advertisements based on a user's web browsing history information, and specifically comprises the following steps.

[0559] First, the user's device collects browsing history information for multiple web pages visited by the user, including the URLs of the pages visited, the duration of stay, the links clicked, the scrolling depth, and the search queries. This data collection is done using browser cookies and local storage.

[0560] The collected web page browsing history information is analyzed on the user's device. A machine learning algorithm is used for the analysis. The user's interests in specific categories (e.g., travel, cooking, technology, etc.) are identified through this analysis. Data such as the ads the user clicked and the length of time they spent on the site is also used for the analysis. The results of this analysis are encrypted and sent to the server. Standard encryption algorithms such as AES are used for encryption.

[0561] The server receives and decrypts the encrypted data sent from the device. At this time, a unique user profile is created. The profile includes the user's areas of interest, past behavioral history, and clicked ad history. The server understands the user's behavioral patterns and builds a foundation for providing appropriate advertisements.

[0562] The server then generates personalized ads based on the user's profile and contextual information about the current web page. This ad generation process may also use natural language processing (NLP) and image generation techniques. For example, if the user is interested in cooking, the server might generate ads for cooking equipment and ingredients. The ads are then formatted in HTML5 and JavaScript and converted into a deliverable format.

[0563] The generated advertisements are delivered to the user's device in real time, where they are interpreted and inserted into the currently displayed web page at the appropriate location, ensuring that the user sees relevant advertisements on the page they are viewing.

[0564] Finally, when a user clicks on an ad, the click information is recorded on the user's device. The user's device then sends this click data back to the server to track the effectiveness of the ad. The server then collects and analyzes data such as ad click rates and conversion rates to evaluate the ad's performance.

[0565] To illustrate this in a concrete example, if a user frequently visits gardening-related web pages, personalized ads such as "Discount sales on gardening tools" or "Special fertilizer for home gardens" can be generated. The prompts for the AI ​​model to generate these ads are as follows:

[0566] Example prompt:

[0567] The user's interest category is "Gardening". His recent search queries include terms like "How to grow roses" and "Vegetable garden". Generate the best ads for this user.

[0568] To implement this invention, the user's terminal and the server each require a web browser, software for data collection and analysis, and a program with encryption and decryption functions, and the server requires an advertisement generation system incorporating machine learning models and natural language processing technology.

[0569] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0570] Step 1:

[0571] The user's device collects web page browsing history information. The input is the web pages the user visited, the time spent there, the links clicked, the scrolling depth, and the search queries. The output is a dataset that compiles and records this information. The data collection is done using browser extensions, cookies, local storage, etc.

[0572] Step 2:

[0573] The user's device analyzes the collected web page browsing history information. The input is the browsing history information collected in step 1. The output is the analyzed user's areas of interest. The analysis uses a machine learning algorithm to identify the user's interest in specific categories (e.g., travel, cooking, technology, etc.). Data such as the ads the user clicked and the length of time they stayed on the site are also used for the analysis.

[0574] Step 3:

[0575] The user's device encrypts the analysis results and sends them to the server. The input is the analysis results obtained in step 2. The output is the encrypted data. A standard encryption algorithm such as AES is used for encryption.

[0576] Step 4:

[0577] The server receives and decrypts the encrypted data. The input is the encrypted data sent from the user's device. The output is the decrypted analysis result. The server creates and updates the user's profile based on the decrypted data.

[0578] Step 5:

[0579] The server generates personalized ads based on the user's profile and the context of the current web page. The input is the user's profile and the context of the web page. The output is a personalized ad. Natural language processing (NLP) and image generation techniques may be used to generate the ads.

[0580] Step 6:

[0581] The server delivers the generated advertisement to the user's device in real time. The input is the advertisement generated in step 5. The output is the delivered advertisement. The advertisement is formatted in HTML5 or JavaScript format and sent to the user's device.

[0582] Step 7:

[0583] The user's device interprets the received advertisement and inserts it into the appropriate location on the currently displayed web page. The input is the advertisement delivered in step 6. The output is the advertisement displayed within the web page. This allows the user to see advertisements based on their interests.

[0584] Step 8:

[0585] When a user clicks on an ad, the click information is recorded on the user's device. The input is the information about the ad the user clicked on. The output is the recorded click information. The device then sends this click data back to the server and uses it to track the effectiveness of the ad.

[0586] 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.

[0587] The system of the present invention generates and delivers personalized advertisements by linking the server of an advertisement distribution platform with the user's device, taking into consideration not only the user's interests and the context of the web page being viewed, but also the user's emotional state. The present invention is composed of the following steps.

[0588] Collecting data on user behavior

[0589] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling. In addition, more detailed data can be obtained by collecting text information entered by the user and rich media (voice, facial expressions, etc.).

[0590] User behavior and sentiment data analysis

[0591] The device analyzes the user's behavior and emotions based on the collected browsing history information and input text information. This analysis uses machine learning algorithms and natural language processing (NLP) technology. It also uses an emotion engine to identify the user's emotional state from the user's text information and rich media. For example, it can recognize emotional states such as joy, sadness, and anger from the comments and reactions entered by the user.

[0592] Sending data

[0593] The device compiles the results of the behavioral and emotional analysis and encrypts them using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0594] Receiving Data and Profiling

[0595] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a user profile. The profile includes the user's areas of interest, past behavior, clicked ads, emotional state, and more.

[0596] Ad Generation

[0597] The server generates personalized ads based on the user's profile, contextual information about the current web page, and the user's emotional state. This process also uses natural language processing and image generation techniques. For example, if a user is interested in travel and is in a positive emotional state, ads for travel promotions and special offers will be generated.

[0598] Ad delivery and display

[0599] The server formats the generated advertisement in HTML5 or JavaScript format and delivers it to the user's device. The advertisement data also includes tags for tracking. The device interprets the received advertisement and displays it in the appropriate position on the currently viewed web page. This allows the user to see advertisements that are highly relevant to the page they are viewing.

[0600] Tracking advertising effectiveness

[0601] When a user clicks on an ad, the click information is recorded on the device. The device then sends this click data back to the server, where it is used to evaluate the effectiveness of the ad. The server then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[0602] Specific examples

[0603] Example 1: Viewing a cooking recipe site

[0604] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. If the user posts a positive comment about a recipe, the device also analyzes the user's emotions. The device analyzes this data to determine that the user is interested in cooking and is in a positive emotional state. The analysis results are encrypted and sent to a server. The server updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent again from the device to the server, and the effectiveness of the advertisement is tracked.

[0605] The system of the present invention delivers advertisements in real time that are adaptive to the user's interests, context, and emotions, improving the user experience while maximizing advertising effectiveness.

[0606] The processing flow will be explained below.

[0607] Step 1:

[0608] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling, as well as text information and rich media (voice, facial expressions, etc.) entered by the user.

[0609] Step 2:

[0610] The device analyzes the collected browsing history information and input text information, and uses machine learning algorithms and natural language processing (NLP) technology to identify the user's behavior and emotions. For example, if a user inputs a positive comment, the content is analyzed to recognize the user's emotional state.

[0611] Step 3:

[0612] The emotion engine analyzes the user's emotional state from text information and rich media to detect specific emotions such as joy, sadness, anger, etc. The device then adds this emotional information to the analysis results.

[0613] Step 4:

[0614] The device encrypts the results of behavioral and emotional analysis using encryption algorithms such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0615] Step 5:

[0616] The device then sends the encrypted analysis results to a server, using security protocols to keep the data safe.

[0617] Step 6:

[0618] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a profile of the user, including their interests, past behavior, and emotional state.

[0619] Step 7:

[0620] The server generates personalized advertisements based on the user's profile, contextual information about the current web page, and the user's emotional state. For example, if a user is interested in cooking and is in a positive emotional state, the server generates advertisements for cooking-related products and services.

[0621] Step 8:

[0622] The server then formats the generated advertisements in HTML5 or JavaScript format and delivers them to the user's device. The advertisement data also includes tags for later tracking the effectiveness of the advertisements.

[0623] Step 9:

[0624] The device interprets the received advertisement and displays it in the appropriate location on the currently viewed web page, allowing the user to see the most relevant advertisement on the page they are viewing.

[0625] Step 10:

[0626] When a user clicks on an ad, the click information is recorded on the device, which then sends the click data back to the server and uses it to track the effectiveness of the ad.

[0627] Step 11:

[0628] The server analyzes the click rate and conversion rate of the advertisement based on the received click data, thereby evaluating the performance of the advertisement and helping to generate subsequent advertisements.

[0629] Example 2

[0630] 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."

[0631] Conventional ad delivery systems deliver ads based solely on a user's interests and browsing history, which limits the effectiveness of ads because they are unable to take into account changes in the user's interests or emotional state. It is also difficult to properly analyze user behavior data and click information to generate and deliver personalized ads in real time. This has created a need for systems that can simultaneously improve user experience and maximize advertising effectiveness.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0633] In this invention, the server includes means for analyzing user behavioral data and emotional state, means for creating and updating a user profile based on the analysis results, means for generating personalized advertisements based on the user profile and context information of the current web page, and means for tracking advertisement effectiveness, thereby enabling highly personalized advertisement delivery based on the user's behavior and emotions, maximizing the effectiveness of advertisements and improving the user experience.

[0634] "User's terminal" refers to an information processing device that allows a user to access the Internet, view web pages, and input and send data.

[0635] "Web page browsing history information" is data related to the web pages accessed by the user, and includes information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[0636] "Text information" refers to character data entered or displayed by a user on a web page, including comments, reviews, search queries, etc.

[0637] "Rich media" refers to a variety of data formats other than text, including audio, video, images, and user facial expressions.

[0638] "Emotional state" refers to the emotion a user feels from specific text information or rich media, and identifies psychological states such as joy, sadness, or anger.

[0639] A "machine learning algorithm" is a technology used to process data, extract patterns and relationships, and build models to predict user behavior and identify interests.

[0640] "Natural Language Processing (NLP)" is a technology for understanding, interpreting, and generating human language, and is used to analyze user text information.

[0641] "AES (Advanced Encryption Standard)" is an encryption method for protecting data and is used to securely transmit analysis results to the server.

[0642] A "user profile" is a data set that includes information such as a user's interests, past behavioral history, clicked ads, and emotional state.

[0643] "Current web page context" refers to information about the content or subject of the web page most recently accessed by the user.

[0644] A "personalized ad" is an ad that is optimized for a particular user, generated based on the user's profile and the context of the current web page.

[0645] "Image generation technology" means technology for generating or editing digital images used to create the visual elements of an advertisement.

[0646] "Advertising tracking" is the process of measuring how an ad impacts users, and refers to the collection and analysis of data such as click-through rates and conversion rates.

[0647] "Click information" refers to data when a user clicks on an advertisement, and is used to analyze the time of the click and the relevance of the advertisement.

[0648] The system of the present invention works in cooperation with the advertising distribution platform server and the user's device to generate and deliver personalized advertisements that take into account the user's interests, the context of the web page being viewed, and even the user's emotional state.

[0649] Hardware and software used

[0650] Hardware used

[0651] User's device: Information processing device such as computer, smartphone, tablet, etc.

[0652] Servers: Cloud servers, physical servers, servers in data centers

[0653] Software used

[0654] Web browser: Google Chrome, Mozilla Firefox, Apple Safari, Microsoft Edge, etc.

[0655] Machine learning algorithms: Scikit-learn, TensorFlow, PyTorch, etc.

[0656] Natural Language Processing (NLP): NLTK, spaCy, BERT, etc.

[0657] Encryption algorithm: AES (Advanced Encryption Standard)

[0658] Data analysis tools: Google Analytics, Apache Hadoop, etc.

[0659] Specific examples of program processing

[0660] Collecting data on user behavior

[0661] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling. Furthermore, text information and rich media (voice, facial expressions, etc.) entered by the user are also collected. For example, if a user posts a comment on a blog post, the content of the comment and facial expression data are collected.

[0662] User behavior and sentiment data analysis

[0663] The device uses machine learning algorithms to analyze user behavior and identify their interests based on the collected browsing history and input text information. It also uses natural language processing technology and an emotion engine to recognize the user's emotional state from their comments and reactions. For example, if a user comments, "This book is really great!", it identifies that positive emotion.

[0664] Sending data

[0665] The device compiles the results of the behavioral and emotional analysis and encrypts them using the Advanced Encryption Standard (AES). The encrypted data is then sent to a server using HTTPS to prevent unauthorized access by third parties.

[0666] Receiving Data and Profiling

[0667] The server decrypts the received data and creates and updates a user profile, which includes information such as the user's interests, past behavior, clicked ads, and emotional state.

[0668] Ad generation and delivery

[0669] The server generates personalized ads based on the user's profile and the context of the current web page using generative AI models, natural language processing, and image generation technologies. The ads are formatted in HTML5 or JavaScript and delivered to the user's device with tracking tags. The device interprets the received ads and displays them in the appropriate location on the currently viewed web page.

