system

Abstract

The system according to this embodiment aims to provide optimal advertisements based on the characteristics of individuals. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a selection unit, and a display unit. The acquisition unit acquires the characteristics of a person. The analysis unit analyzes the characteristics acquired by the acquisition unit. The selection unit selects an appropriate advertisement based on the characteristics analyzed by the analysis unit. The display unit displays the advertisement selected by the selection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since advertisements are displayed uniformly, there is a problem that it is impossible to provide an optimal advertisement according to the characteristics of each individual.

[0005] The system according to the embodiment aims to provide an optimal advertisement based on the characteristics of a person.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a selection unit, and a display unit. The acquisition unit acquires the characteristics of a person. The analysis unit analyzes the characteristics acquired by the acquisition unit. The selection unit selects an appropriate advertisement based on the characteristics analyzed by the analysis unit. The display unit displays the advertisement selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal advertisements based on the characteristics of individuals. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 display system according to an embodiment of the present invention is a system that recognizes the characteristics of a person in front of a street advertisement using an AI camera and presents the most suitable advertisement from a database based on those characteristics. The advertising display system installs an AI camera in front of a street advertisement to understand the characteristics of the person viewing the advertisement (gender, age, body type, clothing, etc.). Next, the generating AI learns the advertisement and target information provided by the advertiser. Finally, the generating AI estimates the person's profile and selects and displays the most suitable advertisement. This mechanism is expected to reduce advertising costs and create new revenue streams. For example, the advertising display system installs an AI camera in front of a street advertisement. The AI ​​camera recognizes the characteristics of the person viewing the advertisement and analyzes the image information to convert information such as gender, age, body type, and clothing into text. For example, the AI ​​camera takes an image of a person standing in front of an advertisement and estimates their gender and age from the image. This information is input to the generating AI. Next, the generating AI learns the advertisement and target information provided by the advertiser. The advertiser provides the advertisement image and target information to the database. The generating AI learns this information and understands the target audience for each product. For example, if a particular product is targeted at young people, the generating AI learns this information. Finally, the generating AI estimates the person's profile and selects and displays the most suitable advertisement. The generating AI estimates the person's profile based on transcribed personal characteristics information, and then compares that profile with the target information for each product to select the most suitable advertisement. For example, if the AI ​​camera recognizes a young male, the generating AI will select and display an advertisement targeted at young people. This system is expected to reduce advertising costs. Advertisers can choose between a fixed-rate subscription model regardless of the number of impressions or a pay-per-impression model, thus reducing unnecessary expenses. Furthermore, creating an electronic advertising platform can generate new revenue streams. For example, the fees paid by companies when placing advertisements become a new source of revenue. In this way, a system that recognizes the characteristics of people in front of street advertisements using an AI camera and presents the most suitable advertisements based on those characteristics is efficient for both companies and consumers, and is expected to reduce advertising costs and create new revenue streams. As a result, the advertising display system can maximize the effectiveness of advertisements by displaying the most suitable advertisements based on the characteristics of the person.

[0029] The advertising display system according to the embodiment comprises an acquisition unit, an analysis unit, a selection unit, and a display unit. The acquisition unit acquires the characteristics of a person. The acquisition unit acquires information such as the gender, age, body type, and clothing of a person viewing an advertisement, for example, using an AI camera. The acquisition unit can identify gender, for example, using facial recognition technology. The acquisition unit can also estimate age, for example, using image analysis technology. Furthermore, the acquisition unit can acquire information on body type and clothing, for example, using image analysis technology. The analysis unit analyzes the characteristics acquired by the acquisition unit. The analysis unit analyzes the acquired information, for example, using AI, and identifies the characteristics of a person. The analysis unit can identify characteristics such as gender, age, body type, and clothing, for example, using data analysis methods. The analysis unit can also identify characteristics, for example, using machine learning algorithms. The selection unit selects the optimal advertisement based on the characteristics analyzed by the analysis unit. The selection unit selects the optimal advertisement from a database, for example, using generative AI. The selection unit can select advertisements based on target information, for example. The selection unit can, for example, use a generating AI to learn from advertisers' provided ad and target information and select the most suitable ad. The display unit displays the ad selected by the selection unit. The display unit can, for example, display the ad using digital signage. The display unit can also, for example, display the ad using an electronic advertising platform. The display unit can also, for example, display the ad using a mobile device. This allows the ad display system to maximize the effectiveness of advertising by displaying the most suitable ad based on the characteristics of the individual.

[0030] The acquisition unit acquires the characteristics of a person. For example, the acquisition unit uses an AI camera to acquire information such as the gender, age, body type, and clothing of a person viewing an advertisement. Specifically, the AI ​​camera captures high-resolution video in real time and identifies the gender using facial recognition technology. Facial recognition technology analyzes facial feature points and can determine with high accuracy whether the person is male or female. In addition, deep learning-based image analysis technology is used for age estimation, estimating age based on features such as facial wrinkles and skin texture. Furthermore, body type and clothing information are acquired by analyzing a full-body image. For example, body type is classified into categories such as slim, standard, and chubby by analyzing the skeleton and body proportions. Clothing information is classified into categories such as casual, formal, and sportswear by analyzing color, design, and brand logos. As a result, the acquisition unit can acquire detailed characteristic information of the person viewing the advertisement in real time and transmit it to the next analysis unit.

[0031] The analysis unit analyzes the characteristics acquired by the acquisition unit. For example, the analysis unit analyzes the acquired information using AI to identify a person's characteristics. Specifically, it uses data analysis techniques to identify characteristics such as gender, age, body type, and clothing. The analysis unit can also identify characteristics using machine learning algorithms. Machine learning algorithms have the ability to learn from large amounts of data and identify patterns in characteristics. For example, to identify gender, a model that has learned facial feature points and skeletal differences is used, and to estimate age, a model that has learned facial wrinkles and skin texture is used. To identify body type, a model that has learned overall proportions and body silhouette is used, and to identify clothing, a model that has learned color, design, and brand logos is used. This allows the analysis unit to quickly and accurately analyze the acquired information and identify a person's characteristics. Furthermore, the analysis unit can utilize past data and statistical information to analyze trends and patterns in characteristics and perform more accurate analysis. For example, it can analyze fluctuations in characteristics at specific times of day or locations to optimize ad display.

[0032] The selection unit selects the most suitable advertisement based on the characteristics analyzed by the analysis unit. For example, the selection unit uses generative AI to select the most suitable advertisement from a database. Specifically, the generative AI learns from advertisements and target information provided by advertisers and selects the most suitable advertisement. The generative AI uses deep learning to analyze the content and target information of advertisements and selects the advertisement that is most suitable for specific characteristics. For example, it can select fashion-related advertisements for young women and sports goods advertisements for people wearing sportswear. Furthermore, the selection unit can continuously optimize its advertisement selection based on data that is updated in real time. For example, if a particular advertisement shows a high click-through rate or conversion rate, it can be adjusted to prioritize the display of that advertisement. In this way, the selection unit can select the most suitable advertisement based on the analyzed characteristics and maximize the effectiveness of the advertisement.

