System

Abstract

A system is provided.SOLUTION: A system including means for collecting data of an installation place from a camera or a sensor, means for transmitting the collected data to a server, means for analyzing the received data using a generation model and determining optimal advertisement content, means for transmitting the determined advertisement content to an advertisement display device, means for displaying the advertisement content on the advertisement display device, means for collecting data again during an advertisement campaign period and evaluating an effect, and means for proposing an advertisement strategy on the basis of an effect measurement result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Current ad display devices often display fixed ads once they have been set up, preventing effective approaches to target audiences. This often results in poor advertising effectiveness. Furthermore, measuring the effectiveness of advertising campaigns is often done manually, making real-time measurement and analysis difficult. This invention aims to solve these issues by using a generative AI model to optimize ads in real time and precisely measure the effectiveness of advertising campaigns. [Means for solving the problem]

[0005] The present invention provides a system including the following means: means for collecting installation location data from cameras and sensors, means for transmitting the collected data to a server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for transmitting the determined advertising content to an advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating its effectiveness, and means for proposing advertising strategies based on the results of effectiveness measurement. This system enables real-time analysis and advertising optimization, enabling precise effectiveness measurement and effective approaches to target demographics.

[0006] "Cameras and sensors" are devices used to collect data from the location where they are installed, and have the ability to acquire video, images, location information, etc.

[0007] "Location data" refers to information about the environment in which the advertisement is placed and the trends, attributes, and behavior of people in the surrounding area.

[0008] "Server" means a computer system that receives, stores, and analyzes the collected data, uses a generative AI model to determine optimal advertising content, and transmits it to the display device.

[0009] "Generative AI model" refers to an artificial intelligence algorithm that analyzes received data and predicts and determines optimal advertising content in real time.

[0010] "Advertising content" refers to digital media such as advertising images, videos, and text displayed on a display device.

[0011] An "advertising display device" is a device including a digital signage or monitor for visually displaying advertising content transmitted from a server.

[0012] "Data during the advertising campaign" refers to information such as the flow of people at the installation location and the length of time they stay there, which is collected again while the advertising campaign is being carried out.

[0013] "Means for evaluating effectiveness" refers to methods and tools for analyzing data collected during the advertising campaign and quantitatively evaluating the effectiveness of the advertising.

[0014] "Means for proposing advertising strategies" refers to methods and tools that, based on the results of effectiveness measurement, suggest effective approaches for the next advertising campaign or for specific target audiences. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] MODE FOR CARRYING OUT THE INVENTION

[0037] This invention is a system that utilizes generative AI models to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location to optimally display advertising.

[0038] Data collection

[0039] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. This data is collected in real time and periodically sent to a server.

[0040] Data transmission and storage

[0041] The device compiles the collected data into packets and sends them to the server via API. The server receives the transmitted data and stores it in a database. The stored data is used for subsequent analysis and optimization of ad display.

[0042] Analyzing data and determining optimal advertising content

[0043] The server analyzes the received data and inputs it into a generative AI model. The generative AI model predicts and determines the optimal advertising content at the current time based on the obtained data. For example, if the analysis results show that there are many young people, it will select advertisements for fashion brands aimed at that demographic.

[0044] Sending and displaying advertising content

[0045] The server sends the selected advertising content to the advertising display device (terminal). The terminal displays the received advertising content in real time. The displayed content is optimized for the target user demographic.

[0046] Measuring the effectiveness of advertising campaigns

[0047] During the advertising campaign, the device collects data again to measure the changes before and after. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as the ad's view rate, increase or decrease in dwell time, and changes in the number of passersby.

[0048] Visualization and proposal of effects

[0049] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of their advertising at a glance. Furthermore, based on the analysis results, the server suggests ways to approach specific target demographics and new advertising strategies.

[0050] Specific examples

[0051] For example, the target is digital signage advertisements installed in large shopping malls.

[0052] The device uses a camera to detect the age group and walking speed of people passing through the mall and sends the data to a server.

[0053] The server analyzes the received data using a generative AI model and detects that there are many young people in the evening hours.

[0054] The server selects advertisements for new fashion brands aimed at young people and sends them to the digital signage.

[0055] The terminal displays the advertisement received from the server in real time.

[0056] During the advertising campaign, the device continuously collects data and transmits it to the server.

[0057] The server analyzes the effectiveness of the advertisement based on the newly collected data and evaluates factors such as increases in viewer ratings and length of stay.

[0058] The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[0059] In this way, the system of the present invention provides an effective advertising strategy through real-time advertising optimization and precise effect measurement.

[0060] The processing flow will be explained below.

[0061] Program processing steps

[0062] Step 1:

[0063] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity.

[0064] Step 2:

[0065] The device collects data and sends it to a server via an API, along with metadata such as time, location, and person attributes.

[0066] Step 3:

[0067] The server receives the data sent from the device and stores it in a database, which is used for subsequent analysis and to optimize the display of advertisements.

[0068] Step 4:

[0069] The server removes noise from the received data and completes missing data, as well as detecting and correcting outliers and completing missing values.

[0070] Step 5:

[0071] The server inputs the preprocessed data into a generative AI model, which predicts optimal advertising content based on the target's attribute data.

