system

Abstract

The system according to this embodiment aims to automatically optimize advertising creatives and reduce the burden on engineers, designers, and marketers. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an optimization unit, and a monitoring unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit. The optimization unit optimizes the advertising creative based on the analysis results obtained by the analysis unit. The monitoring unit monitors the effectiveness of the advertising creative optimized by the optimization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, since the optimization of advertising creatives is performed manually, there is a problem that the burden on engineers, designers, and marketers is large.

[0005] The system according to the embodiment aims to automatically optimize advertising creatives and reduce the burden on engineers, designers, and marketers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an optimization unit, and a monitoring unit. The data collection unit collects user behavior data. The analysis unit analyzes the data collected by the data collection unit. The optimization unit optimizes the advertising creative based on the analysis results obtained by the analysis unit. The monitoring unit monitors the effectiveness of the advertising creative optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically optimize advertising creatives, reducing the burden on engineers, designers, and marketers. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An advertising optimization system according to an embodiment of the present invention is a system in which an AI agent embedded in a website or browser analyzes user behavior data and market trends linked to user attributes in real time, and automatically optimizes the displayed advertising creative. This advertising optimization system can maximize advertising effectiveness without compromising usability and reduce the burden on engineers, designers, and marketers who create advertising creatives. First, the advertising optimization system is embedded in a website or browser and collects user behavior data in real time. Behavioral data includes page viewing history, click history, and time spent on the site. User attribute information (age, place of residence, etc.) is also collected. This data is analyzed by the advertising optimization system to identify the user's interests and preferences. Next, the advertising optimization system analyzes market trends based on the collected data. Market trends include currently popular products and services, and seasonal fluctuations in demand. This identifies the optimal advertising creative tailored to the user's attributes. The advertising optimization system automatically optimizes the identified advertising creative. Specifically, it adjusts the font size of the ad, processes images, and adjusts the color tones. This improves the click-through rate and conversion rate of the ad. Furthermore, the ad optimization system monitors ad performance in real time and re-optimizes ad creatives as needed. This ensures that the most effective ads are always displayed. This mechanism significantly reduces the time and resources required for advertisers, advertising agencies, and marketers to analyze data and optimize creatives to improve ad performance. It also maximizes ad effectiveness without compromising usability. For example, when a user is browsing a website, the ad optimization system collects the user's behavioral data and identifies ads that the user might be interested in. It then optimizes the font size and images of those ads and displays them to the user. When the user clicks on an ad, the ad optimization system collects that data and monitors the ad's effectiveness. If the effectiveness is low, it re-optimizes the ad creative and changes the ad displayed.In this way, ad optimization systems revolutionize efficiency and effectiveness in the advertising industry by automating the optimization of ad creatives and establishing new standards for marketing. This allows ad optimization systems to maximize the effectiveness of ads and reduce the burden on engineers, designers, and marketers.

[0029] The advertising optimization system according to this embodiment comprises a collection unit, an analysis unit, an optimization unit, and a monitoring unit. The collection unit collects user behavior data. User behavior data includes, but is not limited to, page viewing history, click history, and time spent on a page. For example, to collect page viewing history, the collection unit records the URLs and viewing times of pages viewed by the user. The collection unit may also record the URLs and click counts of links clicked by the user to collect click history. The collection unit may also measure the time a user spends on a specific page to collect time spent on a page. For example, the collection unit records the URLs of pages viewed by the user and measures the viewing time. The collection unit may also record the URLs of links clicked by the user and measure the click counts. The collection unit may also measure the time a user spends on a specific page and record the time spent on the page. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies user interests and preferences based on the collected data. The analysis unit may also analyze user behavior patterns based on the collected data. The analytics department can also identify user attribute information based on the collected data. For example, the analytics department can identify user interests and preferences based on collected page browsing history. The analytics department can also analyze user behavior patterns based on collected click history. The analytics department can also identify user attribute information based on collected time spent on the site. The optimization department optimizes ad creatives based on the analysis results obtained by the analytics department. For example, the optimization department adjusts the font size, image processing, and color tones of ads. The optimization department can also adjust the placement and layout of ads. The optimization department can also optimize the content and message of ads. For example, the optimization department adjusts the font size of ads to improve visibility. The optimization department can also process ad images to enhance their visual appeal. The optimization department can adjust the color tones of ads to ensure visual consistency. The monitoring department monitors the effectiveness of ad creatives optimized by the optimization department. For example, the monitoring department measures ad click-through rates and conversion rates.The monitoring unit can also measure the number of times an ad is displayed and the number of impressions. The monitoring unit can monitor the effectiveness of an ad in real time and re-optimize the ad creative as needed. For example, the monitoring unit can measure the click-through rate of an ad and evaluate its effectiveness. The monitoring unit can also measure the conversion rate of an ad and evaluate its effectiveness. The monitoring unit can also measure the number of times an ad is displayed and evaluate its effectiveness. As a result, the ad optimization system according to this embodiment can maximize the effectiveness of ad creatives by collecting, analyzing, optimizing, and monitoring user behavior data.

[0030] The data collection unit collects user behavior data. This data includes, but is not limited to, page viewing history, click history, and time spent on pages. For example, to collect page viewing history, the data collection unit records the URLs of pages viewed by the user and the time spent on each page. Specifically, the data collection unit records the URL of each page accessed by the user and measures the time spent on each page in seconds. This allows the system to understand which pages the user is interested in. The data collection unit can also record the URLs of links clicked by the user and the number of clicks to collect click history. For example, the data collection unit records the URLs of links clicked by the user and records the date and time of the click and the number of clicks in detail. This allows the system to identify which links the user is interested in. The data collection unit can also measure the time a user spends on a specific page to collect time spent on it. For example, the data collection unit measures the time a user spends on a specific page in milliseconds and records the time spent. This allows the system to understand which pages the user spends the longest time on. Furthermore, the data collection unit can also collect technical information such as the user's device information, browser type, and operating system version. This allows for a more detailed analysis of user behavior data. The data collection unit centrally manages this data and stores it in a database in real time. This enables the data collection unit to efficiently collect user behavior data and improve the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department identifies user interests and preferences based on the collected data. Specifically, based on the collected page browsing history, the analysis department identifies pages and categories that users frequently access and infers user interests and preferences. For example, if a user frequently views sports-related pages, it can be determined that the user is interested in sports. The analysis department can also analyze user behavior patterns based on collected click history. For example, if a user frequently clicks on a particular link, it can be determined that they are interested in content related to that link. Furthermore, the analysis department can identify user attribute information based on collected time spent on pages. For example, if a user spends a long time on a particular page, it can be determined that they have a strong interest in the content of that page. The analysis department integrates this data to analyze user behavior patterns and interests in detail. In addition, the analysis department can use machine learning algorithms to analyze user behavior data and predict future behavior. For example, based on past behavior data, it can predict which pages a user is most likely to view next. This allows the analytics department to gain a detailed understanding of user behavior and use that information to optimize advertising.

[0032] The optimization unit optimizes ad creatives based on the analysis results obtained by the analysis unit. For example, the optimization unit adjusts the font size, image processing, and color scheme of the ad. Specifically, the optimization unit adjusts the font size of the ad based on the user's interests to improve readability. For example, in ads aimed at seniors, the font size can be increased for easier reading. The optimization unit can also process the images of the ad to enhance their visual appeal. For example, if a user is interested in sports, sports-related images can be used to enhance the visual appeal of the ad. The optimization unit can also adjust the color scheme of the ad to ensure visual consistency. For example, by adjusting the color scheme of the ad to match the brand's color scheme, brand consistency can be maintained. Furthermore, the optimization unit can also adjust the placement and layout of the ad. For example, based on user behavior patterns, the ad can be placed in the optimal position to improve the click-through rate. The optimization unit can also optimize the content and message of the ad. For example, based on the user's interests, the ad message can be customized to make it more appealing to the user. In this way, the optimization unit can maximize the effectiveness of the ad and attract user attention.