[0033] The display unit displays the advertisement selected by the selection unit. The display unit can display advertisements using, for example, digital signage. Digital signage uses a high-resolution display to show clear and highly visible advertisements. The display unit can also display advertisements using, for example, an electronic advertising platform. Electronic advertising platforms can deliver advertisements over the internet and reach a wide range of users. The display unit can also display advertisements using, for example, a mobile device. Mobile devices can display personalized advertisements based on the user's location information and browsing history. This allows the display unit to display selected advertisements on a variety of platforms and maximize the effectiveness of the advertisements. Furthermore, the display unit can monitor the display status of advertisements and user reactions in real time and evaluate the effectiveness of the advertisements. For example, it can analyze the visibility rate and click-through rate of digital signage, and the tap rate and conversion rate of mobile devices to measure the effectiveness of the advertisements. This allows the display unit to continuously improve the way advertisements are displayed and the content of the advertisements, maximizing their effectiveness.

[0034] The acquisition unit can acquire information on the gender, age, body type, and clothing of a person viewing an advertisement. The acquisition unit can, for example, identify gender using facial recognition technology. The acquisition unit can also, for example, estimate age using image analysis technology. The acquisition unit can also, for example, acquire information on body type and clothing using image analysis technology. This allows for more accurate advertisement selection by acquiring detailed characteristic information of the person viewing the advertisement. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit identifies gender using facial recognition technology, it can use AI to analyze the facial image and determine gender.

[0035] The analysis unit can analyze the information acquired by the acquisition unit and identify the characteristics of a person. The analysis unit can, for example, use AI to analyze the acquired information and identify the characteristics of a person. The analysis unit can, for example, use data analysis techniques to identify characteristics such as gender, age, body type, and clothing. The analysis unit can also, for example, use machine learning algorithms to identify characteristics. In this way, by analyzing the acquired information, the characteristics of a person can be accurately identified. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data acquired by the acquisition unit into the AI, and the AI ​​can analyze the image data to identify characteristics.

[0036] The selection unit can select appropriate advertisements from the database based on the characteristics identified by the analysis unit. The selection unit can, for example, use a generative AI to select the optimal advertisement from the database. The selection unit can, for example, select advertisements based on target information. The selection unit can also, for example, have a generative AI learn advertisements and target information provided by advertisers and then select the optimal advertisement. This maximizes the effectiveness of advertisements by selecting the optimal advertisement based on characteristics. Some or all of the above-described processes in the selection unit may be performed using a generative AI, for example, or without a generative AI. For example, the selection unit can input the characteristics identified by the analysis unit into a generative AI, and the generative AI can select the optimal advertisement from the database.

[0037] The display unit can display advertisements selected by the selection unit. The display unit can display advertisements using, for example, digital signage. The display unit can also display advertisements using, for example, an electronic advertising platform. The display unit can also display advertisements using, for example, a mobile device. This allows for the provision of advertisements that are best suited to the target audience by displaying selected advertisements. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the advertisements selected by the selection unit into the AI, which can then determine the best way to display the advertisements.

[0038] The selection unit can learn based on ad and target information provided by advertisers. For example, the selection unit can learn ad and target information provided by advertisers using generative AI. For example, the selection unit can learn ad and target information using machine learning algorithms. This allows for more accurate ad selection by learning the information provided by advertisers. Some or all of the above processing in the selection unit may be performed using generative AI, or without generative AI. For example, the selection unit can input ad and target information provided by advertisers into a generative AI, which can then learn this information.

[0039] The data acquisition unit can analyze the past behavioral history of people viewing advertisements and select an appropriate data acquisition method. For example, the data acquisition unit can analyze what kinds of advertisements people have shown interest in in the past and prioritize acquiring similar characteristics. For example, the data acquisition unit can acquire relevant characteristics based on places and events that people have visited in the past. For example, the data acquisition unit can acquire characteristics that will attract interest by referring to the past purchase history of people viewing advertisements. This makes it possible to acquire characteristics more effectively by analyzing past behavioral history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the past behavioral history of people viewing advertisements into AI, and the AI ​​can analyze the behavioral history and select the optimal data acquisition method.

[0040] The acquisition unit can filter the acquisition of characteristics based on the current activities and areas of interest of the person viewing the advertisement. For example, the acquisition unit can prioritize the acquisition of characteristics related to an event the person viewing the advertisement is currently participating in. For example, the acquisition unit can acquire characteristics based on topics the person viewing the advertisement is currently interested in. For example, the acquisition unit can acquire characteristics based on the device or app the person viewing the advertisement is currently using. This allows for the acquisition of more relevant characteristics by filtering characteristics based on current activities and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the current activities and areas of interest of the person viewing the advertisement into the AI, which can then filter characteristics based on this information.

[0041] The acquisition unit can preferentially acquire relevant characteristics based on the geographical location information of the person viewing the advertisement. For example, the acquisition unit can preferentially acquire characteristics related to the place where the person viewing the advertisement is currently located. For example, the acquisition unit can preferentially acquire characteristics related to places the person viewing the advertisement has visited in the past. For example, the acquisition unit can preferentially acquire characteristics related to places the person viewing the advertisement plans to visit in the future. This allows for the provision of more relevant advertisements by acquiring characteristics based on geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the geographical location information of the person viewing the advertisement into AI, and the AI ​​can preferentially acquire relevant characteristics.

[0042] The acquisition unit can analyze the social media activity of people who view advertisements and acquire relevant characteristics. For example, the acquisition unit can acquire characteristics based on information shared by people who view advertisements on social media. For example, the acquisition unit can acquire characteristics related to accounts that people who view advertisements follow on social media. For example, the acquisition unit can acquire characteristics related to posts that people who view advertisements "like" on social media. By analyzing social media activity, more relevant characteristics can be acquired. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the social media activity of people who view advertisements into AI, and the AI ​​can acquire relevant characteristics.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the acquired characteristics. For example, the analysis unit can perform a detailed analysis on highly important characteristics to produce highly accurate results. For example, the analysis unit can perform a simplified analysis on less important characteristics to produce results quickly. For example, the analysis unit can perform a balanced analysis on characteristics of moderate importance to produce appropriate results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the characteristics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the acquired characteristics into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0044] The analysis unit can apply different analysis methods depending on the category of the acquired characteristics. For example, for characteristics related to gender, the analysis unit can apply a gender-specific analysis algorithm. For example, for characteristics related to age, the analysis unit can apply an age-specific analysis algorithm. For example, for characteristics related to clothing, the analysis unit can apply a clothing-specific analysis algorithm. By applying an analysis algorithm according to the category of characteristics, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of acquired characteristics into the AI, and the AI ​​can apply different analysis algorithms depending on the category.

[0045] The analysis unit can determine the order of analysis based on the submission timing of the acquired characteristics. For example, the analysis unit may prioritize the analysis of recently acquired characteristics. For example, the analysis unit may postpone the analysis of characteristics acquired in the past. For example, the analysis unit may prioritize the analysis of characteristics for which the submission timing is important. This enables efficient analysis by determining the priority of analysis based on the submission timing. 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 submission timing of the acquired characteristics into the AI, and the AI ​​can determine the order of analysis based on the submission timing.