[0072] Step 6:

[0073] The server selects the optimal advertising content based on the output of the generative AI model. For example, if it determines that a large number of young people are in the audience, it will select advertisements for fashion brands aimed at that demographic.

[0074] Step 7:

[0075] The server then transmits the selected advertising content to the device, using an encryption protocol to ensure security.

[0076] Step 8:

[0077] The terminal displays the advertising content received from the server on an advertising display device, for example, by projecting advertising videos or images onto a digital signage.

[0078] Step 9:

[0079] During the advertising campaign, the device will again use its cameras and sensors to collect data, again capturing information such as the number of passersby, their attributes, and their behavior.

[0080] Step 10:

[0081] The device sends data during the ad campaign to the server, which also packets the data and sends it via API.

[0082] Step 11:

[0083] The server analyzes the newly received data from the advertising campaign period and evaluates the effectiveness of the advertisement based on factors such as viewer ratings, changes in dwell time, and changes in the number of people passing by.

[0084] Step 12:

[0085] The server visualizes the analysis results in graphs and charts and provides them to the user, who can use them to see the effectiveness of their advertising at a glance.

[0086] Step 13:

[0087] The server then proposes new advertising strategies based on the analysis results, for example, showing effective approaches to specific target demographics.

[0088] Step 14:

[0089] The user checks the proposals sent from the server and plans and implements a new advertising strategy.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] Conventional advertising display systems mainly display fixed content, making it difficult to display optimal ads in real time based on viewer attributes and behavior. Furthermore, they lacked a mechanism for precisely measuring the effectiveness of advertising campaigns and reflecting this information in subsequent advertising strategies. This made it difficult to maximize advertising effectiveness.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for collecting installation location data from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for transmitting the determined advertising content to the advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating effectiveness, and means for visualizing the evaluation results in graphs and charts and proposing configurable advertising strategies. This makes it possible to display advertisements based on viewer attributes and behavior in real time, and to precisely measure the effectiveness of the advertising campaign and reflect it in the next advertising strategy.

[0095] A "camera" is a device that captures images of the surrounding area where it is installed and collects visual data.

[0096] A "sensor" is a device that detects physical environmental information (e.g., temperature, light, sound, motion, etc.) and converts it into a digital signal.

[0097] A "terminal" is a device that transmits data collected from cameras and sensors to a server, and also functions as an advertising display device.

[0098] "Server" refers to a central processing unit that receives and stores data sent from the device, analyzes the data using a generative AI model, and determines the optimal advertising content.

[0099] A "generative AI model" is a model that uses machine learning technology to analyze collected data and predict and determine optimal advertising content.

[0100] "Advertising content" refers to the content of the advertisement displayed on the display device (for example, images, videos, text, etc.).

[0101] An "advertising display device" is a device for visually displaying determined advertising content, and generally a display or digital signage is used.

[0102] "Effectiveness measurement" is the process of analyzing data collected during an advertising campaign to evaluate factors such as ad viewership and increase or decrease in time spent on the ad.

[0103] "Evaluation results" refer to indicators and information obtained through analysis of effectiveness measurement data, which indicate the effectiveness of an advertising campaign.

[0104] "Advertising strategy" refers to the specific approach and plan for the next advertising campaign based on the evaluation results.

[0105] "Graphs and charts" are visual tools for visually displaying evaluation results, presenting trends and patterns in the data in an easily understandable format.

[0106] This invention is a system that utilizes generative AI models to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location to optimally display advertising.

[0107] Data collection

[0108] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. This data is collected in real time and periodically sent to a server. Examples of specific hardware used include network cameras (e.g., Hikvision) and motion detection sensors.

[0109] Data transmission and storage

[0110] The device assembles the collected data into packets and sends them to a server via API. The server receives the sent data and stores it in a database. An example of specific software used is data transmission using a RESTful API. The stored data is used for subsequent analysis and optimization of ad display.

[0111] Analyzing data and determining optimal advertising content

[0112] The server analyzes the received data and inputs it into a generative AI model. The generative AI model predicts and determines the optimal advertising content at the current time based on the obtained data. For example, a model built with the TensorFlow library using Python can be used. For example, if the analysis results show that there are many young people, advertisements for fashion brands aimed at that demographic can be selected. As a concrete example, the following prompt sentence can be input:

[0113] "Based on data showing that many young people (aged 18 to 25) visit the shopping mall between 6:00 PM and 9:00 PM, please suggest advertisements for fashion brands that will appeal to them."

[0114] Sending and displaying advertising content

[0115] The server sends the selected advertising content to the advertising display device (terminal). The terminal displays the received advertising content in real time. Examples of specific hardware used include digital signage and large displays. The content displayed is optimized for the target user demographic.

[0116] Measuring the effectiveness of advertising campaigns

[0117] During the advertising campaign, the device collects data again to measure the changes before and after. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as the ad's view rate, increase or decrease in dwell time, and changes in the number of passersby.

[0118] Visualization and proposal of effects

[0119] The server visualizes the analysis results in graphs and charts and provides them to the user. Specific software that can be used includes data visualization tools (e.g., D3.js, Matplotlib). This allows users to see the effectiveness of their advertising at a glance. Furthermore, based on the analysis results, the server suggests approaches to specific target demographics and new advertising strategies.