[0033] The monitoring unit monitors the effectiveness of ad creatives optimized by the optimization unit. For example, the monitoring unit measures ad click-through rates (CTR) and conversion rates. Specifically, it records the number of times an ad is displayed and clicked, and calculates the CTR. This allows for an evaluation of how effectively the ad is attracting user attention. The monitoring unit can also measure and evaluate the effectiveness of ad conversion rates. For example, it records the number of times a user who clicked on an ad actually purchased a product or registered for a service, and calculates the conversion rate. This allows for an evaluation of how effectively the ad is prompting user action. The monitoring unit can also measure ad impressions and the number of times an ad is displayed. For example, it records the number of times an ad is displayed and calculates the number of impressions. This allows for an evaluation of how many users the ad is being shown to. Furthermore, the monitoring unit can monitor the effectiveness of ads in real time and re-optimize ad creatives as needed. For example, if an ad's CTR or conversion rate is low, the monitoring unit can provide feedback to the optimization unit and instruct them to re-optimize the ad creatives. This allows the monitoring unit to continuously evaluate and optimize the effectiveness of advertisements, thereby maximizing their impact.

[0034] The attribute collection unit can collect user attribute information. For example, the attribute collection unit can collect attribute information such as the user's age, gender, and occupation. The attribute collection unit can also collect information about the user's place of residence and interests. For example, the attribute collection unit can collect the user's age and use it for advertising targeting. The attribute collection unit can also collect the user's gender and adjust the content of the advertisement. The attribute collection unit can also collect the user's occupation and optimize the message of the advertisement. By collecting user attribute information, it becomes possible to optimize advertising creatives with greater accuracy. Some or all of the above processing in the attribute collection unit may be performed using AI, for example, or without AI. For example, the attribute collection unit can input user attribute information into a generating AI and have the generating AI perform the collection of attribute information.

[0035] The optimization unit can adjust the font size, image processing, and color tone of advertisements. For example, the optimization unit can adjust the font size of advertisements to improve readability. The optimization unit can also process advertisement images to enhance their visual appeal. The optimization unit can adjust the color tone of advertisements to ensure visual consistency. For example, the optimization unit can increase the font size of advertisements to improve readability. The optimization unit can also crop advertisement images to highlight important parts. The optimization unit can adjust the color tone of advertisements to match the brand image. In this way, by adjusting the font size, image processing, and color tone of advertisements, the readability and effectiveness of advertisements can be improved. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the font size, image processing, and color tone adjustments of advertisements into a generating AI and have the generating AI execute them.

[0036] The monitoring unit can measure the click-through rate and conversion rate of advertisements. For example, the monitoring unit can measure the click-through rate of advertisements and evaluate their effectiveness. The monitoring unit can also measure the conversion rate of advertisements and evaluate their effectiveness. The monitoring unit can also measure the number of times an advertisement is displayed and evaluate its effectiveness. For example, the monitoring unit can measure the click-through rate of advertisements in real time and evaluate their effectiveness. The monitoring unit can also measure the conversion rate of advertisements in real time and evaluate their effectiveness. The monitoring unit can also measure the number of times an advertisement is displayed in real time and evaluate its effectiveness. This allows for real-time understanding of the effectiveness of advertisements by measuring the click-through rate and conversion rate of advertisements. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the measurement of the click-through rate and conversion rate of advertisements into a generating AI and have the generating AI perform the measurement.

[0037] The optimization unit can re-optimize ad creatives when their effectiveness is low. For example, if the click-through rate or conversion rate of an ad is low, the optimization unit will re-adjust the font size, image processing, and color tones of the ad. The optimization unit can also re-adjust the placement and layout of the ad. The optimization unit can also re-optimize the content and message of the ad. For example, the optimization unit can re-adjust the font size of the ad to improve readability. The optimization unit can also re-process the images of the ad to enhance their visual appeal. The optimization unit can also re-adjust the color tones of the ad to ensure visual consistency. This ensures that the optimal ad creative is always provided by re-optimizing when the ad's effectiveness is low. Some or all of the above processes in the optimization unit may be performed using AI, for example, or not using AI. For example, if the ad's effectiveness is low, the optimization unit can input the re-optimization of the ad creative into the generating AI and have the generating AI perform the optimization.

[0038] The data collection unit can collect behavioral data such as page viewing history, click history, and time spent on a page. For example, to collect page viewing history, the data collection unit records the URLs of pages viewed by the user and the viewing time. To collect click history, the data collection unit can also record the URLs of links clicked by the user and the number of clicks. To collect time spent on a page, the data collection unit can also measure the time a user spends on a specific page. For example, the data collection unit records the URLs of pages viewed by the user and measures the viewing time. The data collection unit can also record the URLs of links clicked by the user and measure the number of clicks. The data collection unit can also measure the time a user spends on a specific page and record the time spent. By collecting behavioral data such as page viewing history, click history, and time spent on a page, it is possible to understand user behavior in detail. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input behavioral data such as page viewing history, click history, and time spent on a page into a generating AI and have the generating AI execute it.

[0039] The data collection unit can analyze the user's past behavior data and select the optimal collection method. For example, the data collection unit can prioritize collecting data from pages the user has frequently visited in the past. The data collection unit can also concentrate data collection during times when there are many clicks, based on the user's past click history. The data collection unit can also analyze the user's past time spent on a page and focus on collecting data from pages where the user spends a long time. For example, the data collection unit can prioritize collecting data from pages the user has frequently visited in the past. The data collection unit can also concentrate data collection during times when there are many clicks, based on the user's past click history. The data collection unit can also analyze the user's past time spent on a page and focus on collecting data from pages where the user spends a long time. This allows the optimal collection method to be selected by analyzing the user's past behavior data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter behavioral data based on the user's current interests. For example, the data collection unit can prioritize collecting relevant behavioral data based on the content of the page the user is currently viewing. The data collection unit can also filter relevant behavioral data based on keywords the user has recently searched for. The data collection unit can also collect behavioral data related to interests based on content the user has shared on social media. For example, the data collection unit can prioritize collecting relevant behavioral data based on the content of the page the user is currently viewing. The data collection unit can also filter relevant behavioral data based on keywords the user has recently searched for. The data collection unit can also collect behavioral data related to interests based on content the user has shared on social media. This allows for the collection of highly relevant behavioral data by filtering based on the user's current interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input filtering based on the user's current interests into a generating AI and have the generating AI perform it.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific region, the data collection unit will prioritize collecting behavioral data related to that region. If the user is traveling, the data collection unit can also prioritize collecting behavioral data related to the travel destination. If the user is at home, the data collection unit can also prioritize collecting behavioral data around the user's home. This allows for the optimization of region-specific advertising creatives by collecting highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect relevant behavioral data based on content shared by the user on social media. The data collection unit can also collect relevant behavioral data based on accounts followed by the user on social media. The data collection unit can also collect relevant behavioral data based on groups the user participates in on social media. For example, the data collection unit can collect relevant behavioral data based on content shared by the user on social media. The data collection unit can also collect relevant behavioral data based on accounts followed by the user on social media. The data collection unit can also collect relevant behavioral data based on groups the user participates in on social media. This allows for the collection of relevant behavioral data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important behavioral data. The analysis unit can also perform a simplified analysis on less important behavioral data. The analysis unit can also perform an analysis with a moderate level of detail on behavioral data of moderate importance. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the level of detail of the analysis based on the importance of the behavioral data into a generating AI and have the generating AI execute it.