[0046] The analysis unit can determine the order of analysis based on the relevance of the acquired characteristics. For example, the analysis unit may prioritize the analysis of characteristics highly relevant to advertising. For example, the analysis unit may postpone the analysis of characteristics less relevant to advertising. For example, the analysis unit may balance the analysis of characteristics with moderate relevance. By adjusting the order of analysis based on relevance, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the acquired characteristics into the AI, and the AI ​​can determine the order of analysis based on relevance.

[0047] The selection unit can adjust the precision of its selections based on the importance of the advertisements. For example, the selection unit can apply detailed selection criteria to highly important advertisements. For example, it can apply simplified selection criteria to less important advertisements. For example, it can apply balanced selection criteria to moderately important advertisements. This allows for efficient advertisement selection by adjusting the level of detail of the selections based on the importance of the advertisements. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the importance of the advertisements into a generative AI, which can then adjust the level of detail of the selections based on the importance.

[0048] The selection unit can apply different selection methods depending on the category of the advertisement. For example, for fashion advertisements, the selection unit can apply a selection algorithm specific to fashion. For example, for food advertisements, the selection unit can apply a selection algorithm specific to food. For example, for electronic device advertisements, the selection unit can apply a selection algorithm specific to electronic devices. By applying a selection algorithm according to the category of the advertisement, more accurate advertisement selection becomes possible. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the category of the advertisement into a generative AI, and the generative AI can apply a different selection algorithm depending on the category.

[0049] The selection unit can determine the order of selection based on the submission date of the advertisements. For example, the selection unit may prioritize recently submitted advertisements. For example, the selection unit may postpone the selection of advertisements submitted in the past. For example, the selection unit may prioritize advertisements for which the submission date is important. This enables efficient advertisement selection by determining the priority of selection based on the submission date. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the submission dates of the advertisements into a generative AI, and the generative AI can determine the order of selection based on the submission dates.

[0050] The selection unit can determine the order of selection based on the relevance of the advertisements. For example, the selection unit may prioritize selecting characteristics that are highly relevant to the advertisement. For example, the selection unit may postpone selecting characteristics that are less relevant to the advertisement. For example, the selection unit may balance the selection of characteristics with a moderate level of relevance. By adjusting the order of selection based on relevance, more effective advertisement selection becomes possible. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the relevance of the advertisements into a generative AI, and the generative AI can determine the order of selection based on relevance.

[0051] The display unit can select an appropriate display method when displaying an advertisement by referring to the viewer's past behavioral history. For example, the display unit can refer to the display methods of advertisements that the viewer has shown interest in in the past. For example, the display unit can select a display method related to places or events that the viewer has visited in the past. For example, the display unit can select the optimal display method based on the viewer's past purchase history. This makes it possible to display advertisements more effectively by referring to past behavioral history. 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 viewer's past behavioral history into AI, and the AI ​​can select the optimal display method.

[0052] The display unit can select an appropriate display method based on the person's device information when displaying an advertisement. For example, if the person viewing the advertisement is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the person viewing the advertisement is using a tablet, the display unit can provide a display method optimized for a larger screen. For example, if the person viewing the advertisement is using a smartwatch, the display unit can provide a concise and highly visible display method. This enables more effective advertisement display by selecting the optimal display method based on 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 device information of the person viewing the advertisement into the AI, and the AI ​​can select the optimal display method.

[0053] The display unit can provide multilingual support when displaying advertisements, depending on the user's language settings. For example, the display unit can automatically set the language of the advertisement based on the language settings of the user's device. For example, the display unit can provide a language switching function if the user uses multiple languages. For example, the display unit can display the advertisement in a specific language if the user selects one. This enables more effective advertisement display by providing multilingual support according to language settings. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the user's language settings into the AI, which can then provide multilingual support.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The advertising display system may also include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit collects and analyzes data on products and services that the user has purchased in the past. For example, it can analyze the categories and price ranges of products the user has purchased in the past to understand the user's purchasing trends. Next, based on the analysis results, the purchase history analysis unit estimates the user's interests and preferences and provides information to select the most appropriate advertisements. For example, if the user has purchased luxury brand products in the past, advertisements for luxury brands can be displayed preferentially. Also, if the user has purchased products related to a particular season, advertisements related to that season can be displayed. In this way, the effectiveness of advertisements can be further enhanced by selecting advertisements based on the user's purchase history.

[0056] The advertising display system may also include a social media analytics unit that analyzes users' social media activity. This unit collects and analyzes information shared by users on social media, accounts they follow, and posts they like. For example, if a user frequently shares posts about a particular brand or product, the system can prioritize displaying advertisements for that brand or product. It can also display advertisements related to products or services promoted by influencers the user follows. Furthermore, it can analyze the content of posts a user likes to estimate their interests. This allows the system to select advertisements based on social media activity, providing ads that are relevant to the user's interests.

[0057] The advertising display system can also include an activity analysis unit that understands the user's current activity status. This unit collects and analyzes information about events the user is currently participating in, apps they are using, and devices they are using. For example, if a user is participating in a sports event, sports-related advertisements can be prioritized. Similarly, if a user is using a specific app, advertisements related to that app can be displayed. Furthermore, if a user is using a device such as a smartphone or tablet, advertisements optimized for that device can be displayed. This allows for the delivery of more relevant advertisements by selecting them based on the user's current activity status.

[0058] The advertising display system may also include a location information analysis unit that analyzes the user's geographical location. The location information analysis unit collects and analyzes information related to the user's current location, past visits, and future planned visits. For example, it can prioritize displaying advertisements related to the user's current location. It can also display advertisements related to places the user has visited in the past. Furthermore, it can display advertisements related to places the user plans to visit in the future. This allows for the provision of more relevant advertisements by selecting them based on geographical location information. Location information is collected, for example, using GPS or Wi-Fi location information. For example, the user's current location can be determined using the GPS function of a smartphone, and advertisements can be selected based on that information.

[0059] The advertising display system may also include a device analysis unit that analyzes the user's device information. The device analysis unit collects and analyzes information about the type and settings of the device the user is using. For example, if the user is using a smartphone, the system can display advertisements optimized for the smartphone's screen size and resolution. Similarly, if the user is using a tablet, the system can display advertisements optimized for the larger screen. Furthermore, if the user is using a smartwatch, the system can display concise and highly visible advertisements. This allows for more effective ad display by selecting advertisements based on device information. Device information is collected, for example, by analyzing device settings and usage. For instance, smartphone settings information can be collected, and advertisements can be selected based on that information.

