system

Abstract

The system according to this embodiment aims to collect and analyze environmental and people's reactions in real time, and to select and deliver the most appropriate advertisements. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a selection unit, and a distribution unit. The collection unit collects environmental and people's reactions. The analysis unit analyzes the data collected by the collection unit. The selection unit selects the most suitable advertisement based on the analysis results obtained by the analysis unit. The distribution unit distributes 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that in order to maximize the effect of advertisements, collecting and analyzing the environment and people's reactions in real time and selecting and distributing optimal advertisements have not been sufficiently carried out.

[0005] The system according to the embodiment aims to collect and analyze the environment and people's reactions in real time and select and distribute optimal advertisements.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, and a distribution unit. The collection unit collects environmental and people's reactions. The analysis unit analyzes the data collected by the collection unit. The selection unit selects the most suitable advertisement based on the analysis results obtained by the analysis unit. The distribution unit distributes the advertisement selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze environmental and people's reactions in real time, and select and deliver the most suitable advertisements. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An advertising agent system according to an embodiment of the present invention is a system that optimizes advertisements in the city and on trains in real time and displays the most suitable advertisements to target users. The advertising agent system uses an AI agent to analyze the environment and people's reactions, and selects and delivers the most effective advertisements. This system maximizes the effectiveness of advertisements and provides a meaningful experience for both advertisers and end users. For example, the advertising agent system uses an AI agent to analyze the environment. In this process, cameras and sensors are used to monitor the surrounding situation and people's movements in real time. For example, in an advertising display on a train, the age group, gender, and facial expressions of passengers can be analyzed. This allows for an understanding of the attributes of target users. Next, the advertising agent system's AI agent analyzes people's reactions. For example, it can analyze eye movements and changes in facial expressions in response to advertisements to evaluate the effectiveness of the advertisements. This allows for a determination of which advertisement is most effective. Furthermore, the advertising agent system's AI agent selects and delivers the most effective advertisements. For example, the advertisement content can be instantly switched based on the attributes and behavioral data of target users. This maximizes the effectiveness of advertisements and provides a meaningful experience for both advertisers and end users. This system enables reaching target users that were difficult to achieve with static, traditional advertising, and allows for the immediate measurement and adjustment of advertising effectiveness. Furthermore, because the content of the advertisements is timely, the return on investment improves. For example, public transportation operators, advertising agencies, and marketing departments can maximize the effectiveness of their advertisements in real time. This allows the advertising agent system to maximize the effectiveness of its advertisements.

[0029] The advertising agent system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, and a distribution unit. The collection unit collects the reactions of the environment and people. The collection unit can collect the reactions of the environment and people using, for example, cameras and sensors. The collection unit can, for example, take pictures of the surrounding situation using a CCD camera and detect people's movements using an infrared sensor. The collection unit can also measure the ambient temperature using a temperature sensor. For example, the collection unit can analyze the age group and gender of passengers using a camera. The collection unit can, for example, analyze the facial expressions of passengers using sensors. The collection unit can, for example, measure the ambient temperature using a temperature sensor. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data to understand the attributes of target users. The analysis unit can, for example, analyze the collected data to understand the age group and gender of target users. The analysis unit can, for example, analyze the collected data to understand the interests of target users. The analysis unit can, for example, analyze the collected data to understand the behavioral patterns of target users. The selection unit selects the most suitable advertisement based on the analysis results obtained by the analysis unit. The selection unit can, for example, select the most suitable advertisement based on the attributes and behavioral data of the target user. The selection unit can, for example, select the most suitable advertisement based on the age group and gender of the target user. The selection unit can, for example, select the most suitable advertisement based on the interests of the target user. The selection unit can, for example, select the most suitable advertisement based on the behavioral patterns of the target user. The distribution unit distributes the advertisements selected by the selection unit. The distribution unit can, for example, distribute the selected advertisements in real time. The distribution unit can, for example, display the selected advertisements on a display. The distribution unit can, for example, distribute the selected advertisements to smartphones. The distribution unit can, for example, display the selected advertisements on a website. As a result, the advertising agent system according to this embodiment can maximize the effectiveness of the advertisements.

[0030] The data collection unit collects information about the environment and people's reactions. For example, it can use cameras and sensors to collect information about the environment and people's reactions. Specifically, it can use a CCD camera to capture images of the surroundings and an infrared sensor to detect people's movements. This allows the data collection unit to understand the surrounding environment in real time and respond to dynamic environmental changes. It can also measure ambient temperature using a temperature sensor. For example, it can use a camera to analyze the age group and gender of passengers. This allows for the identification of target audiences for advertisements and enables more effective ad delivery. Furthermore, it can analyze passengers' facial expressions using sensors. Facial expression analysis allows for understanding passengers' emotional states and evaluating the effectiveness of advertisements in real time. By measuring ambient temperature using a temperature sensor, it becomes possible to adjust ad content according to environmental changes. For example, when the temperature is high, it can display advertisements for cooling products, and when the temperature is low, it can display advertisements for heating products. This allows the data collection unit to collect diverse data and provide fundamental information to maximize the effectiveness of advertisements. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and selection departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can analyze the collected data to understand the attributes of target users. Specifically, it can analyze the collected data to understand the age group and gender of target users. This clarifies the target audience for advertisements, enabling more effective ad delivery. Furthermore, it can analyze the collected data to understand the interests of target users. For example, it can identify interests based on advertisements users have viewed in the past and their purchase history, and select highly relevant advertisements. It can also analyze the collected data to understand the behavioral patterns of target users. For example, it can analyze users' movement paths and time spent on a site to identify the optimal timing for displaying advertisements. This allows the analysis department to quickly and accurately analyze the collected data and understand the attributes and behavioral patterns of target users. In addition, the analysis department can utilize historical data and statistical information to evaluate long-term advertising effectiveness and conduct trend analysis. For example, it can predict fluctuations in advertising effectiveness in specific regions and time periods based on past ad delivery data and formulate future advertising strategies. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the analysis unit to not only grasp the situation in real time but also to evaluate the long-term effectiveness of advertising and detect anomalies, thereby improving the reliability and effectiveness of the entire system.

[0032] The selection unit selects the most suitable advertisements based on the analysis results obtained by the analysis unit. For example, the selection unit can select the most suitable advertisements based on the attributes and behavioral data of target users. Specifically, it can select the most suitable advertisements based on the age group and gender of target users. This enables effective ad delivery tailored to the target audience. Furthermore, it can also select the most suitable advertisements based on the interests of target users. For example, it can select highly relevant advertisements based on advertisements that users have previously viewed and purchase history. It can also select the most suitable advertisements based on the behavioral patterns of target users. For example, it can analyze users' movement paths and time spent on a site to identify the optimal timing for displaying advertisements. As a result, the selection unit can quickly and accurately select the most suitable advertisements based on the analysis results, maximizing the effectiveness of the advertisements. In addition, the selection unit can continuously improve its ad selection criteria and algorithms. For example, it can review its selection criteria and algorithms based on advertisement effectiveness measurement data to perform more accurate ad selection. Furthermore, the selection unit can manage multiple ad campaigns simultaneously and formulate optimal ad delivery strategies. As a result, the selection unit can always provide highly accurate ad selection based on the latest information, maximizing the effectiveness of the advertisements.

