system

Abstract

The system according to this embodiment aims to identify user interests and provide users with advertisements that are beneficial to them. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a presentation unit, and a reception unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit and identifies the user's interests. The generation unit generates advertisements based on the interests identified by the analysis unit. The presentation unit presents the advertisements generated by the generation unit to the user. The reception unit receives the user's response to the advertisements presented by the presentation unit.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that even if an advertiser spends a large amount of budget, the conversion is poor, and users feel stressed by advertisements that they are not interested in.

[0005] The system according to the embodiment aims to identify the interests of users and provide advertisements beneficial to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a presentation unit, and a reception unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit and identifies the user's interests. The generation unit generates advertisements based on the interests identified by the analysis unit. The presentation unit presents the advertisements generated by the generation unit to the user. The reception unit receives the user's responses to the advertisements presented by the presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify user interests and provide users with advertisements that are beneficial to them. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) An advertising distribution system according to an embodiment of the present invention is an AI agent for achieving efficient and beneficial advertising distribution for both advertisers and users. Unlike conventional push advertising, this advertising distribution system provides a mechanism for realizing pull advertising, where users pull the information they want. First, the advertising distribution system uses AI to analyze the user's usual behavior patterns and identify the user's interests. For example, the advertising distribution system collects data such as websites the user visits daily, search history, and purchase history, and the AI ​​analyzes this data. Based on this analysis, the advertising distribution system asks the user questions about information that might be of interest to them. The user answers the advertising distribution system about what they are interested in. Next, the advertising distribution system interprets the user's answers and converts them into specific products and information. For example, if the user answers that they are interested in day trips, the advertising distribution system generates advertisements related to day trips based on that information. The advertising distribution system registers the generated advertisements with the advertising system and provides them to advertisers. The advertisers analyze the advertisements generated by the advertising distribution system and propose products and solutions they currently offer. After that, the advertising distribution system presents the advertiser's proposals to the user and asks the user if they are interested. Users select only those they are interested in and submit their responses to the ad delivery system. Based on the user's responses, the ad delivery system generates more specific ads and registers them in the ad delivery system. By repeating this process, more accurate ads can be provided. As a result, users receive ads that match their interests, and advertisers can deliver ads efficiently. This creates a win-win relationship for both advertisers and users. In this way, the ad delivery system analyzes user interests and provides useful information to users as ads, reducing the workload for advertisers, and creating a win-win relationship where users receive the information they wanted directly.

[0029] The advertising distribution system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a presentation unit, and a reception unit. The collection unit collects user behavior data. For example, the collection unit collects data such as the user's website browsing history, search history, and purchase history. For example, the collection unit can collect the URLs and browsing time of websites that the user visits on a daily basis. The collection unit can also collect keywords and search dates and times that the user searches for. Furthermore, the collection unit can also collect information on products purchased by the user, the purchase date and time, and the purchase amount. The analysis unit analyzes the data collected by the collection unit to identify the user's interests. For example, the analysis unit analyzes the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit can identify interests based on the user's browsing frequency and click count. The analysis unit can also identify interests based on the user's search history and purchase history. The generation unit generates advertisements based on the interests identified by the analysis unit. For example, the generation unit generates banner ads, text ads, video ads, etc., based on the user's interests. The generation unit can, for example, generate advertisements related to day trips if the user responds that they are interested in day trips. The generation unit can also generate advertisements for specific products or services based on the user's interests. The presentation unit presents the advertisements generated by the generation unit to the user. The presentation unit can, for example, display the generated advertisements on a web page. The presentation unit can also send the generated advertisements to the user via email or push notification. Furthermore, the presentation unit can also present the generated advertisements to the user on social media. The reception unit receives the user's response to the advertisements presented by the presentation unit. The reception unit can, for example, confirm whether the user clicked on the advertisement. The reception unit can also confirm whether the user provided feedback comments on the advertisement. Furthermore, the reception unit can confirm whether the user answered a questionnaire. As a result, the advertising distribution system according to this embodiment can achieve efficient and beneficial advertising distribution by generating and presenting advertisements to the user based on user behavior data.

[0030] The data collection unit collects user behavior data. For example, the data collection unit collects data such as users' website browsing history, search history, and purchase history. Specifically, it can collect URLs of websites that users visit on a daily basis and the time spent browsing them. This allows the system to understand what kind of content users are interested in. The data collection unit can also collect keywords that users search for and the date and time of those searches. This allows the system to identify what kind of information users are looking for. Furthermore, the data collection unit can collect information about products that users have purchased, including the date and time of purchase and the purchase amount. This allows the system to understand users' purchasing trends and consumption patterns. The data collection unit collects this data in real time and transmits it to a central database. The data is transmitted using a secure communication protocol and encrypted to protect privacy. The data collection unit collects data with the user's consent and manages it appropriately in accordance with the privacy policy. This allows the data collection unit to collect user behavior data efficiently and securely, improving the overall performance of the system.

[0031] The analytics department analyzes data collected by the data collection department to identify user interests. For example, the analytics department analyzes collected data using statistical analysis and machine learning algorithms. Specifically, it can identify interests based on user browsing frequency and click counts. For instance, users who frequently visit a particular website can be determined to be interested in the content of that website. The analytics department can also identify interests based on user search and purchase history. For example, users who frequently search for specific keywords can be determined to be interested in information related to those keywords. Furthermore, the analytics department can use machine learning algorithms to analyze user behavior patterns and identify potential interests. For example, clustering algorithms can be used to group users with similar behavior patterns and identify the interests of each group. This allows the analytics department to quickly and accurately analyze collected data and identify user interests. Additionally, the analytics department can utilize historical data and statistical information to predict long-term changes in interests and formulate future advertising strategies. This enables the analytics department to not only understand the situation in real time but also to formulate long-term advertising strategies, improving the overall reliability and effectiveness of the system.

[0032] The generation unit generates advertisements based on interests identified by the analysis unit. For example, the generation unit generates banner ads, text ads, and video ads based on user interests. Specifically, if a user answers that they are interested in day trips, the generation unit can generate advertisements related to day trips. The generation unit uses natural language processing and image generation technologies to automatically generate the content and design of advertisements. For example, it can use generation AI to generate ad copy tailored to the user's interests and image generation technology to generate related images and videos. The generation unit can also generate advertisements for specific products or services based on user interests. For example, if a user answers that they are interested in outdoor equipment, it can generate advertisements related to outdoor equipment. The generation unit can also optimize the timing and location of advertisement display to maximize the effectiveness of the advertisements. This allows the generation unit to generate effective advertisements based on user interests and improve the overall advertising effectiveness of the system.

