system

Abstract

The system according to this embodiment aims to dynamically change the content of advertisements based on the user's interests. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a feedback unit, and a reporting unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit and determines the user's interests, concerns, and emotions. The generation unit selects the optimal advertising parts based on the results determined by the analysis unit and generates advertisements. The feedback unit collects performance data of the advertisements generated by the generation unit and improves the AI ​​model. The reporting unit reports the results of the advertisement changes based on the AI ​​model improved by the feedback unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the advertisement content has not been sufficiently changed dynamically based on the interests and concerns of users, and there is room for improvement.

[0005] The system according to the embodiment aims to dynamically change the advertisement content based on the interests and concerns of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a feedback unit, and a reporting unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit and determines the user's interests, concerns, and emotions. The generation unit selects the optimal advertising components based on the results determined by the analysis unit and generates advertisements. The feedback unit collects performance data of the advertisements generated by the generation unit and improves the AI ​​model. The reporting unit reports the results of the advertisement changes based on the AI ​​model improved by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can dynamically change the content of advertisements based on the user's interests. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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) The adaptive ad system according to an embodiment of the present invention is a next-generation advertising system that uses AI to dynamically change ad content based on the user's interests. This adaptive ad system provides ads optimized for individual users, maximizing engagement and advertising effectiveness. Specifically, it has the following six functions and features. For example, the adaptive ad system collects the user's "browsing history," "searches," and "selections" in real time. Next, based on the collected data, the adaptive ad system analyzes the user's "interests," "concerns," and "emotions" and selects the most suitable ad components. Furthermore, the adaptive ad system uses AI to generate the optimal combination from ad components registered in advance by the company, updating the ad each time. For example, it supports dynamic formats such as video ads and interactive ads. The adaptive ad system can also generate static ad formats such as banner ads and text ads. Next, the adaptive ad system collects ad performance data and continuously improves the AI ​​model. Finally, the adaptive ad system reports the results of the ad changes, providing advertisers with new perspectives and viewpoints. This system provides users with highly relevant information through personalized ads, reducing stress and improving engagement. Businesses can maximize advertising effectiveness with user-optimized ads and discover new marketing strategies by analyzing ad performance data. They can also see the results of ad changes in real time. For example, if a user previously searched for "skincare products" and viewed related product pages, their next visit will display ads for "serums" and "face masks." Furthermore, ads containing information such as "skincare tips" and "seasonal skincare methods" are generated based on the user's interests. Specifically, video ads may display how to use serums and user reviews, and interactive ads may provide users with tools to choose skincare products that suit their skin type. Additionally, banner ads may display discount information for specific serums, and text ads may provide "points to consider when choosing skincare products" and "skincare routines."This allows the adaptive ad system to maximize advertising effectiveness by collecting and analyzing user data, generating optimal ads, and continuously improving through feedback.

[0029] The adaptive ad system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a feedback unit, and a reporting unit. The collection unit collects user data. The collection unit can collect data such as the user's "browsing history," "searches," and "selections" in real time. For example, the collection unit collects data such as the URLs of websites the user has visited, keywords entered into search engines, and links clicked. The collection unit can also collect data such as detailed information about products the user has viewed and purchase history. The analysis unit analyzes the data collected by the collection unit to determine the user's interests, concerns, and emotions. For example, the analysis unit can analyze the user's interests using machine learning algorithms. For example, the analysis unit identifies topics and products that the user is interested in based on the user's search history and browsing history. The analysis unit can also determine the user's emotions using sentiment analysis algorithms. For example, the analysis unit analyzes the user's text data to determine positive and negative emotions. The generation unit selects the most suitable ad components based on the results determined by the analysis unit and generates the ad. The generation unit can, for example, generate the optimal combination of ad components pre-registered by a company based on the user's interests and preferences. The generation unit can generate dynamic ad formats such as video ads and interactive ads. It can also generate static ad formats such as banner ads and text ads. The feedback unit collects performance data of the ads generated by the generation unit and improves the AI ​​model. The feedback unit can collect performance data such as click-through rates and conversion rates of ads. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. The reporting unit reports the results of the ad changes based on the AI ​​model improved by the feedback unit. The reporting unit can report the ad performance data as graphs and statistical data, for example, providing advertisers with new perspectives and viewpoints.As a result, the adaptive ad system according to the embodiment can maximize advertising effectiveness by collecting and analyzing user data, generating optimal advertisements, and continuously improving them through feedback.

[0030] The data collection unit collects user data. For example, the data collection unit can collect data such as users' "browsing history," "searches," and "selections" in real time. Specifically, it collects data such as the URLs of websites visited by users, keywords entered into search engines, and links clicked. This allows for detailed tracking of users' online behavior and understanding the interests and preferences of individual users. The data collection unit can also collect data such as detailed information about products viewed by users and their purchase history. For example, it collects the categories and price ranges of products viewed by users on online shopping sites, as well as a list of purchased items. This allows for understanding users' purchasing trends and consumption patterns, enabling more accurate advertising targeting. Furthermore, the data collection unit can also collect data such as social media activity and comments and reviews posted by users. This allows for understanding users' emotions and opinions, providing valuable information for optimizing the content and format of advertisements. By centrally managing this data and providing it to the analysis and generation units in real time, the data collection unit can improve the overall efficiency and accuracy of the system.

[0031] The analysis unit analyzes the data collected by the collection unit to determine the user's interests, concerns, and emotions. For example, the analysis unit can use machine learning algorithms to analyze user interests. Specifically, it identifies topics and products that users are interested in based on their search and browsing history. For example, it analyzes keywords that users frequently search for and the content of web pages they spend a long time viewing to identify areas of interest. The analysis unit can also determine user emotions using sentiment analysis algorithms. For example, it analyzes user text data to determine positive and negative emotions. This allows the system to understand what emotions users are feeling and adjust the content and tone of advertisements accordingly. Furthermore, the analysis unit can analyze user behavior patterns and activity at different times of the day to identify the optimal timing for ad delivery. For example, if a user frequently shops online during a specific time period, delivering ads to that time period can maximize advertising effectiveness. The analysis unit provides these analysis results to the generation unit, which uses them as basic data for generating optimal advertisements.

[0032] The generation unit selects the optimal ad components based on the results determined by the analysis unit and generates the ad. For example, the generation unit can generate the optimal combination of ad components registered in advance by a company, based on the user's interests. Specifically, it selects ad components related to products or services that the user is interested in and combines them to generate a single ad. The generation unit can generate dynamic ad formats, such as video ads and interactive ads. This makes it easier to attract the user's attention and increase engagement. The generation unit can also generate static ad formats, such as banner ads and text ads. This allows it to support different ad formats and provide ads that are optimal for the user's browsing environment and device. Furthermore, the generation unit can customize the design and message of the ad to generate personalized ads that match the user's interests and emotions. For example, if a user has positive emotions, it uses bright designs and messages that emphasize those emotions. On the other hand, if a user has negative emotions, it uses designs and messages that provide a sense of security. The generation unit can generate these ads in real time and deliver them to users immediately.