[0120] Specific examples

[0121] For example, consider digital signage advertisements installed in large shopping malls. The device uses a camera to detect the age group and walking speed of passersby in the mall and sends the data to a server. The server analyzes the received data using a generative AI model and detects that there are many young people in the mall during the evening hours. The server selects advertisements for new fashion brands aimed at young people and sends them to the digital signage. The device displays the advertisements received from the server in real time. During the advertising campaign, the device continuously collects data and sends it to the server. The server analyzes the effectiveness of the advertisements based on the newly collected data and evaluates things like viewer rates and increases in dwell time. The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[0122] In this way, the system of the present invention provides an effective advertising strategy through real-time advertising optimization and precise effect measurement.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1: Collect data

[0125] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. Specifically, the camera photographs passersby and uses facial recognition software to estimate their age and gender. Motion detection sensors also measure walking speed and length of stay. This data is collected in real time. The input is raw data from the cameras and sensors, and the output is a dataset of analyzed attribute information and behavioral information.

[0126] Step 2: Sending data

[0127] The device compiles the collected data into a list and sends it to the server via API at regular intervals (e.g., every 5 minutes). Specifically, it constructs an HTTP request and sends the collected data to the server in JSON format. For example, it sends a request to the "POST / data" endpoint. The input is a dataset of collected attribute information and behavioral information, and the output is the response to the HTTP request sent to the server.

[0128] Step 3: Save your data

[0129] The server receives data sent from the device and stores it in a database. Specifically, it analyzes the received data, generates SQL statements to insert into a database (e.g., Amazon RDS MySQL), and executes them. The input is the JSON data sent from the device, and the output is the record stored in the database.

[0130] Step 4: Data analysis

[0131] The server analyzes the data stored in the database and inputs it into the generative AI model. Specifically, it extracts the necessary data from the database, performs data preprocessing, and then inputs it into the generative AI model built using Python and the TensorFlow library. The input is the dataset extracted from the database, and the output is the analysis result of the AI ​​model.

[0132] Step 5: Determine the best ad content

[0133] The server analyzes the output from the generative AI model and determines the optimal advertising content. As a specific example, if data shows a high proportion of young people, it selects an advertisement for a fashion brand aimed at that demographic. In this process, it selects the optimal advertisement from a pre-prepared list of advertisements. The input is the analysis result of the AI ​​model, and the output is the selected advertising content.

[0134] Step 6: Submit your advertising content

[0135] The server sends the selected ad content to the ad display device (terminal). Specifically, it sends the ad content as an HTTP response. For example, it sends a request including an ad file to the "POST / display_ad" endpoint. The input is the selected ad content, and the output is the ad content sent to the terminal.

[0136] Step 7: Displaying the Ad

[0137] The terminal displays the advertising content received from the server in real time. Specifically, it sends commands to play video or image advertisements on the display of the advertising display device. The input is the received advertising content, and the output is the displayed advertising content.

[0138] Step 8: Measure your results

[0139] During the advertising campaign, the device collects data again to measure changes before and after the ad is displayed. Specifically, based on the re-collected data, it measures changes in viewer rate, increase or decrease in stay time, and change in the number of passersby. The input is the data collected during the advertising campaign, and the output is a dataset of the effectiveness measurement results.

[0140] Step 9: Evaluate and visualize the effects

[0141] The server analyzes the measurement results and evaluates the actual impact of the advertisement. Specifically, it uses a data visualization tool to generate graphs and charts and provide them to the user. The input is a dataset of the measurement results, and the output is the visualized evaluation results.

[0142] Step 10: Generate proposals

[0143] The server proposes a new advertising strategy based on the results of the effectiveness measurement. Specifically, it further applies the AI ​​model to generate a specific approach for the next advertising campaign. The input is the analyzed effectiveness measurement results, and the output is a proposal for a new advertising strategy.

[0144] (Application example 1)

[0145] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0146] The modern advertising industry requires the real-time delivery of optimal advertising content to specific target audiences. However, current advertising systems make it difficult to precisely measure advertising effectiveness and develop new advertising strategies based on the results. Furthermore, when displaying advertisements via wearable devices such as smart glasses, there is a lack of technology that can display optimal advertisements based on the user's surrounding environment. Therefore, there is an urgent need to develop an integrated system that can dynamically select and display optimal advertisements under specific conditions and measure their effectiveness.

[0147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0148] In this invention, the server includes means for collecting data on the installation location from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative model and determining optimal advertising content, means for displaying the advertising content on the display of the smart glasses, means for re-collecting data during the advertising campaign period and evaluating its effectiveness, and means for proposing an advertising strategy based on the results of the effectiveness measurement. This makes it possible to display optimal advertising content to a specific target demographic in real time and precisely measure the effectiveness of the advertising.

[0149] "Cameras and sensors" are optical and sensing devices used to collect data at their locations.

[0150] "Installation location data" refers to information about the environment around the advertisement installation location and information about people's attributes and behavior.

[0151] "Collection means" refers to devices or software that acquire environmental and behavioral data from cameras or sensors.

[0152] A "server" is a computer system that receives collected data and performs analysis and advertising content decisions.

[0153] The "transmission means" is a device or software for assembling collected data into packets and transmitting the data to a server.

[0154] A "generative model" is an artificial intelligence algorithm that analyzes received data and generates or selects advertising content.