[0033] The presentation unit presents the advertisements generated by the generation unit to the user. For example, the presentation unit can display the generated advertisements on a web page. Specifically, it can display the advertisement in an appropriate location on the web page the user is viewing to attract the user's attention. The presentation unit can also send the generated advertisements to the user via email or push notification. For example, it can send the advertisement to the email address registered by the user and display the advertisement on the user's smartphone via push notification. Furthermore, the presentation unit can also present the generated advertisements to the user on social media. For example, it can display the advertisement in the feed of the social media that the user is using to attract the user's interest. The presentation unit can maximize user interest by optimizing the frequency and timing of advertisement display. In this way, the presentation unit can effectively present the generated advertisements to the user and improve the overall advertising effectiveness of the system.

[0034] The reception unit receives user responses to advertisements presented by the presentation unit. For example, the reception unit can verify whether a user clicked on an advertisement. Specifically, it can record the number of clicks and the date and time of the clicks to evaluate the advertisement's effectiveness. The reception unit can also verify whether a user provided feedback comments on an advertisement. For example, if a user leaves a comment on an advertisement, this comment can be collected and used to improve the advertisement. Furthermore, the reception unit can verify whether a user has responded to a survey. For example, by presenting a survey related to the advertisement and collecting user responses, the effectiveness of the advertisement and user interest can be evaluated. The reception unit collects this data in real time and transmits it to a central database. The data is transmitted using a secure communication protocol and encrypted for privacy protection. This allows the reception unit to efficiently and securely collect user responses and improve the overall advertising effectiveness of the system.

[0035] The data collection unit can collect data such as the user's website browsing history, search history, and purchase history. For example, the data collection unit can collect the user's website browsing history. For example, the data collection unit can collect the URLs and viewing times of the pages the user viewed. The data collection unit can also collect the user's search history. For example, the data collection unit can collect the keywords and search dates and times the user searched. Furthermore, the data collection unit can also collect the user's purchase history. For example, the data collection unit can collect information on the products the user purchased, the purchase date and time, and the purchase amount. In this way, the data collection unit can provide data to identify the user's interests by collecting user behavior data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the user's website browsing history, search history, and purchase history into AI, and the AI ​​can collect the data.

[0036] The analysis unit can analyze the collected data and identify user interests. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit can identify interests based on the user's browsing frequency or click count. The analysis unit can also identify interests based on the user's search history or purchase history. In this way, the analysis unit can identify user interests by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze the data.

[0037] The generation unit can generate specific advertisements based on user interests. For example, the generation unit can generate banner ads, text ads, video ads, etc., based on user interests. For example, if a user answers that they are interested in day trips, the generation unit can generate advertisements related to day trips. The generation unit can also generate advertisements for specific products or services based on user interests. In this way, the generation unit can provide advertisements that are beneficial to the user by generating advertisements based on user interests. 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 interest data into AI, and the AI ​​can generate advertisements.

[0038] The presentation unit can present the generated advertisement to the user and ask the user if they are interested. The presentation unit can, for example, display the generated advertisement on a web page. The presentation unit can also send the generated advertisement to the user via email or push notification. Furthermore, the presentation unit can present the generated advertisement to the user on social media. The presentation unit can, for example, check whether the user clicked on the advertisement. The presentation unit can also check whether the user provided feedback comments on the advertisement. Furthermore, the presentation unit can check whether the user answered a survey. In this way, the presentation unit can confirm the user's interest by presenting the generated advertisement to the user. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the generated advertisement into AI, and the AI ​​can present the advertisement to the user.

[0039] The reception desk can receive user responses and provide information for the AI ​​to generate more specific advertisements. For example, the reception desk can check whether the user clicked on an advertisement. It can also check whether the user provided feedback comments on the advertisement. Furthermore, it can check whether the user answered a survey. In this way, by receiving user responses, the reception desk can provide information for generating more specific advertisements. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user response data into the AI ​​and provide information for the AI ​​to generate more specific advertisements.

[0040] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can collect data from websites that the user frequently visits. For example, the data collection unit can collect relevant data based on the user's search history. The data collection unit can also collect data on products of interest based on the user's purchase history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can filter data based on keywords the user has recently searched for. For example, the data collection unit can filter data based on the categories of articles the user has viewed. The data collection unit can also filter data based on the categories of products the user has purchased. In this way, the data collection unit can collect highly relevant data by filtering data based on the user's current 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 areas of interest into the AI, and the AI ​​can filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect event information for the area where the user is currently located. For example, if the user is traveling, the data collection unit can collect tourist information for the travel destination. Furthermore, if the user is commuting, the data collection unit can collect information related to the commute route. In this way, the data collection unit can achieve efficient data collection by collecting highly relevant data by considering the user's geographical location information. 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 the AI, which can then prioritize the collection of highly relevant data.

[0043] 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 analyze the content of posts from accounts the user follows and collect relevant data. For example, the data collection unit can analyze the activities of groups the user participates in and collect relevant data. The data collection unit can also analyze the content of articles the user shares and collect relevant data. In this way, the data collection unit can collect 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 the user's social media activity data into AI, and the AI ​​can collect relevant data.

[0044] The analysis unit can adjust the level of detail of its analysis based on user behavior patterns during data analysis. For example, the analysis unit can perform a detailed analysis of data from websites that users frequently visit. For example, the analysis unit can perform a detailed analysis of relevant data based on the user's search history. Furthermore, the analysis unit can perform a detailed analysis of data on products of interest based on the user's purchase history. In this way, the analysis unit can achieve efficient data analysis by adjusting the level of detail of its analysis based on user behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user behavior pattern data into AI, which can then adjust the level of detail of its analysis.

[0045] The analysis unit can apply different analysis algorithms to data analysis depending on the user's interest categories. For example, the analysis unit can apply a specialized analysis algorithm to categories that the user is interested in. For example, the analysis unit can apply different analysis methods based on categories that the user has shown interest in. The analysis unit can also select the optimal analysis algorithm according to the user's interest categories. In this way, the analysis unit can achieve efficient data analysis by applying different analysis algorithms according to the user's interest categories. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user interest category data into AI, and the AI ​​can apply different analysis algorithms.