[0033] The feedback unit collects performance data from ads generated by the generation unit and uses it to improve the AI ​​model. For example, the feedback unit can collect performance data such as click-through rates and conversion rates. Specifically, it collects data such as the number of times users clicked on an ad and the number of times they made a purchase through the ad, and evaluates the effectiveness of the ad. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. For example, it identifies ad components with low click-through rates and ad components with high conversion rates, and uses this data to optimize the AI ​​model. This allows for the selection of more effective ad components in the next ad generation. Furthermore, the feedback unit can also collect user feedback and comments to help improve the ads. For example, it collects what users think of the ads and adjusts the ad design and message based on that information. The feedback unit provides this data to the analysis and generation units, enabling continuous improvement of the overall system's accuracy and effectiveness.

[0034] The reporting department reports on the results of ad changes based on the AI ​​model improved by the feedback department. For example, the reporting department can report ad performance data as graphs and statistical data, providing advertisers with new perspectives and viewpoints. Specifically, it can graph the trends in ad click-through rates and conversion rates to visually show which ad components were most effective. It can also compile changes in user interests and preferences as statistical data and provide it to advertisers. This allows advertisers to understand which ad strategies are effective and use this information for future ad campaigns. Furthermore, the reporting department can update ad performance data in real time, providing the latest information. For example, it can monitor the progress of ad campaigns in real time and adjust ad strategies as needed. The reporting department can also provide customized reports according to the advertiser's requests. For example, it can analyze ad performance in detail for a specific period or region and provide the results as a report. In this way, the reporting department can provide valuable information to advertisers and help maximize the effectiveness of their ad campaigns.

[0035] The data collection unit can collect user data such as browsing history, searches, and selections in real time. For example, the data collection unit can collect data such as the URLs of websites visited by the user, keywords entered into search engines, and links clicked. The data collection unit can also collect data such as detailed information on products viewed by the user and purchase history. This allows for more accurate analysis by collecting user behavior data in real time. 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 collect user behavior data in real time, input it into an AI model, and output the analysis results.

[0036] The analysis unit can determine a user's "interests," "concerns," and "emotions" based on the collected data. For example, the analysis unit can analyze a user's interests and concerns using machine learning algorithms. For example, the analysis unit can identify topics and products that a user is interested in based on their search and browsing history. The analysis unit can also determine a user's emotions using sentiment analysis algorithms. For example, the analysis unit can analyze a user's text data to determine positive and negative emotions. This allows for the generation of more personalized advertisements by accurately determining a user's interests, concerns, and emotions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user data into an AI model and output the results of its interest, concern, and emotion determination.

[0037] The generation unit can generate the optimal combination from advertising parts registered in advance by a company, and update the advertisement each time. For example, the generation unit can generate the optimal combination from advertising parts registered in advance by a company based on the user's interests and preferences. The generation unit can generate dynamic advertisements such as video advertisements and interactive advertisements. In addition, the generation unit can also generate static advertisements such as banner advertisements and text advertisements. This ensures that the latest information is always provided to the user by updating the advertisement each time. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input advertising parts registered in advance by a company into a generation AI and generate the optimal combination.

[0038] The generation unit can generate dynamic forms of advertising, such as video ads and interactive ads. For example, the generation unit can generate video ads. For example, the generation unit can generate interactive ads. For example, the generation unit can generate interactive ads that provide users with a tool to choose skincare products that suit their skin type. By generating dynamic forms of advertising, user engagement can be increased. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI perform the generation of video ads and interactive ads.

[0039] The generation unit can generate static advertisements such as banner ads and text ads. For example, the generation unit can generate banner ads. For example, the generation unit can generate text ads. For example, the generation unit can generate banner ads that display discount information for a specific beauty serum. For example, the generation unit can generate text ads that provide "tips for choosing skincare products" or "skincare routines." By generating static advertisements, it is possible to provide advertisements based on user interests. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI perform the generation of banner ads and text ads.

[0040] The feedback unit can collect advertising performance data and continuously improve the AI ​​model. For example, the feedback unit can collect performance data such as the click-through rate and conversion rate of ads. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. In this way, the effectiveness of the ads can be maximized by collecting advertising performance data and continuously improving the AI ​​model. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input advertising performance data into the AI ​​model and perform model retraining.

[0041] The reporting unit can report the results of changes to advertisements, providing advertisers with new perspectives and viewpoints. For example, the reporting unit can report advertisement performance data as graphs and statistical data, providing advertisers with new perspectives and viewpoints. For example, the reporting unit can create reports that evaluate the effectiveness of advertisements based on performance data such as click-through rates and conversion rates. In this way, by reporting the results of changes to advertisements, it can provide advertisers with new perspectives and viewpoints. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input advertisement performance data into an AI model and perform report generation.

[0042] The data collection unit can analyze the user's past browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites that the user frequently visits. For example, the data collection unit can focus on collecting data related to topics that the user has shown interest in in the past. For example, if the user tends to access the site during certain time periods, the data collection unit can concentrate data collection during those times. This allows the optimal data collection method to be selected by analyzing the user's past browsing history. 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 browsing history into an AI model and select the optimal data collection method.

[0043] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is at work, the data collection unit can prioritize collecting work-related data. For example, if the user is on vacation, the data collection unit can collect data related to travel and leisure. For example, if the user is working on a specific project, the data collection unit can collect data related to that project. This allows for the collection of more relevant data by filtering the data based on the user's current activities and areas of interest. 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 current activities and areas of interest into an AI model and perform data filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific city, the data collection unit can collect event information related to that city. For example, if the user is traveling, the data collection unit can collect tourist information and restaurant information for the travel destination. For example, if the user is at home, the data collection unit can collect data about nearby shops and services. This allows for the collection of more relevant data by 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 an AI model and prioritize the collection of highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to posts that a user has "liked" on social media. For example, the data collection unit can collect data based on the content of posts from accounts that a user follows. For example, the data collection unit can collect data related to content that a user has shared. This allows for the collection of more relevant data by analyzing a 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 user social media activity data into an AI model and collect relevant data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a concise analysis on less important data. For example, the analysis unit can perform a detailed analysis on data of high user interest and a concise analysis on data of low interest. For example, the analysis unit can perform a detailed analysis on important data specified by the advertiser and a concise analysis on data not specified. By adjusting the level of detail of the analysis based on the importance of the data, more effective analysis 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 the importance of the data into an AI model and adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an interest analysis algorithm to data related to user interests. For example, the analysis unit can apply an emotion analysis algorithm to data related to user emotions. For example, the analysis unit can apply an action analysis algorithm to data related to user behavior. By applying different analysis algorithms depending on the data category, more accurate analysis 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 the data category into an AI model and apply an appropriate analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. For example, the analysis unit can prioritize the analysis of data collected during a specific period. For example, the analysis unit can prioritize the analysis of data collected during an advertising campaign. This allows for more effective analysis by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into an AI model to determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of data related to user interests. For example, the analysis unit can prioritize the analysis of important data specified by advertisers. For example, the analysis unit can prioritize the analysis of data related to user behavior. By adjusting the order of analysis based on the relevance of the data, more effective analysis 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 the relevance of the data into an AI model and adjust the order of analysis.