[0155] "Advertising content" refers to the content of the advertisement that is optimized and displayed using a generative model.

[0156] An "advertising display device" is a device for visually displaying determined advertising content to a user.

[0157] "Smart glasses" are wearable devices that display information through a built-in display.

[0158] "Data during the advertising campaign" refers to environmental and behavioral data that is continuously collected while the advertisement is displayed.

[0159] The "means for evaluating the effectiveness" refers to a device or software for analyzing the collected data and quantitatively analyzing the impact of the advertisement.

[0160] The "means for proposing advertising strategies" refers to a device or software for planning the next advertising strategy based on the results of effectiveness measurement.

[0161] The present invention relates to a system for displaying advertising content on smart glasses, the implementation of which includes the following steps:

[0162] First, the server installs cameras and sensors at the installation location to collect data on the surrounding environment and people's attributes and behavior. The camera acts as an optical device and captures images in real time. The sensor is a device for detecting attributes such as age, gender, and movement speed. This data is constantly acquired through the collection means.

[0163] The collected data is then sent to a server via API. The sending means is a communication device or software that packages the collected data into packets and sends them to the server. The server receives and stores this data. On the server side, it is recommended to use a database solution such as PostgreSQL.

[0164] A generative AI model analyzes this received data in real time on the server and determines the optimal advertising content. The generative AI model is built on common deep learning frameworks such as TensorFlow and PyTorch. It analyzes user attributes and behavioral patterns and generates advertisements tailored to the target demographic. For example, during times when many young people gather, advertisements for fashion brands appropriate for that demographic are selected.

[0165] The advertising content determined based on the analysis results is sent to the smart glasses, which then display the advertising content in real time within the user's field of view using a display means. Examples of smart glasses that can be used include Google Glass and Microsoft HoloLens.

[0166] During the advertising campaign, the server again collects data through cameras and sensors to evaluate its effectiveness. Specifically, it analyzes parameters such as ad viewing rate, changes in dwell time, and changes in the number of passersby. The evaluated effectiveness measurement data serves as the basis for proposing the next advertising strategy. Data visualization tools such as Matplotlib and Plotly are suitable for evaluation.

[0167] As a concrete example, the following prompt sentence is input into the generative AI model for a young person in their 20s walking through a busy downtown area during the day.

[0168] Example prompt sentence:

[0169] "Currently, many young people in their 20s walking around busy areas during the day are using smart glasses. Therefore, think about the advertising content that is most suitable for them. Specifically, advertisements for fashion brands and restaurants based on their age group, gender, and speed of movement would be good."

[0170] In this way, the system of the present invention can display optimal advertising content in real time and precisely measure its effectiveness, thereby dramatically improving the accuracy and effectiveness of advertising strategies.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] Data on the installation location is collected using cameras and sensors.

[0174] (operation)

[0175] The cameras and sensors connected to the server capture real-time environmental information about the location and the attributes and behavioral data of people around it. Specifically, the cameras capture images, and the sensors detect information such as age, gender, and movement speed.

[0176] (input)

[0177] Physical environment of the installation site, passerby attributes and behavioral data

[0178] (output)

[0179] Raw environmental information and people's attributes and behavior data

[0180] Step 2:

[0181] The collected data is sent to the server.

[0182] (operation)

[0183] The device collects data and sends it to a server via an API, using an internet connection.

[0184] (input)

[0185] Raw data collected in step 1

[0186] (output)

[0187] Environmental information and people data sent to the server

[0188] Step 3:

[0189] A generative AI model is used to analyze the received data and determine the optimal advertising content.

[0190] (operation)

[0191] The server stores the received data in a database and inputs it into a generative AI model for analysis. The generative AI model uses TensorFlow or PyTorch to predict and determine the optimal advertisement based on the input data.

[0192] (input)

[0193] Environmental information and people's attributes and behavior data stored on the server

[0194] (output)

[0195] Optimal advertising content as a result of analysis

[0196] Step 4:

[0197] The determined advertising content is transmitted to the smart glasses.

[0198] (operation)

[0199] The server transmits the determined advertising content to the smart glasses via an API for displaying advertisements.

[0200] (input)

[0201] Ad content determined by generative AI model

[0202] (output)

[0203] Advertising content sent to smart glasses

[0204] Step 5:

[0205] Displaying advertising content on smart glasses.

[0206] (operation)

[0207] The smart glasses then display the received advertising content on their display, with the timing and location of the display controlled in real time.

[0208] (input)

[0209] Advertising content sent to smart glasses

[0210] (output)

[0211] Advertising content displayed in the user's field of view

[0212] Step 6:

[0213] Data will be collected again during the advertising campaign to evaluate its effectiveness.

[0214] (operation)

[0215] During the campaign period, the server will again collect data via cameras and sensors, and analyze that data to evaluate changes in ad viewing rates and dwell times.

[0216] (input)

[0217] Environmental and behavioral data collected again

[0218] (output)

[0219] Advertising effectiveness evaluation results

[0220] Step 7:

[0221] We propose advertising strategies based on the results of effectiveness measurements.

[0222] (operation)

[0223] The server proposes strategies for the next advertising campaign based on the collected and analyzed performance measurement data, and uses visualization tools to display the results in graphs and charts.