[0033] The distribution unit delivers the advertisements selected by the selection unit. The distribution unit can, for example, deliver selected advertisements in real time. Specifically, it can display selected advertisements on screens. This allows for effective delivery of advertisements to the target audience. Furthermore, it can deliver selected advertisements to smartphones. For example, it can send push notifications to users' smartphones and display advertisements. It can also display selected advertisements on websites. For example, it can display advertisements on websites that users are browsing, effectively delivering advertisements to target users. This allows the distribution unit to deliver selected advertisements quickly and accurately, maximizing their effectiveness. In addition, the distribution unit can monitor the delivery status and effectiveness of advertisements in real time and adjust the content and timing of delivery as needed. For example, it can monitor the click-through rate and conversion rate of advertisements and review the content if the effectiveness is low. The distribution unit can also manage multiple distribution channels simultaneously and develop optimal distribution strategies. This allows the distribution unit to always provide highly accurate ad delivery based on the latest information, maximizing the effectiveness of advertisements.

[0034] The data collection unit can collect information about the environment and people's reactions using cameras and sensors. For example, the data collection unit can photograph the surrounding environment using a camera. For example, the data collection unit can detect people's movements using sensors. For example, the data collection unit can measure the ambient temperature using a temperature sensor. For example, the data collection unit can photograph the surrounding environment using a CCD camera and detect people's movements using an infrared sensor. For example, the data collection unit can measure the ambient temperature using a temperature sensor. This allows for accurate collection of information about the environment and people's reactions using cameras and sensors. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data acquired by a camera into a generating AI and analyze the environment and people's reactions from the image data.

[0035] The analysis department can analyze the collected data and understand the attributes of the target users. For example, the analysis department can analyze the collected data and understand the age group and gender of the target users. For example, the analysis department can analyze the collected data and understand the interests of the target users. For example, the analysis department can analyze the collected data and understand the behavioral patterns of the target users. For example, the analysis department can analyze the collected data and understand the age group and gender of the target users. For example, the analysis department can analyze the collected data and understand the interests of the target users. For example, the analysis department can analyze the collected data and understand the behavioral patterns of the target users. This allows for the selection of more effective advertisements by understanding the attributes of the target users. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the collected data into a generating AI and analyze the attributes of the target users.

[0036] The selection unit can select the most suitable advertisement based on the attributes and behavioral data of the target user. For example, the selection unit can select the most suitable advertisement based on the age group and gender of the target user. For example, the selection unit can select the most suitable advertisement based on the interests of the target user. For example, the selection unit can select the most suitable advertisement based on the behavioral patterns of the target user. For example, the selection unit can select the most suitable advertisement based on the age group and gender of the target user. For example, the selection unit can select the most suitable advertisement based on the interests of the target user. For example, the selection unit can select the most suitable advertisement based on the behavioral patterns of the target user. This maximizes the effectiveness of advertisements by selecting them based on the attributes and behavioral data of the target user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the attributes and behavioral data of the target user into a generating AI and select the most suitable advertisement.

[0037] The distribution unit can deliver selected advertisements in real time. The distribution unit can, for example, display selected advertisements on a display. The distribution unit can, for example, deliver selected advertisements to a smartphone. The distribution unit can, for example, display selected advertisements on a website. The distribution unit can, for example, display selected advertisements on a display. The distribution unit can, for example, deliver selected advertisements to a smartphone. The distribution unit can, for example, display selected advertisements on a website. This enables timely advertisement display by delivering advertisements in real time. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input selected advertisements into a generation AI and deliver them in real time.

[0038] The distribution unit can evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. This allows for continuous improvement of advertisement effectiveness by evaluating and providing feedback on the effectiveness of advertisements. Some or all of the above-described processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the effectiveness of advertisements into a generation AI and provide feedback.

[0039] The data collection unit can adjust the frequency of data collection based on specific time periods or events. For example, during rush hour, the data collection unit can frequently collect passenger movements and congestion levels. For example, during events, the data collection unit can collect participant reactions and behaviors in real time. For example, at night, the data collection unit can collect ambient sounds and changes in lighting. By adjusting the frequency of data collection based on specific time periods or events, more effective data collection becomes possible. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the frequency of data collection based on specific time periods or events into a generating AI and adjust it.

[0040] The data collection unit can collect additional information such as ambient sounds and temperature to perform a more detailed environmental analysis. For example, the data collection unit can collect ambient noise levels and adjust the volume of advertisements. For example, the data collection unit can collect temperature data and display advertisements appropriate for the season. For example, the data collection unit can collect lighting brightness and select advertisements with high visibility. This allows for a more detailed environmental analysis by collecting additional information such as ambient sounds and temperature. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input additional information such as ambient sounds and temperature into a generating AI and perform an environmental analysis.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's location information. For example, if the user is at a train station, the data collection unit can prioritize the collection of surrounding advertisements and passenger movements. For example, if the user is at a shopping mall, the data collection unit can prioritize the collection of store advertisements and shopper behavior. For example, if the user is at a park, the data collection unit can prioritize the collection of surrounding environmental sounds and people's activities. This enables more effective data collection by prioritizing the collection of highly relevant data based on the user's location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location information into a generating AI and prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data. For example, the data collection unit can collect information on nearby advertisements and events based on location information shared by the user on social media. For example, the data collection unit can analyze photos and videos posted by the user on social media and collect relevant advertisements. For example, the data collection unit can analyze a user's interests on social media and collect relevant data. This allows for the effective collection of relevant data by analyzing a user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's social media activity into a generating AI and collect relevant data.

[0043] The analysis unit can evaluate changes in current data by comparing it with past data. For example, the analysis unit can compare current data with past advertising effectiveness data to evaluate changes in effectiveness. For example, the analysis unit can compare current data with past user response data to evaluate changes in responses. For example, the analysis unit can compare current data with past environmental data to evaluate changes in the environment. This allows for an accurate evaluation of changes in current data by comparing it with past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data and current data into a generating AI to evaluate changes in the data.