[0050] The generation unit can adjust the level of detail in ad generation based on the importance of the ad components. For example, the generation unit can perform detailed generation for important ad components and simplified generation for less important components. For example, the generation unit can perform detailed generation for important components specified by the advertiser and simplified generation for components not specified. For example, the generation unit can perform detailed generation for components of high user interest and simplified generation for components of low user interest. By adjusting the level of detail in generation based on the importance of the ad components, more effective ads can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of ad components into an AI model and adjust the level of detail in generation.

[0051] The generation unit can apply different generation algorithms depending on the category of the ad part when generating ads. For example, the generation unit can apply a video generation algorithm to video ad parts. For example, the generation unit can apply an interactive generation algorithm to interactive ad parts. For example, the generation unit can apply a static generation algorithm to static ad parts. By applying different generation algorithms depending on the category of the ad part, more effective ads can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the ad part into an AI model and apply an appropriate generation algorithm.

[0052] The generation unit can determine the generation priority based on the registration date of the ad parts when generating ads. For example, the generation unit can prioritize the generation of the newest ad parts and postpone older parts. For example, the generation unit can prioritize the generation of parts registered during the ad campaign period. For example, the generation unit can prioritize the generation of important parts specified by the advertiser. This allows for the generation of more effective ads by determining the generation priority based on the registration date of the ad parts. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the registration date of the ad parts into an AI model to determine the generation priority.

[0053] The generation unit can adjust the generation order based on the relevance of ad components when generating ads. For example, the generation unit can prioritize the generation of components related to the user's interests. For example, the generation unit can prioritize the generation of important components specified by the advertiser. For example, the generation unit can prioritize the generation of components related to the user's behavior. By adjusting the generation order based on the relevance of ad components, more effective ads can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of ad components into an AI model and adjust the generation order.

[0054] The feedback unit can optimize its feedback algorithm by referring to past performance data during the feedback process. For example, the feedback unit can adjust the feedback algorithm based on past advertising performance data. For example, the feedback unit can optimize the feedback algorithm based on key performance metrics specified by the advertiser. For example, the feedback unit can improve the feedback algorithm by referring to user response data. This allows for more effective feedback by optimizing the feedback algorithm by referring to past performance data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past performance data into an AI model to optimize the feedback algorithm.

[0055] The feedback unit can apply different feedback methods to each ad category when providing feedback. For example, the feedback unit can provide feedback to video ads based on viewing time and click-through rate. For example, the feedback unit can provide feedback to interactive ads based on user engagement data. For example, the feedback unit can provide feedback to static ads based on impressions and click-through rate. This allows for more effective feedback by applying different feedback methods to each ad category. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the ad category into an AI model and apply an appropriate feedback method.

[0056] The feedback unit can weight the feedback based on when the advertising performance data was collected. For example, the feedback unit can prioritize the inclusion of the most recent performance data in the feedback. For example, the feedback unit can give more weight to the data during the advertising campaign period when providing feedback. For example, the feedback unit can give more weight to the data during a period designated by the advertiser when providing feedback. This allows for more effective feedback by weighting the feedback based on when the advertising performance data was collected. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the timing of advertising performance data collection into an AI model and weight the feedback.

[0057] The feedback unit can provide feedback by referring to relevant market data for the advertisement. For example, the feedback unit can provide feedback by referring to trend data for the market targeted by the advertisement. For example, the feedback unit can provide feedback by referring to the advertising performance data of competitors. For example, the feedback unit can provide feedback by referring to seasonal market fluctuation data. This allows for more effective feedback by referring to relevant market data for the advertisement. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input relevant market data for the advertisement into an AI model and provide feedback.

[0058] The reporting unit can optimize its reporting algorithm by referring to past reporting data when generating reports. For example, the reporting unit can adjust its reporting algorithm based on past reporting data. For example, the reporting unit can optimize its reporting algorithm based on key reporting metrics specified by advertisers. For example, the reporting unit can improve its reporting algorithm by referring to user response data. This allows for the provision of more effective reports by optimizing the reporting algorithm by referring to past reporting data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting data into an AI model to optimize its reporting algorithm.

[0059] The reporting unit can apply different reporting methods to each ad category when generating reports. For example, the reporting unit can provide reports for video ads based on viewing time and click-through rates. For example, the reporting unit can provide reports for interactive ads based on user engagement data. For example, the reporting unit can provide reports for static ads based on impressions and click-through rates. This allows for more effective reports to be provided by applying different reporting methods to each ad category. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input ad categories into an AI model and apply an appropriate reporting method.

[0060] The reporting unit can weight reports based on when the results of advertising changes were collected. For example, the reporting unit can prioritize the most recent changes in the report. For example, the reporting unit can prioritize changes during the advertising campaign period in the report. For example, the reporting unit can prioritize changes during a period of importance specified by the advertiser in the report. This allows for more effective reports by weighting them based on when the results of advertising changes were collected. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the collection timing of advertising change results into an AI model and weight the report.

[0061] The reporting unit can generate reports by referencing relevant market data for advertising. For example, the reporting unit can generate reports by referencing trend data for the market targeted by the advertising. For example, the reporting unit can generate reports by referencing the advertising performance data of competitors. For example, the reporting unit can generate reports by referencing seasonal market fluctuation data. By referencing relevant market data for advertising, a more effective report can be provided. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input relevant market data for advertising into an AI model and generate a report.

[0062] The reporting unit can provide customized reports based on advertiser requests when generating reports. For example, the reporting unit can customize reports based on specific metrics specified by the advertiser. For example, the reporting unit can provide reports that emphasize data for a specific period requested by the advertiser. For example, the reporting unit can provide reports that include specific market data of interest to the advertiser. By providing customized reports based on advertiser requests, more effective reports can be provided. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input advertiser requests into an AI model and generate customized reports.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can analyze the user's past browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites that the user frequently visits. For example, the data collection unit can focus on collecting data related to topics that the user has shown interest in in the past. For example, if the user tends to access the site during certain time periods, the data collection unit can concentrate data collection during those times. This allows the optimal data collection method to be selected by analyzing the user's past browsing history. 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 browsing history into an AI model and select the optimal data collection method.