[0046] The analysis unit can improve the accuracy of its analysis by referring to the user's past interest data during data analysis. For example, the analysis unit can improve the accuracy of its analysis based on data that the user has shown interest in in the past. For example, the analysis unit can refer to the user's past interest data to analyze relevant data in detail. The analysis unit can also select the optimal analysis method based on the user's past interest data. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past interest data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past interest data into AI, and the AI ​​can improve the accuracy of its analysis.

[0047] The analysis unit can perform data analysis by referring to the user's relevant market data. For example, the analysis unit can perform analysis by referring to market data that the user is interested in. For example, the analysis unit can perform detailed analysis of relevant data based on the user's market of interest. The analysis unit can also select the optimal analysis method based on the user's market of interest data. In this way, the analysis unit can achieve efficient data analysis by referring to the user's relevant market data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's relevant market data into AI, and the AI ​​can perform the analysis.

[0048] The generation unit can adjust the level of detail in ads based on the user's level of interest when generating ads. For example, if the user shows high interest, the generation unit can generate an ad with detailed information. For example, if the user shows low interest, the generation unit can generate an ad with concise information. The generation unit can also select the optimal level of detail in the ad according to the user's level of interest. In this way, the generation unit can achieve efficient ad generation by adjusting the level of detail in the ad based on the user's level of interest. 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 interest data into AI, and the AI ​​can adjust the level of detail in the ad.

[0049] The generation unit can apply different generation algorithms depending on the user's interest categories when generating ads. For example, the generation unit can apply a specialized generation algorithm to categories that the user is interested in. For example, the generation unit can apply different generation methods based on categories that the user has shown interest in. The generation unit can also select the optimal generation algorithm depending on the user's interest categories. In this way, the generation unit can achieve efficient ad generation by applying different generation algorithms depending on the user's interest categories. 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 interest category data into AI, and the AI ​​can apply different generation algorithms.

[0050] The generation unit can improve the accuracy of ad generation by referring to the user's past ad response data. For example, the generation unit can improve the accuracy of generation based on ads that the user has shown interest in in the past. For example, the generation unit can generate relevant ads in detail by referring to the user's past ad response data. The generation unit can also select the optimal generation method based on the user's past ad response data. In this way, the generation unit can improve the accuracy of generation by referring to the user's past ad response data. 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 user's past ad response data into AI, and the AI ​​can improve the accuracy of generation.

[0051] The generation unit can generate advertisements by referencing the user's relevant market data. For example, the generation unit can generate advertisements by referencing data on markets that the user is interested in. For example, the generation unit can generate relevant advertisements in detail based on the user's market interests. The generation unit can also select the optimal generation method based on the user's market interest data. In this way, the generation unit can achieve efficient advertisement generation by referencing the user's relevant market data. 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 user's relevant market data into AI, and the AI ​​can perform the generation.

[0052] The presentation unit can select the optimal presentation method by referring to the user's past ad response data when presenting an ad. For example, the presentation unit can select the optimal presentation method based on ads the user has shown interest in in the past. For example, the presentation unit can refer to the user's past ad response data to present relevant ads in detail. The presentation unit can also select the optimal presentation method based on the user's past ad response data. In this way, the presentation unit can select the optimal presentation method by referring to the user's past ad response data. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit can input the user's past ad response data into AI, and the AI ​​can select the optimal presentation method.

[0053] The display unit can adjust the level of detail displayed based on the user's level of interest when displaying advertisements. For example, if the user shows high interest, the display unit can display an advertisement containing detailed information. For example, if the user shows low interest, the display unit can display an advertisement containing concise information. The display unit can also select the optimal level of detail based on the user's level of interest. In this way, the display unit can achieve efficient advertisement display by adjusting the level of detail based on the user's level of interest. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user interest data into AI, and the AI ​​can adjust the level of detail of the display.

[0054] The display unit can select the optimal display method when displaying advertisements, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can select the optimal display method by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0055] The display unit can prioritize displaying highly relevant advertisements by considering the user's geographical location when displaying advertisements. For example, the display unit can prioritize displaying event information in the user's current location. For example, if the user is traveling, the display unit can prioritize displaying tourist information for their travel destination. Furthermore, if the user is commuting, the display unit can prioritize displaying advertisements related to their commute route. In this way, the display unit can achieve efficient advertisement display by prioritizing highly relevant advertisements by considering the user's geographical location. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location information into AI, which can then prioritize displaying highly relevant advertisements.

[0056] The reception unit can select the optimal reception method by referring to the user's past response data when receiving a response. For example, the reception unit can select the optimal reception method based on the response methods the user has shown interest in in the past. For example, the reception unit can refer to the user's past response data to present relevant response methods in detail. The reception unit can also select the optimal reception method based on the user's past response data. In this way, the reception unit can select the optimal reception method by referring to the user's past response data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past response data into AI, and the AI ​​can select the optimal reception method.

[0057] The reception unit can adjust the level of detail in the response based on the user's level of interest. For example, if the user shows a high level of interest, the reception unit can provide an interface requesting a detailed response. For example, if the user shows a low level of interest, the reception unit can provide an interface requesting a concise response. The reception unit can also select the optimal level of detail in the response according to the user's level of interest. In this way, the reception unit can achieve efficient response reception by adjusting the level of detail in the response based on the user's level of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user interest data into the AI, and the AI ​​can adjust the level of detail in the response.

[0058] The reception unit can select the optimal reception method when receiving a response, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method that matches the screen size. If the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a concise and highly visible reception method. In this way, the reception unit can select the optimal reception method by taking into account the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's device information into the AI, and the AI ​​can select the optimal reception method.

[0059] The reception desk can prioritize receiving responses that are highly relevant, taking into account the user's geographical location. For example, the reception desk can prioritize responses related to event information in the user's current location. If the user is traveling, the reception desk can prioritize responses related to tourist information in their travel destination. If the user is commuting, the reception desk can also prioritize responses related to their commute route. This allows the reception desk to efficiently receive responses by prioritizing highly relevant responses while considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize receiving highly relevant responses.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can adjust the timing of data collection considering the battery level of the user's device. For example, it can refrain from collecting data when the battery level is low and collect data when charging or when the battery level is sufficient. The data collection unit can also prioritize data collection when the user's device is connected to Wi-Fi. This allows the data collection unit to achieve efficient data collection according to the status of the user's device.