[0044] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase behavior data. The analysis unit can also apply a browsing pattern analysis algorithm to browsing behavior data. The analysis unit can also apply a click pattern analysis algorithm to click behavior data. By applying different analysis algorithms depending on the category of behavioral data, appropriate analysis according to the characteristics of the data becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms depending on the category of behavioral data into a generating AI and have the generating AI execute them.

[0045] The analysis unit can determine the priority of analysis based on when the behavioral data was collected. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. The analysis unit may also prioritize the most recent data while referring to past behavioral data. The analysis unit may also prioritize the analysis of behavioral data collected during a specific period. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. The analysis unit may also prioritize the most recent data while referring to past behavioral data. The analysis unit may also prioritize the analysis of behavioral data collected during a specific period. This makes it possible to perform analysis that emphasizes the most recent data by determining the priority of analysis based on when the behavioral data was collected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the analysis priority based on when the behavioral data was collected into a generating AI and have the generating AI execute it.

[0046] The analysis unit can adjust the order of analysis based on the relevance of behavioral data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant behavioral data. It may also analyze moderately relevant behavioral data next. It may also analyze lowly relevant behavioral data last. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of behavioral data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the order of analysis based on the relevance of behavioral data into a generating AI and have the generating AI execute it.

[0047] The optimization unit can adjust the level of detail of the optimization based on the importance of the advertisements during the optimization process. For example, the optimization unit can perform detailed optimization for highly important advertisements. For less important advertisements, the optimization unit can perform simplified optimization. For moderately important advertisements, the optimization unit can perform optimization with a moderate level of detail. This allows for efficient advertisement optimization by adjusting the level of detail of the optimization based on the importance of the advertisements. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the level of detail of the optimization based on the importance of the advertisements into a generating AI and have the generating AI execute it.

[0048] The optimization unit can apply different optimization algorithms depending on the ad category during optimization. For example, the optimization unit can apply an optimization algorithm that increases purchase intent to product ads. The optimization unit can also apply an optimization algorithm that emphasizes the appeal of a service to service ads. The optimization unit can also apply an optimization algorithm that strengthens the brand image to brand ads. For example, the optimization unit can apply an optimization algorithm that increases purchase intent to product ads. The optimization unit can also apply an optimization algorithm that emphasizes the appeal of a service to service ads. The optimization unit can also apply an optimization algorithm that strengthens the brand image to brand ads. By applying different optimization algorithms depending on the ad category, appropriate optimization tailored to the characteristics of the ad becomes possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input different optimization algorithms depending on the ad category into a generating AI and have the generating AI execute them.

[0049] The optimization unit can adjust its optimization method based on the ad's display location during optimization. For example, if the ad is displayed on the top page, the optimization unit can perform optimizations to improve visibility. If the ad is displayed on a product page, the optimization unit can also perform optimizations to increase purchase intent. If the ad is displayed on a blog page, the optimization unit can also perform optimizations to improve relevance to the content. For example, if the ad is displayed on the top page, the optimization unit can perform optimizations to improve visibility. If the ad is displayed on a product page, the optimization unit can also perform optimizations to increase purchase intent. If the ad is displayed on a blog page, the optimization unit can also perform optimizations to improve relevance to the content. By adjusting the optimization method based on the ad's display location, appropriate ad optimization according to the display location becomes possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the optimization method based on the ad's display location into a generating AI and have the generating AI execute it.

[0050] The optimization unit can adjust the order of optimization based on the relevance of the ads during the optimization process. For example, the optimization unit may prioritize optimizing ads with high relevance. It may also optimize ads with moderate relevance next. It may also optimize ads with low relevance last. For example, the optimization unit may prioritize optimizing ads with high relevance. It may also optimize ads with moderate relevance next. It may also optimize ads with low relevance last. This allows for efficient ad optimization by adjusting the order of optimization based on the relevance of the ads. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the order of optimization based on the relevance of the ads to a generating AI and have the generating AI execute it.

[0051] The monitoring unit can analyze the effectiveness of advertisements in real time during monitoring and select the optimal monitoring method. For example, the monitoring unit can analyze the click-through rate of advertisements in real time and prioritize monitoring high-performing advertisements. The monitoring unit can also analyze the conversion rate of advertisements in real time and prioritize monitoring high-performing advertisements. The monitoring unit can also analyze the number of times advertisements are displayed in real time and prioritize monitoring high-performing advertisements. For example, the monitoring unit can analyze the click-through rate of advertisements in real time and prioritize monitoring high-performing advertisements. The monitoring unit can also analyze the conversion rate of advertisements in real time and prioritize monitoring high-performing advertisements. The monitoring unit can also analyze the number of times advertisements are displayed in real time and prioritize monitoring high-performing advertisements. This allows for the prioritization of monitoring of high-performing advertisements by analyzing their effectiveness in real time. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data for analyzing the effectiveness of advertisements in real time into a generating AI and have the generating AI select the optimal monitoring method.

[0052] The monitoring unit can apply different monitoring algorithms depending on the category of the advertisement during monitoring. For example, the monitoring unit can apply a monitoring algorithm that increases purchase intent to product advertisements. The monitoring unit can also apply a monitoring algorithm that emphasizes the appeal of a service to service advertisements. The monitoring unit can also apply a monitoring algorithm that strengthens the brand image to brand advertisements. For example, the monitoring unit can apply a monitoring algorithm that increases purchase intent to product advertisements. The monitoring unit can also apply a monitoring algorithm that emphasizes the appeal of a service to service advertisements. The monitoring unit can also apply a monitoring algorithm that strengthens the brand image to brand advertisements. By applying different monitoring algorithms depending on the category of the advertisement, appropriate monitoring tailored to the characteristics of the advertisement becomes possible. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input different monitoring algorithms depending on the category of the advertisement into a generating AI and have the generating AI execute them.

[0053] The monitoring unit can adjust its monitoring method based on the ad's display location during monitoring. For example, if the ad is displayed on the top page, the monitoring unit can perform monitoring to improve visibility. If the ad is displayed on a product page, the monitoring unit can also perform monitoring to increase purchase intent. If the ad is displayed on a blog page, the monitoring unit can also perform monitoring to improve relevance to the content. For example, if the ad is displayed on the top page, the monitoring unit can perform monitoring to improve visibility. If the ad is displayed on a product page, the monitoring unit can also perform monitoring to increase purchase intent. If the ad is displayed on a blog page, the monitoring unit can also perform monitoring to improve relevance to the content. By adjusting the monitoring method based on the ad's display location, appropriate monitoring according to the display location becomes possible. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the monitoring method based on the ad's display location into a generating AI and have the generating AI execute it.

[0054] The monitoring unit can adjust the monitoring order based on the relevance of the advertisements during monitoring. For example, the monitoring unit may prioritize monitoring highly relevant advertisements. It may also monitor moderately relevant advertisements next. It may also monitor lowly relevant advertisements last. This allows for efficient monitoring by adjusting the monitoring order based on the relevance of the advertisements. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the monitoring order based on the relevance of the advertisements into a generating AI and have the generating AI execute it.

[0055] The attribute collection unit can analyze the user's past attribute information and select the optimal collection method. For example, the attribute collection unit can select the optimal collection method based on attribute information previously provided by the user. The attribute collection unit can also adjust the collection frequency based on the user's past attribute information. The attribute collection unit can also analyze the user's past attribute information and determine the priority of the attribute information to be collected. For example, the attribute collection unit can select the optimal collection method based on attribute information previously provided by the user. The attribute collection unit can also adjust the collection frequency based on the user's past attribute information. The attribute collection unit can also analyze the user's past attribute information and determine the priority of the attribute information to be collected. This allows the optimal collection method to be selected by analyzing the user's past attribute information. Some or all of the above processing in the attribute collection unit may be performed using AI, for example, or without AI. For example, the attribute collection unit can input the user's past attribute information into a generating AI and have the generating AI select the optimal collection method.