[0670] Tracking advertising effectiveness

[0671] When a user clicks on an ad, the click information is recorded on the device and sent back to the server, which then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[0672] Specific examples

[0673] Example 1: Viewing a cooking recipe site

[0674] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. If the user posts a positive comment about a recipe, the device also analyzes the user's sentiment. The device analyzes this data and determines that the user is interested in cooking and is in a positive emotional state. The analysis results are encrypted and sent to a server. The server updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent again from the device to the server, and the effectiveness of the advertisement is tracked.

[0675] Prompt Sentence Examples

[0676] "If a user is browsing a cooking recipe site, how would you optimize your ads?"

[0677] "How can we personalize ads based on user behavioral data and emotional state?"

[0678] This system enables the generation and delivery of advertisements based on user behavior, emotions, and context, improving the user experience and maximizing advertising effectiveness.

[0679] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0680] Step 1: Collecting data on user behavior

[0681] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling, as well as text information and rich media (voice, facial expressions, etc.) entered by the user.

[0682] Input: Information about web pages you visit, text and rich media you enter

[0683] Data processing: storing information in browser cookies and local storage

[0684] Output: Browsing history information, text information, rich media data

[0685] Step 2: Analyzing user behavior and sentiment data

[0686] The device uses machine learning algorithms to analyze user behavior and identify their interests based on collected browsing history and input text information, and uses natural language processing technology and an emotion engine to recognize the user's emotional state from their comments and reactions.

[0687] Input: browsing history information, text information, rich media data

[0688] Data Computing: Behavioral analysis using machine learning algorithms, emotion analysis using natural language processing technology and emotion engines

[0689] Output: User interests, emotional state

[0690] Step 3: Sending data

[0691] The device compiles the behavioral and emotional analysis results and encrypts them using AES (Advanced Encryption Standard), which is then sent to a server using HTTPS.

[0692] Input: User interests, emotional state

[0693] Data processing: AES encryption

[0694] Output: Encrypted analysis results

[0695] Step 4: Receiving data and creating a profile

[0696] The server receives the data sent from the device and decrypts the AES-encrypted data. Based on the decrypted data, it creates and updates a user profile. The profile includes the user's areas of interest, past behavioral history, clicked ad history, emotional state, etc.

[0697] Input: Encrypted analysis results

[0698] Data calculation: AES decryption, profile creation and updating

[0699] Output: User profile

[0700] Step 5: Generate Ads

[0701] The server generates personalized ads based on the user's profile and contextual information from the current web page using generative AI models, natural language processing techniques, and image generation techniques.

[0702] Input: User profile, current web page context

[0703] Data Computing: Ad Generation with Generative AI Models and Natural Language Processing Techniques

[0704] Output: Personalized ads

[0705] Step 6: Serving and displaying ads

[0706] The server then formats the generated ad in HTML5 or JavaScript format, includes a tracking tag, and delivers it to the user's device, where it is interpreted and displayed in the appropriate location on the currently viewed web page.

[0707] Input: Personalized ads (HTML5 and JavaScript formats)

[0708] Data processing: Formatting into HTML5 or JavaScript format

[0709] Output: Advertisement displayed on a web page

[0710] Step 7: Track your advertising success

[0711] When a user clicks on an ad, the click information is recorded on the device and sent back to the server, which then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[0712] Input: Ad click information

[0713] Data processing: Record click data and send it to the server

[0714] Output: Ad performance data (click-through rate, conversion rate)

[0715] (Application example 2)

[0716] 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."

[0717] Conventional ad delivery systems only consider users' interests and concerns, but are unable to provide personalized ads that reflect the user's emotional state. As a result, improving the user experience and maximizing advertising effectiveness have not been fully achieved. Furthermore, because emotion analysis technology has not been integrated into the ad generation system, it has been difficult to display ads that are optimized for the user's current psychological state.

[0718] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a personalized advertisement based on the user profile and the context of the current web page, means for creating and updating the user profile using the received analysis results, and means for delivering the generated advertisement to the user's terminal. This makes it possible to provide optimal personalized advertisements that reflect the user's interests and real-time emotional state.

[0719] "Web pages" refers to multiple different websites or web pages that a user accesses on the Internet.

[0720] "Browser history information" is information that is left behind when a user browses a web page, and includes data such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[0721] "Analyzing emotions" refers to identifying a user's emotional state (e.g., joy, sadness, anger, etc.) from collected data using machine learning algorithms and natural language processing techniques.

[0722] "Identifying user interests" refers to analyzing the collected browsing history information and emotional information to identify what themes and products the user is interested in.

[0723] "Encrypting the analysis results and sending them to the server" refers to encrypting the analyzed information to protect data security and sending it to the server via the Internet.

[0724] "Server" refers to a central processing unit that receives, analyzes, stores, generates and distributes data, and is the core of this system.

[0725] A "user profile" is a data set that compiles various data about a user (interests, browsing history, emotional state, etc.) to provide customized information to each individual user.

[0726] "Current web page context" refers to the content, theme, and related information of the web page that the user is currently viewing.

[0727] "Personalized advertising" refers to advertising that is individually optimized based on a user's profile and the context of the current web page.

[0728] "Generating an ad" refers to creating ad content, such as text, images, or video, based on the user's profile and contextual information about the current web page.

[0729] "Distributing the generated advertisement" refers to sending the advertisement generated by the server to the user's terminal and sending the data necessary for displaying it.

[0730] "Displaying an advertisement on a web page" refers to positioning and visually presenting an advertisement within the web page that a user is viewing.

[0731] This invention is a system that generates and delivers personalized advertisements using web page browsing history information and emotional data collected by the user's device. This system analyzes the user's browsing behavior and emotional state in real time and displays appropriate advertisements based on that analysis.

[0732] Hardware and Software Used

[0733] Hardware: Smartphone (including camera, microphone, and sensors)

[0734] Software: Python programming language, TextBlob (for sentiment analysis), Scikit-learn (for machine learning), AES (for encryption), Requests (for data transmission)

[0735] Data processing and calculation

[0736] 1. Data Collection

[0737] The user's device collects browsing history information for each web page the user visits. The collected information includes the URL of the visited page, the time spent on the page, the links clicked, the scrolling depth, and emotional data such as the user's comments and facial expressions. This allows for comprehensive collection of user behavioral and emotional data.

[0738] 2. Emotion analysis

[0739] The device analyzes collected comments and rich media data and uses machine learning algorithms and natural language processing techniques to identify the user's emotional state. Specifically, it uses the TextBlob library to distinguish between positive, negative, and neutral sentiment from comments. In addition, it uses Scikit-learn to cluster the data and classify the user's interests.

[0740] 3. Data Encryption and Transmission

[0741] The analysis results are encrypted using the AES encryption algorithm, and then the encrypted data is sent to the server, ensuring secure communication.

[0742] 4. Profile Creation and Updates

[0743] The server then decrypts the encrypted data and creates or updates a user profile, which includes information about the user's interests, browsing history, ad clicks, emotional state, and more.

[0744] 5. Ad Generation

[0745] The server generates personalized advertisements using natural language processing and sentiment analysis techniques based on the user's profile and contextual information of the current web page. For example, if the user is interested in travel and is in a positive emotional state, advertisements for travel promotions and special offers will be generated.

[0746] 6. Advertisement Delivery and Display

[0747] The generated advertisements are delivered from the server to the user's device, and are then displayed at appropriate positions on the web page on the smartphone, allowing the user to view advertisements related to the content they are viewing.

[0748] Specific examples

[0749] If a user views a "chocolate cake" recipe on a cooking recipe site and posts a positive comment, their profile will be updated with that information, and ads for suitable kitchen supplies and ingredients will be displayed.

[0750] Prompt Sentence Examples

[0751] "Write a Python program that analyzes the URLs of pages visited by users, the time spent on them, the links clicked, the scroll depth, and their emotional state, and then suggests ads based on that."

[0752] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0753] Step 1:

[0754] Input: URLs of web pages visited by users, time spent on the page, links clicked, scroll depth, and emotional data such as user comments and facial expressions.

[0755] Processing: The user's device collects this browsing history information and emotional data using browser cookies and local storage.

[0756] Output: Collected web page browsing history information and user emotion data

[0757] While visiting a web page, the user's device collects information such as the actions the user takes and the time spent on the page, as well as emotional data from the user's comments and facial expressions.

[0758] Step 2:

[0759] Input: Collected browsing history information and emotional data

[0760] Processing: The device performs sentiment analysis on the collected data using the TextBlob library, and uses Scikit-learn to cluster page content. It also identifies emotional states (positive, negative, neutral) and classifies interests based on user comments and media data.

[0761] Output: Parsed emotional state and clustering results

[0762] The device analyzes user comments to understand their interests and focuses on specific areas of interest, and then categorizes browsing data into specific clusters.

[0763] Step 3:

[0764] Input: Parsed emotional states and clustering results

[0765] Processing: The analysis results are encrypted using the AES encryption algorithm.

[0766] Output: Encrypted analysis results

[0767] The device ensures data security by encrypting the results of sentiment analysis and clustering.

[0768] Step 4:

[0769] Input: Encrypted analysis results

[0770] Action: Send the encrypted data to the server.

[0771] Output: The server where the analysis results are sent

[0772] The server receives the encrypted data and prepares it for decryption.

[0773] Step 5:

[0774] Input: Encrypted analysis results

[0775] Processing: The server decrypts the received data and creates and updates a profile based on the user's interests, browsing history, and emotional state.

[0776] Output: Updated user profile

[0777] The server uses the decrypted data to create and update a detailed profile of the user for further processing.

[0778] Step 6:

[0779] Input: User profile and current web page context information

[0780] Processing: The server generates personalized advertisements using natural language processing and sentiment analysis techniques.

[0781] Output: Generated personalized ad

[0782] The server uses the user's profile and the context of the current web page to generate appropriate ads that match the user's interests and emotional state.

[0783] Step 7:

[0784] Input: Generated personalized ad

[0785] Processing: The server delivers the generated advertisement to the user's device and sends data to display it on the web page.

[0786] Output: Personalized ads delivered

[0787] The server delivers the generated advertisement to the user's terminal, which then displays it appropriately on a web page.

[0788] Step 8:

[0789] Input: Personalized ads delivered

[0790] Processing: The user's device displays the delivered advertisement on the web page.

[0791] Output: The ad shown to the user

[0792] The user's device displays ads that match the content of the web page, allowing the user to see ads that are directly related to the web page they are viewing.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] [Third embodiment]

[0797] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0798] 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.

[0799] 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).

[0800] 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.

[0801] 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.

[0802] 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).

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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."

[0809] The system of the present invention personalizes advertisements based on the user's interests and the context of the web page being viewed by linking the server on the advertisement distribution platform side with the user's terminal. This system is comprised of the following steps.

[0810] Collecting data on user behavior

[0811] When a user browses a web page, their device collects browsing history information, including the URL of the page visited, the time spent on the page, the links clicked, the scrolling depth, and the search queries. This data is collected using browser cookies and local storage.

[0812] User behavior data analysis

[0813] The device analyzes the user's interests based on the collected data. This analysis may involve the use of machine learning algorithms to identify interests in specific categories (e.g., travel, cooking, technology, etc.) by analyzing the pages the user frequently visits and search queries. Data such as the ads the user clicks on and the duration of their visit may also be used for analysis.

[0814] Sending data

[0815] The device then encrypts the analysis results and sends them to the server. This encryption uses a standard encryption algorithm such as AES (Advanced Encryption Standard). The encrypted data is securely sent to the server, preventing eavesdropping or tampering by third parties.

[0816] Receiving Data and Profiling

[0817] The server receives and decrypts the encrypted data sent from the device. At this time, a unique user profile is created. The profile includes the user's areas of interest, past behavioral history, and clicked ad history. This allows the server to understand the user's behavioral patterns and build a foundation for providing appropriate advertisements.

[0818] Ad Generation

[0819] The server generates personalized ads based on the user's profile and contextual information about the current web page. This ad generation process may also use natural language processing (NLP) and image generation techniques. For example, if the user is interested in travel, the server generates promotional ads for flights and hotels. The generated ads are then formatted in HTML5 and JavaScript and converted into a deliverable format.

[0820] Ad delivery and display

[0821] The server delivers the generated advertisements to the user's device in real time, and the device interprets the received advertisements and inserts them into the appropriate positions on the currently displayed web page, allowing the user to see advertisements that are relevant to the page they are viewing.

[0822] Tracking advertising effectiveness

[0823] When a user clicks on an ad, the click information is recorded on the device. The device then sends this click data back to the server, which uses it to track the effectiveness of the ad. The server collects and analyzes data such as ad click rates and conversion rates to evaluate the ad's performance.

[0824] Specific examples

[0825] Example 1: Viewing a cooking recipe site

[0826] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. The device analyzes this data and determines that the user is interested in cooking. The analysis results are encrypted and sent to a server, which updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent back from the device to the server, and the effectiveness of the advertisement is tracked.

[0827] This allows users to receive more relevant ads and also allows advertisers to deliver more targeted ads.