[0062] The analytics department can identify ad patterns that users are likely to be interested in by referring to their past ad click history. For example, it can analyze the content and format of ads that users have clicked in the past and generate similar ads. Furthermore, if the analytics department finds that users tend to click during specific time periods, it can present ads accordingly. This allows the analytics department to leverage users' past behavioral data to deliver more effective ads.

[0063] The ad generation unit can dynamically change the ad display format based on user interests. For example, if a user shows a high interest in video ads, it can prioritize generating video ads. It can also generate text ads if the user prefers text ads. Furthermore, the generation unit can customize the ad's color and design according to user interests. This allows the generation unit to provide ads tailored to user preferences.

[0064] The display unit can adjust the ad layout according to the screen size of the user's device. For example, it can use a simple layout on a small smartphone screen and a layout with detailed information on a larger tablet or desktop screen. Furthermore, the display unit can change how the ad is displayed depending on whether the user's device is in portrait or landscape orientation. This allows the display unit to achieve ad display optimized for the user's device.

[0065] The reception desk can dynamically change the content of the next questions presented based on the user's responses. For example, if a user shows interest in a particular product, it can present detailed questions related to that product. Conversely, if a user shows no interest in a particular category, questions related to that category can be omitted. This allows the reception desk to efficiently present questions in response to the user's answers.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects user behavior data. For example, it collects data such as the user's website browsing history, search history, and purchase history. The data collection unit can collect URLs of websites the user visits on a daily basis, browsing time, keywords searched, search date and time, information on purchased products, purchase date and time, and purchase amount. Step 2: The analysis department analyzes the data collected by the collection department to identify user interests. For example, they analyze the collected data using statistical analysis and machine learning algorithms to identify interests based on user browsing frequency, click counts, search history, and purchase history. Step 3: The generation unit generates advertisements based on the interests identified by the analysis unit. For example, it generates banner ads, text ads, video ads, etc., based on the user's interests. If a user answers that they are interested in day trips, it can generate advertisements related to day trips. Step 4: The presentation unit presents the advertisement generated by the generation unit to the user. For example, the generated advertisement can be displayed on a web page, sent to the user via email or push notification, or presented to the user on social media. Step 5: The reception desk receives user responses to the advertisements presented by the presentation desk. For example, it can check whether the user clicked on the advertisement, provided feedback comments on the advertisement, or answered a survey.

[0068] (Example of form 2) An advertising distribution system according to an embodiment of the present invention is an AI agent for achieving efficient and beneficial advertising distribution for both advertisers and users. Unlike conventional push advertising, this advertising distribution system provides a mechanism for realizing pull advertising, where users pull the information they want. First, the advertising distribution system uses AI to analyze the user's usual behavior patterns and identify the user's interests. For example, the advertising distribution system collects data such as websites the user visits daily, search history, and purchase history, and the AI ​​analyzes this data. Based on this analysis, the advertising distribution system asks the user questions about information that might be of interest to them. The user answers the advertising distribution system about what they are interested in. Next, the advertising distribution system interprets the user's answers and converts them into specific products and information. For example, if the user answers that they are interested in day trips, the advertising distribution system generates advertisements related to day trips based on that information. The advertising distribution system registers the generated advertisements with the advertising system and provides them to advertisers. The advertisers analyze the advertisements generated by the advertising distribution system and propose products and solutions they currently offer. After that, the advertising distribution system presents the advertiser's proposals to the user and asks the user if they are interested. Users select only those they are interested in and submit their responses to the ad delivery system. Based on the user's responses, the ad delivery system generates more specific ads and registers them in the ad delivery system. By repeating this process, more accurate ads can be provided. As a result, users receive ads that match their interests, and advertisers can deliver ads efficiently. This creates a win-win relationship for both advertisers and users. In this way, the ad delivery system analyzes user interests and provides useful information to users as ads, reducing the workload for advertisers, and creating a win-win relationship where users receive the information they wanted directly.

[0069] The advertising distribution system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a presentation unit, and a reception unit. The collection unit collects user behavior data. For example, the collection unit collects data such as the user's website browsing history, search history, and purchase history. For example, the collection unit can collect the URLs and browsing time of websites that the user visits on a daily basis. The collection unit can also collect keywords and search dates and times that the user searches for. Furthermore, the collection unit can also collect information on products purchased by the user, the purchase date and time, and the purchase amount. The analysis unit analyzes the data collected by the collection unit to identify the user's interests. For example, the analysis unit analyzes the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit can identify interests based on the user's browsing frequency and click count. The analysis unit can also identify interests based on the user's search history and purchase history. The generation unit generates advertisements based on the interests identified by the analysis unit. For example, the generation unit generates banner ads, text ads, video ads, etc., based on the user's interests. The generation unit can, for example, generate advertisements related to day trips if the user responds that they are interested in day trips. The generation unit can also generate advertisements for specific products or services based on the user's interests. The presentation unit presents the advertisements generated by the generation unit to the user. The presentation unit can, for example, display the generated advertisements on a web page. The presentation unit can also send the generated advertisements to the user via email or push notification. Furthermore, the presentation unit can also present the generated advertisements to the user on social media. The reception unit receives the user's response to the advertisements presented by the presentation unit. The reception unit can, for example, confirm whether the user clicked on the advertisement. The reception unit can also confirm whether the user provided feedback comments on the advertisement. Furthermore, the reception unit can confirm whether the user answered a questionnaire. As a result, the advertising distribution system according to this embodiment can achieve efficient and beneficial advertising distribution by generating and presenting advertisements to the user based on user behavior data.

[0070] The data collection unit collects user behavior data. For example, the data collection unit collects data such as users' website browsing history, search history, and purchase history. Specifically, it can collect URLs of websites that users visit on a daily basis and the time spent browsing them. This allows the system to understand what kind of content users are interested in. The data collection unit can also collect keywords that users search for and the date and time of those searches. This allows the system to identify what kind of information users are looking for. Furthermore, the data collection unit can collect information about products that users have purchased, including the date and time of purchase and the purchase amount. This allows the system to understand users' purchasing trends and consumption patterns. The data collection unit collects this data in real time and transmits it to a central database. The data is transmitted using a secure communication protocol and encrypted to protect privacy. The data collection unit collects data with the user's consent and manages it appropriately in accordance with the privacy policy. This allows the data collection unit to collect user behavior data efficiently and securely, improving the overall performance of the system.