[0065] The feedback unit can collect advertising performance data and continuously improve the AI ​​model. For example, the feedback unit can collect performance data such as the click-through rate and conversion rate of ads. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. In this way, the effectiveness of the ads can be maximized by collecting advertising performance data and continuously improving the AI ​​model. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input advertising performance data into the AI ​​model and perform model retraining.

[0066] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is at work, the data collection unit can prioritize collecting work-related data. For example, if the user is on vacation, the data collection unit can collect data related to travel and leisure. For example, if the user is working on a specific project, the data collection unit can collect data related to that project. This allows for the collection of more relevant data by filtering the data based on the user's current activities and areas of interest. 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 current activities and areas of interest into an AI model and perform data filtering.

[0067] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a concise analysis on less important data. For example, the analysis unit can perform a detailed analysis on data of high user interest and a concise analysis on data of low interest. For example, the analysis unit can perform a detailed analysis on important data specified by the advertiser and a concise analysis on data not specified. By adjusting the level of detail of the analysis based on the importance of the data, more effective analysis 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 the importance of the data into an AI model and adjust the level of detail of the analysis.

[0068] The generation unit can adjust the level of detail in ad generation based on the importance of the ad components. For example, the generation unit can perform detailed generation for important ad components and simplified generation for less important components. For example, the generation unit can perform detailed generation for important components specified by the advertiser and simplified generation for components not specified. For example, the generation unit can perform detailed generation for components of high user interest and simplified generation for components of low user interest. By adjusting the level of detail in generation based on the importance of the ad components, more effective ads can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of ad components into an AI model and adjust the level of detail in generation.

[0069] The reporting unit can apply different reporting methods to each ad category when generating reports. For example, the reporting unit can provide reports for video ads based on viewing time and click-through rates. For example, the reporting unit can provide reports for interactive ads based on user engagement data. For example, the reporting unit can provide reports for static ads based on impressions and click-through rates. This allows for more effective reports to be provided by applying different reporting methods to each ad category. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input ad categories into an AI model and apply an appropriate reporting method.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects user data. The data collection unit can collect data such as the user's "browsing history," "searches," and "selections" in real time. For example, the data collection unit collects data such as the URLs of websites the user has visited, keywords entered into search engines, and links clicked. The data collection unit can also collect data such as detailed information about products the user has viewed and purchase history. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the user's interests, concerns, and emotions. The analysis unit can, for example, use machine learning algorithms to analyze the user's interests and concerns. For example, the analysis unit can identify topics and products that the user is interested in based on the user's search history and browsing history. The analysis unit can also determine the user's emotions using sentiment analysis algorithms. For example, the analysis unit can analyze the user's text data to determine positive and negative emotions. Step 3: The generation unit selects the optimal ad components based on the results determined by the analysis unit and generates the ad. For example, the generation unit can generate the optimal combination of ad components registered in advance by a company based on the user's interests. The generation unit can generate dynamic ad formats such as video ads and interactive ads. In addition, the generation unit can also generate static ad formats such as banner ads and text ads. Step 4: The feedback unit collects performance data from the ads generated by the generation unit and improves the AI ​​model. The feedback unit can collect performance data such as click-through rates and conversion rates for ads. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. Step 5: The reporting unit reports the results of the ad changes based on the AI ​​model improved by the feedback unit. The reporting unit can, for example, report ad performance data as graphs and statistical data, providing advertisers with new perspectives and viewpoints.

[0072] (Example of form 2) The adaptive ad system according to an embodiment of the present invention is a next-generation advertising system that uses AI to dynamically change ad content based on the user's interests. This adaptive ad system provides ads optimized for individual users, maximizing engagement and advertising effectiveness. Specifically, it has the following six functions and features. For example, the adaptive ad system collects the user's "browsing history," "searches," and "selections" in real time. Next, based on the collected data, the adaptive ad system analyzes the user's "interests," "concerns," and "emotions" and selects the most suitable ad components. Furthermore, the adaptive ad system uses AI to generate the optimal combination from ad components registered in advance by the company, updating the ad each time. For example, it supports dynamic formats such as video ads and interactive ads. The adaptive ad system can also generate static ad formats such as banner ads and text ads. Next, the adaptive ad system collects ad performance data and continuously improves the AI ​​model. Finally, the adaptive ad system reports the results of the ad changes, providing advertisers with new perspectives and viewpoints. This system provides users with highly relevant information through personalized ads, reducing stress and improving engagement. Businesses can maximize advertising effectiveness with user-optimized ads and discover new marketing strategies by analyzing ad performance data. They can also see the results of ad changes in real time. For example, if a user previously searched for "skincare products" and viewed related product pages, their next visit will display ads for "serums" and "face masks." Furthermore, ads containing information such as "skincare tips" and "seasonal skincare methods" are generated based on the user's interests. Specifically, video ads may display how to use serums and user reviews, and interactive ads may provide users with tools to choose skincare products that suit their skin type. Additionally, banner ads may display discount information for specific serums, and text ads may provide "points to consider when choosing skincare products" and "skincare routines."This allows the adaptive ad system to maximize advertising effectiveness by collecting and analyzing user data, generating optimal ads, and continuously improving through feedback.

[0073] The adaptive ad system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a feedback unit, and a reporting unit. The collection unit collects user data. The collection unit can collect data such as the user's "browsing history," "searches," and "selections" in real time. For example, the collection unit collects data such as the URLs of websites the user has visited, keywords entered into search engines, and links clicked. The collection unit can also collect data such as detailed information about products the user has viewed and purchase history. The analysis unit analyzes the data collected by the collection unit to determine the user's interests, concerns, and emotions. For example, the analysis unit can analyze the user's interests using machine learning algorithms. For example, the analysis unit identifies topics and products that the user is interested in based on the user's search history and browsing history. The analysis unit can also determine the user's emotions using sentiment analysis algorithms. For example, the analysis unit analyzes the user's text data to determine positive and negative emotions. The generation unit selects the most suitable ad components based on the results determined by the analysis unit and generates the ad. The generation unit can, for example, generate the optimal combination of ad components pre-registered by a company based on the user's interests and preferences. The generation unit can generate dynamic ad formats such as video ads and interactive ads. It can also generate static ad formats such as banner ads and text ads. The feedback unit collects performance data of the ads generated by the generation unit and improves the AI ​​model. The feedback unit can collect performance data such as click-through rates and conversion rates of ads. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. The reporting unit reports the results of the ad changes based on the AI ​​model improved by the feedback unit. The reporting unit can report the ad performance data as graphs and statistical data, for example, providing advertisers with new perspectives and viewpoints.As a result, the adaptive ad system according to the embodiment can maximize advertising effectiveness by collecting and analyzing user data, generating optimal advertisements, and continuously improving them through feedback.