[0044] The analysis unit can apply different analytical methods to each data category. For example, the analysis unit can apply clustering methods to user attribute data to classify target users. For example, the analysis unit can apply time series analysis methods to user behavior data to analyze behavioral patterns. For example, the analysis unit can apply regression analysis methods to environmental data to evaluate the impact of the environment. By applying different analytical methods to each data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analytical methods for each data category into a generating AI and perform the analysis.

[0045] The analysis unit can determine the priority of analysis based on the timing of data collection. For example, the analysis unit can prioritize the analysis of the latest data to grasp the real-time situation. For example, the analysis unit can prioritize the analysis of data during a specific event period to evaluate the effect of the event. For example, the analysis unit can prioritize the analysis of current data while referring to past data. This allows for a real-time understanding of the situation by determining the priority of analysis based on the timing of data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of data collection into a generating AI to determine the priority of analysis.

[0046] The analysis department can improve the accuracy of its analysis by referring to relevant market data. For example, the analysis department can refer to market trend data to evaluate the effectiveness of advertising. For example, the analysis department can refer to competitors' advertising data to compare the effectiveness of its own advertising. For example, the analysis department can refer to consumer purchasing data to identify target users for advertising. This improves the accuracy of the analysis by referring to relevant market data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input relevant market data into a generating AI to improve the accuracy of the analysis.

[0047] The selection unit can select the most suitable advertisement by referring to past advertising performance data. For example, the selection unit can prioritize the selection of advertisements that have shown high effectiveness in the past. For example, the selection unit can select the most suitable advertisement for the target user based on past performance data. For example, the selection unit can analyze past performance data and select highly effective advertisements. In this way, the most suitable advertisement can be selected by referring to past advertising performance data. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input past advertising performance data into a generating AI and select the most suitable advertisement.

[0048] The selection unit can apply different selection algorithms depending on the attributes of the target user. For example, the selection unit can apply an ad selection algorithm based on age group. For example, the selection unit can apply an ad selection algorithm based on gender. For example, the selection unit can apply an ad selection algorithm based on interests. This allows for the selection of the most suitable ad according to the attributes of the target user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input a selection algorithm based on the attributes of the target user into a generating AI and select an ad.

[0049] The selection unit can select the most suitable advertisement based on the location and time of display. For example, on an advertising display at a train station, the selection unit can select an advertisement that is timed to coincide with commuting hours. For example, on an advertising display at a shopping mall, the selection unit can select an advertisement that is timed to coincide with the busiest times for shoppers. For example, on an advertising display at a park, the selection unit can select an advertisement that coincides with weekend events. By selecting the most suitable advertisement based on the location and time of display, the effectiveness of the advertisement can be maximized. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the location and time of display of the advertisement into a generating AI and select the most suitable advertisement.

[0050] The selection unit can adjust the order of selection based on the relevance of the advertisements. For example, the selection unit can prioritize advertisements related to the interests of the target user. For example, the selection unit can select highly relevant advertisements based on the target user's past behavioral data. For example, the selection unit can select highly relevant advertisements based on the target user's attributes. By adjusting the order of selection based on the relevance of the advertisements, more effective advertisements can be selected. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the relevance of the advertisements into a generating AI and adjust the order of selection.

[0051] The distribution unit can evaluate the effectiveness of advertisements in real time and modify the advertisement content as needed. For example, after an advertisement is delivered, the distribution unit can evaluate user reactions in real time and switch to a different advertisement if the effect is poor. For example, if a user's gaze is not directed at an advertisement during its delivery, the distribution unit can change to an advertisement designed to attract attention. For example, if a user's facial expression does not change after an advertisement is delivered, the distribution unit can change to a more impactful advertisement. This allows for the real-time evaluation of the effectiveness of advertisements and the modification of the advertisement content as needed, thereby maximizing the effectiveness of the advertisements. Some or all of the above processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the effectiveness of the advertisement into a generating AI and modify the advertisement content.

[0052] The distribution unit can apply different distribution methods depending on the attributes of the target user. For example, the distribution unit can deliver visually stimulating advertisements to young people. For example, the distribution unit can deliver gentle and easy-to-understand advertisements to older people. For example, the distribution unit can deliver advertisements that convey the main points in a short amount of time to business people. This enables optimal advertisement delivery according to the attributes of the target user. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input distribution methods according to the attributes of the target user into a generating AI and deliver advertisements.

[0053] The distribution unit can select the optimal distribution method based on the location and time of day the advertisement is displayed. For example, the distribution unit can deliver advertisements on train station displays that are timed to coincide with commuting hours. For example, the distribution unit can deliver advertisements on shopping mall displays that are timed to coincide with peak shopping hours. For example, the distribution unit can deliver advertisements on park displays that are timed to coincide with weekend events. By selecting the optimal distribution method based on the location and time of day the advertisement is displayed, the effectiveness of the advertisement can be maximized. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the location and time of day the advertisement is displayed into a generating AI and select the optimal distribution method.

[0054] The delivery unit can adjust the delivery order based on the relevance of the ads. For example, the delivery unit can prioritize the delivery of ads related to the interests of the target users. For example, the delivery unit can deliver highly relevant ads based on the target users' past behavioral data. For example, the delivery unit can deliver highly relevant ads based on the attributes of the target users. By adjusting the delivery order based on the relevance of the ads, more effective ad delivery becomes possible. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of the ads into a generating AI and adjust the delivery order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The advertising agent system can also collect user purchase history, and the analysis unit can analyze that data. For example, the collection unit can collect data on products and services that users have purchased in the past. The analysis unit can analyze the collected purchase history data to understand the user's purchasing trends. Based on this, the selection unit can select the most suitable advertisements based on the user's purchasing trends. For example, it can prioritize selecting advertisements related to products that the user has purchased in the past. This can further enhance the effectiveness of the advertisements.

[0057] The advertising agent system can also collect user location information, and the analysis unit can analyze that data. For example, the collection unit can collect GPS data from the user's smartphone. The analysis unit can analyze the collected location data to understand the user's movement patterns. This allows the selection unit to select the most suitable advertisements based on the user's movement patterns. For example, it can prioritize selecting advertisements related to places the user frequently visits. This can further enhance the effectiveness of the advertisements.

[0058] The advertising agent system can further collect users' social media activity, and the analysis unit can analyze that data. For example, the collection unit can collect data on posts and photos that users share on social media. The analysis unit can analyze the collected social media data to understand users' interests. This allows the selection unit to select the most suitable advertisements based on users' interests. For example, it can prioritize selecting advertisements related to content that users have shared on social media. This can further enhance the effectiveness of the advertisements.

[0059] The advertising agent system can also collect user health data, which can then be analyzed by the analysis unit. For example, the collection unit can collect health data such as heart rate and steps taken from the user's smartwatch. The analysis unit can analyze the collected health data to understand the user's health status. This allows the selection unit to select the most suitable advertisements based on the user's health status. For example, if the user is exercising, advertisements for sports equipment can be displayed, and if they are relaxing, advertisements for relaxation products can be displayed. This further enhances the effectiveness of the advertisements.