[0071] The analytics department analyzes data collected by the data collection department to identify user interests. For example, the analytics department analyzes collected data using statistical analysis and machine learning algorithms. Specifically, it can identify interests based on user browsing frequency and click counts. For instance, users who frequently visit a particular website can be determined to be interested in the content of that website. The analytics department can also identify interests based on user search and purchase history. For example, users who frequently search for specific keywords can be determined to be interested in information related to those keywords. Furthermore, the analytics department can use machine learning algorithms to analyze user behavior patterns and identify potential interests. For example, clustering algorithms can be used to group users with similar behavior patterns and identify the interests of each group. This allows the analytics department to quickly and accurately analyze collected data and identify user interests. Additionally, the analytics department can utilize historical data and statistical information to predict long-term changes in interests and formulate future advertising strategies. This enables the analytics department to not only understand the situation in real time but also to formulate long-term advertising strategies, improving the overall reliability and effectiveness of the system.

[0072] The generation unit generates advertisements based on interests identified by the analysis unit. For example, the generation unit generates banner ads, text ads, and video ads based on user interests. Specifically, if a user answers that they are interested in day trips, the generation unit can generate advertisements related to day trips. The generation unit uses natural language processing and image generation technologies to automatically generate the content and design of advertisements. For example, it can use generation AI to generate ad copy tailored to the user's interests and image generation technology to generate related images and videos. The generation unit can also generate advertisements for specific products or services based on user interests. For example, if a user answers that they are interested in outdoor equipment, it can generate advertisements related to outdoor equipment. The generation unit can also optimize the timing and location of advertisement display to maximize the effectiveness of the advertisements. This allows the generation unit to generate effective advertisements based on user interests and improve the overall advertising effectiveness of the system.

[0073] The presentation unit presents the advertisements generated by the generation unit to the user. For example, the presentation unit can display the generated advertisements on a web page. Specifically, it can display the advertisement in an appropriate location on the web page the user is viewing to attract the user's attention. The presentation unit can also send the generated advertisements to the user via email or push notification. For example, it can send the advertisement to the email address registered by the user and display the advertisement on the user's smartphone via push notification. Furthermore, the presentation unit can also present the generated advertisements to the user on social media. For example, it can display the advertisement in the feed of the social media that the user is using to attract the user's interest. The presentation unit can maximize user interest by optimizing the frequency and timing of advertisement display. In this way, the presentation unit can effectively present the generated advertisements to the user and improve the overall advertising effectiveness of the system.

[0074] The reception unit receives user responses to advertisements presented by the presentation unit. For example, the reception unit can verify whether a user clicked on an advertisement. Specifically, it can record the number of clicks and the date and time of the clicks to evaluate the advertisement's effectiveness. The reception unit can also verify whether a user provided feedback comments on an advertisement. For example, if a user leaves a comment on an advertisement, this comment can be collected and used to improve the advertisement. Furthermore, the reception unit can verify whether a user has responded to a survey. For example, by presenting a survey related to the advertisement and collecting user responses, the effectiveness of the advertisement and user interest can be evaluated. The reception unit collects this data in real time and transmits it to a central database. The data is transmitted using a secure communication protocol and encrypted for privacy protection. This allows the reception unit to efficiently and securely collect user responses and improve the overall advertising effectiveness of the system.

[0075] The data collection unit can collect data such as the user's website browsing history, search history, and purchase history. For example, the data collection unit can collect the user's website browsing history. For example, the data collection unit can collect the URLs and viewing times of the pages the user viewed. The data collection unit can also collect the user's search history. For example, the data collection unit can collect the keywords and search dates and times the user searched. Furthermore, the data collection unit can also collect the user's purchase history. For example, the data collection unit can collect information on the products the user purchased, the purchase date and time, and the purchase amount. In this way, the data collection unit can provide data to identify the user's interests by collecting user behavior data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the user's website browsing history, search history, and purchase history into AI, and the AI ​​can collect the data.

[0076] The analysis unit can analyze the collected data and identify user interests. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit can identify interests based on the user's browsing frequency or click count. The analysis unit can also identify interests based on the user's search history or purchase history. In this way, the analysis unit can identify user interests by analyzing the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze the data.

[0077] The generation unit can generate specific advertisements based on user interests. For example, the generation unit can generate banner ads, text ads, video ads, etc., based on user interests. For example, if a user answers that they are interested in day trips, the generation unit can generate advertisements related to day trips. The generation unit can also generate advertisements for specific products or services based on user interests. In this way, the generation unit can provide advertisements that are beneficial to the user by generating advertisements based on user interests. 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 interest data into AI, and the AI ​​can generate advertisements.

[0078] The presentation unit can present the generated advertisement to the user and ask the user if they are interested. The presentation unit can, for example, display the generated advertisement on a web page. The presentation unit can also send the generated advertisement to the user via email or push notification. Furthermore, the presentation unit can present the generated advertisement to the user on social media. The presentation unit can, for example, check whether the user clicked on the advertisement. The presentation unit can also check whether the user provided feedback comments on the advertisement. Furthermore, the presentation unit can check whether the user answered a survey. In this way, the presentation unit can confirm the user's interest by presenting the generated advertisement to the user. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the generated advertisement into AI, and the AI ​​can present the advertisement to the user.

[0079] The reception desk can receive user responses and provide information for the AI ​​to generate more specific advertisements. For example, the reception desk can check whether the user clicked on an advertisement. It can also check whether the user provided feedback comments on the advertisement. Furthermore, it can check whether the user answered a survey. In this way, by receiving user responses, the reception desk can provide information for generating more specific advertisements. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user response data into the AI ​​and provide information for the AI ​​to generate more specific advertisements.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data when the user is relaxed. For example, the data collection unit can refrain from collecting data when the user is stressed. The data collection unit can also collect data when the user is focused. In this way, the data collection unit can achieve efficient 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 AI, and the AI ​​can adjust the timing of data collection.