[0060] The advertising display system may also include a language analysis unit that analyzes the user's language settings. This unit collects and analyzes the language settings of the user's device and their past language usage history. For example, if the user's device language setting is English, the system can display English advertisements. It can also provide a language switching function if the user uses multiple languages. Furthermore, if the user selects a specific language, the system can display advertisements in that language. This enables more effective ad display by providing multilingual advertisements based on language settings. Language settings are collected, for example, by analyzing device settings and past language usage history. For instance, the system can collect the language settings of a smartphone and select advertisements based on that information.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The acquisition unit acquires the characteristics of the person. For example, the acquisition unit uses an AI camera to acquire information such as the gender, age, body type, and clothing of the person viewing the advertisement. The acquisition unit can identify gender using facial recognition technology, estimate age using image analysis technology, and acquire information on body type and clothing. Step 2: The analysis unit analyzes the characteristics acquired by the acquisition unit. The analysis unit uses AI to analyze the acquired information and identify the characteristics of the person. Using data analysis methods and machine learning algorithms, characteristics such as gender, age, body type, and clothing can be identified. Step 3: The selection unit selects the optimal advertisement based on the characteristics analyzed by the analysis unit. The selection unit uses generative AI to select the optimal advertisement from the database and selects advertisements based on target information. The generative AI can also learn from advertisements and target information provided by advertisers to select the optimal advertisement. Step 4: The display unit displays the advertisement selected by the selection unit. The display unit can display the advertisement using digital signage, electronic advertising platforms, or mobile devices.

[0063] (Example of form 2) An advertising display system according to an embodiment of the present invention is a system that recognizes the characteristics of a person in front of a street advertisement using an AI camera and presents the most suitable advertisement from a database based on those characteristics. The advertising display system installs an AI camera in front of a street advertisement to understand the characteristics of the person viewing the advertisement (gender, age, body type, clothing, etc.). Next, the generating AI learns the advertisement and target information provided by the advertiser. Finally, the generating AI estimates the person's profile and selects and displays the most suitable advertisement. This mechanism is expected to reduce advertising costs and create new revenue streams. For example, the advertising display system installs an AI camera in front of a street advertisement. The AI ​​camera recognizes the characteristics of the person viewing the advertisement and analyzes the image information to convert information such as gender, age, body type, and clothing into text. For example, the AI ​​camera takes an image of a person standing in front of an advertisement and estimates their gender and age from the image. This information is input to the generating AI. Next, the generating AI learns the advertisement and target information provided by the advertiser. The advertiser provides the advertisement image and target information to the database. The generating AI learns this information and understands the target audience for each product. For example, if a particular product is targeted at young people, the generating AI learns this information. Finally, the generating AI estimates the person's profile and selects and displays the most suitable advertisement. The generating AI estimates the person's profile based on transcribed personal characteristics information, and then compares that profile with the target information for each product to select the most suitable advertisement. For example, if the AI ​​camera recognizes a young male, the generating AI will select and display an advertisement targeted at young people. This system is expected to reduce advertising costs. Advertisers can choose between a fixed-rate subscription model regardless of the number of impressions or a pay-per-impression model, thus reducing unnecessary expenses. Furthermore, creating an electronic advertising platform can generate new revenue streams. For example, the fees paid by companies when placing advertisements become a new source of revenue. In this way, a system that recognizes the characteristics of people in front of street advertisements using an AI camera and presents the most suitable advertisements based on those characteristics is efficient for both companies and consumers, and is expected to reduce advertising costs and create new revenue streams. As a result, the advertising display system can maximize the effectiveness of advertisements by displaying the most suitable advertisements based on the characteristics of the person.

[0064] The advertising display system according to the embodiment comprises an acquisition unit, an analysis unit, a selection unit, and a display unit. The acquisition unit acquires the characteristics of a person. The acquisition unit acquires information such as the gender, age, body type, and clothing of a person viewing an advertisement, for example, using an AI camera. The acquisition unit can identify gender, for example, using facial recognition technology. The acquisition unit can also estimate age, for example, using image analysis technology. Furthermore, the acquisition unit can acquire information on body type and clothing, for example, using image analysis technology. The analysis unit analyzes the characteristics acquired by the acquisition unit. The analysis unit analyzes the acquired information, for example, using AI, and identifies the characteristics of a person. The analysis unit can identify characteristics such as gender, age, body type, and clothing, for example, using data analysis methods. The analysis unit can also identify characteristics, for example, using machine learning algorithms. The selection unit selects the optimal advertisement based on the characteristics analyzed by the analysis unit. The selection unit selects the optimal advertisement from a database, for example, using generative AI. The selection unit can select advertisements based on target information, for example. The selection unit can, for example, use a generating AI to learn from advertisers' provided ad and target information and select the most suitable ad. The display unit displays the ad selected by the selection unit. The display unit can, for example, display the ad using digital signage. The display unit can also, for example, display the ad using an electronic advertising platform. The display unit can also, for example, display the ad using a mobile device. This allows the ad display system to maximize the effectiveness of advertising by displaying the most suitable ad based on the characteristics of the individual.

[0065] The acquisition unit acquires the characteristics of a person. For example, the acquisition unit uses an AI camera to acquire information such as the gender, age, body type, and clothing of a person viewing an advertisement. Specifically, the AI ​​camera captures high-resolution video in real time and identifies the gender using facial recognition technology. Facial recognition technology analyzes facial feature points and can determine with high accuracy whether the person is male or female. In addition, deep learning-based image analysis technology is used for age estimation, estimating age based on features such as facial wrinkles and skin texture. Furthermore, body type and clothing information are acquired by analyzing a full-body image. For example, body type is classified into categories such as slim, standard, and chubby by analyzing the skeleton and body proportions. Clothing information is classified into categories such as casual, formal, and sportswear by analyzing color, design, and brand logos. As a result, the acquisition unit can acquire detailed characteristic information of the person viewing the advertisement in real time and transmit it to the next analysis unit.

[0066] The analysis unit analyzes the characteristics acquired by the acquisition unit. For example, the analysis unit analyzes the acquired information using AI to identify a person's characteristics. Specifically, it uses data analysis techniques to identify characteristics such as gender, age, body type, and clothing. The analysis unit can also identify characteristics using machine learning algorithms. Machine learning algorithms have the ability to learn from large amounts of data and identify patterns in characteristics. For example, to identify gender, a model that has learned facial feature points and skeletal differences is used, and to estimate age, a model that has learned facial wrinkles and skin texture is used. To identify body type, a model that has learned overall proportions and body silhouette is used, and to identify clothing, a model that has learned color, design, and brand logos is used. This allows the analysis unit to quickly and accurately analyze the acquired information and identify a person's characteristics. Furthermore, the analysis unit can utilize past data and statistical information to analyze trends and patterns in characteristics and perform more accurate analysis. For example, it can analyze fluctuations in characteristics at specific times of day or locations to optimize ad display.

[0067] The selection unit selects the most suitable advertisement based on the characteristics analyzed by the analysis unit. For example, the selection unit uses generative AI to select the most suitable advertisement from a database. Specifically, the generative AI learns from advertisements and target information provided by advertisers and selects the most suitable advertisement. The generative AI uses deep learning to analyze the content and target information of advertisements and selects the advertisement that is most suitable for specific characteristics. For example, it can select fashion-related advertisements for young women and sports goods advertisements for people wearing sportswear. Furthermore, the selection unit can continuously optimize its advertisement selection based on data that is updated in real time. For example, if a particular advertisement shows a high click-through rate or conversion rate, it can be adjusted to prioritize the display of that advertisement. In this way, the selection unit can select the most suitable advertisement based on the analyzed characteristics and maximize the effectiveness of the advertisement.