[0056] The attribute collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting attribute information. For example, if the user is in a specific region, the attribute collection unit will prioritize the collection of attribute information related to that region. If the user is traveling, the attribute collection unit can also prioritize the collection of attribute information related to the travel destination. If the user is at home, the attribute collection unit can also prioritize the collection of attribute information around the user's home. This allows for the optimization of region-specific advertising creatives by collecting highly relevant information while considering the user's geographical location. Some or all of the above processing in the attribute collection unit may be performed using AI, for example, or without AI. For example, the attribute collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] An ad optimization system can analyze a user's past purchase history and select the most effective ad creative. For example, it can prioritize displaying ads related to products the user has previously purchased. It can also display ads related to the brands of products the user has previously purchased, or ads related to the categories of products the user has previously purchased. By analyzing a user's past purchase history, it is possible to select more effective ad creatives.

[0059] Ad optimization systems can display location-specific ads by considering the user's geographical location. For example, if a user is in a specific area, ads related to businesses and services in that area will be displayed. If a user is traveling, ads related to tourist attractions and hotels in their destination can be displayed. If a user is at home, ads related to businesses and services near their home can be displayed. This maximizes the effectiveness of ads by displaying location-specific ads that take the user's geographical location into account.

[0060] Ad optimization systems can analyze a user's social media activity and display relevant ads. For example, they can display relevant ads based on content a user shares on social media. They can also display relevant ads based on accounts a user follows. They can also display relevant ads based on groups a user belongs to. In this way, by analyzing a user's social media activity, relevant ads can be displayed.

[0061] Ad optimization systems can analyze a user's past search history and display relevant ads. For example, they can display relevant ads based on keywords the user has searched for in the past. They can also display relevant ads based on product categories the user has searched for in the past. They can also display relevant ads based on brands the user has searched for in the past. In this way, by analyzing a user's past search history, relevant ads can be displayed.

[0062] Ad optimization systems can analyze a user's past browsing history and display relevant ads. For example, they can display ads related to pages the user has previously viewed, product categories the user has previously viewed, or brands the user has previously viewed. This allows for the display of relevant ads by analyzing the user's past browsing history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects user behavior data. This data includes page viewing history, click history, and time spent on each page. For example, the data collection unit records the URLs and viewing times of pages viewed by the user, the URLs and click counts of links clicked, and the time spent on specific pages. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, based on the collected data, it identifies user interests, behavioral patterns, and attribute information. Step 3: The optimization department optimizes the ad creative based on the analysis results obtained by the analysis department. For example, this includes adjusting the font size and image processing of the ad, adjusting the color scheme, adjusting the placement and layout, and optimizing the content and message. Step 4: The monitoring unit monitors the effectiveness of the ad creative optimized by the optimization unit. For example, it measures the ad's click-through rate, conversion rate, impressions, and view count to evaluate its effectiveness. It can also re-optimize the ad creative if necessary.

[0065] (Example of form 2) An advertising optimization system according to an embodiment of the present invention is a system in which an AI agent embedded in a website or browser analyzes user behavior data and market trends linked to user attributes in real time, and automatically optimizes the displayed advertising creative. This advertising optimization system can maximize advertising effectiveness without compromising usability and reduce the burden on engineers, designers, and marketers who create advertising creatives. First, the advertising optimization system is embedded in a website or browser and collects user behavior data in real time. Behavioral data includes page viewing history, click history, and time spent on the site. User attribute information (age, place of residence, etc.) is also collected. This data is analyzed by the advertising optimization system to identify the user's interests and preferences. Next, the advertising optimization system analyzes market trends based on the collected data. Market trends include currently popular products and services, and seasonal fluctuations in demand. This identifies the optimal advertising creative tailored to the user's attributes. The advertising optimization system automatically optimizes the identified advertising creative. Specifically, it adjusts the font size of the ad, processes images, and adjusts the color tones. This improves the click-through rate and conversion rate of the ad. Furthermore, the ad optimization system monitors ad performance in real time and re-optimizes ad creatives as needed. This ensures that the most effective ads are always displayed. This mechanism significantly reduces the time and resources required for advertisers, advertising agencies, and marketers to analyze data and optimize creatives to improve ad performance. It also maximizes ad effectiveness without compromising usability. For example, when a user is browsing a website, the ad optimization system collects the user's behavioral data and identifies ads that the user might be interested in. It then optimizes the font size and images of those ads and displays them to the user. When the user clicks on an ad, the ad optimization system collects that data and monitors the ad's effectiveness. If the effectiveness is low, it re-optimizes the ad creative and changes the ad displayed.In this way, ad optimization systems revolutionize efficiency and effectiveness in the advertising industry by automating the optimization of ad creatives and establishing new standards for marketing. This allows ad optimization systems to maximize the effectiveness of ads and reduce the burden on engineers, designers, and marketers.

[0066] The advertising optimization system according to this embodiment comprises a collection unit, an analysis unit, an optimization unit, and a monitoring unit. The collection unit collects user behavior data. User behavior data includes, but is not limited to, page viewing history, click history, and time spent on a page. For example, to collect page viewing history, the collection unit records the URLs and viewing times of pages viewed by the user. The collection unit may also record the URLs and click counts of links clicked by the user to collect click history. The collection unit may also measure the time a user spends on a specific page to collect time spent on a page. For example, the collection unit records the URLs of pages viewed by the user and measures the viewing time. The collection unit may also record the URLs of links clicked by the user and measure the click counts. The collection unit may also measure the time a user spends on a specific page and record the time spent on the page. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies user interests and preferences based on the collected data. The analysis unit may also analyze user behavior patterns based on the collected data. The analytics department can also identify user attribute information based on the collected data. For example, the analytics department can identify user interests and preferences based on collected page browsing history. The analytics department can also analyze user behavior patterns based on collected click history. The analytics department can also identify user attribute information based on collected time spent on the site. The optimization department optimizes ad creatives based on the analysis results obtained by the analytics department. For example, the optimization department adjusts the font size, image processing, and color tones of ads. The optimization department can also adjust the placement and layout of ads. The optimization department can also optimize the content and message of ads. For example, the optimization department adjusts the font size of ads to improve visibility. The optimization department can also process ad images to enhance their visual appeal. The optimization department can adjust the color tones of ads to ensure visual consistency. The monitoring department monitors the effectiveness of ad creatives optimized by the optimization department. For example, the monitoring department measures ad click-through rates and conversion rates.The monitoring unit can also measure the number of times an ad is displayed and the number of impressions. The monitoring unit can monitor the effectiveness of an ad in real time and re-optimize the ad creative as needed. For example, the monitoring unit can measure the click-through rate of an ad and evaluate its effectiveness. The monitoring unit can also measure the conversion rate of an ad and evaluate its effectiveness. The monitoring unit can also measure the number of times an ad is displayed and evaluate its effectiveness. As a result, the ad optimization system according to this embodiment can maximize the effectiveness of ad creatives by collecting, analyzing, optimizing, and monitoring user behavior data.