[0060] The advertising agent system can also collect user purchase history, and the analysis unit can analyze that data. For example, the collection unit can collect data on products and services that users have purchased in the past. The analysis unit can analyze the collected purchase history data to understand the user's purchasing trends. Based on this, the selection unit can select the most suitable advertisements based on the user's purchasing trends. For example, it can prioritize selecting advertisements related to products that the user has purchased in the past. This can further enhance the effectiveness of the advertisements.

[0061] The advertising agent system can also collect user location information, and the analysis unit can analyze that data. For example, the collection unit can collect GPS data from the user's smartphone. The analysis unit can analyze the collected location data to understand the user's movement patterns. This allows the selection unit to select the most suitable advertisements based on the user's movement patterns. For example, it can prioritize selecting advertisements related to places the user frequently visits. This can further enhance the effectiveness of the advertisements.

[0062] The advertising agent system can further collect users' social media activity, and the analysis unit can analyze that data. For example, the collection unit can collect data on posts and photos that users share on social media. The analysis unit can analyze the collected social media data to understand users' interests. This allows the selection unit to select the most suitable advertisements based on users' interests. For example, it can prioritize selecting advertisements related to content that users have shared on social media. This can further enhance the effectiveness of the advertisements.

[0063] The advertising agent system can also collect user health data, which can then be analyzed by the analysis unit. For example, the collection unit can collect health data such as heart rate and steps taken from the user's smartwatch. The analysis unit can analyze the collected health data to understand the user's health status. This allows the selection unit to select the most suitable advertisements based on the user's health status. For example, if the user is exercising, advertisements for sports equipment can be displayed, and if they are relaxing, advertisements for relaxation products can be displayed. This further enhances the effectiveness of the advertisements.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit collects information about the environment and people's reactions. For example, cameras and sensors can be used to collect information about the environment and people's reactions. Specifically, a CCD camera can be used to photograph the surrounding environment, and an infrared sensor can be used to detect people's movements. A temperature sensor can also be used to measure the ambient temperature. Furthermore, a camera can be used to analyze the age group and gender of passengers, and sensors can be used to analyze passengers' facial expressions. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, by analyzing the collected data, they can understand the attributes of the target users. Specifically, they can understand the target users' age group, gender, interests, and behavioral patterns. Step 3: The selection unit selects the most suitable advertisement based on the analysis results obtained by the analysis unit. For example, it can select the most suitable advertisement based on the attributes and behavioral data of the target users. Specifically, it can select the most suitable advertisement based on the age group, gender, interests, and behavioral patterns of the target users. Step 4: The distribution unit delivers the advertisements selected by the selection unit. For example, the selected advertisements can be delivered in real time. Specifically, the selected advertisements can be displayed on displays, delivered to smartphones, or displayed on websites.

[0066] (Example of form 2) An advertising agent system according to an embodiment of the present invention is a system that optimizes advertisements in the city and on trains in real time and displays the most suitable advertisements to target users. The advertising agent system uses an AI agent to analyze the environment and people's reactions, and selects and delivers the most effective advertisements. This system maximizes the effectiveness of advertisements and provides a meaningful experience for both advertisers and end users. For example, the advertising agent system uses an AI agent to analyze the environment. In this process, cameras and sensors are used to monitor the surrounding situation and people's movements in real time. For example, in an advertising display on a train, the age group, gender, and facial expressions of passengers can be analyzed. This allows for an understanding of the attributes of target users. Next, the advertising agent system's AI agent analyzes people's reactions. For example, it can analyze eye movements and changes in facial expressions in response to advertisements to evaluate the effectiveness of the advertisements. This allows for a determination of which advertisement is most effective. Furthermore, the advertising agent system's AI agent selects and delivers the most effective advertisements. For example, the advertisement content can be instantly switched based on the attributes and behavioral data of target users. This maximizes the effectiveness of advertisements and provides a meaningful experience for both advertisers and end users. This system enables reaching target users that were difficult to achieve with static, traditional advertising, and allows for the immediate measurement and adjustment of advertising effectiveness. Furthermore, because the content of the advertisements is timely, the return on investment improves. For example, public transportation operators, advertising agencies, and marketing departments can maximize the effectiveness of their advertisements in real time. This allows the advertising agent system to maximize the effectiveness of its advertisements.

[0067] The advertising agent system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, and a distribution unit. The collection unit collects the reactions of the environment and people. The collection unit can collect the reactions of the environment and people using, for example, cameras and sensors. The collection unit can, for example, take pictures of the surrounding situation using a CCD camera and detect people's movements using an infrared sensor. The collection unit can also measure the ambient temperature using a temperature sensor. For example, the collection unit can analyze the age group and gender of passengers using a camera. The collection unit can, for example, analyze the facial expressions of passengers using sensors. The collection unit can, for example, measure the ambient temperature using a temperature sensor. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data to understand the attributes of target users. The analysis unit can, for example, analyze the collected data to understand the age group and gender of target users. The analysis unit can, for example, analyze the collected data to understand the interests of target users. The analysis unit can, for example, analyze the collected data to understand the behavioral patterns of target users. The selection unit selects the most suitable advertisement based on the analysis results obtained by the analysis unit. The selection unit can, for example, select the most suitable advertisement based on the attributes and behavioral data of the target user. The selection unit can, for example, select the most suitable advertisement based on the age group and gender of the target user. The selection unit can, for example, select the most suitable advertisement based on the interests of the target user. The selection unit can, for example, select the most suitable advertisement based on the behavioral patterns of the target user. The distribution unit distributes the advertisements selected by the selection unit. The distribution unit can, for example, distribute the selected advertisements in real time. The distribution unit can, for example, display the selected advertisements on a display. The distribution unit can, for example, distribute the selected advertisements to smartphones. The distribution unit can, for example, display the selected advertisements on a website. As a result, the advertising agent system according to this embodiment can maximize the effectiveness of the advertisements.