[0068] The display unit displays the advertisement selected by the selection unit. The display unit can display advertisements using, for example, digital signage. Digital signage uses a high-resolution display to show clear and highly visible advertisements. The display unit can also display advertisements using, for example, an electronic advertising platform. Electronic advertising platforms can deliver advertisements over the internet and reach a wide range of users. The display unit can also display advertisements using, for example, a mobile device. Mobile devices can display personalized advertisements based on the user's location information and browsing history. This allows the display unit to display selected advertisements on a variety of platforms and maximize the effectiveness of the advertisements. Furthermore, the display unit can monitor the display status of advertisements and user reactions in real time and evaluate the effectiveness of the advertisements. For example, it can analyze the visibility rate and click-through rate of digital signage, and the tap rate and conversion rate of mobile devices to measure the effectiveness of the advertisements. This allows the display unit to continuously improve the way advertisements are displayed and the content of the advertisements, maximizing their effectiveness.

[0069] The acquisition unit can acquire information on the gender, age, body type, and clothing of a person viewing an advertisement. The acquisition unit can, for example, identify gender using facial recognition technology. The acquisition unit can also, for example, estimate age using image analysis technology. The acquisition unit can also, for example, acquire information on body type and clothing using image analysis technology. This allows for more accurate advertisement selection by acquiring detailed characteristic information of the person viewing the advertisement. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, when the acquisition unit identifies gender using facial recognition technology, it can use AI to analyze the facial image and determine gender.

[0070] The analysis unit can analyze the information acquired by the acquisition unit and identify the characteristics of a person. The analysis unit can, for example, use AI to analyze the acquired information and identify the characteristics of a person. The analysis unit can, for example, use data analysis techniques to identify characteristics such as gender, age, body type, and clothing. The analysis unit can also, for example, use machine learning algorithms to identify characteristics. In this way, by analyzing the acquired information, the characteristics of a person can be accurately identified. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data acquired by the acquisition unit into the AI, and the AI ​​can analyze the image data to identify characteristics.

[0071] The selection unit can select appropriate advertisements from the database based on the characteristics identified by the analysis unit. The selection unit can, for example, use a generative AI to select the optimal advertisement from the database. The selection unit can, for example, select advertisements based on target information. The selection unit can also, for example, have a generative AI learn advertisements and target information provided by advertisers and then select the optimal advertisement. This maximizes the effectiveness of advertisements by selecting the optimal advertisement based on characteristics. Some or all of the above-described processes in the selection unit may be performed using a generative AI, for example, or without a generative AI. For example, the selection unit can input the characteristics identified by the analysis unit into a generative AI, and the generative AI can select the optimal advertisement from the database.

[0072] The display unit can display advertisements selected by the selection unit. The display unit can display advertisements using, for example, digital signage. The display unit can also display advertisements using, for example, an electronic advertising platform. The display unit can also display advertisements using, for example, a mobile device. This allows for the provision of advertisements that are best suited to the target audience by displaying selected advertisements. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the advertisements selected by the selection unit into the AI, which can then determine the best way to display the advertisements.

[0073] The selection unit can learn based on ad and target information provided by advertisers. For example, the selection unit can learn ad and target information provided by advertisers using generative AI. For example, the selection unit can learn ad and target information using machine learning algorithms. This allows for more accurate ad selection by learning the information provided by advertisers. Some or all of the above processing in the selection unit may be performed using generative AI, or without generative AI. For example, the selection unit can input ad and target information provided by advertisers into a generative AI, which can then learn this information.

[0074] The acquisition unit can estimate the emotions of a person viewing an advertisement and adjust the timing of characteristic acquisition based on the estimated emotions. For example, if the person viewing the advertisement is excited, the acquisition unit can immediately acquire characteristics and perform analysis in real time. For example, if the person viewing the advertisement is relaxed, the acquisition unit can take some time to acquire detailed characteristics. For example, if the person viewing the advertisement is in a hurry, the acquisition unit can quickly acquire characteristics and immediately display the most suitable advertisement. By adjusting the timing of characteristic acquisition based on emotions, characteristics can be acquired at a more appropriate time. 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 processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can capture the facial expression of a person viewing the advertisement with a camera, input it into the generative AI, and the generative AI can estimate the emotion.

[0075] The data acquisition unit can analyze the past behavioral history of people viewing advertisements and select an appropriate data acquisition method. For example, the data acquisition unit can analyze what kinds of advertisements people have shown interest in in the past and prioritize acquiring similar characteristics. For example, the data acquisition unit can acquire relevant characteristics based on places and events that people have visited in the past. For example, the data acquisition unit can acquire characteristics that will attract interest by referring to the past purchase history of people viewing advertisements. This makes it possible to acquire characteristics more effectively by analyzing past behavioral history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the past behavioral history of people viewing advertisements into AI, and the AI ​​can analyze the behavioral history and select the optimal data acquisition method.

[0076] The acquisition unit can filter the acquisition of characteristics based on the current activities and areas of interest of the person viewing the advertisement. For example, the acquisition unit can prioritize the acquisition of characteristics related to an event the person viewing the advertisement is currently participating in. For example, the acquisition unit can acquire characteristics based on topics the person viewing the advertisement is currently interested in. For example, the acquisition unit can acquire characteristics based on the device or app the person viewing the advertisement is currently using. This allows for the acquisition of more relevant characteristics by filtering characteristics based on current activities and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the current activities and areas of interest of the person viewing the advertisement into the AI, which can then filter characteristics based on this information.

[0077] The acquisition unit can estimate the emotions of a person viewing an advertisement and determine the priority of characteristics to acquire based on the estimated emotions. For example, if the person viewing the advertisement is excited, the acquisition unit can prioritize acquiring characteristics that are of interest. For example, if the person viewing the advertisement is relaxed, the acquisition unit can prioritize acquiring detailed characteristics. For example, if the person viewing the advertisement is in a hurry, the acquisition unit can prioritize acquiring characteristics that can be acquired quickly. This makes it possible to acquire characteristics more effectively by prioritizing characteristics based on 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 acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can capture the facial expression of a person viewing the advertisement with a camera, input it into the generative AI, and the generative AI can estimate the emotion.

[0078] The acquisition unit can preferentially acquire relevant characteristics based on the geographical location information of the person viewing the advertisement. For example, the acquisition unit can preferentially acquire characteristics related to the place where the person viewing the advertisement is currently located. For example, the acquisition unit can preferentially acquire characteristics related to places the person viewing the advertisement has visited in the past. For example, the acquisition unit can preferentially acquire characteristics related to places the person viewing the advertisement plans to visit in the future. This allows for the provision of more relevant advertisements by acquiring characteristics based on geographical location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the geographical location information of the person viewing the advertisement into AI, and the AI ​​can preferentially acquire relevant characteristics.