[0081] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can collect data from websites that the user frequently visits. For example, the data collection unit can collect relevant data based on the user's search history. The data collection unit can also collect data on products of interest based on the user's purchase history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI, which can then select the optimal data collection method.

[0082] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can filter data based on keywords the user has recently searched for. For example, the data collection unit can filter data based on the categories of articles the user has viewed. The data collection unit can also filter data based on the categories of products the user has purchased. In this way, the data collection unit can collect highly relevant data by filtering data based on the user's current 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 areas of interest into the AI, and the AI ​​can filter the data.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting entertainment-related data. If the user is relaxed, the data collection unit may prioritize collecting relaxation-related data. Furthermore, if the user is focused, the data collection unit may prioritize collecting learning-related data. This allows the data collection unit to achieve efficient data collection by prioritizing data collection 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 may include, 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, or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of data to collect.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect event information for the area where the user is currently located. For example, if the user is traveling, the data collection unit can collect tourist information for the travel destination. Furthermore, if the user is commuting, the data collection unit can collect information related to the commute route. In this way, the data collection unit can achieve efficient data collection by collecting highly relevant data by considering the user's geographical location information. 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 the AI, which can then prioritize the collection of highly relevant data.

[0085] 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 analyze the content of posts from accounts the user follows and collect relevant data. For example, the data collection unit can analyze the activities of groups the user participates in and collect relevant data. The data collection unit can also analyze the content of articles the user shares and collect relevant data. In this way, the data collection unit can collect 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 the user's social media activity data into AI, and the AI ​​can collect relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can perform a concise analysis. The analysis unit can also provide visually stimulating analysis results if the user is excited. In this way, the analysis unit can achieve efficient data analysis by adjusting the data analysis 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the data analysis method.

[0087] The analysis unit can adjust the level of detail of its analysis based on user behavior patterns during data analysis. For example, the analysis unit can perform a detailed analysis of data from websites that users frequently visit. For example, the analysis unit can perform a detailed analysis of relevant data based on the user's search history. Furthermore, the analysis unit can perform a detailed analysis of data on products of interest based on the user's purchase history. In this way, the analysis unit can achieve efficient data analysis by adjusting the level of detail of its analysis based on user behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user behavior pattern data into AI, which can then adjust the level of detail of its analysis.

[0088] The analysis unit can apply different analysis algorithms to data analysis depending on the user's interest categories. For example, the analysis unit can apply a specialized analysis algorithm to categories that the user is interested in. For example, the analysis unit can apply different analysis methods based on categories that the user has shown interest in. The analysis unit can also select the optimal analysis algorithm according to the user's interest categories. In this way, the analysis unit can achieve efficient data analysis by applying different analysis algorithms according to the user's interest categories. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user interest category data into AI, and the AI ​​can apply different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the analysis unit can achieve efficient data analysis by adjusting the display method of the analysis results 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the display method of the analysis results.

[0090] The analysis unit can improve the accuracy of its analysis by referring to the user's past interest data during data analysis. For example, the analysis unit can improve the accuracy of its analysis based on data that the user has shown interest in in the past. For example, the analysis unit can refer to the user's past interest data to analyze relevant data in detail. The analysis unit can also select the optimal analysis method based on the user's past interest data. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past interest data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past interest data into AI, and the AI ​​can improve the accuracy of its analysis.

[0091] The analysis unit can perform data analysis by referring to the user's relevant market data. For example, the analysis unit can perform analysis by referring to market data that the user is interested in. For example, the analysis unit can perform detailed analysis of relevant data based on the user's market of interest. The analysis unit can also select the optimal analysis method based on the user's market of interest data. In this way, the analysis unit can achieve efficient data analysis by referring to the user's relevant market data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's relevant market data into AI, and the AI ​​can perform the analysis.

[0092] The generation unit can estimate the user's emotions and adjust the ad generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate ads that proceed at a relaxed pace. If the user is in a hurry, the generation unit can generate ads that emphasize the shortest route. Furthermore, if the user is excited, the generation unit can generate ads with visually stimulating effects. In this way, the generation unit can achieve efficient ad generation by adjusting the ad generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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, and the AI ​​can adjust the ad generation method.

[0093] The generation unit can adjust the level of detail in ads based on the user's level of interest when generating ads. For example, if the user shows high interest, the generation unit can generate an ad with detailed information. For example, if the user shows low interest, the generation unit can generate an ad with concise information. The generation unit can also select the optimal level of detail in the ad according to the user's level of interest. In this way, the generation unit can achieve efficient ad generation by adjusting the level of detail in the ad based on the user's level of interest. 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 interest data into AI, and the AI ​​can adjust the level of detail in the ad.

[0094] The generation unit can apply different generation algorithms depending on the user's interest categories when generating ads. For example, the generation unit can apply a specialized generation algorithm to categories that the user is interested in. For example, the generation unit can apply different generation methods based on categories that the user has shown interest in. The generation unit can also select the optimal generation algorithm depending on the user's interest categories. In this way, the generation unit can achieve efficient ad generation by applying different generation algorithms depending on the user's interest categories. 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 interest category data into AI, and the AI ​​can apply different generation algorithms.

[0095] The generation unit can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is stressed, the generation unit can provide a simple and highly visible display. If the user is relaxed, the generation unit can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a concise display. This allows the generation unit to achieve efficient ad display by adjusting how ads are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into an AI, which can then adjust how ads are displayed.

[0096] The generation unit can improve the accuracy of ad generation by referring to the user's past ad response data. For example, the generation unit can improve the accuracy of generation based on ads that the user has shown interest in in the past. For example, the generation unit can generate relevant ads in detail by referring to the user's past ad response data. The generation unit can also select the optimal generation method based on the user's past ad response data. In this way, the generation unit can improve the accuracy of generation by referring to the user's past ad response data. 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 user's past ad response data into AI, and the AI ​​can improve the accuracy of generation.

[0097] The generation unit can generate advertisements by referencing the user's relevant market data. For example, the generation unit can generate advertisements by referencing data on markets that the user is interested in. For example, the generation unit can generate relevant advertisements in detail based on the user's market interests. The generation unit can also select the optimal generation method based on the user's market interest data. In this way, the generation unit can achieve efficient advertisement generation by referencing the user's relevant market data. 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 user's relevant market data into AI, and the AI ​​can perform the generation.