[0224] (input)

[0225] Advertising effectiveness evaluation results

[0226] (output)

[0227] Advertisement strategy suggestions and visualized data

[0228] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0229] MODE FOR CARRYING OUT THE INVENTION

[0230] This invention is a system that utilizes a generative AI model and an emotion engine to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location, and recognizing user emotions to optimize advertising display.

[0231] Data collection

[0232] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. At the same time, it uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data. This data is collected in real time and periodically sent to a server.

[0233] Data transmission and storage

[0234] The device compiles the collected data into packets and sends them to a server via API. This data includes metadata such as time, location, person's attributes, behavior, and emotional state. The server receives the transmitted data and stores it in a database. The stored data is used for subsequent analysis and optimization of ad display.

[0235] Analyzing data and determining optimal advertising content

[0236] The server filters out noise from the received data and fills in missing data, then inputs the preprocessed data into the generative AI model and emotion engine. The generative AI model predicts optimal advertising content based on the target's attribute data. The emotion engine then analyzes the user's emotional data and determines the optimal advertising content for the user's current emotional state.

[0237] Sending and displaying advertising content

[0238] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. An encryption protocol is used to ensure security during transmission. The device displays the advertising content received from the server on an advertising display device in real time. For example, a user who is smiling might be shown an advertisement that emphasizes fun.

[0239] Measuring the effectiveness of advertising campaigns

[0240] During the advertising campaign, the device again collects data using cameras and sensors to measure changes before and after the campaign. For example, it compares passerby attributes, length of stay, and user emotion data before and after the campaign. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as viewer ratings, increases or decreases in length of stay, changes in the number of passersby, and changes in user emotion.

[0241] Visualization and proposal of effects

[0242] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of the advertisements at a glance. Furthermore, the server proposes new advertising strategies based on the analysis results. For example, it suggests the type of advertising content that is more effective at a specific time or location.

[0243] Specific examples

[0244] For example, when targeting a digital signage advertisement installed in a large shopping mall, the operation is as follows.

[0245] The device uses a camera to detect the age group and behavioral data of passersby in the mall, and then uses an emotion engine to collect emotional data from the user's facial expressions and voice, and sends this data to a server.

[0246] The server analyzes the received data using a generative AI model and emotion engine, and detects, for example, that there are many young people in the evening hours and that their emotional state is excited.

[0247] The server selects advertisements for new fashion brands that are aimed at young people and match their excitement level, and transmits them to the digital signage.

[0248] The terminal displays the advertisement received from the server in real time.

[0249] During the advertising campaign, the device again collects data and sends it to the server.

[0250] The server analyzes the effectiveness of the advertising campaign based on the newly collected data, evaluating things like viewership rates, increases in dwell time, and changes in user emotions.

[0251] The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[0252] In this way, the system of the present invention provides effective advertising strategies through real-time advertising optimization and precise effectiveness measurement, and by utilizing user emotional data.

[0253] The processing flow will be explained below.

[0254] Program processing steps

[0255] Step 1:

[0256] The device uses cameras and sensors installed at the advertisement location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people around it. It also uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data.

[0257] Step 2:

[0258] The device collects data and sends it to a server via an API, along with metadata such as time, location, person's attributes, behavior, and emotional state.

[0259] Step 3:

[0260] The server receives the data sent from the device and stores it in a database, which is used for subsequent analysis and to optimize the display of advertisements.

[0261] Step 4:

[0262] The server removes noise from the received data and fills in missing data, specifically detecting and correcting outliers and filling in missing values.

[0263] Step 5:

[0264] The server inputs the preprocessed data into a generative AI model, which predicts optimal advertising content based on the target's attribute data.

[0265] Step 6:

[0266] The server also inputs pre-processed emotional data into an emotion engine, which analyzes the user's emotional state and determines emotion-based advertising content.

[0267] Step 7:

[0268] The server combines the output of the generative AI model and the emotion engine to select the most appropriate advertising content for that moment, for example, selecting entertainment advertising for excited young users.

[0269] Step 8:

[0270] The server then transmits the selected advertising content to the device, using an encryption protocol to ensure security.

[0271] Step 9:

[0272] The terminal displays the advertising content received from the server on an advertising display device in real time, for example, by projecting advertising videos or images onto a digital signage.

[0273] Step 10:

[0274] During the advertising campaign, the device will again use its cameras and sensors to collect data and measure changes before and after the campaign, such as comparing passerby attributes, length of stay, and user emotion data before and after the campaign.

[0275] Step 11:

[0276] The device sends data during the ad campaign to the server, which also packets the data and sends it via API.

[0277] Step 12:

[0278] The server analyzes the newly received data from the advertising campaign period and evaluates the effectiveness of the advertisement based on factors such as viewer ratings, changes in time spent on the site, changes in the number of people passing by, and changes in user emotions.

[0279] Step 13:

[0280] The server visualizes the analysis results in graphs and charts and provides them to the user, who can use them to see the effectiveness of their advertising at a glance.

[0281] Step 14:

[0282] The server then suggests new advertising strategies based on the analysis, for example, suggesting the type of advertising content that would be more effective at a particular time or location.

[0283] Step 15:

[0284] The user checks the proposals sent from the server, plans and implements a new advertising strategy, evaluates the proposals, and reflects them in the next advertising campaign.