[0079] The acquisition unit can analyze the social media activity of people who view advertisements and acquire relevant characteristics. For example, the acquisition unit can acquire characteristics based on information shared by people who view advertisements on social media. For example, the acquisition unit can acquire characteristics related to accounts that people who view advertisements follow on social media. For example, the acquisition unit can acquire characteristics related to posts that people who view advertisements "like" on social media. By analyzing social media activity, more relevant characteristics can be acquired. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the social media activity of people who view advertisements into AI, and the AI ​​can acquire relevant characteristics.

[0080] The analysis unit can estimate the emotions of a person viewing an advertisement and adjust the analysis method based on the estimated emotions. For example, if the person viewing the advertisement is excited, the analysis unit can perform a rapid analysis and produce results immediately. For example, if the person viewing the advertisement is relaxed, the analysis unit can perform a detailed analysis and produce highly accurate results. For example, if the person viewing the advertisement is in a hurry, the analysis unit can perform a simplified analysis and produce results quickly. This allows for more appropriate analysis by adjusting the analysis method based on 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-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can capture the facial expressions of a person viewing the advertisement with a camera, input them into the generative AI, and the generative AI can estimate emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the acquired characteristics. For example, the analysis unit can perform a detailed analysis on highly important characteristics to produce highly accurate results. For example, the analysis unit can perform a simplified analysis on less important characteristics to produce results quickly. For example, the analysis unit can perform a balanced analysis on characteristics of moderate importance to produce appropriate results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the characteristics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the acquired characteristics into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0082] The analysis unit can apply different analysis methods depending on the category of the acquired characteristics. For example, for characteristics related to gender, the analysis unit can apply a gender-specific analysis algorithm. For example, for characteristics related to age, the analysis unit can apply an age-specific analysis algorithm. For example, for characteristics related to clothing, the analysis unit can apply a clothing-specific analysis algorithm. By applying an analysis algorithm according to the category of characteristics, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of acquired characteristics into the AI, and the AI ​​can apply different analysis algorithms depending on the category.

[0083] The analysis unit can estimate the emotions of a person viewing an advertisement and determine the priority of analysis based on the estimated emotions. For example, if the person viewing the advertisement is excited, the analysis unit may prioritize the analysis of characteristics that attract interest. For example, if the person viewing the advertisement is relaxed, the analysis unit may prioritize the analysis of detailed characteristics. For example, if the person viewing the advertisement is in a hurry, the analysis unit may prioritize characteristics that can be analyzed quickly. This allows for more effective analysis by determining the priority of analysis based on 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can capture the facial expression of a person viewing the advertisement with a camera, input it into the generative AI, and the generative AI can estimate the emotion.

[0084] The analysis unit can determine the order of analysis based on the submission timing of the acquired characteristics. For example, the analysis unit may prioritize the analysis of recently acquired characteristics. For example, the analysis unit may postpone the analysis of characteristics acquired in the past. For example, the analysis unit may prioritize the analysis of characteristics for which the submission timing is important. This enables efficient analysis by determining the priority of analysis based on the submission timing. 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 submission timing of the acquired characteristics into the AI, and the AI ​​can determine the order of analysis based on the submission timing.

[0085] The analysis unit can determine the order of analysis based on the relevance of the acquired characteristics. For example, the analysis unit may prioritize the analysis of characteristics highly relevant to advertising. For example, the analysis unit may postpone the analysis of characteristics less relevant to advertising. For example, the analysis unit may balance the analysis of characteristics with moderate relevance. By adjusting the order of analysis based on relevance, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the acquired characteristics into the AI, and the AI ​​can determine the order of analysis based on relevance.

[0086] The selection unit can estimate the emotions of a person viewing an advertisement and adjust its ad selection method based on the estimated emotions. For example, if the viewer is excited, the selection unit can select a visually stimulating advertisement. For example, if the viewer is relaxed, the selection unit can select a calming advertisement. For example, if the viewer is in a hurry, the selection unit can select a concise and to-the-point advertisement. By adjusting the ad selection method based on emotions, more effective ad selection becomes possible. 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 selection unit may be performed using a generative AI, or not using a generative AI. For example, the selection unit can capture the facial expression of a person viewing an advertisement with a camera, input it into a generative AI, and the generative AI can estimate the emotion.

[0087] The selection unit can adjust the precision of its selections based on the importance of the advertisements. For example, the selection unit can apply detailed selection criteria to highly important advertisements. For example, it can apply simplified selection criteria to less important advertisements. For example, it can apply balanced selection criteria to moderately important advertisements. This allows for efficient advertisement selection by adjusting the level of detail of the selections based on the importance of the advertisements. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the importance of the advertisements into a generative AI, which can then adjust the level of detail of the selections based on the importance.

[0088] The selection unit can apply different selection methods depending on the category of the advertisement. For example, for fashion advertisements, the selection unit can apply a selection algorithm specific to fashion. For example, for food advertisements, the selection unit can apply a selection algorithm specific to food. For example, for electronic device advertisements, the selection unit can apply a selection algorithm specific to electronic devices. By applying a selection algorithm according to the category of the advertisement, more accurate advertisement selection becomes possible. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the category of the advertisement into a generative AI, and the generative AI can apply a different selection algorithm depending on the category.

[0089] The selection unit can estimate the emotions of a person viewing an advertisement and prioritize advertisements based on the estimated emotions. For example, if the viewer is excited, the selection unit may prioritize visually stimulating advertisements. For example, if the viewer is relaxed, the selection unit may prioritize calming advertisements. For example, if the viewer is in a hurry, the selection unit may prioritize concise and to-the-point advertisements. This allows for more effective advertisement selection by prioritizing advertisements based on 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 selection unit may be performed using generative AI, or not. For example, the selection unit can capture the facial expression of a person viewing an advertisement with a camera, input it into the generative AI, and the generative AI can estimate the emotion.

[0090] The selection unit can determine the order of selection based on the submission date of the advertisements. For example, the selection unit may prioritize recently submitted advertisements. For example, the selection unit may postpone the selection of advertisements submitted in the past. For example, the selection unit may prioritize advertisements for which the submission date is important. This enables efficient advertisement selection by determining the priority of selection based on the submission date. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the submission dates of the advertisements into a generative AI, and the generative AI can determine the order of selection based on the submission dates.

[0091] The selection unit can determine the order of selection based on the relevance of the advertisements. For example, the selection unit may prioritize selecting characteristics that are highly relevant to the advertisement. For example, the selection unit may postpone selecting characteristics that are less relevant to the advertisement. For example, the selection unit may balance the selection of characteristics with a moderate level of relevance. By adjusting the order of selection based on relevance, more effective advertisement selection becomes possible. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the relevance of the advertisements into a generative AI, and the generative AI can determine the order of selection based on relevance.

[0092] The display unit can estimate the emotions of a person viewing an advertisement and adjust the way the advertisement is displayed based on the estimated emotions. For example, if the viewer is excited, the display unit can provide a visually stimulating display method. For example, if the viewer is relaxed, the display unit can provide a calm display method. For example, if the viewer is in a hurry, the display unit can provide a concise and to-the-point display method. By adjusting the display method of the advertisement based on emotions, more effective advertisement display becomes possible. 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, or not using AI. For example, the display unit can capture the facial expression of a person viewing the advertisement with a camera, input it into the generative AI, and the generative AI can estimate the emotion.