[0067] The data collection unit collects user behavior data. This data includes, but is not limited to, page viewing history, click history, and time spent on pages. For example, to collect page viewing history, the data collection unit records the URLs of pages viewed by the user and the time spent on each page. Specifically, the data collection unit records the URL of each page accessed by the user and measures the time spent on each page in seconds. This allows the system to understand which pages the user is interested in. The data collection unit can also record the URLs of links clicked by the user and the number of clicks to collect click history. For example, the data collection unit records the URLs of links clicked by the user and records the date and time of the click and the number of clicks in detail. This allows the system to identify which links the user is interested in. The data collection unit can also measure the time a user spends on a specific page to collect time spent on it. For example, the data collection unit measures the time a user spends on a specific page in milliseconds and records the time spent. This allows the system to understand which pages the user spends the longest time on. Furthermore, the data collection unit can also collect technical information such as the user's device information, browser type, and operating system version. This allows for a more detailed analysis of user behavior data. The data collection unit centrally manages this data and stores it in a database in real time. This enables the data collection unit to efficiently collect user behavior data and improve the overall system performance.

[0068] The analysis department analyzes the data collected by the data collection department. For example, the analysis department identifies user interests and preferences based on the collected data. Specifically, based on the collected page browsing history, the analysis department identifies pages and categories that users frequently access and infers user interests and preferences. For example, if a user frequently views sports-related pages, it can be determined that the user is interested in sports. The analysis department can also analyze user behavior patterns based on collected click history. For example, if a user frequently clicks on a particular link, it can be determined that they are interested in content related to that link. Furthermore, the analysis department can identify user attribute information based on collected time spent on pages. For example, if a user spends a long time on a particular page, it can be determined that they have a strong interest in the content of that page. The analysis department integrates this data to analyze user behavior patterns and interests in detail. In addition, the analysis department can use machine learning algorithms to analyze user behavior data and predict future behavior. For example, based on past behavior data, it can predict which pages a user is most likely to view next. This allows the analytics department to gain a detailed understanding of user behavior and use that information to optimize advertising.

[0069] The optimization unit optimizes ad creatives based on the analysis results obtained by the analysis unit. For example, the optimization unit adjusts the font size, image processing, and color scheme of the ad. Specifically, the optimization unit adjusts the font size of the ad based on the user's interests to improve readability. For example, in ads aimed at seniors, the font size can be increased for easier reading. The optimization unit can also process the images of the ad to enhance their visual appeal. For example, if a user is interested in sports, sports-related images can be used to enhance the visual appeal of the ad. The optimization unit can also adjust the color scheme of the ad to ensure visual consistency. For example, by adjusting the color scheme of the ad to match the brand's color scheme, brand consistency can be maintained. Furthermore, the optimization unit can also adjust the placement and layout of the ad. For example, based on user behavior patterns, the ad can be placed in the optimal position to improve the click-through rate. The optimization unit can also optimize the content and message of the ad. For example, based on the user's interests, the ad message can be customized to make it more appealing to the user. In this way, the optimization unit can maximize the effectiveness of the ad and attract user attention.

[0070] The monitoring unit monitors the effectiveness of ad creatives optimized by the optimization unit. For example, the monitoring unit measures ad click-through rates (CTR) and conversion rates. Specifically, it records the number of times an ad is displayed and clicked, and calculates the CTR. This allows for an evaluation of how effectively the ad is attracting user attention. The monitoring unit can also measure and evaluate the effectiveness of ad conversion rates. For example, it records the number of times a user who clicked on an ad actually purchased a product or registered for a service, and calculates the conversion rate. This allows for an evaluation of how effectively the ad is prompting user action. The monitoring unit can also measure ad impressions and the number of times an ad is displayed. For example, it records the number of times an ad is displayed and calculates the number of impressions. This allows for an evaluation of how many users the ad is being shown to. Furthermore, the monitoring unit can monitor the effectiveness of ads in real time and re-optimize ad creatives as needed. For example, if an ad's CTR or conversion rate is low, the monitoring unit can provide feedback to the optimization unit and instruct them to re-optimize the ad creatives. This allows the monitoring unit to continuously evaluate and optimize the effectiveness of advertisements, thereby maximizing their impact.

[0071] The attribute collection unit can collect user attribute information. For example, the attribute collection unit can collect attribute information such as the user's age, gender, and occupation. The attribute collection unit can also collect information about the user's place of residence and interests. For example, the attribute collection unit can collect the user's age and use it for advertising targeting. The attribute collection unit can also collect the user's gender and adjust the content of the advertisement. The attribute collection unit can also collect the user's occupation and optimize the message of the advertisement. By collecting user attribute information, it becomes possible to optimize advertising creatives with greater accuracy. Some or all of the above processing in the attribute collection unit may be performed using AI, for example, or without AI. For example, the attribute collection unit can input user attribute information into a generating AI and have the generating AI perform the collection of attribute information.

[0072] The optimization unit can adjust the font size, image processing, and color tone of advertisements. For example, the optimization unit can adjust the font size of advertisements to improve readability. The optimization unit can also process advertisement images to enhance their visual appeal. The optimization unit can adjust the color tone of advertisements to ensure visual consistency. For example, the optimization unit can increase the font size of advertisements to improve readability. The optimization unit can also crop advertisement images to highlight important parts. The optimization unit can adjust the color tone of advertisements to match the brand image. In this way, by adjusting the font size, image processing, and color tone of advertisements, the readability and effectiveness of advertisements can be improved. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the font size, image processing, and color tone adjustments of advertisements into a generating AI and have the generating AI execute them.

[0073] The monitoring unit can measure the click-through rate and conversion rate of advertisements. For example, the monitoring unit can measure the click-through rate of advertisements and evaluate their effectiveness. The monitoring unit can also measure the conversion rate of advertisements and evaluate their effectiveness. The monitoring unit can also measure the number of times an advertisement is displayed and evaluate its effectiveness. For example, the monitoring unit can measure the click-through rate of advertisements in real time and evaluate their effectiveness. The monitoring unit can also measure the conversion rate of advertisements in real time and evaluate their effectiveness. The monitoring unit can also measure the number of times an advertisement is displayed in real time and evaluate its effectiveness. This allows for real-time understanding of the effectiveness of advertisements by measuring the click-through rate and conversion rate of advertisements. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the measurement of the click-through rate and conversion rate of advertisements into a generating AI and have the generating AI perform the measurement.

[0074] The optimization unit can re-optimize ad creatives when their effectiveness is low. For example, if the click-through rate or conversion rate of an ad is low, the optimization unit will re-adjust the font size, image processing, and color tones of the ad. The optimization unit can also re-adjust the placement and layout of the ad. The optimization unit can also re-optimize the content and message of the ad. For example, the optimization unit can re-adjust the font size of the ad to improve readability. The optimization unit can also re-process the images of the ad to enhance their visual appeal. The optimization unit can also re-adjust the color tones of the ad to ensure visual consistency. This ensures that the optimal ad creative is always provided by re-optimizing when the ad's effectiveness is low. Some or all of the above processes in the optimization unit may be performed using AI, for example, or not using AI. For example, if the ad's effectiveness is low, the optimization unit can input the re-optimization of the ad creative into the generating AI and have the generating AI perform the optimization.

[0075] The data collection unit can collect behavioral data such as page viewing history, click history, and time spent on a page. For example, to collect page viewing history, the data collection unit records the URLs of pages viewed by the user and the viewing time. To collect click history, the data collection unit can also record the URLs of links clicked by the user and the number of clicks. To collect time spent on a page, the data collection unit can also measure the time a user spends on a specific page. For example, the data collection unit records the URLs of pages viewed by the user and measures the viewing time. The data collection unit can also record the URLs of links clicked by the user and measure the number of clicks. The data collection unit can also measure the time a user spends on a specific page and record the time spent. By collecting behavioral data such as page viewing history, click history, and time spent on a page, it is possible to understand user behavior in detail. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input behavioral data such as page viewing history, click history, and time spent on a page into a generating AI and have the generating AI execute it.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and wait until the user's behavior stabilizes. If the user is relaxed, the data collection unit can also accelerate the collection timing to collect behavioral data in real time. If the user is excited, the data collection unit can also accelerate the collection timing to collect detailed behavioral data. By adjusting the collection timing based on the user's emotions, behavioral data can be collected at a more appropriate time. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0077] The data collection unit can analyze the user's past behavior data and select the optimal collection method. For example, the data collection unit can prioritize collecting data from pages the user has frequently visited in the past. The data collection unit can also concentrate data collection during times when there are many clicks, based on the user's past click history. The data collection unit can also analyze the user's past time spent on a page and focus on collecting data from pages where the user spends a long time. For example, the data collection unit can prioritize collecting data from pages the user has frequently visited in the past. The data collection unit can also concentrate data collection during times when there are many clicks, based on the user's past click history. The data collection unit can also analyze the user's past time spent on a page and focus on collecting data from pages where the user spends a long time. This allows the optimal collection method to be selected by analyzing the user's past behavior data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal collection method.