[0828] The system of the present invention provides advertisements that adapt to a user's interests and context in real time, improving the user experience and maximizing the effectiveness of advertisements.

[0829] The processing flow will be explained below.

[0830] Step 1:

[0831] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[0832] Step 2:

[0833] The device analyzes user behavior based on the collected browsing history information. For example, it uses machine learning algorithms to analyze the patterns of frequently visited web pages and search queries to identify user interests.

[0834] Step 3:

[0835] The device compiles the analysis results and encrypts them using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0836] Step 4:

[0837] The device then sends the encrypted analysis results to the ad distribution platform's server, using a protocol to protect the user's privacy.

[0838] Step 5:

[0839] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a user profile. The profile includes the user's areas of interest, past behavior, and clicked ads.

[0840] Step 6:

[0841] The server generates personalized ads based on the user's profile and contextual information about the current web page, optionally using natural language processing (NLP) and image generation techniques.

[0842] Step 7:

[0843] The server then formats the generated advertisements in HTML5 or JavaScript format and delivers them to the user's device. The advertisement data also includes tags for tracking purposes.

[0844] Step 8:

[0845] The device interprets the received advertisement and displays it in an appropriate location on the currently viewed web page, without disrupting the user's browsing experience.

[0846] Step 9:

[0847] When a user clicks on a displayed advertisement, the click information is recorded on the terminal. The terminal then sends this click data back to the server, where it is used to evaluate the effectiveness of the advertisement.

[0848] Step 10:

[0849] The server analyzes the click data received and evaluates the ad's click rate, conversion rate, etc. This feedback is used to generate future ads.

[0850] Example 1

[0851] 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."

[0852] Conventional ad delivery systems were unable to fully utilize user behavioral data and had low accuracy in delivering personalized ads, making it difficult to provide ads based on users' interests. Furthermore, tracking of advertising effectiveness was also insufficient, making it difficult to accurately measure effectiveness for advertisers. This made it difficult to improve the user experience and maximize the effectiveness of advertising.

[0853] 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.

[0854] In this invention, the server includes means for creating and updating a user profile, means for generating personalized advertisements, and means for delivering the generated advertisements to the user's terminal, thereby enabling personalized advertisements based on the user's interests to be provided in real time.

[0855] A "user terminal" is an electronic device that a user uses to browse web pages and that has the functionality to display personalized advertisements.

[0856] "Web page browsing history information" is data that includes information such as the URLs of web pages accessed by a user, the length of time spent on those pages, the links clicked, and search queries.

[0857] "Analysis" is the process of using collected data to identify user behavior patterns and interests.

[0858] "Encryption" is the act of converting data using a specific algorithm to protect it from eavesdropping or tampering by third parties.

[0859] A "server" is a computer system that receives data sent from a user's terminal and creates and updates a user's profile based on the analysis results.

[0860] A "profile" is a data set that compiles user-specific information, including the user's interests, past behavioral history, and history of clicked ads.

[0861] "Personalized ads" are ads that are optimally generated for an individual user based on the user's profile and contextual information of the current web page.

[0862] "Delivery" is the process of transmitting the generated advertisement to the user's terminal via the Internet.

[0863] "Tracking" is the process of recording users' responses and actions to advertisements and measuring the effectiveness of the advertisements.

[0864] A "machine learning algorithm" is a program or method that allows a computer to analyze large amounts of data and learn based on that data.

[0865] "Natural language processing technology" is a computer technology for understanding and generating human language.

[0866] "Image generation technology" is technology for generating images using a computer.

[0867] The system of this invention connects an advertising distribution platform server with a user's device to personalize advertisements based on the user's interests and the context of the web page they are viewing. This system includes a series of processes: collecting data on user behavior, analyzing the data, generating advertisements, and delivering and tracking the advertisements.

[0868] When a user browses a web page, their device collects their browsing history information. This information includes the URL of the web page, the time spent on the page, the links clicked, and the search queries. Specifically, this data is collected by using browser cookies and local storage. For this purpose, the user's PC, smartphone, and web browser (e.g., Google Chrome, Mozilla Firefox) are used.

[0869] The device then analyzes the collected data to identify the user's interests. This analysis is performed using machine learning algorithms, specifically software such as TensorFlow and Scikit-learn. For example, if the user visits many travel-related pages, the device will determine that the user is interested in travel.

[0870] The analysis results are encrypted and sent to the server using the AES (Advanced Encryption Standard) algorithm. The encrypted data is then securely sent to the server via the Internet.

[0871] The server then decrypts the encrypted data and creates and updates a unique user profile, which includes information about the user's interests, past behavior, and the ads they have clicked.

[0872] The server then generates ads based on the user's profile and the context of the currently viewed webpage. This ad generation process uses natural language processing (NLP) and image generation techniques, including libraries such as BERT and GANs. For example, if a user's profile indicates an interest in travel, the server generates promotional ads for flights and hotels.

[0873] The generated ads are formatted in HTML5 or JavaScript format and delivered in real time from the server to the device. The device then inserts the received ads into the appropriate location on the web page, allowing the user to see ads that are highly relevant to the page they are currently viewing.

[0874] Finally, when a user clicks on an ad, the click information is again recorded on the device, encrypted, and sent to the server. The server analyzes this information and uses it to track the effectiveness of the ad. Specifically, data such as click-through rates and conversion rates are collected and used to evaluate the performance of the ad.

[0875] Specific examples

[0876] Viewing recipe sites

[0877] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. The device analyzes this data and determines that the user is interested in cooking. The results of this analysis are encrypted and sent to a server, which updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent back from the device to the server, and the effectiveness of the advertisement is tracked.

[0878] Example prompts for generative AI models

[0879] "Create a program that generates personalized advertisements for cooking utensils for users browsing a cooking recipe website. Explain the entire process, from collecting user behavior data to tracking the effectiveness of the advertisements."

[0880] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0881] Step 1:

[0882] The device collects the user's browsing history information.

[0883] Input: User's web page visits, links clicked, search queries, and time spent.

[0884] Data processing: Record information in browser cookies or local storage. For example, if a user enters the search query "pasta recipes," that information is stored in a cookie.

[0885] Output: Browsing history information (e.g. URLs visited, time spent, links clicked).

[0886] Step 2:

[0887] Analyze the data collected by the device.

[0888] Input: Browsing history information collected in step 1.

[0889] Data Computing: Identifying user interests using machine learning algorithms. Performing data analysis using TensorFlow and Scikit-learn.

[0890] Specific operation: If the user visits many travel-related pages, the device will analyze that the user is interested in travel.

[0891] Output: Interest analysis results (e.g. "User is interested in travel").

[0892] Step 3:

[0893] The device encrypts the analysis results and sends them to the server.

[0894] Input: Analysis results obtained in step 2.

[0895] Data processing: Encrypt data using AES (Advanced Encryption Standard).

[0896] Specific operation: The analysis result "The user is interested in traveling" is encrypted as "encrypted_data".

[0897] Output: The encrypted data.

[0898] Step 4:

[0899] The server receives and decrypts the encrypted data to create and update the user profile.

[0900] Input: The encrypted data sent in step 3.

[0901] Data operations: Decrypt AES encryption and decode user interests. Update user profile.

[0902] Specific behavior: The decrypted data "user is interested in travel" is added to the profile.

[0903] Output: The updated user profile.

[0904] Step 5:

[0905] A server generates advertisements based on the user's profile and contextual information.

[0906] Input: Updated user profile, context information for the current web page.

[0907] Data Computation: Generate ads using natural language processing (NLP) and image generation, using BERT and GANs.

[0908] Specific Operation: If the user's profile indicates that they are "interested in travel," the server generates promotional ads for flights and hotels.

[0909] Output: The generated ad (in HTML5 and JavaScript formats).

[0910] Step 6:

[0911] The server distributes the generated advertisement to the user's terminal.

[0912] Input: The ad generated in step 5.

[0913] Data transmission: Sending advertisements over the internet in real time.

[0914] Specific operation: The generated advertisement is sent to the terminal and inserted into the current web page.

[0915] Output: The ad that was delivered.

[0916] Step 7:

[0917] A user clicks on a delivered ad.

[0918] Input: Personalized ads displayed on web pages.

[0919] Specific operation: When a user clicks on an advertisement, the click information is recorded on the device.

[0920] Output: Click data (e.g. clicked ad ID, click timestamp).

[0921] Step 8:

[0922] The device sends click information to a server to track advertising effectiveness.

[0923] Input: Click data recorded in step 7.

[0924] Data transmission: Click data is encrypted and sent to the server.

[0925] Specific operation: The device encrypts the click information using AES and sends it to the server.

[0926] Output: Encrypted click data, ad effectiveness tracking data.

[0927] (Application example 1)

[0928] 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."

[0929] Conventional ad delivery systems only perform basic analysis of user behavior data, limiting their ability to provide effective personalized ads. Furthermore, they lacked the functionality to collect ad click information and track ad effectiveness, preventing ad performance optimization. This resulted in ads that were not relevant to users, making it difficult to increase purchasing motivation and for advertisers to deliver ads efficiently.

[0930] 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.

[0931] In this invention, the server includes means for creating and updating a user profile using the received analysis results, means for generating personalized advertisements based on the user profile and the context of the current web page, and means for delivering the generated advertisements to the user's terminal, thereby enabling effective generation and delivery of personalized advertisements based on user behavior data and tracking of advertising effectiveness.

[0932] A "user device" is a computing device used to browse web pages, collect and analyze data, and deliver and display personalized advertisements.

[0933] "Web page browsing history information" refers to data such as the URLs of the web pages visited by the user, the time spent on the page, the links clicked, the scrolling depth, and the search queries.

[0934] "Analysis results" are data that indicate areas of interest and behavioral patterns generated using machine learning algorithms, etc., based on browsing history information collected to identify user interests.

[0935] "Encryption" is the process of transforming digital data using an encryption algorithm such as AES to protect the data from eavesdropping or tampering by third parties.

[0936] "Server" refers to a central computing device that receives data sent from a user's device, creates and updates the user's profile based on the analysis results, and generates and delivers personalized advertisements.

[0937] A "user profile" is a collection of information that compiles a user's areas of interest, past behavioral history, clicked ad history, etc., and is a data set used for ad personalization.

[0938] "Current web page context" refers to the content, theme, text information, metadata, etc. of the web page currently being viewed by the user, and is information that can be used to generate personalized ads.

[0939] "Personalized advertising" refers to advertising that is tailored to a specific user based on the user's interests or the content of the web page currently being viewed.

[0940] "Advertisement click information" is data when a user clicks on a displayed advertisement, and includes the type of advertisement clicked, the timing of the click, location information, and the like.

[0941] "Advertising effectiveness tracking" is the process of collecting and analyzing data such as the number of times an ad is displayed, click rate, and conversion rate in order to measure how effective the ad was.

[0942] The present invention is a system for generating and delivering personalized advertisements based on a user's web browsing history information, and specifically comprises the following steps.

[0943] First, the user's device collects browsing history information for multiple web pages visited by the user, including the URLs of the pages visited, the duration of stay, the links clicked, the scrolling depth, and the search queries. This data collection is done using browser cookies and local storage.

[0944] The collected web page browsing history information is analyzed on the user's device. A machine learning algorithm is used for the analysis. The user's interests in specific categories (e.g., travel, cooking, technology, etc.) are identified through this analysis. Data such as the ads the user clicked and the length of time they spent on the site is also used for the analysis. The results of this analysis are encrypted and sent to the server. Standard encryption algorithms such as AES are used for encryption.

[0945] The server receives and decrypts the encrypted data sent from the device. At this time, a unique user profile is created. The profile includes the user's areas of interest, past behavioral history, and clicked ad history. The server understands the user's behavioral patterns and builds a foundation for providing appropriate advertisements.

[0946] The server then generates personalized ads based on the user's profile and contextual information about the current web page. This ad generation process may also use natural language processing (NLP) and image generation techniques. For example, if the user is interested in cooking, the server might generate ads for cooking equipment and ingredients. The ads are then formatted in HTML5 and JavaScript and converted into a deliverable format.

[0947] The generated advertisements are delivered to the user's device in real time, where they are interpreted and inserted into the currently displayed web page at the appropriate location, ensuring that the user sees relevant advertisements on the page they are viewing.

[0948] Finally, when a user clicks on an ad, the click information is recorded on the user's device. The user's device then sends this click data back to the server to track the effectiveness of the ad. The server then collects and analyzes data such as ad click rates and conversion rates to evaluate the ad's performance.

[0949] To illustrate this in a concrete example, if a user frequently visits gardening-related web pages, personalized ads such as "Discount sales on gardening tools" or "Special fertilizer for home gardens" can be generated. The prompts for the AI ​​model to generate these ads are as follows:

[0950] Example prompt:

[0951] The user's interest category is "Gardening". His recent search queries include terms like "How to grow roses" and "Vegetable garden". Generate the best ads for this user.

[0952] To implement this invention, the user's terminal and the server each require a web browser, software for data collection and analysis, and a program with encryption and decryption functions, and the server requires an advertisement generation system incorporating machine learning models and natural language processing technology.