[0093] The display unit can select an appropriate display method when displaying an advertisement by referring to the viewer's past behavioral history. For example, the display unit can refer to the display methods of advertisements that the viewer has shown interest in in the past. For example, the display unit can select a display method related to places or events that the viewer has visited in the past. For example, the display unit can select the optimal display method based on the viewer's past purchase history. This makes it possible to display advertisements more effectively by referring to past behavioral history. 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 viewer's past behavioral history into AI, and the AI ​​can select the optimal display method.

[0094] The display unit can estimate the emotions of the person viewing the advertisement and adjust the display order of the advertisements based on the estimated emotions. For example, if the person viewing the advertisement is excited, the display unit can display visually stimulating advertisements first. For example, if the person viewing the advertisement is relaxed, the display unit can display calming advertisements first. For example, if the person viewing the advertisement is in a hurry, the display unit can display concise and to-the-point advertisements first. By adjusting the display order of advertisements based on emotions, more effective advertisement display becomes possible. 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, or not using AI. For example, the display unit can capture the facial expression of the person viewing the advertisement with a camera, input it into the generative AI, and the generative AI can estimate the emotion.

[0095] The display unit can select an appropriate display method based on the person's device information when displaying an advertisement. For example, if the person viewing the advertisement is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the person viewing the advertisement is using a tablet, the display unit can provide a display method optimized for a larger screen. For example, if the person viewing the advertisement is using a smartwatch, the display unit can provide a concise and highly visible display method. This enables more effective advertisement display by selecting the optimal display method based on 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 device information of the person viewing the advertisement into the AI, and the AI ​​can select the optimal display method.

[0096] The display unit can provide multilingual support when displaying advertisements, depending on the user's language settings. For example, the display unit can automatically set the language of the advertisement based on the language settings of the user's device. For example, the display unit can provide a language switching function if the user uses multiple languages. For example, the display unit can display the advertisement in a specific language if the user selects one. This enables more effective advertisement display by providing multilingual support according to language settings. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the user's language settings into the AI, which can then provide multilingual support.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The advertising display system may also include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit collects and analyzes data on products and services that the user has purchased in the past. For example, it can analyze the categories and price ranges of products the user has purchased in the past to understand the user's purchasing trends. Next, based on the analysis results, the purchase history analysis unit estimates the user's interests and preferences and provides information to select the most appropriate advertisements. For example, if the user has purchased luxury brand products in the past, advertisements for luxury brands can be displayed preferentially. Also, if the user has purchased products related to a particular season, advertisements related to that season can be displayed. In this way, the effectiveness of advertisements can be further enhanced by selecting advertisements based on the user's purchase history.

[0099] The advertising display system may also include a social media analytics unit that analyzes users' social media activity. This unit collects and analyzes information shared by users on social media, accounts they follow, and posts they like. For example, if a user frequently shares posts about a particular brand or product, the system can prioritize displaying advertisements for that brand or product. It can also display advertisements related to products or services promoted by influencers the user follows. Furthermore, it can analyze the content of posts a user likes to estimate their interests. This allows the system to select advertisements based on social media activity, providing ads that are relevant to the user's interests.

[0100] The ad display system can further estimate the user's emotions and adjust the timing of ad display based on those emotions. For example, if the user is excited, an ad can be displayed immediately to capture their attention. Conversely, if the user is relaxed, a more detailed ad can be displayed over a longer period of time. Also, if the user is in a hurry, an ad can be displayed quickly to convey information in a short amount of time. By adjusting the timing of ad display based on the user's emotions, more effective ad display becomes possible. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice. For example, the user's facial expressions can be captured with a camera, and AI can analyze the expressions to estimate emotions. Alternatively, the user's voice can be collected with a microphone, and emotions can be estimated using voice analysis technology.

[0101] The advertising display system can also include an activity analysis unit that understands the user's current activity status. This unit collects and analyzes information about events the user is currently participating in, apps they are using, and devices they are using. For example, if a user is participating in a sports event, sports-related advertisements can be prioritized. Similarly, if a user is using a specific app, advertisements related to that app can be displayed. Furthermore, if a user is using a device such as a smartphone or tablet, advertisements optimized for that device can be displayed. This allows for the delivery of more relevant advertisements by selecting them based on the user's current activity status.

[0102] The advertising display system may also include a location information analysis unit that analyzes the user's geographical location. The location information analysis unit collects and analyzes information related to the user's current location, past visits, and future planned visits. For example, it can prioritize displaying advertisements related to the user's current location. It can also display advertisements related to places the user has visited in the past. Furthermore, it can display advertisements related to places the user plans to visit in the future. This allows for the provision of more relevant advertisements by selecting them based on geographical location information. Location information is collected, for example, using GPS or Wi-Fi location information. For example, the user's current location can be determined using the GPS function of a smartphone, and advertisements can be selected based on that information.

[0103] The advertising display system can further estimate the user's emotions and customize the ad content based on those emotions. For example, if the user is excited, a visually stimulating ad can be displayed. Conversely, if the user is relaxed, a calming ad can be displayed. Also, if the user is in a hurry, a concise and to-the-point ad can be displayed. By customizing the ad content based on the user's emotions, more effective ad display becomes possible. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice. For example, the user's facial expressions can be captured with a camera, and AI can analyze the expressions to estimate emotions. Alternatively, the user's voice can be collected with a microphone, and emotions can be estimated using voice analysis technology.

[0104] The advertising display system may also include a device analysis unit that analyzes the user's device information. The device analysis unit collects and analyzes information about the type and settings of the device the user is using. For example, if the user is using a smartphone, the system can display advertisements optimized for the smartphone's screen size and resolution. Similarly, if the user is using a tablet, the system can display advertisements optimized for the larger screen. Furthermore, if the user is using a smartwatch, the system can display concise and highly visible advertisements. This allows for more effective ad display by selecting advertisements based on device information. Device information is collected, for example, by analyzing device settings and usage. For instance, smartphone settings information can be collected, and advertisements can be selected based on that information.

[0105] The ad display system can further estimate the user's emotions and adjust the order in which ads are displayed based on those emotions. For example, if a user is excited, visually stimulating ads can be displayed first. Conversely, if a user is relaxed, calming ads can be displayed first. Also, if a user is in a hurry, concise and to-the-point ads can be displayed first. By adjusting the order in which ads are displayed based on the user's emotions, more effective ad display becomes possible. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice. For example, the user's facial expressions can be captured with a camera, and AI can analyze the expressions to estimate emotions. Alternatively, the user's voice can be collected with a microphone, and emotions can be estimated using voice analysis technology.

[0106] The advertising display system may also include a language analysis unit that analyzes the user's language settings. This unit collects and analyzes the language settings of the user's device and their past language usage history. For example, if the user's device language setting is English, the system can display English advertisements. It can also provide a language switching function if the user uses multiple languages. Furthermore, if the user selects a specific language, the system can display advertisements in that language. This enables more effective ad display by providing multilingual advertisements based on language settings. Language settings are collected, for example, by analyzing device settings and past language usage history. For instance, the system can collect the language settings of a smartphone and select advertisements based on that information.