[0285] With these detailed processing steps, the system of the present invention can provide effective advertising strategies through real-time advertising optimization and precise effectiveness measurement, and by utilizing user sentiment data.

[0286] Example 2

[0287] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0288] Conventional advertising display systems determine advertising content based solely on target user attributes and behavioral data, which limits the effectiveness of advertising. Furthermore, real-time ad optimization and measurement of advertising campaign effectiveness are insufficient, resulting in delays in improving advertising strategies.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0290] In this invention, the server includes means for collecting data on the installation location from cameras and sensors, means for analyzing the collected data in real time and detecting user attributes, behavior, and emotional state, means for transmitting the collected data to the server, means for predicting optimal advertising content from the received data using a generative AI model, means for analyzing user emotional data using an emotion engine and determining optimal advertising content, means for transmitting the determined advertising content to an advertising display device and displaying it in real time, means for re-collecting data during the advertising campaign period and measuring and evaluating effectiveness, and means for proposing advertising strategies based on the effectiveness measurement results and visualizing the analysis results, thereby enabling real-time advertising optimization and precise effectiveness measurement.

[0291] A "camera" is a device for capturing images and collecting the data.

[0292] A "sensor" is a device that senses environmental information and collects that data.

[0293] "Installation location data" refers to information about the area around where the advertisement is installed, including information such as people's attributes and behavior.

[0294] "Real-time analysis" refers to the process of processing data as it is collected and obtaining analytical results.

[0295] "User attributes" refers to basic information about individual users, such as age, gender, and occupation.

[0296] "Behavior" refers to the actions and situations that a user can take, such as walking speed and length of stay.

[0297] "Emotional state" refers to the emotion the user is currently feeling (e.g., joy, anger, etc.).

[0298] "Generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate or predict optimal advertising content.

[0299] "Emotion engine" refers to a system for analyzing a user's emotion data and outputting corresponding results.

[0300] "Advertisement display device" refers to a hardware device for displaying determined advertisement content.

[0301] "Campaign Data" means all data collected during the implementation of a particular campaign.

[0302] "Measuring and evaluating effectiveness" refers to the process of quantitatively and qualitatively analyzing the success and impact of an advertising campaign.

[0303] "Proposing an advertising strategy" means showing the direction of future advertising activities and a specific action plan based on the results of effectiveness measurements.

[0304] "Visualizing the analysis results" refers to displaying the results of data analysis in a form that is easy for users to understand (e.g., graphs, charts, etc.).

[0305] This invention is a system that utilizes a generative AI model and an emotion engine to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location, and recognizing user emotions to optimize advertising display.

[0306] Data collection

[0307] The device uses high-resolution cameras and sensors (such as rangefinders and infrared sensors) installed at the advertising location to collect passersby's age, gender, and behavioral data (walking speed, length of stay, etc.) in real time. Furthermore, the device uses a high-performance microphone and emotion recognition software linked to an emotion engine to analyze and collect emotional data from the user's facial expressions and voice. This allows the emotion engine to accurately grasp the user's current emotional state.

[0308] Data transmission and storage

[0309] The device collects data and sends it securely to a server using a REST API. The data packet contains detailed metadata such as time, location, person's attributes, behavior, and emotional state. The transmitted data is received in real time by the server and stored in a cloud database (e.g., Amazon RDS or Google BigQuery).

[0310] Analyzing data and determining optimal advertising content

[0311] The server removes noise from the received data, fills in missing data, and then inputs the preprocessed data into a generative AI model (e.g., OpenAI GPT series) and an emotion engine. The generative AI model predicts optimal advertising content based on the collected attribute data, and the emotion engine analyzes the user's emotional data and determines the optimal advertising content based on the user's current emotional state.

[0312] For example, give the generative AI model the following prompt:

[0313] "Generate the most effective ads for your current audience."

[0314] Sending and displaying advertising content

[0315] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. TLS / SSL is used to ensure data transmission security. The device displays the advertising content received from the server on the advertising display in real time. For example, a music festival advertisement is displayed to a young, smiling user.

[0316] Measuring the effectiveness of advertising campaigns

[0317] During the advertising campaign, the device will again collect data using cameras and sensors to measure changes before and after. The server will analyze the collected effectiveness measurement data and evaluate the actual impact of the advertisement. This evaluation includes multiple parameters such as viewer rate, increase or decrease in dwell time, change in number of passersby, and change in user emotions.

[0318] Visualization and proposal of effects

[0319] The server visualizes the analysis results in graphs and charts (e.g., heat maps and time series graphs) and provides them to the user. Furthermore, it provides specific suggestions for new advertising strategies based on the analysis results. For example, it may suggest that "displaying more ads aimed at younger demographics in the evening will improve effectiveness."

[0320] As described above, the system of the present invention utilizes emotional data to provide an effective advertising strategy through real-time advertising optimization and precise effectiveness measurement.

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] Step 1:

[0323] Data collection

[0324] The devices use high-resolution cameras and sensors installed at the advertising location to collect real-time data from surrounding passersby, including:

[0325] User attributes such as age and gender

[0326] Behavioral data such as walking speed and length of stay

[0327] Using a camera and microphone linked to the emotion engine, emotional data is obtained from the user's facial expressions and voice.