[0074] The data collection unit collects user data. For example, the data collection unit can collect data such as users' "browsing history," "searches," and "selections" in real time. Specifically, it collects data such as the URLs of websites visited by users, keywords entered into search engines, and links clicked. This allows for detailed tracking of users' online behavior and understanding the interests and preferences of individual users. The data collection unit can also collect data such as detailed information about products viewed by users and their purchase history. For example, it collects the categories and price ranges of products viewed by users on online shopping sites, as well as a list of purchased items. This allows for understanding users' purchasing trends and consumption patterns, enabling more accurate advertising targeting. Furthermore, the data collection unit can also collect data such as social media activity and comments and reviews posted by users. This allows for understanding users' emotions and opinions, providing valuable information for optimizing the content and format of advertisements. By centrally managing this data and providing it to the analysis and generation units in real time, the data collection unit can improve the overall efficiency and accuracy of the system.

[0075] The analysis unit analyzes the data collected by the collection unit to determine the user's interests, concerns, and emotions. For example, the analysis unit can use machine learning algorithms to analyze user interests. Specifically, it identifies topics and products that users are interested in based on their search and browsing history. For example, it analyzes keywords that users frequently search for and the content of web pages they spend a long time viewing to identify areas of interest. The analysis unit can also determine user emotions using sentiment analysis algorithms. For example, it analyzes user text data to determine positive and negative emotions. This allows the system to understand what emotions users are feeling and adjust the content and tone of advertisements accordingly. Furthermore, the analysis unit can analyze user behavior patterns and activity at different times of the day to identify the optimal timing for ad delivery. For example, if a user frequently shops online during a specific time period, delivering ads to that time period can maximize advertising effectiveness. The analysis unit provides these analysis results to the generation unit, which uses them as basic data for generating optimal advertisements.

[0076] The generation unit selects the optimal ad components based on the results determined by the analysis unit and generates the ad. For example, the generation unit can generate the optimal combination of ad components registered in advance by a company, based on the user's interests. Specifically, it selects ad components related to products or services that the user is interested in and combines them to generate a single ad. The generation unit can generate dynamic ad formats, such as video ads and interactive ads. This makes it easier to attract the user's attention and increase engagement. The generation unit can also generate static ad formats, such as banner ads and text ads. This allows it to support different ad formats and provide ads that are optimal for the user's browsing environment and device. Furthermore, the generation unit can customize the design and message of the ad to generate personalized ads that match the user's interests and emotions. For example, if a user has positive emotions, it uses bright designs and messages that emphasize those emotions. On the other hand, if a user has negative emotions, it uses designs and messages that provide a sense of security. The generation unit can generate these ads in real time and deliver them to users immediately.

[0077] The feedback unit collects performance data from ads generated by the generation unit and uses it to improve the AI ​​model. For example, the feedback unit can collect performance data such as click-through rates and conversion rates. Specifically, it collects data such as the number of times users clicked on an ad and the number of times they made a purchase through the ad, and evaluates the effectiveness of the ad. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. For example, it identifies ad components with low click-through rates and ad components with high conversion rates, and uses this data to optimize the AI ​​model. This allows for the selection of more effective ad components in the next ad generation. Furthermore, the feedback unit can also collect user feedback and comments to help improve the ads. For example, it collects what users think of the ads and adjusts the ad design and message based on that information. The feedback unit provides this data to the analysis and generation units, enabling continuous improvement of the overall system's accuracy and effectiveness.

[0078] The reporting department reports on the results of ad changes based on the AI ​​model improved by the feedback department. For example, the reporting department can report ad performance data as graphs and statistical data, providing advertisers with new perspectives and viewpoints. Specifically, it can graph the trends in ad click-through rates and conversion rates to visually show which ad components were most effective. It can also compile changes in user interests and preferences as statistical data and provide it to advertisers. This allows advertisers to understand which ad strategies are effective and use this information for future ad campaigns. Furthermore, the reporting department can update ad performance data in real time, providing the latest information. For example, it can monitor the progress of ad campaigns in real time and adjust ad strategies as needed. The reporting department can also provide customized reports according to the advertiser's requests. For example, it can analyze ad performance in detail for a specific period or region and provide the results as a report. In this way, the reporting department can provide valuable information to advertisers and help maximize the effectiveness of their ad campaigns.

[0079] The data collection unit can collect user data such as browsing history, searches, and selections in real time. For example, the data collection unit can collect data such as the URLs of websites visited by the user, keywords entered into search engines, and links clicked. The data collection unit can also collect data such as detailed information on products viewed by the user and purchase history. This allows for more accurate analysis by collecting user behavior data in real time. 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 collect user behavior data in real time, input it into an AI model, and output the analysis results.

[0080] The analysis unit can determine a user's "interests," "concerns," and "emotions" based on the collected data. For example, the analysis unit can analyze a user's interests and concerns using machine learning algorithms. For example, the analysis unit can identify topics and products that a user is interested in based on their search and browsing history. The analysis unit can also determine a user's emotions using sentiment analysis algorithms. For example, the analysis unit can analyze a user's text data to determine positive and negative emotions. This allows for the generation of more personalized advertisements by accurately determining a user's interests, concerns, and emotions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user data into an AI model and output the results of its interest, concern, and emotion determination.

[0081] The generation unit can generate the optimal combination from advertising parts registered in advance by a company, and update the advertisement each time. For example, the generation unit can generate the optimal combination from advertising parts registered in advance by a company based on the user's interests and preferences. The generation unit can generate dynamic advertisements such as video advertisements and interactive advertisements. In addition, the generation unit can also generate static advertisements such as banner advertisements and text advertisements. This ensures that the latest information is always provided to the user by updating the advertisement each time. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input advertising parts registered in advance by a company into a generation AI and generate the optimal combination.

[0082] The generation unit can generate dynamic forms of advertising, such as video ads and interactive ads. For example, the generation unit can generate video ads. For example, the generation unit can generate interactive ads. For example, the generation unit can generate interactive ads that provide users with a tool to choose skincare products that suit their skin type. By generating dynamic forms of advertising, user engagement can be increased. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI perform the generation of video ads and interactive ads.

[0083] The generation unit can generate static advertisements such as banner ads and text ads. For example, the generation unit can generate banner ads. For example, the generation unit can generate text ads. For example, the generation unit can generate banner ads that display discount information for a specific beauty serum. For example, the generation unit can generate text ads that provide "tips for choosing skincare products" or "skincare routines." By generating static advertisements, it is possible to provide advertisements based on user interests. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can have a generation AI perform the generation of banner ads and text ads.

[0084] The feedback unit can collect advertising performance data and continuously improve the AI ​​model. For example, the feedback unit can collect performance data such as the click-through rate and conversion rate of ads. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. In this way, the effectiveness of the ads can be maximized by collecting advertising performance data and continuously improving the AI ​​model. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input advertising performance data into the AI ​​model and perform model retraining.