[0107] The ad display system can further estimate the user's emotions and prioritize ads based on those emotions. For example, if a user is excited, visually stimulating ads can be prioritized. Conversely, if a user is relaxed, calming ads can be prioritized. Also, if a user is in a hurry, concise and to-the-point ads can be prioritized. This allows for more effective ad display by prioritizing ads based on the user's emotions. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice. For example, a camera can capture the user's facial expressions, and AI can analyze them to estimate emotions. Alternatively, the user's voice can be collected with a microphone, and emotions can be estimated using voice analysis technology.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The acquisition unit acquires the characteristics of the person. For example, the acquisition unit uses an AI camera to acquire information such as the gender, age, body type, and clothing of the person viewing the advertisement. The acquisition unit can identify gender using facial recognition technology, estimate age using image analysis technology, and acquire information on body type and clothing. Step 2: The analysis unit analyzes the characteristics acquired by the acquisition unit. The analysis unit uses AI to analyze the acquired information and identify the characteristics of the person. Using data analysis methods and machine learning algorithms, characteristics such as gender, age, body type, and clothing can be identified. Step 3: The selection unit selects the optimal advertisement based on the characteristics analyzed by the analysis unit. The selection unit uses generative AI to select the optimal advertisement from the database and selects advertisements based on target information. The generative AI can also learn from advertisements and target information provided by advertisers to select the optimal advertisement. Step 4: The display unit displays the advertisement selected by the selection unit. The display unit can display the advertisement using digital signage, electronic advertising platforms, or mobile devices.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the acquisition unit, analysis unit, selection unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit uses the camera 42 of the smart device 14 to acquire the characteristics of a person viewing an advertisement. The analysis unit analyzes the information acquired by the identification processing unit 290 of the data processing unit 12 to identify the characteristics of the person. The selection unit selects the most suitable advertisement based on the characteristics analyzed by the identification processing unit 290 of the data processing unit 12. The display unit displays the selected advertisement using the output device 40 of the smart device 14. 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the acquisition unit, analysis unit, selection unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit uses the camera 42 of the smart glasses 214 to acquire the characteristics of a person viewing an advertisement. The analysis unit analyzes the information acquired by the identification processing unit 290 of the data processing unit 12 to identify the characteristics of the person. The selection unit selects the most suitable advertisement based on the characteristics analyzed by the identification processing unit 290 of the data processing unit 12. The display unit uses the speaker 240 of the smart glasses 214 to display the selected advertisement. 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.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the acquisition unit, analysis unit, selection unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit uses the camera 42 of the headset terminal 314 to acquire the characteristics of a person viewing an advertisement. The analysis unit analyzes the information acquired by the identification processing unit 290 of the data processing unit 12 to identify the characteristics of the person. The selection unit selects the most suitable advertisement based on the characteristics analyzed by the identification processing unit 290 of the data processing unit 12. The display unit displays the selected advertisement using the display 343 of the headset terminal 314. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the acquisition unit, analysis unit, selection unit, and display unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit uses the camera 42 of the robot 414 to acquire the characteristics of a person viewing an advertisement. The analysis unit analyzes the information acquired by the identification processing unit 290 of the data processing unit 12 to identify the characteristics of the person. The selection unit selects the most suitable advertisement based on the characteristics analyzed by the identification processing unit 290 of the data processing unit 12. The display unit displays the selected advertisement using the LEDs in the eyes of the robot 414. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) An acquisition unit that acquires the characteristics of a person, An analysis unit analyzes the characteristics acquired by the acquisition unit, A selection unit that selects an appropriate advertisement based on the characteristics analyzed by the analysis unit, The system includes a display unit that displays the advertisement selected by the selection unit. A system characterized by the following features. (Note 2) The acquisition unit is, Obtain information on the gender, age, body type, and clothing of people viewing the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The information acquired by the acquisition unit is analyzed, and the characteristics of the acquisition unit are identified. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Based on the characteristics identified by the analysis unit, an appropriate advertisement is selected from the database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is The advertisement selected by the aforementioned selection unit is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is It learns based on the advertisements and target information provided by advertisers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the emotions of people viewing the advertisement and adjusts the timing of characteristic acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the past behavioral history of people who view advertisements and select the appropriate method of acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, Filter the acquisition of characteristics based on the current activities and areas of interest of the person viewing the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the emotions of people viewing the advertisement and determines the priority of the characteristics to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, Prioritize obtaining relevant characteristics based on the geographical location information of the person viewing the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, Analyze the social media activity of people who view advertisements and obtain relevant characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of people who view advertisements and adjust the analysis method of characteristics based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Adjust the accuracy of the analysis based on the importance of the acquired characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Apply different analysis methods depending on the category of the acquired characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of people viewing the advertisement and determines the priority of analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The order of analysis is determined based on when the acquired characteristics were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The order of analysis is determined based on the relevance of the acquired characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is It estimates the emotions of people viewing the ads and adjusts the ad selection process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is Adjust the accuracy of selections based on the importance of the advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is Apply different selection methods depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is The system estimates the emotions of people viewing the advertisements and prioritizes the advertisements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is The order of selection is determined based on the timing of ad submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is The order of selections is determined based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is The system estimates the emotions of people viewing the advertisement and adjusts how the advertisement is displayed 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 advertisement, the system selects the appropriate display method by referring to the person's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is The system estimates the emotions of the person viewing the ad and adjusts the order in which the ads are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying an advertisement, the system selects the appropriate display method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying an ad, multilingual support will be provided according to the language settings of the person viewing the ad. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 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. An acquisition unit that acquires the characteristics of a person, An analysis unit analyzes the characteristics acquired by the acquisition unit, A selection unit that selects an appropriate advertisement based on the characteristics analyzed by the analysis unit, The system includes a display unit that displays the advertisement selected by the selection unit. A system characterized by the following features.

2. The acquisition unit is, Obtain information on the gender, age, body type, and clothing of people viewing the advertisement. The system according to feature 1.

3. The aforementioned analysis unit, The information acquired by the acquisition unit is analyzed, and the characteristics of the acquisition unit are identified. The system according to feature 1.

4. The aforementioned selection unit is Based on the characteristics identified by the analysis unit, an appropriate advertisement is selected from the database. The system according to feature 1.

5. The aforementioned display unit is The advertisement selected by the aforementioned selection unit is displayed. The system according to feature 1.

6. The aforementioned selection unit is It learns based on the advertisements and target information provided by advertisers. The system according to feature 1.

7. The acquisition unit is, The system estimates the emotions of people viewing the advertisement and adjusts the timing of characteristic acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the past behavioral history of people who view advertisements and select the appropriate method of acquisition. The system according to feature 1.

9. The acquisition unit is, Filter the acquisition of characteristics based on the current activities and areas of interest of the person viewing the advertisement. The system according to feature 1.

10. The acquisition unit is, The system estimates the emotions of people viewing the advertisement and determines the priority of the characteristics to acquire based on those estimated emotions. The system according to feature 1.

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