[0078] The data collection unit can filter behavioral data based on the user's current interests. For example, the data collection unit can prioritize collecting relevant behavioral data based on the content of the page the user is currently viewing. The data collection unit can also filter relevant behavioral data based on keywords the user has recently searched for. The data collection unit can also collect behavioral data related to interests based on content the user has shared on social media. For example, the data collection unit can prioritize collecting relevant behavioral data based on the content of the page the user is currently viewing. The data collection unit can also filter relevant behavioral data based on keywords the user has recently searched for. The data collection unit can also collect behavioral data related to interests based on content the user has shared on social media. This allows for the collection of highly relevant behavioral data by filtering based on the user's current interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input filtering based on the user's current interests into a generating AI and have the generating AI perform it.

[0079] The data collection unit can estimate the user's emotions and determine the priority of behavioral data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting behavioral data related to stress reduction. If the user is relaxed, the data collection unit may also prioritize collecting behavioral data related to relaxation. If the user is excited, the data collection unit may also prioritize collecting behavioral data related to excitement. For example, if the user is stressed, the data collection unit will prioritize collecting behavioral data related to stress reduction. If the user is relaxed, the data collection unit may also prioritize collecting behavioral data related to relaxation. If the user is excited, the data collection unit may also prioritize collecting behavioral data related to excitement. This allows for the priority collection of important data by determining the priority of behavioral data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific region, the data collection unit will prioritize collecting behavioral data related to that region. If the user is traveling, the data collection unit can also prioritize collecting behavioral data related to the travel destination. If the user is at home, the data collection unit can also prioritize collecting behavioral data around the user's home. This allows for the optimization of region-specific advertising creatives by collecting highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0081] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect relevant behavioral data based on content shared by the user on social media. The data collection unit can also collect relevant behavioral data based on accounts followed by the user on social media. The data collection unit can also collect relevant behavioral data based on groups the user participates in on social media. For example, the data collection unit can collect relevant behavioral data based on content shared by the user on social media. The data collection unit can also collect relevant behavioral data based on accounts followed by the user on social media. The data collection unit can also collect relevant behavioral data based on groups the user participates in on social media. This allows for the collection of relevant behavioral data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is excited, the analysis unit can also provide visually stimulating analysis results. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is excited, the analysis unit can also provide visually stimulating analysis results. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important behavioral data. The analysis unit can also perform a simplified analysis on less important behavioral data. The analysis unit can also perform an analysis with a moderate level of detail on behavioral data of moderate importance. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the level of detail of the analysis based on the importance of the behavioral data into a generating AI and have the generating AI execute it.

[0084] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase behavior data. The analysis unit can also apply a browsing pattern analysis algorithm to browsing behavior data. The analysis unit can also apply a click pattern analysis algorithm to click behavior data. By applying different analysis algorithms depending on the category of behavioral data, appropriate analysis according to the characteristics of the data becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms depending on the category of behavioral data into a generating AI and have the generating AI execute them.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, to-the-point analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with detailed explanations. If the user is excited, the analysis unit can also provide an analysis with visually stimulating effects. For example, if the user is in a hurry, the analysis unit provides a short, to-the-point analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with detailed explanations. If the user is excited, the analysis unit can also provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, it is possible to provide an appropriate analysis result according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can determine the priority of analysis based on when the behavioral data was collected. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. The analysis unit may also prioritize the most recent data while referring to past behavioral data. The analysis unit may also prioritize the analysis of behavioral data collected during a specific period. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. The analysis unit may also prioritize the most recent data while referring to past behavioral data. The analysis unit may also prioritize the analysis of behavioral data collected during a specific period. This makes it possible to perform analysis that emphasizes the most recent data by determining the priority of analysis based on when the behavioral data was collected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the analysis priority based on when the behavioral data was collected into a generating AI and have the generating AI execute it.

[0087] The analysis unit can adjust the order of analysis based on the relevance of behavioral data during the analysis process. For example, the analysis unit may prioritize analyzing highly relevant behavioral data. It may also analyze moderately relevant behavioral data next. It may also analyze lowly relevant behavioral data last. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of behavioral data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the order of analysis based on the relevance of behavioral data into a generating AI and have the generating AI execute it.

[0088] The optimization unit can estimate the user's emotions and adjust the optimization method of the ad creative based on the estimated user emotions. For example, if the user is stressed, the optimization unit can provide a simple and highly visible ad creative. If the user is relaxed, the optimization unit can also provide an ad creative that includes detailed information. If the user is excited, the optimization unit can also provide a visually stimulating ad creative. For example, if the user is stressed, the optimization unit can provide a simple and highly visible ad creative. If the user is relaxed, the optimization unit can also provide an ad creative that includes detailed information. If the user is excited, the optimization unit can also provide a visually stimulating ad creative. This allows the system to provide the user with the most suitable ad by adjusting the optimization method of the ad creative based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0089] The optimization unit can adjust the level of detail of the optimization based on the importance of the advertisements during the optimization process. For example, the optimization unit can perform detailed optimization for highly important advertisements. For less important advertisements, the optimization unit can perform simplified optimization. For moderately important advertisements, the optimization unit can perform optimization with a moderate level of detail. This allows for efficient advertisement optimization by adjusting the level of detail of the optimization based on the importance of the advertisements. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the level of detail of the optimization based on the importance of the advertisements into a generating AI and have the generating AI execute it.

[0090] The optimization unit can apply different optimization algorithms depending on the ad category during optimization. For example, the optimization unit can apply an optimization algorithm that increases purchase intent to product ads. The optimization unit can also apply an optimization algorithm that emphasizes the appeal of a service to service ads. The optimization unit can also apply an optimization algorithm that strengthens the brand image to brand ads. For example, the optimization unit can apply an optimization algorithm that increases purchase intent to product ads. The optimization unit can also apply an optimization algorithm that emphasizes the appeal of a service to service ads. The optimization unit can also apply an optimization algorithm that strengthens the brand image to brand ads. By applying different optimization algorithms depending on the ad category, appropriate optimization tailored to the characteristics of the ad becomes possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input different optimization algorithms depending on the ad category into a generating AI and have the generating AI execute them.