[0068] The data collection unit collects information about the environment and people's reactions. For example, it can use cameras and sensors to collect information about the environment and people's reactions. Specifically, it can use a CCD camera to capture images of the surroundings and an infrared sensor to detect people's movements. This allows the data collection unit to understand the surrounding environment in real time and respond to dynamic environmental changes. It can also measure ambient temperature using a temperature sensor. For example, it can use a camera to analyze the age group and gender of passengers. This allows for the identification of target audiences for advertisements and enables more effective ad delivery. Furthermore, it can analyze passengers' facial expressions using sensors. Facial expression analysis allows for understanding passengers' emotional states and evaluating the effectiveness of advertisements in real time. By measuring ambient temperature using a temperature sensor, it becomes possible to adjust ad content according to environmental changes. For example, when the temperature is high, it can display advertisements for cooling products, and when the temperature is low, it can display advertisements for heating products. This allows the data collection unit to collect diverse data and provide fundamental information to maximize the effectiveness of advertisements. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and selection departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0069] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can analyze the collected data to understand the attributes of target users. Specifically, it can analyze the collected data to understand the age group and gender of target users. This clarifies the target audience for advertisements, enabling more effective ad delivery. Furthermore, it can analyze the collected data to understand the interests of target users. For example, it can identify interests based on advertisements users have viewed in the past and their purchase history, and select highly relevant advertisements. It can also analyze the collected data to understand the behavioral patterns of target users. For example, it can analyze users' movement paths and time spent on a site to identify the optimal timing for displaying advertisements. This allows the analysis department to quickly and accurately analyze the collected data and understand the attributes and behavioral patterns of target users. In addition, the analysis department can utilize historical data and statistical information to evaluate long-term advertising effectiveness and conduct trend analysis. For example, it can predict fluctuations in advertising effectiveness in specific regions and time periods based on past ad delivery data and formulate future advertising strategies. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the analysis unit to not only grasp the situation in real time but also to evaluate the long-term effectiveness of advertising and detect anomalies, thereby improving the reliability and effectiveness of the entire system.

[0070] The selection unit selects the most suitable advertisements based on the analysis results obtained by the analysis unit. For example, the selection unit can select the most suitable advertisements based on the attributes and behavioral data of target users. Specifically, it can select the most suitable advertisements based on the age group and gender of target users. This enables effective ad delivery tailored to the target audience. Furthermore, it can also select the most suitable advertisements based on the interests of target users. For example, it can select highly relevant advertisements based on advertisements that users have previously viewed and purchase history. It can also select the most suitable advertisements based on the behavioral patterns of target users. For example, it can analyze users' movement paths and time spent on a site to identify the optimal timing for displaying advertisements. As a result, the selection unit can quickly and accurately select the most suitable advertisements based on the analysis results, maximizing the effectiveness of the advertisements. In addition, the selection unit can continuously improve its ad selection criteria and algorithms. For example, it can review its selection criteria and algorithms based on advertisement effectiveness measurement data to perform more accurate ad selection. Furthermore, the selection unit can manage multiple ad campaigns simultaneously and formulate optimal ad delivery strategies. As a result, the selection unit can always provide highly accurate ad selection based on the latest information, maximizing the effectiveness of the advertisements.

[0071] The distribution unit delivers the advertisements selected by the selection unit. The distribution unit can, for example, deliver selected advertisements in real time. Specifically, it can display selected advertisements on screens. This allows for effective delivery of advertisements to the target audience. Furthermore, it can deliver selected advertisements to smartphones. For example, it can send push notifications to users' smartphones and display advertisements. It can also display selected advertisements on websites. For example, it can display advertisements on websites that users are browsing, effectively delivering advertisements to target users. This allows the distribution unit to deliver selected advertisements quickly and accurately, maximizing their effectiveness. In addition, the distribution unit can monitor the delivery status and effectiveness of advertisements in real time and adjust the content and timing of delivery as needed. For example, it can monitor the click-through rate and conversion rate of advertisements and review the content if the effectiveness is low. The distribution unit can also manage multiple distribution channels simultaneously and develop optimal distribution strategies. This allows the distribution unit to always provide highly accurate ad delivery based on the latest information, maximizing the effectiveness of advertisements.

[0072] The data collection unit can collect information about the environment and people's reactions using cameras and sensors. For example, the data collection unit can photograph the surrounding environment using a camera. For example, the data collection unit can detect people's movements using sensors. For example, the data collection unit can measure the ambient temperature using a temperature sensor. For example, the data collection unit can photograph the surrounding environment using a CCD camera and detect people's movements using an infrared sensor. For example, the data collection unit can measure the ambient temperature using a temperature sensor. This allows for accurate collection of information about the environment and people's reactions using cameras and sensors. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data acquired by a camera into a generating AI and analyze the environment and people's reactions from the image data.

[0073] The analysis department can analyze the collected data and understand the attributes of the target users. For example, the analysis department can analyze the collected data and understand the age group and gender of the target users. For example, the analysis department can analyze the collected data and understand the interests of the target users. For example, the analysis department can analyze the collected data and understand the behavioral patterns of the target users. For example, the analysis department can analyze the collected data and understand the age group and gender of the target users. For example, the analysis department can analyze the collected data and understand the interests of the target users. For example, the analysis department can analyze the collected data and understand the behavioral patterns of the target users. This allows for the selection of more effective advertisements by understanding the attributes of the target users. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the collected data into a generating AI and analyze the attributes of the target users.

[0074] The selection unit can select the most suitable advertisement based on the attributes and behavioral data of the target user. For example, the selection unit can select the most suitable advertisement based on the age group and gender of the target user. For example, the selection unit can select the most suitable advertisement based on the interests of the target user. For example, the selection unit can select the most suitable advertisement based on the behavioral patterns of the target user. For example, the selection unit can select the most suitable advertisement based on the age group and gender of the target user. For example, the selection unit can select the most suitable advertisement based on the interests of the target user. For example, the selection unit can select the most suitable advertisement based on the behavioral patterns of the target user. This maximizes the effectiveness of advertisements by selecting them based on the attributes and behavioral data of the target user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the attributes and behavioral data of the target user into a generating AI and select the most suitable advertisement.

[0075] The distribution unit can deliver selected advertisements in real time. The distribution unit can, for example, display selected advertisements on a display. The distribution unit can, for example, deliver selected advertisements to a smartphone. The distribution unit can, for example, display selected advertisements on a website. The distribution unit can, for example, display selected advertisements on a display. The distribution unit can, for example, deliver selected advertisements to a smartphone. The distribution unit can, for example, display selected advertisements on a website. This enables timely advertisement display by delivering advertisements in real time. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input selected advertisements into a generation AI and deliver them in real time.

[0076] The distribution unit can evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. The distribution unit can, for example, evaluate the effectiveness of advertisements and provide feedback as needed. This allows for continuous improvement of advertisement effectiveness by evaluating and providing feedback on the effectiveness of advertisements. Some or all of the above-described processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the effectiveness of advertisements into a generation AI and provide feedback.

[0077] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is excited, the data collection unit can focus on collecting changes in facial expressions and movements. For example, if the user is relaxed, the data collection unit can collect data such as ambient sounds and temperature. For example, if the user is stressed, the data collection unit can collect biometric data such as eye movements and heart rate. By adjusting the type of data collected based on the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into the generative AI and adjust the type of data to be collected.