[0328] (input)

[0329] Video, audio and environmental data of passersby

[0330] (output)

[0331] User attribute data, behavioral data, emotional data

[0332] Specific behavior:

[0333] The cameras use facial recognition technology to estimate the age and gender of passersby.

[0334] The sensors aggregate data and analyze pedestrian movement patterns.

[0335] The microphone uses voice analysis technology to capture the user's tone of voice and emotional index.

[0336] Step 2:

[0337] Data transmission and storage

[0338] The device collects data and sends it to the server using a REST API. The data is encrypted using the TLS / SSL protocol, and the server stores the received data in a database.

[0339] (input)

[0340] User attribute data, behavioral data, emotional data

[0341] (output)

[0342] Encrypted data packets, database entries

[0343] Specific behavior:

[0344] The device generates a data packet and sends an HTTP request to the API endpoint.

[0345] The server interprets the received request and inserts it into a database.

[0346] Step 3:

[0347] Analyzing data and determining optimal advertising content

[0348] The server cleanses the data, removes noise, and fills in missing data. It then inputs the data into a generative AI model to predict optimal ad content. Furthermore, an emotion engine analyzes the user's emotional data and determines ad content appropriate for their current emotional state.

[0349] (input)

[0350] Cleansed user attribute data, behavioral data, and emotional data

[0351] (output)

[0352] Optimal advertising content

[0353] Specific behavior:

[0354] The server applies algorithms to remove outliers and impute missing data.

[0355] Enter the following prompt into your generative AI model: "Generate the most effective ad for my current audience."

[0356] The emotion engine analyzes the emotion data and selects the advertisement that best suits the emotional state.

[0357] Step 4:

[0358] Sending and displaying advertising content

[0359] The server receives optimal advertising content from the generative AI model and emotion engine and sends it to the device, which then displays the received advertising content on the advertising display in real time.

[0360] (input)

[0361] Optimal advertising content

[0362] (output)

[0363] Advertisements displayed on advertising displays

[0364] Specific behavior:

[0365] The server encrypts the content in JSON format and sends it to the terminal.

[0366] The device receives the data and pushes it to the display in real time.

[0367] Step 5:

[0368] Measuring the effectiveness of advertising campaigns

[0369] During the advertising campaign, the device again uses cameras and sensors to collect data and measure changes before and after. The server analyzes this data to assess the actual impact of the ads.

[0370] (input)

[0371] User attribute data, behavioral data, and emotional data during the advertising campaign

[0372] (output)

[0373] Advertising effectiveness measurement results

[0374] Specific behavior:

[0375] The device continuously records the viewing time and length of time that passersby spend there.

[0376] The server uses the before and after data sets to statistically analyze the effectiveness of the advertisement.

[0377] Step 6:

[0378] Visualization and proposal of effects

[0379] The server analyzes the acquired effectiveness measurement data, visualizes the results in graphs and charts, and proposes new advertising strategies based on the analysis results.

[0380] (input)

[0381] Advertising effectiveness measurement results

[0382] (output)

[0383] Visualized analysis results and proposals for new advertising strategies

[0384] Specific behavior:

[0385] The server uses visual tools to visualize the data and displays it in a dashboard for users to view.

[0386] New advertising strategy scenarios are generated from the analysis results and proposed to the user.

[0387] These are the specific processing steps of this system, which enables real-time ad optimization and precise measurement of effectiveness.

[0388] (Application example 2)

[0389] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0390] Conventional advertising display systems simply collect data about the surrounding area of ​​an advertisement and display fixed advertising content based on that data. This means that a single advertisement is not necessarily effective for all users, making it difficult to maximize advertising effectiveness. Furthermore, there are limited means for precisely measuring the effectiveness of advertising campaigns, resulting in a lack of information to utilize for future advertising strategies. The present invention aims to solve these problems by providing a system that displays optimal advertising content in real time based on user emotions and attributes and precisely measures advertising effectiveness.

[0391] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting installation location data from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for analyzing emotional data using an emotion engine and determining advertising content tailored to the user's current emotional state, means for transmitting the determined advertising content to the advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating effectiveness, and means for proposing an advertising strategy based on the effectiveness measurement results. This makes it possible to display optimal advertisements for each user in real time and precisely measure the effectiveness of the advertisements.

[0392] A "camera" is a device that can capture images and has the role of collecting visual data at the location where it is installed.

[0393] A "sensor" is a device that detects physical information (such as movement, temperature, humidity, etc.) and converts it into an electrical signal.

[0394] "Location data" refers to information collected by cameras and sensors about the people and environment around a particular location.

[0395] "Server" means the central computing device that receives, analyzes, generates, manages, and distributes the advertising content collected.

[0396] A "generative AI model" is a module that includes an artificial intelligence algorithm for predicting and generating optimal advertising content based on input data.

[0397] An "emotion engine" refers to an artificial intelligence algorithm or software that analyzes a user's emotional state from their facial expressions, voice, etc.

[0398] "Advertising Content" means information or visual material (e.g., images, videos, text advertisements, etc.) generated or selected for display to users.

[0399] "Advertising display device" refers to a monitor or display for visually displaying optimized advertising content to a user.

[0400] "Effectiveness of advertising campaign" refers to measuring and evaluating changes in user behavior and emotions before, during, and after the advertisement display period.