[0091] The optimization unit can estimate the user's emotions and determine the priority of ad creative optimization based on the estimated user emotions. For example, if the user is stressed, the optimization unit will prioritize optimizing ad creatives related to stress reduction. If the user is relaxed, the optimization unit can also prioritize optimizing ad creatives related to relaxation. If the user is excited, the optimization unit can also prioritize optimizing ad creatives related to excitement. For example, if the user is stressed, the optimization unit will prioritize optimizing ad creatives related to stress reduction. If the user is relaxed, the optimization unit can also prioritize optimizing ad creatives related to relaxation. If the user is excited, the optimization unit can also prioritize optimizing ad creatives related to excitement. This allows for the prioritization of important ads by determining the priority of ad creative optimization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0092] The optimization unit can adjust its optimization method based on the ad's display location during optimization. For example, if the ad is displayed on the top page, the optimization unit can perform optimizations to improve visibility. If the ad is displayed on a product page, the optimization unit can also perform optimizations to increase purchase intent. If the ad is displayed on a blog page, the optimization unit can also perform optimizations to improve relevance to the content. For example, if the ad is displayed on the top page, the optimization unit can perform optimizations to improve visibility. If the ad is displayed on a product page, the optimization unit can also perform optimizations to increase purchase intent. If the ad is displayed on a blog page, the optimization unit can also perform optimizations to improve relevance to the content. By adjusting the optimization method based on the ad's display location, appropriate ad optimization according to the display location becomes possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the optimization method based on the ad's display location into a generating AI and have the generating AI execute it.

[0093] The optimization unit can adjust the order of optimization based on the relevance of the ads during the optimization process. For example, the optimization unit may prioritize optimizing ads with high relevance. It may also optimize ads with moderate relevance next. It may also optimize ads with low relevance last. For example, the optimization unit may prioritize optimizing ads with high relevance. It may also optimize ads with moderate relevance next. It may also optimize ads with low relevance last. This allows for efficient ad optimization by adjusting the order of optimization based on the relevance of the ads. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the order of optimization based on the relevance of the ads to a generating AI and have the generating AI execute it.

[0094] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit provides simple and easy-to-understand monitoring results. If the user is relaxed, the monitoring unit can also provide detailed monitoring results. If the user is excited, the monitoring unit can also provide visually stimulating monitoring results. For example, if the user is stressed, the monitoring unit provides simple and easy-to-understand monitoring results. If the user is relaxed, the monitoring unit can also provide detailed monitoring results. If the user is excited, the monitoring unit can also provide visually stimulating monitoring results. This makes it possible to provide monitoring results that are easy for the user to understand by adjusting the monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0095] The monitoring unit can analyze the effectiveness of advertisements in real time during monitoring and select the optimal monitoring method. For example, the monitoring unit can analyze the click-through rate of advertisements in real time and prioritize monitoring high-performing advertisements. The monitoring unit can also analyze the conversion rate of advertisements in real time and prioritize monitoring high-performing advertisements. The monitoring unit can also analyze the number of times advertisements are displayed in real time and prioritize monitoring high-performing advertisements. For example, the monitoring unit can analyze the click-through rate of advertisements in real time and prioritize monitoring high-performing advertisements. The monitoring unit can also analyze the conversion rate of advertisements in real time and prioritize monitoring high-performing advertisements. The monitoring unit can also analyze the number of times advertisements are displayed in real time and prioritize monitoring high-performing advertisements. This allows for the prioritization of monitoring of high-performing advertisements by analyzing their effectiveness in real time. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data for analyzing the effectiveness of advertisements in real time into a generating AI and have the generating AI select the optimal monitoring method.

[0096] The monitoring unit can apply different monitoring algorithms depending on the category of the advertisement during monitoring. For example, the monitoring unit can apply a monitoring algorithm that increases purchase intent to product advertisements. The monitoring unit can also apply a monitoring algorithm that emphasizes the appeal of a service to service advertisements. The monitoring unit can also apply a monitoring algorithm that strengthens the brand image to brand advertisements. For example, the monitoring unit can apply a monitoring algorithm that increases purchase intent to product advertisements. The monitoring unit can also apply a monitoring algorithm that emphasizes the appeal of a service to service advertisements. The monitoring unit can also apply a monitoring algorithm that strengthens the brand image to brand advertisements. By applying different monitoring algorithms depending on the category of the advertisement, appropriate monitoring tailored to the characteristics of the advertisement becomes possible. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input different monitoring algorithms depending on the category of the advertisement into a generating AI and have the generating AI execute them.

[0097] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring ads related to stress reduction. If the user is relaxed, the monitoring unit may also prioritize monitoring ads related to relaxation. If the user is excited, the monitoring unit may also prioritize monitoring ads related to excitement. For example, if the user is stressed, the monitoring unit will prioritize monitoring ads related to stress reduction. If the user is relaxed, the monitoring unit may also prioritize monitoring ads related to relaxation. If the user is excited, the monitoring unit may also prioritize monitoring ads related to excitement. This allows for priority monitoring of important ads by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0098] The monitoring unit can adjust its monitoring method based on the ad's display location during monitoring. For example, if the ad is displayed on the top page, the monitoring unit can perform monitoring to improve visibility. If the ad is displayed on a product page, the monitoring unit can also perform monitoring to increase purchase intent. If the ad is displayed on a blog page, the monitoring unit can also perform monitoring to improve relevance to the content. For example, if the ad is displayed on the top page, the monitoring unit can perform monitoring to improve visibility. If the ad is displayed on a product page, the monitoring unit can also perform monitoring to increase purchase intent. If the ad is displayed on a blog page, the monitoring unit can also perform monitoring to improve relevance to the content. By adjusting the monitoring method based on the ad's display location, appropriate monitoring according to the display location becomes possible. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the monitoring method based on the ad's display location into a generating AI and have the generating AI execute it.

[0099] The monitoring unit can adjust the monitoring order based on the relevance of the advertisements during monitoring. For example, the monitoring unit may prioritize monitoring highly relevant advertisements. It may also monitor moderately relevant advertisements next. It may also monitor lowly relevant advertisements last. This allows for efficient monitoring by adjusting the monitoring order based on the relevance of the advertisements. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the monitoring order based on the relevance of the advertisements into a generating AI and have the generating AI execute it.

[0100] The attribute collection unit can estimate the user's emotions and adjust the timing of attribute information collection based on the estimated emotions. For example, if the user is stressed, the attribute collection unit can delay the collection timing and wait until the user's behavior stabilizes. If the user is relaxed, the attribute collection unit can also speed up the collection timing and collect attribute information in real time. If the user is excited, the attribute collection unit can also speed up the collection timing and collect detailed attribute information. By adjusting the timing of attribute information collection based on the user's emotions, attribute information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the attribute collection unit may be performed using AI, for example, or without AI. For example, the attribute collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0101] The attribute collection unit can analyze the user's past attribute information and select the optimal collection method. For example, the attribute collection unit can select the optimal collection method based on attribute information previously provided by the user. The attribute collection unit can also adjust the collection frequency based on the user's past attribute information. The attribute collection unit can also analyze the user's past attribute information and determine the priority of the attribute information to be collected. For example, the attribute collection unit can select the optimal collection method based on attribute information previously provided by the user. The attribute collection unit can also adjust the collection frequency based on the user's past attribute information. The attribute collection unit can also analyze the user's past attribute information and determine the priority of the attribute information to be collected. This allows the optimal collection method to be selected by analyzing the user's past attribute information. Some or all of the above processing in the attribute collection unit may be performed using AI, for example, or without AI. For example, the attribute collection unit can input the user's past attribute information into a generating AI and have the generating AI select the optimal collection method.