[0098] The presentation unit can estimate the user's emotions and adjust the way ads are presented based on those emotions. For example, if the user is relaxed, the presentation unit may present ads that proceed at a leisurely pace. If the user is in a hurry, the presentation unit may present ads that emphasize the shortest route. If the user is excited, the presentation unit may also present ads with visually stimulating effects. In this way, the presentation unit can achieve efficient ad presentation by adjusting the way ads are presented 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 presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input user emotion data into AI, and the AI ​​can adjust the way ads are presented.

[0099] The presentation unit can select the optimal presentation method by referring to the user's past ad response data when presenting an ad. For example, the presentation unit can select the optimal presentation method based on ads the user has shown interest in in the past. For example, the presentation unit can refer to the user's past ad response data to present relevant ads in detail. The presentation unit can also select the optimal presentation method based on the user's past ad response data. In this way, the presentation unit can select the optimal presentation method by referring to the user's past ad response data. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit can input the user's past ad response data into AI, and the AI ​​can select the optimal presentation method.

[0100] The display unit can adjust the level of detail displayed based on the user's level of interest when displaying advertisements. For example, if the user shows high interest, the display unit can display an advertisement containing detailed information. For example, if the user shows low interest, the display unit can display an advertisement containing concise information. The display unit can also select the optimal level of detail based on the user's level of interest. In this way, the display unit can achieve efficient advertisement display by adjusting the level of detail based on the user's level of interest. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user interest data into AI, and the AI ​​can adjust the level of detail of the display.

[0101] The display unit can estimate the user's emotions and adjust the order in which ads are displayed based on the estimated emotions. For example, if the user is relaxed, the display unit may display ads that proceed at a leisurely pace. If the user is in a hurry, the display unit may display ads that emphasize the shortest route. Furthermore, if the user is excited, the display unit may display ads with visually stimulating effects. In this way, the display unit can achieve efficient ad delivery by adjusting the order in which ads are displayed 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 display unit may be performed using AI, for example, or not using AI. For example, the display unit can input user emotion data into AI, and the AI ​​can adjust the order in which ads are displayed.

[0102] The display unit can select the optimal display method when displaying advertisements, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can select the optimal display method by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0103] The display unit can prioritize displaying highly relevant advertisements by considering the user's geographical location when displaying advertisements. For example, the display unit can prioritize displaying event information in the user's current location. For example, if the user is traveling, the display unit can prioritize displaying tourist information for their travel destination. Furthermore, if the user is commuting, the display unit can prioritize displaying advertisements related to their commute route. In this way, the display unit can achieve efficient advertisement display by prioritizing highly relevant advertisements by considering the user's geographical location. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location information into AI, which can then prioritize displaying highly relevant advertisements.

[0104] The reception unit can estimate the user's emotions and adjust the response processing method based on the estimated emotions. For example, if the user is relaxed, the reception unit can provide an interface requesting detailed answers. If the user is in a hurry, the reception unit can provide an interface requesting concise answers. The reception unit can also provide a visually stimulating interface if the user is excited. This allows the reception unit to efficiently process responses by adjusting the response processing method 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 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 reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into the AI, which can then adjust the response processing method.

[0105] The reception unit can select the optimal reception method by referring to the user's past response data when receiving a response. For example, the reception unit can select the optimal reception method based on the response methods the user has shown interest in in the past. For example, the reception unit can refer to the user's past response data to present relevant response methods in detail. The reception unit can also select the optimal reception method based on the user's past response data. In this way, the reception unit can select the optimal reception method by referring to the user's past response data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past response data into AI, and the AI ​​can select the optimal reception method.

[0106] The reception unit can adjust the level of detail in the response based on the user's level of interest. For example, if the user shows a high level of interest, the reception unit can provide an interface requesting a detailed response. For example, if the user shows a low level of interest, the reception unit can provide an interface requesting a concise response. The reception unit can also select the optimal level of detail in the response according to the user's level of interest. In this way, the reception unit can achieve efficient response reception by adjusting the level of detail in the response based on the user's level of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user interest data into the AI, and the AI ​​can adjust the level of detail in the response.

[0107] The reception desk can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user is relaxed, the reception desk may prioritize detailed responses. If the user is in a hurry, the reception desk may prioritize concise responses. Furthermore, if the user is excited, the reception desk may prioritize visually stimulating responses. This allows the reception desk to efficiently receive responses by prioritizing responses 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into an AI, which can then determine the priority of responses.

[0108] The reception unit can select the optimal reception method when receiving a response, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method that matches the screen size. If the user is using a tablet, the reception unit can provide a reception method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a concise and highly visible reception method. In this way, the reception unit can select the optimal reception method by taking into account the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's device information into the AI, and the AI ​​can select the optimal reception method.

[0109] The reception desk can prioritize receiving responses that are highly relevant, taking into account the user's geographical location. For example, the reception desk can prioritize responses related to event information in the user's current location. If the user is traveling, the reception desk can prioritize responses related to tourist information in their travel destination. If the user is commuting, the reception desk can also prioritize responses related to their commute route. This allows the reception desk to efficiently receive responses by prioritizing highly relevant responses while considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize receiving highly relevant responses.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can adjust the timing of data collection considering the battery level of the user's device. For example, it can refrain from collecting data when the battery level is low and collect data when charging or when the battery level is sufficient. The data collection unit can also prioritize data collection when the user's device is connected to Wi-Fi. This allows the data collection unit to achieve efficient data collection according to the status of the user's device.

[0112] The analytics department can identify ad patterns that users are likely to be interested in by referring to their past ad click history. For example, it can analyze the content and format of ads that users have clicked in the past and generate similar ads. Furthermore, if the analytics department finds that users tend to click during specific time periods, it can present ads accordingly. This allows the analytics department to leverage users' past behavioral data to deliver more effective ads.

[0113] The ad generation unit can dynamically change the ad display format based on user interests. For example, if a user shows a high interest in video ads, it can prioritize generating video ads. It can also generate text ads if the user prefers text ads. Furthermore, the generation unit can customize the ad's color and design according to user interests. This allows the generation unit to provide ads tailored to user preferences.

[0114] The display unit can adjust the ad layout according to the screen size of the user's device. For example, it can use a simple layout on a small smartphone screen and a layout with detailed information on a larger tablet or desktop screen. Furthermore, the display unit can change how the ad is displayed depending on whether the user's device is in portrait or landscape orientation. This allows the display unit to achieve ad display optimized for the user's device.