[0085] The reporting unit can report the results of changes to advertisements, providing advertisers with new perspectives and viewpoints. For example, the reporting unit can report advertisement performance data as graphs and statistical data, providing advertisers with new perspectives and viewpoints. For example, the reporting unit can create reports that evaluate the effectiveness of advertisements based on performance data such as click-through rates and conversion rates. In this way, by reporting the results of changes to advertisements, it can provide advertisers with new perspectives and viewpoints. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input advertisement performance data into an AI model and perform report generation.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce data collection and collect data when the user is relaxed. For example, if the user is excited, the data collection unit can collect data in real time and reflect it immediately. For example, if the user is tired, the data collection unit can temporarily stop data collection and resume it after the user has rested. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI model and adjust the timing of data collection.

[0087] The data collection unit can analyze the user's past browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites that the user frequently visits. For example, the data collection unit can focus on collecting data related to topics that the user has shown interest in in the past. For example, if the user tends to access the site during certain time periods, the data collection unit can concentrate data collection during those times. This allows the optimal data collection method to be selected by analyzing the user's past browsing history. 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 browsing history into an AI model and select the optimal data collection method.

[0088] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is at work, the data collection unit can prioritize collecting work-related data. For example, if the user is on vacation, the data collection unit can collect data related to travel and leisure. For example, if the user is working on a specific project, the data collection unit can collect data related to that project. This allows for the collection of more relevant data by filtering the data based on the user's current activities and areas of interest. 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 current activities and areas of interest into an AI model and perform data filtering.

[0089] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit can prioritize collecting data related to topics of interest. For example, if the user is stressed, the data collection unit can prioritize collecting data that helps reduce stress. For example, if the user is excited, the data collection unit can prioritize collecting data related to entertainment. This allows for more effective data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI model to determine the priority of the data.

[0090] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific city, the data collection unit can collect event information related to that city. For example, if the user is traveling, the data collection unit can collect tourist information and restaurant information for the travel destination. For example, if the user is at home, the data collection unit can collect data about nearby shops and services. This allows for the collection of more relevant data by 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 an AI model and prioritize the collection of highly relevant data.

[0091] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to posts that a user has "liked" on social media. For example, the data collection unit can collect data based on the content of posts from accounts that a user follows. For example, the data collection unit can collect data related to content that a user has shared. This allows for the collection of more relevant data by analyzing a 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 user social media activity data into an AI model and collect relevant data.

[0092] 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 relaxed, the analysis unit can provide detailed analysis results. For example, if the user is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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-described processes 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 an AI model and adjust the presentation of the analysis.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a concise analysis on less important data. For example, the analysis unit can perform a detailed analysis on data of high user interest and a concise analysis on data of low interest. For example, the analysis unit can perform a detailed analysis on important data specified by the advertiser and a concise analysis on data not specified. By adjusting the level of detail of the analysis based on the importance of the data, more effective analysis 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 the importance of the data into an AI model and adjust the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an interest analysis algorithm to data related to user interests. For example, the analysis unit can apply an emotion analysis algorithm to data related to user emotions. For example, the analysis unit can apply an action analysis algorithm to data related to user behavior. By applying different analysis algorithms depending on the data category, more accurate analysis 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 the data category into an AI model and apply an appropriate analysis algorithm.

[0095] 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 can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 an AI model and adjust the length of the analysis.

[0096] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. For example, the analysis unit can prioritize the analysis of data collected during a specific period. For example, the analysis unit can prioritize the analysis of data collected during an advertising campaign. This allows for more effective analysis by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into an AI model to determine the priority of analysis.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of data related to user interests. For example, the analysis unit can prioritize the analysis of important data specified by advertisers. For example, the analysis unit can prioritize the analysis of data related to user behavior. By adjusting the order of analysis based on the relevance of the data, more effective analysis 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 the relevance of the data into an AI model and adjust the order of analysis.

[0098] The generation unit can estimate the user's emotions and adjust the presentation of the generated advertisements based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate advertisements with a calm tone. For example, if the user is excited, the generation unit can generate visually stimulating advertisements. For example, if the user is stressed, the generation unit can generate simple and calming advertisements. This allows for the generation of more effective advertisements by adjusting the presentation of advertisements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI model and adjust the presentation of advertisements.

[0099] The generation unit can adjust the level of detail in ad generation based on the importance of the ad components. For example, the generation unit can perform detailed generation for important ad components and simplified generation for less important components. For example, the generation unit can perform detailed generation for important components specified by the advertiser and simplified generation for components not specified. For example, the generation unit can perform detailed generation for components of high user interest and simplified generation for components of low user interest. By adjusting the level of detail in generation based on the importance of the ad components, more effective ads can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of ad components into an AI model and adjust the level of detail in generation.

[0100] The generation unit can apply different generation algorithms depending on the category of the ad part when generating ads. For example, the generation unit can apply a video generation algorithm to video ad parts. For example, the generation unit can apply an interactive generation algorithm to interactive ad parts. For example, the generation unit can apply a static generation algorithm to static ad parts. By applying different generation algorithms depending on the category of the ad part, more effective ads can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the ad part into an AI model and apply an appropriate generation algorithm.

[0101] The generation unit can estimate the user's emotions and adjust the length of the ads it generates based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point ad. If the user is relaxed, the generation unit can generate a longer ad with detailed explanations. If the user is excited, the generation unit can generate a visually stimulating ad. By adjusting the ad length based on the user's emotions, more effective ads can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI model and adjust the ad length.

[0102] The generation unit can determine the generation priority based on the registration date of the ad parts when generating ads. For example, the generation unit can prioritize the generation of the newest ad parts and postpone older parts. For example, the generation unit can prioritize the generation of parts registered during the ad campaign period. For example, the generation unit can prioritize the generation of important parts specified by the advertiser. This allows for the generation of more effective ads by determining the generation priority based on the registration date of the ad parts. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the registration date of the ad parts into an AI model to determine the generation priority.

[0103] The generation unit can adjust the generation order based on the relevance of ad components when generating ads. For example, the generation unit can prioritize the generation of components related to the user's interests. For example, the generation unit can prioritize the generation of important components specified by the advertiser. For example, the generation unit can prioritize the generation of components related to the user's behavior. By adjusting the generation order based on the relevance of ad components, more effective ads can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of ad components into an AI model and adjust the generation order.

[0104] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. For example, if the user is stressed, the feedback unit can provide concise and to-the-point feedback. For example, if the user is excited, the feedback unit can provide visually appealing feedback. This allows for more effective feedback by adjusting the feedback method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into an AI model and adjust the feedback method.