[0078] The data collection unit can adjust the frequency of data collection based on specific time periods or events. For example, during rush hour, the data collection unit can frequently collect passenger movements and congestion levels. For example, during events, the data collection unit can collect participant reactions and behaviors in real time. For example, at night, the data collection unit can collect ambient sounds and changes in lighting. By adjusting the frequency of data collection based on specific time periods or events, more effective data collection becomes possible. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the frequency of data collection based on specific time periods or events into a generating AI and adjust it.

[0079] The data collection unit can collect additional information such as ambient sounds and temperature to perform a more detailed environmental analysis. For example, the data collection unit can collect ambient noise levels and adjust the volume of advertisements. For example, the data collection unit can collect temperature data and display advertisements appropriate for the season. For example, the data collection unit can collect lighting brightness and select advertisements with high visibility. This allows for a more detailed environmental analysis by collecting additional information such as ambient sounds and temperature. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input additional information such as ambient sounds and temperature into a generating AI and perform an environmental analysis.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting changes in facial expressions and movements. For example, if the user is relaxed, the data collection unit can prioritize collecting data such as ambient sounds and temperature. For example, if the user is stressed, the data collection unit can prioritize collecting biometric data such as eye movements and heart rate. This allows for the collection of more important data by prioritizing the data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI and determine the priority of data to collect.

[0081] The data collection unit can prioritize the collection of highly relevant data based on the user's location information. For example, if the user is at a train station, the data collection unit can prioritize the collection of surrounding advertisements and passenger movements. For example, if the user is at a shopping mall, the data collection unit can prioritize the collection of store advertisements and shopper behavior. For example, if the user is at a park, the data collection unit can prioritize the collection of surrounding environmental sounds and people's activities. This enables more effective data collection by prioritizing the collection of highly relevant data based on the user's location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location information into a generating AI and prioritize the collection of highly relevant data.

[0082] The data collection unit can analyze a user's social media activity and collect relevant data. For example, the data collection unit can collect information on nearby advertisements and events based on location information shared by the user on social media. For example, the data collection unit can analyze photos and videos posted by the user on social media and collect relevant advertisements. For example, the data collection unit can analyze a user's interests on social media and collect relevant data. This allows for the effective collection of relevant data by analyzing a user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's social media activity into a generating AI and collect relevant data.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is excited, the analysis unit can apply an algorithm that emphasizes emotional changes. For example, if the user is relaxed, the analysis unit can apply an algorithm that captures gentle emotional changes. For example, if the user is stressed, the analysis unit can apply an algorithm that identifies the cause of the stress. By adjusting the analysis algorithm based on the user's emotions, a more accurate analysis 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and adjust the analysis algorithm.

[0084] The analysis unit can evaluate changes in current data by comparing it with past data. For example, the analysis unit can compare current data with past advertising effectiveness data to evaluate changes in effectiveness. For example, the analysis unit can compare current data with past user response data to evaluate changes in responses. For example, the analysis unit can compare current data with past environmental data to evaluate changes in the environment. This allows for an accurate evaluation of changes in current data by comparing it with past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data and current data into a generating AI to evaluate changes in the data.

[0085] The analysis unit can apply different analytical methods to each data category. For example, the analysis unit can apply clustering methods to user attribute data to classify target users. For example, the analysis unit can apply time series analysis methods to user behavior data to analyze behavioral patterns. For example, the analysis unit can apply regression analysis methods to environmental data to evaluate the impact of the environment. By applying different analytical methods to each data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analytical methods for each data category into a generating AI and perform the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. For example, if the user is relaxed, the analysis unit can provide a calm display method. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-read display method. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and adjust the display method of the analysis results.

[0087] The analysis unit can determine the priority of analysis based on the timing of data collection. For example, the analysis unit can prioritize the analysis of the latest data to grasp the real-time situation. For example, the analysis unit can prioritize the analysis of data during a specific event period to evaluate the effect of the event. For example, the analysis unit can prioritize the analysis of current data while referring to past data. This allows for a real-time understanding of the situation by determining the priority of analysis based on the timing of data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of data collection into a generating AI to determine the priority of analysis.

[0088] The analysis department can improve the accuracy of its analysis by referring to relevant market data. For example, the analysis department can refer to market trend data to evaluate the effectiveness of advertising. For example, the analysis department can refer to competitors' advertising data to compare the effectiveness of its own advertising. For example, the analysis department can refer to consumer purchasing data to identify target users for advertising. This improves the accuracy of the analysis by referring to relevant market data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input relevant market data into a generating AI to improve the accuracy of the analysis.

[0089] The selection unit can estimate the user's emotions and adjust the ad selection criteria based on the estimated emotions. For example, if the user is excited, the selection unit can select visually stimulating ads. For example, if the user is relaxed, the selection unit can select calming ads. For example, if the user is stressed, the selection unit can select ads with a relaxing effect. This allows for the selection of more effective ads by adjusting the ad selection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into a generative AI and adjust the ad selection criteria.

[0090] The selection unit can select the most suitable advertisement by referring to past advertising performance data. For example, the selection unit can prioritize the selection of advertisements that have shown high effectiveness in the past. For example, the selection unit can select the most suitable advertisement for the target user based on past performance data. For example, the selection unit can analyze past performance data and select highly effective advertisements. In this way, the most suitable advertisement can be selected by referring to past advertising performance data. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input past advertising performance data into a generating AI and select the most suitable advertisement.

[0091] The selection unit can apply different selection algorithms depending on the attributes of the target user. For example, the selection unit can apply an ad selection algorithm based on age group. For example, the selection unit can apply an ad selection algorithm based on gender. For example, the selection unit can apply an ad selection algorithm based on interests. This allows for the selection of the most suitable ad according to the attributes of the target user. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input a selection algorithm based on the attributes of the target user into a generating AI and select an ad.

[0092] The selection unit can estimate the user's emotions and determine the priority of ad selection based on the estimated user emotions. For example, if the user is excited, the selection unit can prioritize visually stimulating ads. For example, if the user is relaxed, the selection unit can prioritize calming ads. For example, if the user is stressed, the selection unit can prioritize ads with a relaxing effect. This allows for the selection of more effective ads by prioritizing ad selection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user emotion data into a generative AI to determine the priority of ad selection.

[0093] The selection unit can select the most suitable advertisement based on the location and time of display. For example, on an advertising display at a train station, the selection unit can select an advertisement that is timed to coincide with commuting hours. For example, on an advertising display at a shopping mall, the selection unit can select an advertisement that is timed to coincide with the busiest times for shoppers. For example, on an advertising display at a park, the selection unit can select an advertisement that coincides with weekend events. By selecting the most suitable advertisement based on the location and time of display, the effectiveness of the advertisement can be maximized. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the location and time of display of the advertisement into a generating AI and select the most suitable advertisement.