[0115] The reception desk can dynamically change the content of the next questions presented based on the user's responses. For example, if a user shows interest in a particular product, it can present detailed questions related to that product. Conversely, if a user shows no interest in a particular category, questions related to that category can be omitted. This allows the reception desk to efficiently present questions in response to the user's answers.

[0116] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced. Conversely, if the user is relaxed, the frequency of data collection can be increased. Furthermore, the data collection unit can also change the content of the data collection according to the user's emotions. This enables the data collection unit to perform data collection that takes the user's emotions into consideration.

[0117] The analytics department can estimate the user's emotions and customize the data analysis results based on those estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is excited, it can provide visually appealing analysis results. In this way, the analytics department can provide analysis results that are tailored to the user's emotions.

[0118] The generation unit can estimate the user's emotions and adjust the ad content based on those emotions. For example, if the user is relaxed, it can generate an ad with a calm tone. If the user is excited, it can generate an ad with an energetic tone. Furthermore, if the user is sad, it can generate an ad that includes a message of encouragement. In this way, the generation unit can provide ads that match the user's emotions.

[0119] The display unit can estimate the user's emotions and adjust the timing of ad display based on those emotions. For example, if the user is relaxed, the ad can be displayed immediately. If the user is stressed, the ad display can be delayed. Furthermore, if the user is concentrating, the ad display can be withheld altogether. In this way, the display unit can achieve ad display that takes the user's emotions into consideration.

[0120] The reception desk can estimate the user's emotions and adjust the response method based on those estimates. For example, if the user is relaxed, it can provide an interface that requests detailed answers. If the user is in a hurry, it can provide an interface that requests concise answers. Furthermore, if the user is excited, it can provide a visually stimulating interface. In this way, the reception desk can provide responses that are tailored to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects user behavior data. For example, it collects data such as the user's website browsing history, search history, and purchase history. The data collection unit can collect URLs of websites the user visits on a daily basis, browsing time, keywords searched, search date and time, information on purchased products, purchase date and time, and purchase amount. Step 2: The analysis department analyzes the data collected by the collection department to identify user interests. For example, they analyze the collected data using statistical analysis and machine learning algorithms to identify interests based on user browsing frequency, click counts, search history, and purchase history. Step 3: The generation unit generates advertisements based on the interests identified by the analysis unit. For example, it generates banner ads, text ads, video ads, etc., based on the user's interests. If a user answers that they are interested in day trips, it can generate advertisements related to day trips. Step 4: The presentation unit presents the advertisement generated by the generation unit to the user. For example, the generated advertisement can be displayed on a web page, sent to the user via email or push notification, or presented to the user on social media. Step 5: The reception desk receives user responses to the advertisements presented by the presentation desk. For example, it can check whether the user clicked on the advertisement, provided feedback comments on the advertisement, or answered a survey.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, presentation unit, and reception unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects data such as the user's website browsing history, search history, and purchase history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the user's interests. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates advertisements based on the user's interests. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents the generated advertisements to the user. The reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's responses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, presentation unit, and reception unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects data such as the user's website browsing history, search history, and purchase history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the user's interests. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates advertisements based on the user's interests. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents the generated advertisements to the user. The reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's responses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, presentation unit, and reception unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects data such as the user's website browsing history, search history, and purchase history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the user's interests. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates advertisements based on the user's interests. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents the generated advertisements to the user. The reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's responses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, presentation unit, and reception unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects data such as the user's website browsing history, search history, and purchase history. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the user's interests. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates advertisements based on the user's interests. The presentation unit is implemented by, for example, the control unit 46A of the robot 414 and presents the generated advertisements to the user. The reception unit is implemented by, for example, the control unit 46A of the robot 414 and receives the user's response. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit and identifies user interests, A generation unit that generates advertisements based on the interests identified by the analysis unit, A presentation unit that presents the advertisement generated by the generation unit to the user, The system includes a receiving unit that receives user responses to advertisements presented by the aforementioned presentation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as users' website browsing history, search history, and purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the collected data to identify user interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate targeted ads based on user interests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, The generated advertisement is presented to the user, and they are asked if they are interested. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It accepts user responses and provides information for the AI ​​to generate more specific advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, 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 12) 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 13) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing data, adjust the level of detail of the analysis based on user behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing data, different analytical algorithms are applied depending on the user's interest category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing data, referencing users' past interest data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When performing data analysis, the analysis is conducted by referencing relevant market data for the user. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the user's emotions and adjust how ads are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating ads, adjust the level of detail based on user interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating ads, different generation algorithms are applied depending on the user's interest category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating ads, we improve the accuracy of the generation process by referencing users' past ad response data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating ads, the system references relevant market data for the user. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, It estimates the user's emotions and adjusts how ads are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When displaying an ad, the system selects the optimal display method by referring to the user's past ad response data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When displaying ads, adjust the level of detail based on the user's level of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, It estimates the user's emotions and adjusts the order in which ads are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When displaying advertisements, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When displaying ads, the system prioritizes showing highly relevant ads by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reception unit is The system estimates the user's emotions and adjusts the response processing method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reception unit is When receiving responses, the system selects the most suitable submission method by referring to the user's past response data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reception unit is When receiving responses, adjust the level of detail based on the user's level of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reception unit is The system estimates the user's emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned reception unit is When receiving responses, the system selects the most suitable submission method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned reception unit is When receiving responses, the system prioritizes accepting responses that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit and identifies user interests, A generation unit that generates advertisements based on the interests identified by the analysis unit, A presentation unit that presents the advertisement generated by the generation unit to the user, The system includes a receiving unit that receives user responses to advertisements presented by the aforementioned presentation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as users' website browsing history, search history, and purchase history. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the collected data to identify user interests. The system according to feature 1.

4. The generating unit is Generate targeted ads based on user interests. The system according to feature 1.

5. The aforementioned display unit is, The generated advertisement is presented to the user, and they are asked if they are interested. The system according to feature 1.

6. The aforementioned reception unit is It accepts user responses and provides information for the AI ​​to generate more specific advertisements. The system according to feature 1.

7. 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 according to feature 1.

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

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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