[0102] The attribute collection unit can estimate the user's emotions and determine the priority of attribute information to collect based on the estimated user emotions. For example, if the user is stressed, the attribute collection unit will prioritize collecting attribute information related to stress reduction. If the user is relaxed, the attribute collection unit can also prioritize collecting attribute information related to relaxation. If the user is excited, the attribute collection unit can also prioritize collecting attribute information related to excitement. For example, if the user is stressed, the attribute collection unit will prioritize collecting attribute information related to stress reduction. If the user is relaxed, the attribute collection unit can also prioritize collecting attribute information related to relaxation. If the user is excited, the attribute collection unit can also prioritize collecting attribute information related to excitement. This allows for the priority collection of important information by determining the priority of attribute information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the attribute collection unit may be performed using AI, for example, or without AI. For example, the attribute collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0103] The attribute collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting attribute information. For example, if the user is in a specific region, the attribute collection unit will prioritize the collection of attribute information related to that region. If the user is traveling, the attribute collection unit can also prioritize the collection of attribute information related to the travel destination. If the user is at home, the attribute collection unit can also prioritize the collection of attribute information around the user's home. This allows for the optimization of region-specific advertising creatives by collecting highly relevant information while considering the user's geographical location. Some or all of the above processing in the attribute collection unit may be performed using AI, for example, or without AI. For example, the attribute collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] Ad optimization systems can estimate a user's emotions and adjust the timing of ad display based on those emotions. For example, if a user is stressed, the ad display can be delayed until the user relaxes. If the user is relaxed, the ad can be displayed earlier, at a time when the user is more likely to be interested. If the user is excited, the ad can be displayed more frequently to keep the user interested. In this way, the effectiveness of ads can be maximized by adjusting the timing of ad display based on the user's emotions.

[0106] An ad optimization system can analyze a user's past purchase history and select the most effective ad creative. For example, it can prioritize displaying ads related to products the user has previously purchased. It can also display ads related to the brands of products the user has previously purchased, or ads related to the categories of products the user has previously purchased. By analyzing a user's past purchase history, it is possible to select more effective ad creatives.

[0107] Ad optimization systems can estimate a user's emotions and adjust ad content based on those emotions. For example, if a user is stressed, they can display ads with relaxing effects. If a user is relaxed, they can display ads containing detailed information that pique their interest. If a user is excited, they can display visually stimulating ads. This allows for maximizing ad effectiveness by tailoring ad content based on user emotions.

[0108] Ad optimization systems can display location-specific ads by considering the user's geographical location. For example, if a user is in a specific area, ads related to businesses and services in that area will be displayed. If a user is traveling, ads related to tourist attractions and hotels in their destination can be displayed. If a user is at home, ads related to businesses and services near their home can be displayed. This maximizes the effectiveness of ads by displaying location-specific ads that take the user's geographical location into account.

[0109] Ad optimization systems can estimate a user's emotions and adjust ad design based on those emotions. For example, if a user is stressed, they can display a simple, highly visible ad design. If a user is relaxed, they can display an ad design with more detailed information. If a user is excited, they can display an ad design that is visually stimulating. This allows for maximizing ad effectiveness by adjusting ad design based on user emotions.

[0110] Ad optimization systems can analyze a user's social media activity and display relevant ads. For example, they can display relevant ads based on content a user shares on social media. They can also display relevant ads based on accounts a user follows. They can also display relevant ads based on groups a user belongs to. In this way, by analyzing a user's social media activity, relevant ads can be displayed.

[0111] Ad optimization systems can estimate a user's emotions and adjust the frequency of ad display based on those emotions. For example, if a user is stressed, the frequency of ad display can be reduced to lessen the user's burden. If a user is relaxed, the frequency of ad display can be increased to capture their interest. If a user is excited, the frequency of ad display can be increased even further to maintain their interest. In this way, the effectiveness of ads can be maximized by adjusting the frequency of ad display based on the user's emotions.

[0112] Ad optimization systems can analyze a user's past search history and display relevant ads. For example, they can display relevant ads based on keywords the user has searched for in the past. They can also display relevant ads based on product categories the user has searched for in the past. They can also display relevant ads based on brands the user has searched for in the past. In this way, by analyzing a user's past search history, relevant ads can be displayed.

[0113] Ad optimization systems can estimate a user's emotions and adjust ad targeting based on those emotions. For example, if a user is stressed, they can be shown ads for products or services that promote relaxation. If a user is relaxed, they can be shown ads for products or services that contain detailed information that will pique their interest. If a user is excited, they can be shown ads for products or services that are visually stimulating. This allows for maximizing ad effectiveness by adjusting ad targeting based on user emotions.

[0114] Ad optimization systems can analyze a user's past browsing history and display relevant ads. For example, they can display ads related to pages the user has previously viewed, product categories the user has previously viewed, or brands the user has previously viewed. This allows for the display of relevant ads by analyzing the user's past browsing history.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The data collection unit collects user behavior data. This data includes page viewing history, click history, and time spent on each page. For example, the data collection unit records the URLs and viewing times of pages viewed by the user, the URLs and click counts of links clicked, and the time spent on specific pages. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, based on the collected data, it identifies user interests, behavioral patterns, and attribute information. Step 3: The optimization department optimizes the ad creative based on the analysis results obtained by the analysis department. For example, this includes adjusting the font size and image processing of the ad, adjusting the color scheme, adjusting the placement and layout, and optimizing the content and message. Step 4: The monitoring unit monitors the effectiveness of the ad creative optimized by the optimization unit. For example, it measures the ad's click-through rate, conversion rate, impressions, and view count to evaluate its effectiveness. It can also re-optimize the ad creative if necessary.

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, monitoring unit, and attribute collection unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects user behavior data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes the advertising creative. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the effectiveness of the advertisement. The attribute collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects user attribute information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, monitoring unit, and attribute collection unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects user behavior data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes the advertising creative. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the effectiveness of the advertisement. The attribute collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects user attribute information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, monitoring unit, and attribute collection unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects user behavior data. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes the advertising creative. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the effectiveness of the advertisement. The attribute collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects user attribute information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the collection unit, analysis unit, optimization unit, monitoring unit, and attribute collection unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects user behavior data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and optimizes the advertising creative. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414 and monitors the effectiveness of the advertisement. The attribute collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and collects user attribute information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0170] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, An optimization unit that optimizes advertising creatives based on the analysis results obtained by the aforementioned analysis unit, The system includes a monitoring unit that monitors the effectiveness of the advertising creative optimized by the optimization unit. A system characterized by the following features. (Note 2) It includes an attribute collection unit that collects user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, Adjust the font size, image processing, and color tones of the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, Measure the click-through rate and conversion rate of ads. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, If the ad is not performing well, re-optimize the ad creative. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We collect behavioral data such as page browsing history, click history, and time spent on pages. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, We estimate user emotions and adjust how ad creatives are optimized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, During optimization, adjust the level of optimization based on the importance of the ad. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, During optimization, different optimization algorithms are applied depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, We estimate user emotions and determine the optimization priorities for ad creatives based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, the optimization method is adjusted based on where the ad is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, During optimization, the order of optimization is adjusted based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, the effectiveness of the advertisement is analyzed in real time, and the optimal monitoring method is selected. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, During monitoring, different monitoring algorithms are applied depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, adjust the monitoring method based on where the ad is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, During monitoring, adjust the order of monitoring based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 31) The attribute collection unit, The system estimates the user's emotions and adjusts the timing of attribute information collection based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The attribute collection unit, Analyze the user's past attribute information and select the optimal data collection method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The attribute collection unit, It estimates the user's emotions and determines the priority of attribute information to collect based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The attribute collection unit, When collecting attribute information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, An optimization unit that optimizes advertising creatives based on the analysis results obtained by the aforementioned analysis unit, The system includes a monitoring unit that monitors the effectiveness of the advertising creative optimized by the optimization unit. A system characterized by the following features.

2. It includes an attribute collection unit that collects user attribute information. The system according to feature 1.

3. The optimization unit, Adjust the font size, image processing, and color tones of the advertisement. The system according to feature 1.

4. The monitoring unit, Measure the click-through rate and conversion rate of ads. The system according to feature 1.

5. The optimization unit, If the ad is not performing well, re-optimize the ad creative. The system according to feature 1.

6. The aforementioned collection unit is We collect behavioral data such as page browsing history, click history, and time spent on pages. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

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