[0094] The selection unit can adjust the order of selection based on the relevance of the advertisements. For example, the selection unit can prioritize advertisements related to the interests of the target user. For example, the selection unit can select highly relevant advertisements based on the target user's past behavioral data. For example, the selection unit can select highly relevant advertisements based on the target user's attributes. By adjusting the order of selection based on the relevance of the advertisements, more effective advertisements can be selected. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the relevance of the advertisements into a generating AI and adjust the order of selection.

[0095] The ad delivery unit can estimate the user's emotions and adjust the timing of ad delivery based on those emotions. For example, if the user is excited, the ad delivery unit can immediately deliver a visually stimulating ad. For example, if the user is relaxed, the ad delivery unit can slowly deliver a calming ad. For example, if the user is stressed, the ad delivery unit can deliver a relaxing ad at the right time. By adjusting the timing of ad delivery based on the user's emotions, more effective ad delivery becomes possible. 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 ad delivery unit may be performed using AI, for example, or without AI. For example, the ad delivery unit can input user emotion data into a generative AI and adjust the timing of ad delivery.

[0096] The distribution unit can evaluate the effectiveness of advertisements in real time and modify the advertisement content as needed. For example, after an advertisement is delivered, the distribution unit can evaluate user reactions in real time and switch to a different advertisement if the effect is poor. For example, if a user's gaze is not directed at an advertisement during its delivery, the distribution unit can change to an advertisement designed to attract attention. For example, if a user's facial expression does not change after an advertisement is delivered, the distribution unit can change to a more impactful advertisement. This allows for the real-time evaluation of the effectiveness of advertisements and the modification of the advertisement content as needed, thereby maximizing the effectiveness of the advertisements. Some or all of the above processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the effectiveness of the advertisement into a generating AI and modify the advertisement content.

[0097] The distribution unit can apply different distribution methods depending on the attributes of the target user. For example, the distribution unit can deliver visually stimulating advertisements to young people. For example, the distribution unit can deliver gentle and easy-to-understand advertisements to older people. For example, the distribution unit can deliver advertisements that convey the main points in a short amount of time to business people. This enables optimal advertisement delivery according to the attributes of the target user. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input distribution methods according to the attributes of the target user into a generating AI and deliver advertisements.

[0098] The ad delivery unit can estimate the user's emotions and determine the priority of ad delivery based on the estimated emotions. For example, if the user is excited, the ad delivery unit can prioritize delivering visually stimulating ads. For example, if the user is relaxed, the ad delivery unit can prioritize delivering calming ads. For example, if the user is stressed, the ad delivery unit can prioritize delivering relaxing ads. This allows for more effective ad delivery by prioritizing ad delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad delivery unit may be performed using AI, for example, or without AI. For example, the ad delivery unit can input user emotion data into a generative AI to determine the priority of ad delivery.

[0099] The distribution unit can select the optimal distribution method based on the location and time of day the advertisement is displayed. For example, the distribution unit can deliver advertisements on train station displays that are timed to coincide with commuting hours. For example, the distribution unit can deliver advertisements on shopping mall displays that are timed to coincide with peak shopping hours. For example, the distribution unit can deliver advertisements on park displays that are timed to coincide with weekend events. By selecting the optimal distribution method based on the location and time of day the advertisement is displayed, the effectiveness of the advertisement can be maximized. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the location and time of day the advertisement is displayed into a generating AI and select the optimal distribution method.

[0100] The delivery unit can adjust the delivery order based on the relevance of the ads. For example, the delivery unit can prioritize the delivery of ads related to the interests of the target users. For example, the delivery unit can deliver highly relevant ads based on the target users' past behavioral data. For example, the delivery unit can deliver highly relevant ads based on the attributes of the target users. By adjusting the delivery order based on the relevance of the ads, more effective ad delivery becomes possible. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of the ads into a generating AI and adjust the delivery order.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The advertising agent system can also collect user purchase history, and the analysis unit can analyze that data. For example, the collection unit can collect data on products and services that users have purchased in the past. The analysis unit can analyze the collected purchase history data to understand the user's purchasing trends. Based on this, the selection unit can select the most suitable advertisements based on the user's purchasing trends. For example, it can prioritize selecting advertisements related to products that the user has purchased in the past. This can further enhance the effectiveness of the advertisements.

[0103] The advertising agent system can also collect user location information, and the analysis unit can analyze that data. For example, the collection unit can collect GPS data from the user's smartphone. The analysis unit can analyze the collected location data to understand the user's movement patterns. This allows the selection unit to select the most suitable advertisements based on the user's movement patterns. For example, it can prioritize selecting advertisements related to places the user frequently visits. This can further enhance the effectiveness of the advertisements.

[0104] The advertising agent system can further collect users' social media activity, and the analysis unit can analyze that data. For example, the collection unit can collect data on posts and photos that users share on social media. The analysis unit can analyze the collected social media data to understand users' interests. This allows the selection unit to select the most suitable advertisements based on users' interests. For example, it can prioritize selecting advertisements related to content that users have shared on social media. This can further enhance the effectiveness of the advertisements.

[0105] The advertising agent system can further estimate the user's emotions and adjust how ads are displayed based on those estimated emotions. For example, the data collection unit can collect the user's facial expressions and tone of voice to estimate their emotions. The analysis unit can analyze the estimated emotion data to understand the user's emotional state. This allows the selection unit to choose the most appropriate ad based on the user's emotional state. For example, it can display calming ads when the user is relaxed and stimulating ads when the user is excited. This can further enhance the effectiveness of the ads.

[0106] The advertising agent system can also collect user health data, which can then be analyzed by the analysis unit. For example, the collection unit can collect health data such as heart rate and steps taken from the user's smartwatch. The analysis unit can analyze the collected health data to understand the user's health status. This allows the selection unit to select the most suitable advertisements based on the user's health status. For example, if the user is exercising, advertisements for sports equipment can be displayed, and if they are relaxing, advertisements for relaxation products can be displayed. This further enhances the effectiveness of the advertisements.

[0107] The advertising agent system can also collect user purchase history, and the analysis unit can analyze that data. For example, the collection unit can collect data on products and services that users have purchased in the past. The analysis unit can analyze the collected purchase history data to understand the user's purchasing trends. Based on this, the selection unit can select the most suitable advertisements based on the user's purchasing trends. For example, it can prioritize selecting advertisements related to products that the user has purchased in the past. This can further enhance the effectiveness of the advertisements.

[0108] The advertising agent system can also collect user location information, and the analysis unit can analyze that data. For example, the collection unit can collect GPS data from the user's smartphone. The analysis unit can analyze the collected location data to understand the user's movement patterns. This allows the selection unit to select the most suitable advertisements based on the user's movement patterns. For example, it can prioritize selecting advertisements related to places the user frequently visits. This can further enhance the effectiveness of the advertisements.