[0953] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0954] Step 1:

[0955] The user's device collects web page browsing history information. The input is the web pages the user visited, the time spent there, the links clicked, the scrolling depth, and the search queries. The output is a dataset that compiles and records this information. The data collection is done using browser extensions, cookies, local storage, etc.

[0956] Step 2:

[0957] The user's device analyzes the collected web page browsing history information. The input is the browsing history information collected in step 1. The output is the analyzed user's areas of interest. The analysis uses a machine learning algorithm to identify the user's interest in specific categories (e.g., travel, cooking, technology, etc.). Data such as the ads the user clicked and the length of time they stayed on the site are also used for the analysis.

[0958] Step 3:

[0959] The user's device encrypts the analysis results and sends them to the server. The input is the analysis results obtained in step 2. The output is the encrypted data. A standard encryption algorithm such as AES is used for encryption.

[0960] Step 4:

[0961] The server receives and decrypts the encrypted data. The input is the encrypted data sent from the user's device. The output is the decrypted analysis result. The server creates and updates the user's profile based on the decrypted data.

[0962] Step 5:

[0963] The server generates personalized ads based on the user's profile and the context of the current web page. The input is the user's profile and the context of the web page. The output is a personalized ad. Natural language processing (NLP) and image generation techniques may be used to generate the ads.

[0964] Step 6:

[0965] The server delivers the generated advertisement to the user's device in real time. The input is the advertisement generated in step 5. The output is the delivered advertisement. The advertisement is formatted in HTML5 or JavaScript format and sent to the user's device.

[0966] Step 7:

[0967] The user's device interprets the received advertisement and inserts it into the appropriate location on the currently displayed web page. The input is the advertisement delivered in step 6. The output is the advertisement displayed within the web page. This allows the user to see advertisements based on their interests.

[0968] Step 8:

[0969] When a user clicks on an ad, the click information is recorded on the user's device. The input is the information about the ad the user clicked on. The output is the recorded click information. The device then sends this click data back to the server and uses it to track the effectiveness of the ad.

[0970] 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.

[0971] The system of the present invention generates and delivers personalized advertisements by linking the server of an advertisement distribution platform with the user's device, taking into consideration not only the user's interests and the context of the web page being viewed, but also the user's emotional state. The present invention is composed of the following steps.

[0972] Collecting data on user behavior

[0973] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling. In addition, more detailed data can be obtained by collecting text information entered by the user and rich media (voice, facial expressions, etc.).

[0974] User behavior and sentiment data analysis

[0975] The device analyzes the user's behavior and emotions based on the collected browsing history information and input text information. This analysis uses machine learning algorithms and natural language processing (NLP) technology. It also uses an emotion engine to identify the user's emotional state from the user's text information and rich media. For example, it can recognize emotional states such as joy, sadness, and anger from the comments and reactions entered by the user.

[0976] Sending data

[0977] The device compiles the results of the behavioral and emotional analysis and encrypts them using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0978] Receiving Data and Profiling

[0979] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a user profile. The profile includes the user's areas of interest, past behavior, clicked ads, emotional state, and more.

[0980] Ad Generation

[0981] The server generates personalized ads based on the user's profile, contextual information about the current web page, and the user's emotional state. This process also uses natural language processing and image generation techniques. For example, if a user is interested in travel and is in a positive emotional state, ads for travel promotions and special offers will be generated.

[0982] Ad delivery and display

[0983] The server formats the generated advertisement in HTML5 or JavaScript format and delivers it to the user's device. The advertisement data also includes tags for tracking. The device interprets the received advertisement and displays it in the appropriate position on the currently viewed web page. This allows the user to see advertisements that are highly relevant to the page they are viewing.

[0984] Tracking advertising effectiveness

[0985] When a user clicks on an ad, the click information is recorded on the device. The device then sends this click data back to the server, where it is used to evaluate the effectiveness of the ad. The server then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[0986] Specific examples

[0987] Example 1: Viewing a cooking recipe site

[0988] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. If the user posts a positive comment about a recipe, the device also analyzes the user's emotions. The device analyzes this data to determine that the user is interested in cooking and is in a positive emotional state. The analysis results are encrypted and sent to a server. The server updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent again from the device to the server, and the effectiveness of the advertisement is tracked.

[0989] The system of the present invention delivers advertisements in real time that are adaptive to the user's interests, context, and emotions, improving the user experience while maximizing advertising effectiveness.

[0990] The processing flow will be explained below.

[0991] Step 1:

[0992] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling, as well as text information and rich media (voice, facial expressions, etc.) entered by the user.

[0993] Step 2:

[0994] The device analyzes the collected browsing history information and input text information, and uses machine learning algorithms and natural language processing (NLP) technology to identify the user's behavior and emotions. For example, if a user inputs a positive comment, the content is analyzed to recognize the user's emotional state.

[0995] Step 3:

[0996] The emotion engine analyzes the user's emotional state from text information and rich media to detect specific emotions such as joy, sadness, anger, etc. The device then adds this emotional information to the analysis results.

[0997] Step 4:

[0998] The device encrypts the results of behavioral and emotional analysis using encryption algorithms such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[0999] Step 5:

[1000] The device then sends the encrypted analysis results to a server, using security protocols to keep the data safe.

[1001] Step 6:

[1002] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a profile of the user, including their interests, past behavior, and emotional state.

[1003] Step 7:

[1004] The server generates personalized advertisements based on the user's profile, contextual information about the current web page, and the user's emotional state. For example, if a user is interested in cooking and is in a positive emotional state, the server generates advertisements for cooking-related products and services.

[1005] Step 8:

[1006] The server then formats the generated advertisements in HTML5 or JavaScript format and delivers them to the user's device. The advertisement data also includes tags for later tracking the effectiveness of the advertisements.

[1007] Step 9:

[1008] The device interprets the received advertisement and displays it in the appropriate location on the currently viewed web page, allowing the user to see the most relevant advertisement on the page they are viewing.

[1009] Step 10:

[1010] When a user clicks on an ad, the click information is recorded on the device, which then sends the click data back to the server and uses it to track the effectiveness of the ad.

[1011] Step 11:

[1012] The server analyzes the click rate and conversion rate of the advertisement based on the received click data, thereby evaluating the performance of the advertisement and helping to generate subsequent advertisements.

[1013] Example 2

[1014] 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."

[1015] Conventional ad delivery systems deliver ads based solely on a user's interests and browsing history, which limits the effectiveness of ads because they are unable to take into account changes in the user's interests or emotional state. It is also difficult to properly analyze user behavior data and click information to generate and deliver personalized ads in real time. This has created a need for systems that can simultaneously improve user experience and maximize advertising effectiveness.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1017] In this invention, the server includes means for analyzing user behavioral data and emotional state, means for creating and updating a user profile based on the analysis results, means for generating personalized advertisements based on the user profile and context information of the current web page, and means for tracking advertisement effectiveness, thereby enabling highly personalized advertisement delivery based on the user's behavior and emotions, maximizing the effectiveness of advertisements and improving the user experience.

[1018] "User's terminal" refers to an information processing device that allows a user to access the Internet, view web pages, and input and send data.

[1019] "Web page browsing history information" is data related to the web pages accessed by the user, and includes information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[1020] "Text information" refers to character data entered or displayed by a user on a web page, including comments, reviews, search queries, etc.

[1021] "Rich media" refers to a variety of data formats other than text, including audio, video, images, and user facial expressions.

[1022] "Emotional state" refers to the emotion a user feels from specific text information or rich media, and identifies psychological states such as joy, sadness, or anger.

[1023] A "machine learning algorithm" is a technology used to process data, extract patterns and relationships, and build models to predict user behavior and identify interests.

[1024] "Natural Language Processing (NLP)" is a technology for understanding, interpreting, and generating human language, and is used to analyze user text information.

[1025] "AES (Advanced Encryption Standard)" is an encryption method for protecting data and is used to securely transmit analysis results to the server.

[1026] A "user profile" is a data set that includes information such as a user's interests, past behavioral history, clicked ads, and emotional state.

[1027] "Current web page context" refers to information about the content or subject of the web page most recently accessed by the user.

[1028] A "personalized ad" is an ad that is optimized for a particular user, generated based on the user's profile and the context of the current web page.

[1029] "Image generation technology" means technology for generating or editing digital images used to create the visual elements of an advertisement.

[1030] "Advertising tracking" is the process of measuring how an ad impacts users, and refers to the collection and analysis of data such as click-through rates and conversion rates.

[1031] "Click information" refers to data when a user clicks on an advertisement, and is used to analyze the time of the click and the relevance of the advertisement.

[1032] The system of the present invention works in cooperation with the advertising distribution platform server and the user's device to generate and deliver personalized advertisements that take into account the user's interests, the context of the web page being viewed, and even the user's emotional state.

[1033] Hardware and software used

[1034] Hardware used

[1035] User's device: Information processing device such as computer, smartphone, tablet, etc.

[1036] Servers: Cloud servers, physical servers, servers in data centers

[1037] Software used

[1038] Web browser: Google Chrome, Mozilla Firefox, Apple Safari, Microsoft Edge, etc.

[1039] Machine learning algorithms: Scikit-learn, TensorFlow, PyTorch, etc.

[1040] Natural Language Processing (NLP): NLTK, spaCy, BERT, etc.

[1041] Encryption algorithm: AES (Advanced Encryption Standard)

[1042] Data analysis tools: Google Analytics, Apache Hadoop, etc.

[1043] Specific examples of program processing

[1044] Collecting data on user behavior

[1045] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling. Furthermore, text information and rich media (voice, facial expressions, etc.) entered by the user are also collected. For example, if a user posts a comment on a blog post, the content of the comment and facial expression data are collected.

[1046] User behavior and sentiment data analysis

[1047] The device uses machine learning algorithms to analyze user behavior and identify their interests based on the collected browsing history and input text information. It also uses natural language processing technology and an emotion engine to recognize the user's emotional state from their comments and reactions. For example, if a user comments, "This book is really great!", it identifies that positive emotion.

[1048] Sending data

[1049] The device compiles the results of the behavioral and emotional analysis and encrypts them using the Advanced Encryption Standard (AES). The encrypted data is then sent to a server using HTTPS to prevent unauthorized access by third parties.

[1050] Receiving Data and Profiling

[1051] The server decrypts the received data and creates and updates a user profile, which includes information such as the user's interests, past behavior, clicked ads, and emotional state.

[1052] Ad generation and delivery

[1053] The server generates personalized ads based on the user's profile and the context of the current web page using generative AI models, natural language processing, and image generation technologies. The ads are formatted in HTML5 or JavaScript and delivered to the user's device with tracking tags. The device interprets the received ads and displays them in the appropriate location on the currently viewed web page.

[1054] Tracking advertising effectiveness

[1055] When a user clicks on an ad, the click information is recorded on the device and sent back to the server, which then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[1056] Specific examples

[1057] Example 1: Viewing a cooking recipe site

[1058] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. If the user posts a positive comment about a recipe, the device also analyzes the user's sentiment. The device analyzes this data and determines that the user is interested in cooking and is in a positive emotional state. The analysis results are encrypted and sent to a server. The server updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent again from the device to the server, and the effectiveness of the advertisement is tracked.

[1059] Prompt Sentence Examples

[1060] "If a user is browsing a cooking recipe site, how would you optimize your ads?"

[1061] "How can we personalize ads based on user behavioral data and emotional state?"

[1062] This system enables the generation and delivery of advertisements based on user behavior, emotions, and context, improving the user experience and maximizing advertising effectiveness.

[1063] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1064] Step 1: Collecting data on user behavior

[1065] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling, as well as text information and rich media (voice, facial expressions, etc.) entered by the user.

[1066] Input: Information about web pages you visit, text and rich media you enter

[1067] Data processing: storing information in browser cookies and local storage

[1068] Output: Browsing history information, text information, rich media data

[1069] Step 2: Analyzing user behavior and sentiment data

[1070] The device uses machine learning algorithms to analyze user behavior and identify their interests based on collected browsing history and input text information, and uses natural language processing technology and an emotion engine to recognize the user's emotional state from their comments and reactions.

[1071] Input: browsing history information, text information, rich media data

[1072] Data Computing: Behavioral analysis using machine learning algorithms, emotion analysis using natural language processing technology and emotion engines

[1073] Output: User interests, emotional state

[1074] Step 3: Sending data

[1075] The device compiles the behavioral and emotional analysis results and encrypts them using AES (Advanced Encryption Standard), which is then sent to a server using HTTPS.

[1076] Input: User interests, emotional state

[1077] Data processing: AES encryption

[1078] Output: Encrypted analysis results

[1079] Step 4: Receiving data and creating a profile

[1080] The server receives the data sent from the device and decrypts the AES-encrypted data. Based on the decrypted data, it creates and updates a user profile. The profile includes the user's areas of interest, past behavioral history, clicked ad history, emotional state, etc.