[0105] The feedback unit can optimize its feedback algorithm by referring to past performance data during the feedback process. For example, the feedback unit can adjust the feedback algorithm based on past advertising performance data. For example, the feedback unit can optimize the feedback algorithm based on key performance metrics specified by the advertiser. For example, the feedback unit can improve the feedback algorithm by referring to user response data. This allows for more effective feedback by optimizing the feedback algorithm by referring to past performance data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past performance data into an AI model to optimize the feedback algorithm.

[0106] The feedback unit can apply different feedback methods to each ad category when providing feedback. For example, the feedback unit can provide feedback to video ads based on viewing time and click-through rate. For example, the feedback unit can provide feedback to interactive ads based on user engagement data. For example, the feedback unit can provide feedback to static ads based on impressions and click-through rate. This allows for more effective feedback by applying different feedback methods to each ad category. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the ad category into an AI model and apply an appropriate feedback method.

[0107] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit can prioritize detailed feedback. For example, if the user is stressed, the feedback unit can prioritize concise feedback. For example, if the user is excited, the feedback unit can prioritize visually appealing feedback. This allows for more effective feedback by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into an AI model to determine the priority of feedback.

[0108] The feedback unit can weight the feedback based on when the advertising performance data was collected. For example, the feedback unit can prioritize the inclusion of the most recent performance data in the feedback. For example, the feedback unit can give more weight to the data during the advertising campaign period when providing feedback. For example, the feedback unit can give more weight to the data during a period designated by the advertiser when providing feedback. This allows for more effective feedback by weighting the feedback based on when the advertising performance data was collected. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the timing of advertising performance data collection into an AI model and weight the feedback.

[0109] The feedback unit can provide feedback by referring to relevant market data for the advertisement. For example, the feedback unit can provide feedback by referring to trend data for the market targeted by the advertisement. For example, the feedback unit can provide feedback by referring to the advertising performance data of competitors. For example, the feedback unit can provide feedback by referring to seasonal market fluctuation data. This allows for more effective feedback by referring to relevant market data for the advertisement. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input relevant market data for the advertisement into an AI model and provide feedback.

[0110] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is relaxed, the reporting unit can provide a detailed report. For example, if the user is stressed, the reporting unit can provide a concise and to-the-point report. For example, if the user is excited, the reporting unit can provide a visually appealing report. By adjusting how the report is displayed based on the user's emotions, a more effective report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input user emotion data into an AI model and adjust how the report is displayed.

[0111] The reporting unit can optimize its reporting algorithm by referring to past reporting data when generating reports. For example, the reporting unit can adjust its reporting algorithm based on past reporting data. For example, the reporting unit can optimize its reporting algorithm based on key reporting metrics specified by advertisers. For example, the reporting unit can improve its reporting algorithm by referring to user response data. This allows for the provision of more effective reports by optimizing the reporting algorithm by referring to past reporting data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting data into an AI model to optimize its reporting algorithm.

[0112] The reporting unit can apply different reporting methods to each ad category when generating reports. For example, the reporting unit can provide reports for video ads based on viewing time and click-through rates. For example, the reporting unit can provide reports for interactive ads based on user engagement data. For example, the reporting unit can provide reports for static ads based on impressions and click-through rates. This allows for more effective reports to be provided by applying different reporting methods to each ad category. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input ad categories into an AI model and apply an appropriate reporting method.

[0113] The reporting unit can estimate the user's emotions and prioritize reports based on the estimated emotions. For example, if the user is relaxed, the reporting unit can prioritize providing a detailed report. For example, if the user is stressed, the reporting unit can prioritize providing a concise report. For example, if the user is excited, the reporting unit can prioritize providing a visually appealing report. This allows for more effective reporting by prioritizing reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input user emotion data into an AI model to determine report priorities.

[0114] The reporting unit can weight reports based on when the results of advertising changes were collected. For example, the reporting unit can prioritize the most recent changes in the report. For example, the reporting unit can prioritize changes during the advertising campaign period in the report. For example, the reporting unit can prioritize changes during a period of importance specified by the advertiser in the report. This allows for more effective reports by weighting them based on when the results of advertising changes were collected. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the collection timing of advertising change results into an AI model and weight the report.

[0115] The reporting unit can generate reports by referencing relevant market data for advertising. For example, the reporting unit can generate reports by referencing trend data for the market targeted by the advertising. For example, the reporting unit can generate reports by referencing the advertising performance data of competitors. For example, the reporting unit can generate reports by referencing seasonal market fluctuation data. By referencing relevant market data for advertising, a more effective report can be provided. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input relevant market data for advertising into an AI model and generate a report.

[0116] The reporting unit can provide customized reports based on advertiser requests when generating reports. For example, the reporting unit can customize reports based on specific metrics specified by the advertiser. For example, the reporting unit can provide reports that emphasize data for a specific period requested by the advertiser. For example, the reporting unit can provide reports that include specific market data of interest to the advertiser. By providing customized reports based on advertiser requests, more effective reports can be provided. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input advertiser requests into an AI model and generate customized reports.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] 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 relaxed, the analysis unit can provide detailed analysis results. For example, if the user is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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-described processes 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 an AI model and adjust the presentation of the analysis.

[0119] The data collection unit can analyze the user's past browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites that the user frequently visits. For example, the data collection unit can focus on collecting data related to topics that the user has shown interest in in the past. For example, if the user tends to access the site during certain time periods, the data collection unit can concentrate data collection during those times. This allows the optimal data collection method to be selected by analyzing the user's past browsing history. 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 browsing history into an AI model and select the optimal data collection method.

[0120] The generation unit can estimate the user's emotions and adjust the presentation of the generated advertisements based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate advertisements with a calm tone. For example, if the user is excited, the generation unit can generate visually stimulating advertisements. For example, if the user is stressed, the generation unit can generate simple and calming advertisements. This allows for the generation of more effective advertisements by adjusting the presentation of advertisements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI model and adjust the presentation of advertisements.

[0121] The feedback unit can collect advertising performance data and continuously improve the AI ​​model. For example, the feedback unit can collect performance data such as the click-through rate and conversion rate of ads. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. In this way, the effectiveness of the ads can be maximized by collecting advertising performance data and continuously improving the AI ​​model. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input advertising performance data into the AI ​​model and perform model retraining.

[0122] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is relaxed, the reporting unit can provide a detailed report. For example, if the user is stressed, the reporting unit can provide a concise and to-the-point report. For example, if the user is excited, the reporting unit can provide a visually appealing report. By adjusting how the report is displayed based on the user's emotions, a more effective report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input user emotion data into an AI model and adjust how the report is displayed.

[0123] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is at work, the data collection unit can prioritize collecting work-related data. For example, if the user is on vacation, the data collection unit can collect data related to travel and leisure. For example, if the user is working on a specific project, the data collection unit can collect data related to that project. This allows for the collection of more relevant data by filtering the data based on the user's current activities and areas of interest. 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 current activities and areas of interest into an AI model and perform data filtering.