[0109] The advertising agent system can further collect users' social media activity, and the analysis unit can analyze that data. For example, the collection unit can collect data on posts and photos that users share on social media. The analysis unit can analyze the collected social media data to understand users' interests. This allows the selection unit to select the most suitable advertisements based on users' interests. For example, it can prioritize selecting advertisements related to content that users have shared on social media. This can further enhance the effectiveness of the advertisements.

[0110] The advertising agent system can further estimate the user's emotions and adjust how ads are displayed based on those estimated emotions. For example, the data collection unit can collect the user's facial expressions and tone of voice to estimate their emotions. The analysis unit can analyze the estimated emotion data to understand the user's emotional state. This allows the selection unit to choose the most appropriate ad based on the user's emotional state. For example, it can display calming ads when the user is relaxed and stimulating ads when the user is excited. This can further enhance the effectiveness of the ads.

[0111] The advertising agent system can also collect user health data, which can then be analyzed by the analysis unit. For example, the collection unit can collect health data such as heart rate and steps taken from the user's smartwatch. The analysis unit can analyze the collected health data to understand the user's health status. This allows the selection unit to select the most suitable advertisements based on the user's health status. For example, if the user is exercising, advertisements for sports equipment can be displayed, and if they are relaxing, advertisements for relaxation products can be displayed. This further enhances the effectiveness of the advertisements.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit collects information about the environment and people's reactions. For example, cameras and sensors can be used to collect information about the environment and people's reactions. Specifically, a CCD camera can be used to photograph the surrounding environment, and an infrared sensor can be used to detect people's movements. A temperature sensor can also be used to measure the ambient temperature. Furthermore, a camera can be used to analyze the age group and gender of passengers, and sensors can be used to analyze passengers' facial expressions. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, by analyzing the collected data, they can understand the attributes of the target users. Specifically, they can understand the target users' age group, gender, interests, and behavioral patterns. Step 3: The selection unit selects the most suitable advertisement based on the analysis results obtained by the analysis unit. For example, it can select the most suitable advertisement based on the attributes and behavioral data of the target users. Specifically, it can select the most suitable advertisement based on the age group, gender, interests, and behavioral patterns of the target users. Step 4: The distribution unit delivers the advertisements selected by the selection unit. For example, the selected advertisements can be delivered in real time. Specifically, the selected advertisements can be displayed on displays, delivered to smartphones, or displayed on websites.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, and distribution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect environmental and people's reactions using the camera 42 and sensors of the smart device 14. The analysis unit can analyze the collected data using the identification processing unit 290 of the data processing unit 12 to understand the attributes of target users. The selection unit can select the most suitable advertisement using the identification processing unit 290 of the data processing unit 12. The distribution unit can deliver the selected advertisement in real time using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, and distribution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect environmental and people's reactions using the camera 42 and sensors of the smart glasses 214. The analysis unit can analyze the collected data using the identification processing unit 290 of the data processing unit 12 to understand the attributes of target users. The selection unit can select the most suitable advertisement using the identification processing unit 290 of the data processing unit 12. The distribution unit can deliver the selected advertisement in real time using the speaker 240 of the smart glasses 214. 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.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The 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.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 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.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the 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.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, and distribution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect environmental and people's reactions using the camera 42 and sensors of the headset terminal 314. The analysis unit can analyze the collected data using the identification processing unit 290 of the data processing unit 12 to understand the attributes of target users. The selection unit can select the most suitable advertisement using the identification processing unit 290 of the data processing unit 12. The distribution unit can deliver the selected advertisement in real time 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.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The 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.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, and distribution unit, can be implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect environmental and people's reactions using the camera 42 and sensors of the robot 414. The analysis unit can analyze the collected data using the identification processing unit 290 of the data processing unit 12 to understand the attributes of target users. The selection unit can select the most suitable advertisement using the identification processing unit 290 of the data processing unit 12. The distribution unit can deliver the selected advertisement in real time using the speaker 240 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A collection department that collects information on the environment and people's reactions, An analysis unit analyzes the data collected by the aforementioned collection unit, A selection unit that selects the optimal advertisement based on the analysis results obtained by the aforementioned analysis unit, The system includes a distribution unit that delivers the advertisement selected by the selection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Cameras and sensors are used to collect information about the environment and people's reactions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze the collected data to understand the attributes of the target users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Select the most suitable ads based on the attributes and behavioral data of target users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned distribution unit, Deliver selected ads in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned distribution unit, Evaluate the effectiveness of the advertisements and provide feedback as needed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Adjust the frequency of data collection based on specific time periods or events. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Collect additional information such as ambient sounds and temperature to conduct a more detailed environmental analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Prioritize the collection of highly relevant data based on the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Evaluate the changes in current data by comparing it with past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Apply different analytical methods to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Prioritize analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Referencing relevant market data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is We estimate user sentiment and adjust ad selection criteria based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is Select the most suitable advertisement by referring to past advertising performance data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is Apply different selection algorithms depending on the target user's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is It estimates the user's emotions and prioritizes ad selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is Select the most suitable advertisement based on its display location and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is Adjust the order of selections based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned distribution unit, It estimates the user's emotions and adjusts the timing of ad delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned distribution unit, Evaluate the effectiveness of your ads in real time and modify the ad content as needed. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned distribution unit, Apply different delivery methods depending on the attributes of the target user. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned distribution unit, It estimates user sentiment and prioritizes ad delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit, The optimal delivery method is selected based on the location and time of day the ad is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned distribution unit, Adjust the delivery order based on ad relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information on the environment and people's reactions, An analysis unit analyzes the data collected by the aforementioned collection unit, A selection unit that selects the optimal advertisement based on the analysis results obtained by the aforementioned analysis unit, The system includes a distribution unit that delivers the advertisement selected by the selection unit. A system characterized by the following features.

2. The aforementioned collection unit is Cameras and sensors are used to collect information about the environment and people's reactions. The system according to feature 1.

3. The aforementioned analysis unit is Analyze the collected data to understand the attributes of the target users. The system according to feature 1.

4. The aforementioned selection unit is Select the most suitable ads based on the attributes and behavioral data of target users. The system according to feature 1.

5. The aforementioned distribution unit, Deliver selected ads in real time. The system according to feature 1.

6. The aforementioned distribution unit, Evaluate the effectiveness of the advertisements and provide feedback as needed. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Adjust the frequency of data collection based on specific time periods or events. The system according to feature 1.

9. The aforementioned collection unit is Collect additional information such as ambient sounds and temperature to conduct a more detailed environmental analysis. The system according to feature 1.

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

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