[1081] Input: Encrypted analysis results

[1082] Data calculation: AES decryption, profile creation and updating

[1083] Output: User profile

[1084] Step 5: Generate Ads

[1085] The server generates personalized ads based on the user's profile and contextual information from the current web page using generative AI models, natural language processing techniques, and image generation techniques.

[1086] Input: User profile, current web page context

[1087] Data Computing: Ad Generation with Generative AI Models and Natural Language Processing Techniques

[1088] Output: Personalized ads

[1089] Step 6: Serving and displaying ads

[1090] The server then formats the generated ad in HTML5 or JavaScript format, includes a tracking tag, and delivers it to the user's device, where it is interpreted and displayed in the appropriate location on the currently viewed web page.

[1091] Input: Personalized ads (HTML5 and JavaScript formats)

[1092] Data processing: Formatting into HTML5 or JavaScript format

[1093] Output: Advertisement displayed on a web page

[1094] Step 7: Track your advertising success

[1095] When a user clicks on an ad, the click information is recorded on the device and sent back to the server, which then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[1096] Input: Ad click information

[1097] Data processing: Record click data and send it to the server

[1098] Output: Ad performance data (click-through rate, conversion rate)

[1099] (Application example 2)

[1100] 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."

[1101] Conventional ad delivery systems only consider users' interests and concerns, but are unable to provide personalized ads that reflect the user's emotional state. As a result, improving the user experience and maximizing advertising effectiveness have not been fully achieved. Furthermore, because emotion analysis technology has not been integrated into the ad generation system, it has been difficult to display ads that are optimized for the user's current psychological state.

[1102] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a personalized advertisement based on the user profile and the context of the current web page, means for creating and updating the user profile using the received analysis results, and means for delivering the generated advertisement to the user's terminal. This makes it possible to provide optimal personalized advertisements that reflect the user's interests and real-time emotional state.

[1103] "Web pages" refers to multiple different websites or web pages that a user accesses on the Internet.

[1104] "Browser history information" is information that is left behind when a user browses a web page, and includes data such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[1105] "Analyzing emotions" refers to identifying a user's emotional state (e.g., joy, sadness, anger, etc.) from collected data using machine learning algorithms and natural language processing techniques.

[1106] "Identifying user interests" refers to analyzing the collected browsing history information and emotional information to identify what themes and products the user is interested in.

[1107] "Encrypting the analysis results and sending them to the server" refers to encrypting the analyzed information to protect data security and sending it to the server via the Internet.

[1108] "Server" refers to a central processing unit that receives, analyzes, stores, generates and distributes data, and is the core of this system.

[1109] A "user profile" is a data set that compiles various data about a user (interests, browsing history, emotional state, etc.) to provide customized information to each individual user.

[1110] "Current web page context" refers to the content, theme, and related information of the web page that the user is currently viewing.

[1111] "Personalized advertising" refers to advertising that is individually optimized based on a user's profile and the context of the current web page.

[1112] "Generating an ad" refers to creating ad content, such as text, images, or video, based on the user's profile and contextual information about the current web page.

[1113] "Distributing the generated advertisement" refers to sending the advertisement generated by the server to the user's terminal and sending the data necessary for displaying it.

[1114] "Displaying an advertisement on a web page" refers to positioning and visually presenting an advertisement within the web page that a user is viewing.

[1115] This invention is a system that generates and delivers personalized advertisements using web page browsing history information and emotional data collected by the user's device. This system analyzes the user's browsing behavior and emotional state in real time and displays appropriate advertisements based on that analysis.

[1116] Hardware and Software Used

[1117] Hardware: Smartphone (including camera, microphone, and sensors)

[1118] Software: Python programming language, TextBlob (for sentiment analysis), Scikit-learn (for machine learning), AES (for encryption), Requests (for data transmission)

[1119] Data processing and calculation

[1120] 1. Data Collection

[1121] The user's device collects browsing history information for each web page the user visits. The collected information includes the URL of the visited page, the time spent on the page, the links clicked, the scrolling depth, and emotional data such as the user's comments and facial expressions. This allows for comprehensive collection of user behavioral and emotional data.

[1122] 2. Emotion analysis

[1123] The device analyzes collected comments and rich media data and uses machine learning algorithms and natural language processing techniques to identify the user's emotional state. Specifically, it uses the TextBlob library to distinguish between positive, negative, and neutral sentiment from comments. In addition, it uses Scikit-learn to cluster the data and classify the user's interests.

[1124] 3. Data Encryption and Transmission

[1125] The analysis results are encrypted using the AES encryption algorithm, and then the encrypted data is sent to the server, ensuring secure communication.

[1126] 4. Profile Creation and Updates

[1127] The server then decrypts the encrypted data and creates or updates a user profile, which includes information about the user's interests, browsing history, ad clicks, emotional state, and more.

[1128] 5. Ad Generation

[1129] The server generates personalized advertisements using natural language processing and sentiment analysis techniques based on the user's profile and contextual information of the current web page. For example, if the user is interested in travel and is in a positive emotional state, advertisements for travel promotions and special offers will be generated.

[1130] 6. Advertisement Delivery and Display

[1131] The generated advertisements are delivered from the server to the user's device, and are then displayed at appropriate positions on the web page on the smartphone, allowing the user to view advertisements related to the content they are viewing.

[1132] Specific examples

[1133] If a user views a "chocolate cake" recipe on a cooking recipe site and posts a positive comment, their profile will be updated with that information, and ads for suitable kitchen supplies and ingredients will be displayed.

[1134] Prompt Sentence Examples

[1135] "Write a Python program that analyzes the URLs of pages visited by users, the time spent on them, the links clicked, the scroll depth, and their emotional state, and then suggests ads based on that."

[1136] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1137] Step 1:

[1138] Input: URLs of web pages visited by users, time spent on the page, links clicked, scroll depth, and emotional data such as user comments and facial expressions.

[1139] Processing: The user's device collects this browsing history information and emotional data using browser cookies and local storage.

[1140] Output: Collected web page browsing history information and user emotion data

[1141] While visiting a web page, the user's device collects information such as the actions the user takes and the time spent on the page, as well as emotional data from the user's comments and facial expressions.

[1142] Step 2:

[1143] Input: Collected browsing history information and emotional data

[1144] Processing: The device performs sentiment analysis on the collected data using the TextBlob library, and uses Scikit-learn to cluster page content. It also identifies emotional states (positive, negative, neutral) and classifies interests based on user comments and media data.

[1145] Output: Parsed emotional state and clustering results

[1146] The device analyzes user comments to understand their interests and focuses on specific areas of interest, and then categorizes browsing data into specific clusters.

[1147] Step 3:

[1148] Input: Parsed emotional states and clustering results

[1149] Processing: The analysis results are encrypted using the AES encryption algorithm.

[1150] Output: Encrypted analysis results

[1151] The device ensures data security by encrypting the results of sentiment analysis and clustering.

[1152] Step 4:

[1153] Input: Encrypted analysis results

[1154] Action: Send the encrypted data to the server.

[1155] Output: The server where the analysis results are sent

[1156] The server receives the encrypted data and prepares it for decryption.

[1157] Step 5:

[1158] Input: Encrypted analysis results

[1159] Processing: The server decrypts the received data and creates and updates a profile based on the user's interests, browsing history, and emotional state.

[1160] Output: Updated user profile

[1161] The server uses the decrypted data to create and update a detailed profile of the user for further processing.

[1162] Step 6:

[1163] Input: User profile and current web page context information

[1164] Processing: The server generates personalized advertisements using natural language processing and sentiment analysis techniques.

[1165] Output: Generated personalized ad

[1166] The server uses the user's profile and the context of the current web page to generate appropriate ads that match the user's interests and emotional state.

[1167] Step 7:

[1168] Input: Generated personalized ad

[1169] Processing: The server delivers the generated advertisement to the user's device and sends data to display it on the web page.

[1170] Output: Personalized ads delivered

[1171] The server delivers the generated advertisement to the user's terminal, which then displays it appropriately on a web page.

[1172] Step 8:

[1173] Input: Personalized ads delivered

[1174] Processing: The user's device displays the delivered advertisement on the web page.

[1175] Output: The ad shown to the user

[1176] The user's device displays ads that match the content of the web page, allowing the user to see ads that are directly related to the web page they are viewing.

[1177] 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.

[1178] 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.

[1179] 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.

[1180] [Fourth embodiment]

[1181] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1182] 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.

[1183] 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).

[1184] 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.

[1185] 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.

[1186] 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).

[1187] 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.

[1188] 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.

[1189] 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.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] 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."

[1194] The system of the present invention personalizes advertisements based on the user's interests and the context of the web page being viewed by linking the server on the advertisement distribution platform side with the user's terminal. This system is comprised of the following steps.

[1195] Collecting data on user behavior

[1196] When a user browses a web page, their device collects browsing history information, including the URL of the page visited, the time spent on the page, the links clicked, the scrolling depth, and the search queries. This data is collected using browser cookies and local storage.

[1197] User behavior data analysis

[1198] The device analyzes the user's interests based on the collected data. This analysis may involve the use of machine learning algorithms to identify interests in specific categories (e.g., travel, cooking, technology, etc.) by analyzing the pages the user frequently visits and search queries. Data such as the ads the user clicks on and the duration of their visit may also be used for analysis.

[1199] Sending data

[1200] The device then encrypts the analysis results and sends them to the server. This encryption uses a standard encryption algorithm such as AES (Advanced Encryption Standard). The encrypted data is securely sent to the server, preventing eavesdropping or tampering by third parties.

[1201] Receiving Data and Profiling

[1202] The server receives and decrypts the encrypted data sent from the device. At this time, a unique user profile is created. The profile includes the user's areas of interest, past behavioral history, and clicked ad history. This allows the server to understand the user's behavioral patterns and build a foundation for providing appropriate advertisements.

[1203] Ad Generation

[1204] The server generates personalized ads based on the user's profile and contextual information about the current web page. This ad generation process may also use natural language processing (NLP) and image generation techniques. For example, if the user is interested in travel, the server generates promotional ads for flights and hotels. The generated ads are then formatted in HTML5 and JavaScript and converted into a deliverable format.

[1205] Ad delivery and display

[1206] The server delivers the generated advertisements to the user's device in real time, and the device interprets the received advertisements and inserts them into the appropriate positions on the currently displayed web page, allowing the user to see advertisements that are relevant to the page they are viewing.

[1207] Tracking advertising effectiveness

[1208] When a user clicks on an ad, the click information is recorded on the device. The device then sends this click data back to the server, which uses it to track the effectiveness of the ad. The server collects and analyzes data such as ad click rates and conversion rates to evaluate the ad's performance.

[1209] Specific examples

[1210] Example 1: Viewing a cooking recipe site

[1211] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. The device analyzes this data and determines that the user is interested in cooking. The analysis results are encrypted and sent to a server, which updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent back from the device to the server, and the effectiveness of the advertisement is tracked.

[1212] This allows users to receive more relevant ads and also allows advertisers to deliver more targeted ads.

[1213] The system of the present invention provides advertisements that adapt to a user's interests and context in real time, improving the user experience and maximizing the effectiveness of advertisements.

[1214] The processing flow will be explained below.

[1215] Step 1:

[1216] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[1217] Step 2:

[1218] The device analyzes user behavior based on the collected browsing history information. For example, it uses machine learning algorithms to analyze the patterns of frequently visited web pages and search queries to identify user interests.

[1219] Step 3:

[1220] The device compiles the analysis results and encrypts them using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[1221] Step 4:

[1222] The device then sends the encrypted analysis results to the ad distribution platform's server, using a protocol to protect the user's privacy.

[1223] Step 5:

[1224] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a user profile. The profile includes the user's areas of interest, past behavior, and clicked ads.

[1225] Step 6:

[1226] The server generates personalized ads based on the user's profile and contextual information about the current web page, optionally using natural language processing (NLP) and image generation techniques.

[1227] Step 7:

[1228] The server then formats the generated advertisements in HTML5 or JavaScript format and delivers them to the user's device. The advertisement data also includes tags for tracking purposes.

[1229] Step 8:

[1230] The device interprets the received advertisement and displays it in an appropriate location on the currently viewed web page, without disrupting the user's browsing experience.

[1231] Step 9:

[1232] When a user clicks on a displayed advertisement, the click information is recorded on the terminal. The terminal then sends this click data back to the server, where it is used to evaluate the effectiveness of the advertisement.

[1233] Step 10:

[1234] The server analyzes the click data received and evaluates the ad's click rate, conversion rate, etc. This feedback is used to generate future ads.

[1235] Example 1

[1236] 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."

[1237] Conventional ad delivery systems were unable to fully utilize user behavioral data and had low accuracy in delivering personalized ads, making it difficult to provide ads based on users' interests. Furthermore, tracking of advertising effectiveness was also insufficient, making it difficult to accurately measure effectiveness for advertisers. This made it difficult to improve the user experience and maximize the effectiveness of advertising.

[1238] 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.