[0124] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a concise analysis on less important data. For example, the analysis unit can perform a detailed analysis on data of high user interest and a concise analysis on data of low interest. For example, the analysis unit can perform a detailed analysis on important data specified by the advertiser and a concise analysis on data not specified. By adjusting the level of detail of the analysis based on the importance of the data, more effective analysis 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 the importance of the data into an AI model and adjust the level of detail of the analysis.

[0125] The generation unit can adjust the level of detail in ad generation based on the importance of the ad components. For example, the generation unit can perform detailed generation for important ad components and simplified generation for less important components. For example, the generation unit can perform detailed generation for important components specified by the advertiser and simplified generation for components not specified. For example, the generation unit can perform detailed generation for components of high user interest and simplified generation for components of low user interest. By adjusting the level of detail in generation based on the importance of the ad components, more effective ads can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of ad components into an AI model and adjust the level of detail in generation.

[0126] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. For example, if the user is stressed, the feedback unit can provide concise and to-the-point feedback. For example, if the user is excited, the feedback unit can provide visually appealing feedback. This allows for more effective feedback by adjusting the feedback method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into an AI model and adjust the feedback method.

[0127] The reporting unit can apply different reporting methods to each ad category when generating reports. For example, the reporting unit can provide reports for video ads based on viewing time and click-through rates. For example, the reporting unit can provide reports for interactive ads based on user engagement data. For example, the reporting unit can provide reports for static ads based on impressions and click-through rates. This allows for more effective reports to be provided by applying different reporting methods to each ad category. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input ad categories into an AI model and apply an appropriate reporting method.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The data collection unit collects user data. The data collection unit can collect data such as the user's "browsing history," "searches," and "selections" in real time. For example, the data collection unit collects data such as the URLs of websites the user has visited, keywords entered into search engines, and links clicked. The data collection unit can also collect data such as detailed information about products the user has viewed and purchase history. Step 2: The analysis unit analyzes the data collected by the collection unit to determine the user's interests, concerns, and emotions. The analysis unit can, for example, use machine learning algorithms to analyze the user's interests and concerns. For example, the analysis unit can identify topics and products that the user is interested in based on the user's search history and browsing history. The analysis unit can also determine the user's emotions using sentiment analysis algorithms. For example, the analysis unit can analyze the user's text data to determine positive and negative emotions. Step 3: The generation unit selects the optimal ad components based on the results determined by the analysis unit and generates the ad. For example, the generation unit can generate the optimal combination of ad components registered in advance by a company based on the user's interests. The generation unit can generate dynamic ad formats such as video ads and interactive ads. In addition, the generation unit can also generate static ad formats such as banner ads and text ads. Step 4: The feedback unit collects performance data from the ads generated by the generation unit and improves the AI ​​model. The feedback unit can collect performance data such as click-through rates and conversion rates for ads. Based on the collected performance data, the feedback unit retrains the AI ​​model to improve the accuracy of the ads. Step 5: The reporting unit reports the results of the ad changes based on the AI ​​model improved by the feedback unit. The reporting unit can, for example, report ad performance data as graphs and statistical data, providing advertisers with new perspectives and viewpoints.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to determine the user's interests and emotions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal advertisement based on the analysis results. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects advertisement performance data to improve the AI ​​model. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and reports the results of the advertisement changes. 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.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, and reporting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to determine the user's interests and emotions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal advertisement based on the analysis results. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects advertisement performance data to improve the AI ​​model. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and reports the results of the advertisement changes. 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.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to determine the user's interests and emotions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal advertisement based on the analysis results. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects advertisement performance data to improve the AI ​​model. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and reports the results of the advertisement changes. 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.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, and reporting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to determine the user's interests and emotions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal advertisement based on the analysis results. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects advertisement performance data to improve the AI ​​model. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and reports the results of the advertisement changes. 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit to determine the user's interests, concerns, and emotions, Based on the results determined by the analysis unit, a generation unit selects the optimal advertising parts and generates an advertisement. A feedback unit collects the performance data of the advertisements generated by the generation unit and improves the AI ​​model, The system includes a reporting unit that reports the results of changes to advertisements based on the AI ​​model improved by the aforementioned feedback unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects user data such as "browsing history," "searches," and "selections" in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we determine the user's "interests," "concerns," and "emotions." The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The system generates the optimal combination from ad components registered in advance by the company and updates the ad each time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate dynamic forms of advertising, such as video ads and interactive ads. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generates static advertisements such as banner ads and text ads. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is We collect advertising performance data and continuously improve our AI models. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned report section is, We report on the results of changes to advertising and provide advertisers with new perspectives and viewpoints. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past browsing history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, 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 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is We estimate the user's emotions and adjust the way ads are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating ads, adjust the level of detail based on the importance of the ad components. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating ads, different generation algorithms are applied depending on the category of the ad component. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and adjusts the length of the generated ads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating ads, the generation priority is determined based on when the ad components were registered. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating ads, adjust the generation order based on the relevance of the ad components. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is During feedback, the feedback algorithm is optimized by referring to past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, different feedback methods will be applied for each ad category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, weight the feedback based on when the ad performance data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, we refer to relevant market data for advertising. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned report section is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned report section is, When generating reports, the report algorithm is optimized by referring to past report data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned report section is, When generating reports, different reporting methods are applied for each advertising category. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned report section is, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned report section is, When generating reports, the reports are weighted based on when the results of the advertising changes were collected. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned report section is, When generating reports, the report is created by referencing relevant market data for advertising. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned report section is, When generating reports, we provide customized reports based on the advertiser's requests. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0202] 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 data, An analysis unit analyzes the data collected by the aforementioned collection unit to determine the user's interests, concerns, and emotions, Based on the results determined by the analysis unit, a generation unit selects the optimal advertising parts and generates an advertisement. A feedback unit collects the performance data of the advertisements generated by the generation unit and improves the AI ​​model. The system includes a reporting unit that reports the results of changes to advertisements based on the AI ​​model improved by the feedback unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user browsing history, search, and selection data in real time. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we determine the user's interests, concerns, and emotions. The system according to feature 1.

4. The generating unit is The system generates the optimal combination from ad components registered in advance by the company and updates the ad each time. The system according to feature 1.

5. The generating unit is Generate dynamic forms of advertising, such as video ads and interactive ads. The system according to feature 1.

6. The generating unit is Generates static advertisements such as banner ads and text ads. The system according to feature 1.

7. The aforementioned feedback unit is We collect advertising performance data and continuously improve our AI models. The system according to feature 1.

8. The aforementioned report section is, We report on the results of changes to advertising and provide advertisers with new perspectives and viewpoints. The system according to feature 1.

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