[1239] In this invention, the server includes means for creating and updating a user profile, means for generating personalized advertisements, and means for delivering the generated advertisements to the user's terminal, thereby enabling personalized advertisements based on the user's interests to be provided in real time.

[1240] A "user terminal" is an electronic device that a user uses to browse web pages and that has the functionality to display personalized advertisements.

[1241] "Web page browsing history information" is data that includes information such as the URLs of web pages accessed by a user, the length of time spent on those pages, the links clicked, and search queries.

[1242] "Analysis" is the process of using collected data to identify user behavior patterns and interests.

[1243] "Encryption" is the act of converting data using a specific algorithm to protect it from eavesdropping or tampering by third parties.

[1244] A "server" is a computer system that receives data sent from a user's terminal and creates and updates a user's profile based on the analysis results.

[1245] A "profile" is a data set that compiles user-specific information, including the user's interests, past behavioral history, and history of clicked ads.

[1246] "Personalized ads" are ads that are optimally generated for an individual user based on the user's profile and contextual information of the current web page.

[1247] "Delivery" is the process of transmitting the generated advertisement to the user's terminal via the Internet.

[1248] "Tracking" is the process of recording users' responses and actions to advertisements and measuring the effectiveness of the advertisements.

[1249] A "machine learning algorithm" is a program or method that allows a computer to analyze large amounts of data and learn based on that data.

[1250] "Natural language processing technology" is a computer technology for understanding and generating human language.

[1251] "Image generation technology" is technology for generating images using a computer.

[1252] The system of this invention connects an advertising distribution platform server with a user's device to personalize advertisements based on the user's interests and the context of the web page they are viewing. This system includes a series of processes: collecting data on user behavior, analyzing the data, generating advertisements, and delivering and tracking the advertisements.

[1253] When a user browses a web page, their device collects their browsing history information. This information includes the URL of the web page, the time spent on the page, the links clicked, and the search queries. Specifically, this data is collected by using browser cookies and local storage. For this purpose, the user's PC, smartphone, and web browser (e.g., Google Chrome, Mozilla Firefox) are used.

[1254] The device then analyzes the collected data to identify the user's interests. This analysis is performed using machine learning algorithms, specifically software such as TensorFlow and Scikit-learn. For example, if the user visits many travel-related pages, the device will determine that the user is interested in travel.

[1255] The analysis results are encrypted and sent to the server using the AES (Advanced Encryption Standard) algorithm. The encrypted data is then securely sent to the server via the Internet.

[1256] The server then decrypts the encrypted data and creates and updates a unique user profile, which includes information about the user's interests, past behavior, and the ads they have clicked.

[1257] The server then generates ads based on the user's profile and the context of the currently viewed webpage. This ad generation process uses natural language processing (NLP) and image generation techniques, including libraries such as BERT and GANs. For example, if a user's profile indicates an interest in travel, the server generates promotional ads for flights and hotels.

[1258] The generated ads are formatted in HTML5 or JavaScript format and delivered in real time from the server to the device. The device then inserts the received ads into the appropriate location on the web page, allowing the user to see ads that are highly relevant to the page they are currently viewing.

[1259] Finally, when a user clicks on an ad, the click information is again recorded on the device, encrypted, and sent to the server. The server analyzes this information and uses it to track the effectiveness of the ad. Specifically, data such as click-through rates and conversion rates are collected and used to evaluate the performance of the ad.

[1260] Specific examples

[1261] Viewing recipe sites

[1262] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. The device analyzes this data and determines that the user is interested in cooking. The results of this analysis are encrypted and sent to a server, which updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent back from the device to the server, and the effectiveness of the advertisement is tracked.

[1263] Example prompts for generative AI models

[1264] "Create a program that generates personalized advertisements for cooking utensils for users browsing a cooking recipe website. Explain the entire process, from collecting user behavior data to tracking the effectiveness of the advertisements."

[1265] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1266] Step 1:

[1267] The device collects the user's browsing history information.

[1268] Input: User's web page visits, links clicked, search queries, and time spent.

[1269] Data processing: Record information in browser cookies or local storage. For example, if a user enters the search query "pasta recipes," that information is stored in a cookie.

[1270] Output: Browsing history information (e.g. URLs visited, time spent, links clicked).

[1271] Step 2:

[1272] Analyze the data collected by the device.

[1273] Input: Browsing history information collected in step 1.

[1274] Data Computing: Identifying user interests using machine learning algorithms. Performing data analysis using TensorFlow and Scikit-learn.

[1275] Specific operation: If the user visits many travel-related pages, the device will analyze that the user is interested in travel.

[1276] Output: Interest analysis results (e.g. "User is interested in travel").

[1277] Step 3:

[1278] The device encrypts the analysis results and sends them to the server.

[1279] Input: Analysis results obtained in step 2.

[1280] Data processing: Encrypt data using AES (Advanced Encryption Standard).

[1281] Specific operation: The analysis result "The user is interested in traveling" is encrypted as "encrypted_data".

[1282] Output: The encrypted data.

[1283] Step 4:

[1284] The server receives and decrypts the encrypted data to create and update the user profile.

[1285] Input: The encrypted data sent in step 3.

[1286] Data operations: Decrypt AES encryption and decode user interests. Update user profile.

[1287] Specific behavior: The decrypted data "user is interested in travel" is added to the profile.

[1288] Output: The updated user profile.

[1289] Step 5:

[1290] A server generates advertisements based on the user's profile and contextual information.

[1291] Input: Updated user profile, context information for the current web page.

[1292] Data Computation: Generate ads using natural language processing (NLP) and image generation, using BERT and GANs.

[1293] Specific Operation: If the user's profile indicates that they are "interested in travel," the server generates promotional ads for flights and hotels.

[1294] Output: The generated ad (in HTML5 and JavaScript formats).

[1295] Step 6:

[1296] The server distributes the generated advertisement to the user's terminal.

[1297] Input: The ad generated in step 5.

[1298] Data transmission: Sending advertisements over the internet in real time.

[1299] Specific operation: The generated advertisement is sent to the terminal and inserted into the current web page.

[1300] Output: The ad that was delivered.

[1301] Step 7:

[1302] A user clicks on a delivered ad.

[1303] Input: Personalized ads displayed on web pages.

[1304] Specific operation: When a user clicks on an advertisement, the click information is recorded on the device.

[1305] Output: Click data (e.g. clicked ad ID, click timestamp).

[1306] Step 8:

[1307] The device sends click information to a server to track advertising effectiveness.

[1308] Input: Click data recorded in step 7.

[1309] Data transmission: Click data is encrypted and sent to the server.

[1310] Specific operation: The device encrypts the click information using AES and sends it to the server.

[1311] Output: Encrypted click data, ad effectiveness tracking data.

[1312] (Application example 1)

[1313] 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."

[1314] Conventional ad delivery systems only perform basic analysis of user behavior data, limiting their ability to provide effective personalized ads. Furthermore, they lacked the functionality to collect ad click information and track ad effectiveness, preventing ad performance optimization. This resulted in ads that were not relevant to users, making it difficult to increase purchasing motivation and for advertisers to deliver ads efficiently.

[1315] 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.

[1316] In this invention, the server includes means for creating and updating a user profile using the received analysis results, means for generating personalized advertisements based on the user profile and the context of the current web page, and means for delivering the generated advertisements to the user's terminal, thereby enabling effective generation and delivery of personalized advertisements based on user behavior data and tracking of advertising effectiveness.

[1317] A "user device" is a computing device used to browse web pages, collect and analyze data, and deliver and display personalized advertisements.

[1318] "Web page browsing history information" refers to data such as the URLs of the web pages visited by the user, the time spent on the page, the links clicked, the scrolling depth, and the search queries.

[1319] "Analysis results" are data that indicate areas of interest and behavioral patterns generated using machine learning algorithms, etc., based on browsing history information collected to identify user interests.

[1320] "Encryption" is the process of transforming digital data using an encryption algorithm such as AES to protect the data from eavesdropping or tampering by third parties.

[1321] "Server" refers to a central computing device that receives data sent from a user's device, creates and updates the user's profile based on the analysis results, and generates and delivers personalized advertisements.

[1322] A "user profile" is a collection of information that compiles a user's areas of interest, past behavioral history, clicked ad history, etc., and is a data set used for ad personalization.

[1323] "Current web page context" refers to the content, theme, text information, metadata, etc. of the web page currently being viewed by the user, and is information that can be used to generate personalized ads.

[1324] "Personalized advertising" refers to advertising that is tailored to a specific user based on the user's interests or the content of the web page currently being viewed.

[1325] "Advertisement click information" is data when a user clicks on a displayed advertisement, and includes the type of advertisement clicked, the timing of the click, location information, and the like.

[1326] "Advertising effectiveness tracking" is the process of collecting and analyzing data such as the number of times an ad is displayed, click rate, and conversion rate in order to measure how effective the ad was.

[1327] The present invention is a system for generating and delivering personalized advertisements based on a user's web browsing history information, and specifically comprises the following steps.

[1328] First, the user's device collects browsing history information for multiple web pages visited by the user, including the URLs of the pages visited, the duration of stay, the links clicked, the scrolling depth, and the search queries. This data collection is done using browser cookies and local storage.

[1329] The collected web page browsing history information is analyzed on the user's device. A machine learning algorithm is used for the analysis. The user's interests in specific categories (e.g., travel, cooking, technology, etc.) are identified through this analysis. Data such as the ads the user clicked and the length of time they spent on the site is also used for the analysis. The results of this analysis are encrypted and sent to the server. Standard encryption algorithms such as AES are used for encryption.

[1330] The server receives and decrypts the encrypted data sent from the device. At this time, a unique user profile is created. The profile includes the user's areas of interest, past behavioral history, and clicked ad history. The server understands the user's behavioral patterns and builds a foundation for providing appropriate advertisements.

[1331] The server then generates personalized ads based on the user's profile and contextual information about the current web page. This ad generation process may also use natural language processing (NLP) and image generation techniques. For example, if the user is interested in cooking, the server might generate ads for cooking equipment and ingredients. The ads are then formatted in HTML5 and JavaScript and converted into a deliverable format.

[1332] The generated advertisements are delivered to the user's device in real time, where they are interpreted and inserted into the currently displayed web page at the appropriate location, ensuring that the user sees relevant advertisements on the page they are viewing.

[1333] Finally, when a user clicks on an ad, the click information is recorded on the user's device. The user's device then sends this click data back to the server to track the effectiveness of the ad. The server then collects and analyzes data such as ad click rates and conversion rates to evaluate the ad's performance.

[1334] To illustrate this in a concrete example, if a user frequently visits gardening-related web pages, personalized ads such as "Discount sales on gardening tools" or "Special fertilizer for home gardens" can be generated. The prompts for the AI ​​model to generate these ads are as follows:

[1335] Example prompt:

[1336] The user's interest category is "Gardening". His recent search queries include terms like "How to grow roses" and "Vegetable garden". Generate the best ads for this user.

[1337] To implement this invention, the user's terminal and the server each require a web browser, software for data collection and analysis, and a program with encryption and decryption functions, and the server requires an advertisement generation system incorporating machine learning models and natural language processing technology.

[1338] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1339] Step 1:

[1340] The user's device collects web page browsing history information. The input is the web pages the user visited, the time spent there, the links clicked, the scrolling depth, and the search queries. The output is a dataset that compiles and records this information. The data collection is done using browser extensions, cookies, local storage, etc.

[1341] Step 2:

[1342] The user's device analyzes the collected web page browsing history information. The input is the browsing history information collected in step 1. The output is the analyzed user's areas of interest. The analysis uses a machine learning algorithm to identify the user's interest in specific categories (e.g., travel, cooking, technology, etc.). Data such as the ads the user clicked and the length of time they stayed on the site are also used for the analysis.

[1343] Step 3:

[1344] The user's device encrypts the analysis results and sends them to the server. The input is the analysis results obtained in step 2. The output is the encrypted data. A standard encryption algorithm such as AES is used for encryption.

[1345] Step 4:

[1346] The server receives and decrypts the encrypted data. The input is the encrypted data sent from the user's device. The output is the decrypted analysis result. The server creates and updates the user's profile based on the decrypted data.

[1347] Step 5:

[1348] The server generates personalized ads based on the user's profile and the context of the current web page. The input is the user's profile and the context of the web page. The output is a personalized ad. Natural language processing (NLP) and image generation techniques may be used to generate the ads.

[1349] Step 6:

[1350] The server delivers the generated advertisement to the user's device in real time. The input is the advertisement generated in step 5. The output is the delivered advertisement. The advertisement is formatted in HTML5 or JavaScript format and sent to the user's device.

[1351] Step 7:

[1352] The user's device interprets the received advertisement and inserts it into the appropriate location on the currently displayed web page. The input is the advertisement delivered in step 6. The output is the advertisement displayed within the web page. This allows the user to see advertisements based on their interests.

[1353] Step 8:

[1354] When a user clicks on an ad, the click information is recorded on the user's device. The input is the information about the ad the user clicked on. The output is the recorded click information. The device then sends this click data back to the server and uses it to track the effectiveness of the ad.

[1355] 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.

[1356] The system of the present invention generates and delivers personalized advertisements by linking the server of an advertisement distribution platform with the user's device, taking into consideration not only the user's interests and the context of the web page being viewed, but also the user's emotional state. The present invention is composed of the following steps.

[1357] Collecting data on user behavior

[1358] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling. In addition, more detailed data can be obtained by collecting text information entered by the user and rich media (voice, facial expressions, etc.).

[1359] User behavior and sentiment data analysis

[1360] The device analyzes the user's behavior and emotions based on the collected browsing history information and input text information. This analysis uses machine learning algorithms and natural language processing (NLP) technology. It also uses an emotion engine to identify the user's emotional state from the user's text information and rich media. For example, it can recognize emotional states such as joy, sadness, and anger from the comments and reactions entered by the user.

[1361] Sending data

[1362] The device compiles the results of the behavioral and emotional analysis and encrypts them using an encryption algorithm such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[1363] Receiving Data and Profiling

[1364] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a user profile. The profile includes the user's areas of interest, past behavior, clicked ads, emotional state, and more.

[1365] Ad Generation

[1366] The server generates personalized ads based on the user's profile, contextual information about the current web page, and the user's emotional state. This process also uses natural language processing and image generation techniques. For example, if a user is interested in travel and is in a positive emotional state, ads for travel promotions and special offers will be generated.

[1367] Ad delivery and display

[1368] The server formats the generated advertisement in HTML5 or JavaScript format and delivers it to the user's device. The advertisement data also includes tags for tracking. The device interprets the received advertisement and displays it in the appropriate position on the currently viewed web page. This allows the user to see advertisements that are highly relevant to the page they are viewing.

[1369] Tracking advertising effectiveness

[1370] When a user clicks on an ad, the click information is recorded on the device. The device then sends this click data back to the server, where it is used to evaluate the effectiveness of the ad. The server then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[1371] Specific examples

[1372] Example 1: Viewing a cooking recipe site

[1373] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. If the user posts a positive comment about a recipe, the device also analyzes the user's emotions. The device analyzes this data to determine that the user is interested in cooking and is in a positive emotional state. The analysis results are encrypted and sent to a server. The server updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent again from the device to the server, and the effectiveness of the advertisement is tracked.

[1374] The system of the present invention delivers advertisements in real time that are adaptive to the user's interests, context, and emotions, improving the user experience while maximizing advertising effectiveness.

[1375] The processing flow will be explained below.

[1376] Step 1:

[1377] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling, as well as text information and rich media (voice, facial expressions, etc.) entered by the user.

[1378] Step 2:

[1379] The device analyzes the collected browsing history information and input text information, and uses machine learning algorithms and natural language processing (NLP) technology to identify the user's behavior and emotions. For example, if a user inputs a positive comment, the content is analyzed to recognize the user's emotional state.

[1380] Step 3:

[1381] The emotion engine analyzes the user's emotional state from text information and rich media to detect specific emotions such as joy, sadness, anger, etc. The device then adds this emotional information to the analysis results.

[1382] Step 4:

[1383] The device encrypts the results of behavioral and emotional analysis using encryption algorithms such as AES (Advanced Encryption Standard) to prevent unauthorized access by third parties.

[1384] Step 5:

[1385] The device then sends the encrypted analysis results to a server, using security protocols to keep the data safe.

[1386] Step 6:

[1387] The server receives the data sent from the device, decrypts it, and uses the decrypted data to create and update a profile of the user, including their interests, past behavior, and emotional state.

[1388] Step 7:

[1389] The server generates personalized advertisements based on the user's profile, contextual information about the current web page, and the user's emotional state. For example, if a user is interested in cooking and is in a positive emotional state, the server generates advertisements for cooking-related products and services.

[1390] Step 8:

[1391] The server then formats the generated advertisements in HTML5 or JavaScript format and delivers them to the user's device. The advertisement data also includes tags for later tracking the effectiveness of the advertisements.

[1392] Step 9:

[1393] The device interprets the received advertisement and displays it in the appropriate location on the currently viewed web page, allowing the user to see the most relevant advertisement on the page they are viewing.

[1394] Step 10:

[1395] When a user clicks on an ad, the click information is recorded on the device, which then sends the click data back to the server and uses it to track the effectiveness of the ad.

[1396] Step 11:

[1397] The server analyzes the click rate and conversion rate of the advertisement based on the received click data, thereby evaluating the performance of the advertisement and helping to generate subsequent advertisements.

[1398] Example 2

[1399] 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."

[1400] Conventional ad delivery systems deliver ads based solely on a user's interests and browsing history, which limits the effectiveness of ads because they are unable to take into account changes in the user's interests or emotional state. It is also difficult to properly analyze user behavior data and click information to generate and deliver personalized ads in real time. This has created a need for systems that can simultaneously improve user experience and maximize advertising effectiveness.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1402] In this invention, the server includes means for analyzing user behavioral data and emotional state, means for creating and updating a user profile based on the analysis results, means for generating personalized advertisements based on the user profile and context information of the current web page, and means for tracking advertisement effectiveness, thereby enabling highly personalized advertisement delivery based on the user's behavior and emotions, maximizing the effectiveness of advertisements and improving the user experience.

[1403] "User's terminal" refers to an information processing device that allows a user to access the Internet, view web pages, and input and send data.

[1404] "Web page browsing history information" is data related to the web pages accessed by the user, and includes information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[1405] "Text information" refers to character data entered or displayed by a user on a web page, including comments, reviews, search queries, etc.

[1406] "Rich media" refers to a variety of data formats other than text, including audio, video, images, and user facial expressions.

[1407] "Emotional state" refers to the emotion a user feels from specific text information or rich media, and identifies psychological states such as joy, sadness, or anger.

[1408] A "machine learning algorithm" is a technology used to process data, extract patterns and relationships, and build models to predict user behavior and identify interests.

[1409] "Natural Language Processing (NLP)" is a technology for understanding, interpreting, and generating human language, and is used to analyze user text information.

[1410] "AES (Advanced Encryption Standard)" is an encryption method for protecting data and is used to securely transmit analysis results to the server.

[1411] A "user profile" is a data set that includes information such as a user's interests, past behavioral history, clicked ads, and emotional state.

[1412] "Current web page context" refers to information about the content or subject of the web page most recently accessed by the user.

[1413] A "personalized ad" is an ad that is optimized for a particular user, generated based on the user's profile and the context of the current web page.

[1414] "Image generation technology" means technology for generating or editing digital images used to create the visual elements of an advertisement.

[1415] "Advertising tracking" is the process of measuring how an ad impacts users, and refers to the collection and analysis of data such as click-through rates and conversion rates.

[1416] "Click information" refers to data when a user clicks on an advertisement, and is used to analyze the time of the click and the relevance of the advertisement.

[1417] The system of the present invention works in cooperation with the advertising distribution platform server and the user's device to generate and deliver personalized advertisements that take into account the user's interests, the context of the web page being viewed, and even the user's emotional state.

[1418] Hardware and software used

[1419] Hardware used

[1420] User's device: Information processing device such as computer, smartphone, tablet, etc.

[1421] Servers: Cloud servers, physical servers, servers in data centers

[1422] Software used

[1423] Web browser: Google Chrome, Mozilla Firefox, Apple Safari, Microsoft Edge, etc.

[1424] Machine learning algorithms: Scikit-learn, TensorFlow, PyTorch, etc.

[1425] Natural Language Processing (NLP): NLTK, spaCy, BERT, etc.

[1426] Encryption algorithm: AES (Advanced Encryption Standard)

[1427] Data analysis tools: Google Analytics, Apache Hadoop, etc.

[1428] Specific examples of program processing

[1429] Collecting data on user behavior

[1430] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling. Furthermore, text information and rich media (voice, facial expressions, etc.) entered by the user are also collected. For example, if a user posts a comment on a blog post, the content of the comment and facial expression data are collected.

[1431] User behavior and sentiment data analysis

[1432] The device uses machine learning algorithms to analyze user behavior and identify their interests based on the collected browsing history and input text information. It also uses natural language processing technology and an emotion engine to recognize the user's emotional state from their comments and reactions. For example, if a user comments, "This book is really great!", it identifies that positive emotion.

[1433] Sending data

[1434] The device compiles the results of the behavioral and emotional analysis and encrypts them using the Advanced Encryption Standard (AES). The encrypted data is then sent to a server using HTTPS to prevent unauthorized access by third parties.

[1435] Receiving Data and Profiling

[1436] The server decrypts the received data and creates and updates a user profile, which includes information such as the user's interests, past behavior, clicked ads, and emotional state.

[1437] Ad generation and delivery

[1438] The server generates personalized ads based on the user's profile and the context of the current web page using generative AI models, natural language processing, and image generation technologies. The ads are formatted in HTML5 or JavaScript and delivered to the user's device with tracking tags. The device interprets the received ads and displays them in the appropriate location on the currently viewed web page.

[1439] Tracking advertising effectiveness

[1440] When a user clicks on an ad, the click information is recorded on the device and sent back to the server, which then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[1441] Specific examples

[1442] Example 1: Viewing a cooking recipe site

[1443] When a user browses a cooking recipe website, the device collects search queries such as "pasta recipes" and "Italian food" and the time spent on the page. If the user posts a positive comment about a recipe, the device also analyzes the user's sentiment. The device analyzes this data and determines that the user is interested in cooking and is in a positive emotional state. The analysis results are encrypted and sent to a server. The server updates the user's profile and generates advertisements for cooking utensils and ingredients. The generated advertisements are delivered to the device and displayed on the recipe site. If the user clicks on an advertisement, the information is sent again from the device to the server, and the effectiveness of the advertisement is tracked.

[1444] Prompt Sentence Examples

[1445] "If a user is browsing a cooking recipe site, how would you optimize your ads?"

[1446] "How can we personalize ads based on user behavioral data and emotional state?"

[1447] This system enables the generation and delivery of advertisements based on user behavior, emotions, and context, improving the user experience and maximizing advertising effectiveness.

[1448] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1449] Step 1: Collecting data on user behavior

[1450] When a user browses a web page, the device uses browser cookies and local storage to collect browsing history information such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling, as well as text information and rich media (voice, facial expressions, etc.) entered by the user.

[1451] Input: Information about web pages you visit, text and rich media you enter

[1452] Data processing: storing information in browser cookies and local storage

[1453] Output: Browsing history information, text information, rich media data

[1454] Step 2: Analyzing user behavior and sentiment data

[1455] The device uses machine learning algorithms to analyze user behavior and identify their interests based on collected browsing history and input text information, and uses natural language processing technology and an emotion engine to recognize the user's emotional state from their comments and reactions.

[1456] Input: browsing history information, text information, rich media data

[1457] Data Computing: Behavioral analysis using machine learning algorithms, emotion analysis using natural language processing technology and emotion engines

[1458] Output: User interests, emotional state

[1459] Step 3: Sending data

[1460] The device compiles the behavioral and emotional analysis results and encrypts them using AES (Advanced Encryption Standard), which is then sent to a server using HTTPS.

[1461] Input: User interests, emotional state

[1462] Data processing: AES encryption

[1463] Output: Encrypted analysis results

[1464] Step 4: Receiving data and creating a profile

[1465] The server receives the data sent from the device and decrypts the AES-encrypted data. Based on the decrypted data, it creates and updates a user profile. The profile includes the user's areas of interest, past behavioral history, clicked ad history, emotional state, etc.

[1466] Input: Encrypted analysis results

[1467] Data calculation: AES decryption, profile creation and updating

[1468] Output: User profile

[1469] Step 5: Generate Ads

[1470] The server generates personalized ads based on the user's profile and contextual information from the current web page using generative AI models, natural language processing techniques, and image generation techniques.

[1471] Input: User profile, current web page context

[1472] Data Computing: Ad Generation with Generative AI Models and Natural Language Processing Techniques

[1473] Output: Personalized ads

[1474] Step 6: Serving and displaying ads

[1475] The server then formats the generated ad in HTML5 or JavaScript format, includes a tracking tag, and delivers it to the user's device, where it is interpreted and displayed in the appropriate location on the currently viewed web page.

[1476] Input: Personalized ads (HTML5 and JavaScript formats)

[1477] Data processing: Formatting into HTML5 or JavaScript format

[1478] Output: Advertisement displayed on a web page

[1479] Step 7: Track your advertising success

[1480] When a user clicks on an ad, the click information is recorded on the device and sent back to the server, which then collects and analyzes data such as ad click rates and conversion rates to evaluate ad performance.

[1481] Input: Ad click information

[1482] Data processing: Record click data and send it to the server

[1483] Output: Ad performance data (click-through rate, conversion rate)

[1484] (Application example 2)

[1485] 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."

[1486] Conventional ad delivery systems only consider users' interests and concerns, but are unable to provide personalized ads that reflect the user's emotional state. As a result, improving the user experience and maximizing advertising effectiveness have not been fully achieved. Furthermore, because emotion analysis technology has not been integrated into the ad generation system, it has been difficult to display ads that are optimized for the user's current psychological state.

[1487] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a personalized advertisement based on the user profile and the context of the current web page, means for creating and updating the user profile using the received analysis results, and means for delivering the generated advertisement to the user's terminal. This makes it possible to provide optimal personalized advertisements that reflect the user's interests and real-time emotional state.

[1488] "Web pages" refers to multiple different websites or web pages that a user accesses on the Internet.

[1489] "Browser history information" is information that is left behind when a user browses a web page, and includes data such as the URL of the page visited, the time spent on the page, the links clicked, and the depth of scrolling.

[1490] "Analyzing emotions" refers to identifying a user's emotional state (e.g., joy, sadness, anger, etc.) from collected data using machine learning algorithms and natural language processing techniques.

[1491] "Identifying user interests" refers to analyzing the collected browsing history information and emotional information to identify what themes and products the user is interested in.

[1492] "Encrypting the analysis results and sending them to the server" refers to encrypting the analyzed information to protect data security and sending it to the server via the Internet.

[1493] "Server" refers to a central processing unit that receives, analyzes, stores, generates and distributes data, and is the core of this system.

[1494] A "user profile" is a data set that compiles various data about a user (interests, browsing history, emotional state, etc.) to provide customized information to each individual user.

[1495] "Current web page context" refers to the content, theme, and related information of the web page that the user is currently viewing.

[1496] "Personalized advertising" refers to advertising that is individually optimized based on a user's profile and the context of the current web page.

[1497] "Generating an ad" refers to creating ad content, such as text, images, or video, based on the user's profile and contextual information about the current web page.

[1498] "Distributing the generated advertisement" refers to sending the advertisement generated by the server to the user's terminal and sending the data necessary for displaying it.

[1499] "Displaying an advertisement on a web page" refers to positioning and visually presenting an advertisement within the web page that a user is viewing.

[1500] This invention is a system that generates and delivers personalized advertisements using web page browsing history information and emotional data collected by the user's device. This system analyzes the user's browsing behavior and emotional state in real time and displays appropriate advertisements based on that analysis.

[1501] Hardware and Software Used

[1502] Hardware: Smartphone (including camera, microphone, and sensors)

[1503] Software: Python programming language, TextBlob (for sentiment analysis), Scikit-learn (for machine learning), AES (for encryption), Requests (for data transmission)

[1504] Data processing and calculation

[1505] 1. Data Collection

[1506] The user's device collects browsing history information for each web page the user visits. The collected information includes the URL of the visited page, the time spent on the page, the links clicked, the scrolling depth, and emotional data such as the user's comments and facial expressions. This allows for comprehensive collection of user behavioral and emotional data.

[1507] 2. Emotion analysis

[1508] The device analyzes collected comments and rich media data and uses machine learning algorithms and natural language processing techniques to identify the user's emotional state. Specifically, it uses the TextBlob library to distinguish between positive, negative, and neutral sentiment from comments. In addition, it uses Scikit-learn to cluster the data and classify the user's interests.

[1509] 3. Data Encryption and Transmission

[1510] The analysis results are encrypted using the AES encryption algorithm, and then the encrypted data is sent to the server, ensuring secure communication.

[1511] 4. Profile Creation and Updates

[1512] The server then decrypts the encrypted data and creates or updates a user profile, which includes information about the user's interests, browsing history, ad clicks, emotional state, and more.

[1513] 5. Ad Generation

[1514] The server generates personalized advertisements using natural language processing and sentiment analysis techniques based on the user's profile and contextual information of the current web page. For example, if the user is interested in travel and is in a positive emotional state, advertisements for travel promotions and special offers will be generated.

[1515] 6. Advertisement Delivery and Display

[1516] The generated advertisements are delivered from the server to the user's device, and are then displayed at appropriate positions on the web page on the smartphone, allowing the user to view advertisements related to the content they are viewing.

[1517] Specific examples

[1518] If a user views a "chocolate cake" recipe on a cooking recipe site and posts a positive comment, their profile will be updated with that information, and ads for suitable kitchen supplies and ingredients will be displayed.

[1519] Prompt Sentence Examples

[1520] "Write a Python program that analyzes the URLs of pages visited by users, the time spent on them, the links clicked, the scroll depth, and their emotional state, and then suggests ads based on that."

[1521] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1522] Step 1:

[1523] Input: URLs of web pages visited by users, time spent on the page, links clicked, scroll depth, and emotional data such as user comments and facial expressions.

[1524] Processing: The user's device collects this browsing history information and emotional data using browser cookies and local storage.

[1525] Output: Collected web page browsing history information and user emotion data

[1526] While visiting a web page, the user's device collects information such as the actions the user takes and the time spent on the page, as well as emotional data from the user's comments and facial expressions.

[1527] Step 2:

[1528] Input: Collected browsing history information and emotional data

[1529] Processing: The device performs sentiment analysis on the collected data using the TextBlob library, and uses Scikit-learn to cluster page content. It also identifies emotional states (positive, negative, neutral) and classifies interests based on user comments and media data.

[1530] Output: Parsed emotional state and clustering results

[1531] The device analyzes user comments to understand their interests and focuses on specific areas of interest, and then categorizes browsing data into specific clusters.

[1532] Step 3:

[1533] Input: Parsed emotional states and clustering results

[1534] Processing: The analysis results are encrypted using the AES encryption algorithm.

[1535] Output: Encrypted analysis results

[1536] The device ensures data security by encrypting the results of sentiment analysis and clustering.

[1537] Step 4:

[1538] Input: Encrypted analysis results

[1539] Action: Send the encrypted data to the server.

[1540] Output: The server where the analysis results are sent

[1541] The server receives the encrypted data and prepares it for decryption.

[1542] Step 5:

[1543] Input: Encrypted analysis results

[1544] Processing: The server decrypts the received data and creates and updates a profile based on the user's interests, browsing history, and emotional state.

[1545] Output: Updated user profile

[1546] The server uses the decrypted data to create and update a detailed profile of the user for further processing.

[1547] Step 6:

[1548] Input: User profile and current web page context information

[1549] Processing: The server generates personalized advertisements using natural language processing and sentiment analysis techniques.

[1550] Output: Generated personalized ad

[1551] The server uses the user's profile and the context of the current web page to generate appropriate ads that match the user's interests and emotional state.

[1552] Step 7:

[1553] Input: Generated personalized ad

[1554] Processing: The server delivers the generated advertisement to the user's device and sends data to display it on the web page.

[1555] Output: Personalized ads delivered

[1556] The server delivers the generated advertisement to the user's terminal, which then displays it appropriately on a web page.

[1557] Step 8:

[1558] Input: Personalized ads delivered

[1559] Processing: The user's device displays the delivered advertisement on the web page.

[1560] Output: The ad shown to the user

[1561] The user's device displays ads that match the content of the web page, allowing the user to see ads that are directly related to the web page they are viewing.

[1562] 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.

[1563] 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.

[1564] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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).

[1569] 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.

[1570] 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."

[1571] 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.

[1572] 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).

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] The following is further disclosed regarding the above embodiment.

[1584] (Claim 1)

[1585] A means for a user's terminal to collect browsing history information of a plurality of web pages;

[1586] A means for analyzing the collected web page browsing history information and identifying the user's interests and concerns;

[1587] A means for encrypting the analysis results and transmitting them to a server;

[1588] a means for the server to create and update a user profile using the received analysis results;

[1589] means for a server to generate personalized advertisements based on the user's profile and the context of the current web page;

[1590] A means for the server to deliver the generated advertisement to a user's terminal;

[1591] A means for displaying the delivered advertisement on a web page in a user's terminal;

[1592] A system including:

[1593] (Claim 2)

[1594] 2. The system according to claim 1, further comprising means for collecting information about advertisements clicked on by a user on the terminal and transmitting the information to the server.

[1595] (Claim 3)

[1596] 10. The system of claim 1, wherein the server includes means for generating advertisements based on a user's profile and contextual information of the current web page using natural language processing techniques.

[1597] "Example 1"

[1598] (Claim 1)

[1599] A means for a user's terminal to collect browsing history information of a plurality of web pages;

[1600] A means for analyzing the collected web page browsing history information and identifying the user's interests and concerns;

[1601] A means for encrypting the analysis results and transmitting them to a server;

[1602] a means for the server to create and update a user profile using the received analysis results;

[1603] means for a server to generate personalized advertisements based on the user's profile and the context of the current web page;

[1604] A means for the server to deliver the generated advertisement to a user's terminal;

[1605] A means for displaying the delivered advertisement on a web page in a user's terminal;

[1606] A means for recording click information when a delivered advertisement is clicked on in a user's terminal;

[1607] a means for transmitting the recorded click information to a server and tracking the effectiveness of the advertisement;

[1608] A system including:

[1609] (Claim 2)

[1610] 10. The system of claim 1, further comprising means for using a machine learning algorithm to generate the analysis result.

[1611] (Claim 3)

[1612] 10. The system of claim 1, wherein the server comprises means for generating advertisements based on a user's profile and contextual information of a current web page using natural language processing and image generation techniques.

[1613] "Application Example 1"

[1614] (Claim 1)

[1615] A means for a user's terminal to collect browsing history information of a plurality of web pages;

[1616] A means for analyzing the collected web page browsing history information and identifying the user's interests and concerns;

[1617] A means for encrypting the analysis results and transmitting them to a server;

[1618] a means for the server to create and update a user profile using the received analysis results;

[1619] means for a server to generate personalized advertisements based on the user's profile and the context of the current web page;

[1620] A means for the server to deliver the generated advertisement to a user's terminal;

[1621] A means for displaying the delivered advertisement on a web page in a user's terminal;

[1622] A means for transmitting the advertisement click information collected by the user's terminal back to the server and tracking the effectiveness of the advertisement;

[1623] A system including:

[1624] (Claim 2)

[1625] 2. The system according to claim 1, further comprising means for collecting information about advertisements clicked on by a user on the terminal and transmitting the information to the server.

[1626] (Claim 3)

[1627] 10. The system of claim 1, wherein the server includes means for generating advertisements based on a user's profile and contextual information of the current web page using natural language processing techniques.

[1628] "Example 2: Combining Emotion Engines"

[1629] (Claim 1)

[1630] A means for a user's terminal to collect browsing history information of a plurality of web pages;

[1631] A means for analyzing the collected web page browsing history information and identifying the user's interests and concerns;

[1632] means for analyzing the collected text information and rich media to identify the user's emotional state;

[1633] A means for encrypting the analysis results and transmitting them to a server;

[1634] a means for the server to create and update a user profile using the received analysis results;

[1635] means for a server to generate personalized advertisements based on the user's profile and the context of the current web page;

[1636] A means for the server to deliver the generated advertisement to a user's terminal;

[1637] A means for displaying the delivered advertisement on a web page in a user's terminal;

[1638] A system including:

[1639] (Claim 2)

[1640] 2. The system according to claim 1, further comprising means for collecting information about advertisements clicked on by a user on the terminal and transmitting the information to the server.

[1641] (Claim 3)

[1642] 10. The system of claim 1, wherein the server includes means for generating advertisements based on a user's profile and contextual information of the current web page using natural language processing and image generation techniques.

[1643] "Application example 2 when combining emotion engines"

[1644] (Claim 1)

[1645] A means for a user's terminal to collect browsing history information of a plurality of web pages;

[1646] A means of analyzing emotions from collected user comments, etc.,

[1647] A means for analyzing the collected web page browsing history information and emotional information to identify the user's interests and concerns;

[1648] A means for encrypting the analysis results and transmitting them to a server;

[1649] a means for the server to create and update a user profile using the received analysis results;

[1650] means for a server to generate personalized advertisements based on the user's profile and the context of the current web page;

[1651] A means for the server to deliver the generated advertisement to a user's terminal;

[1652] A means for displaying the delivered advertisement on a web page in a user's terminal;

[1653] A system including:

[1654] (Claim 2)

[1655] 2. The system according to claim 1, further comprising means for collecting information about advertisements clicked on by a user on the terminal and transmitting the information to the server.

[1656] (Claim 3)

[1657] 10. The system of claim 1, wherein the server includes means for generating advertisements based on a user's profile and contextual information of the current web page using natural language processing and sentiment analysis techniques. [Explanation of symbols]

[1658] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user's terminal to collect browsing history information of a plurality of web pages; A means for analyzing the collected web page browsing history information and identifying the user's interests and concerns; A means for encrypting the analysis results and transmitting them to a server; a means for the server to create and update a user profile using the received analysis results; means for the server to generate personalized advertisements based on the user's profile and the context of the current web page; A means for the server to deliver the generated advertisement to a user's terminal; A means for displaying the delivered advertisement on a web page in a user's terminal; A system including:

2. 2. The system according to claim 1, further comprising means for collecting information on advertisements clicked on by users on their terminals and transmitting the information to a server.

3. 10. The system of claim 1, wherein the server includes means for generating advertisements based on a user's profile and contextual information of the current web page using natural language processing techniques.

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