[0401] "Effectiveness Measurement Results" means statistical information about the effectiveness of advertising analyzed based on data collected during the advertising campaign.

[0402] An "advertising strategy" refers to a plan or policy for maximizing the effectiveness of advertising, and serves as a guideline for setting the content and timing of the next advertisement based on the results of effectiveness measurement.

[0403] This invention is a system that uses cameras and sensors to collect customer data in physical stores, analyzes the data in real time, and displays optimal advertising content. The system is composed of cameras, sensors, a server, and an advertising display device, and incorporates a generative AI model and emotion engine to optimize advertising based on user attributes and emotion data.

[0404] Data collection

[0405] The device uses cameras and sensors installed in the physical store to collect data on customer attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.). It also uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data. This data is sent to a server in real time.

[0406] Data transmission and storage

[0407] The device sends the collected data via a secure API to a server, which stores it in a database for subsequent analysis and ad display optimization.

[0408] Analyzing data and determining optimal advertising content

[0409] The server performs pre-processing after filtering out noise from the received data and filling in missing data. The generative AI model predicts optimal advertising content based on customer attribute data. Furthermore, the emotion engine analyzes the user's emotional data and determines the optimal advertising content for the user's current emotional state.

[0410] Sending and displaying advertising content

[0411] The server transmits the optimal advertising content selected by the generative AI model and emotion engine to the device, which then displays the advertising content received from the server on an advertising display device in real time.

[0412] Measuring the effectiveness of advertising campaigns

[0413] During the advertising campaign, the device again collects data using cameras and sensors and sends it to the server, which analyzes the collected performance measurement data to evaluate the actual impact of the advertisements, based on viewer ratings, increases or decreases in dwell time, changes in foot traffic, and changes in user sentiment.

[0414] Visualization and proposal of effects

[0415] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of their advertising at a glance. Furthermore, the server proposes new advertising strategies based on the analysis results.

[0416] Specific examples

[0417] For example, consider the case of digital signage advertising installed in a large shopping mall. The device uses a camera to detect the age group and behavioral data of passersby in the mall, and then uses an emotion engine to collect emotional data from users' facial expressions and voices, sending this data to a server. The server analyzes the received data using a generative AI model and emotion engine, and detects, for example, that there are many young people in the evening hours and that their emotional state is excited. The server selects an advertisement for a new fashion brand that is targeted to young people and matches their excited state, and sends it to the digital signage. The device displays the advertisement received from the server in real time.

[0418] Specific input prompt examples:

[0419] Analyze your customer's emotional state based on the following image data and show them the most suitable advertisement:

[0420] Image data:<image_data>

[0421] Attribute data: Age 30, Gender female, Time spent 10 minutes

[0422] Analyze and generate advertising content.

[0423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0424] Step 1:

[0425] The terminal uses cameras and sensors installed in the physical store to collect data on customer attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.). In addition, it uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice to collect emotional data. The input is the collected visual data and audio data, and the output is attribute data and emotional data. Specifically, the camera captures video and the microphone collects audio.

[0426] Step 2:

[0427] The device sends the collected data to the server via a secure API. The input is data collected from the camera and microphone, and the output is packetized data sent to the server through the API. Specifically, the device packs the data into packets and sends them to the server using an encrypted protocol.

[0428] Step 3:

[0429] The server stores the received data in a database. The input is the data sent from the device, and the output is the data stored in the database. Specifically, the server analyzes the data received via the API and stores it in the database in an appropriate format.

[0430] Step 4:

[0431] The server performs preprocessing after removing noise from the received data and completing missing data. The input is raw data stored in a database, and the output is preprocessed data. Specifically, the server uses algorithms to perform noise filtering and completion of missing values.

[0432] Step 5:

[0433] The server uses a generative AI model to predict optimal advertising content based on customer attribute data. The input is preprocessed data, and the output is predicted advertising content. Specifically, the server inputs attribute data into the generative AI model to generate optimal advertising content.

[0434] Step 6:

[0435] The server uses an emotion engine to analyze the user's emotion data and determine the optimal advertising content for the user's current emotional state. The input is emotion data, and the output is emotion-optimized advertising content. Specifically, the emotion data is input into the emotion engine, which selects the optimal advertising content.

[0436] Step 7:

[0437] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. The input is the optimized advertising content, and the output is the advertising data sent to the device. Specifically, the server sends the content data to the device using an encryption protocol.

[0438] Step 8:

[0439] The terminal displays the advertising content received from the server on the advertising display device in real time. The input is advertising data from the server, and the output is advertising content to be displayed on the advertising display device. Specifically, the terminal performs an operation to display the received data on the advertising display device.

[0440] Step 9:

[0441] During the advertising campaign, the device again uses its camera or sensor to collect data and transmits it to the server. The input is new data from the camera or sensor, and the output is data packets sent to the server. Specifically, the device continues to collect data during the campaign and periodically transmits it to the server.

[0442] Step 10:

[0443] The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. The input is the re-collected data, and the output is the analysis results showing the effectiveness of the advertisement. Specifically, the server analyzes parameters such as viewer rate, dwell time, and number of passersby to evaluate the effectiveness of the advertisement.

[0444] Step 11:

[0445] The server visualizes the analysis results in graphs and charts and provides them to the user. It also proposes new advertising strategies based on the analysis results. The input is the analysis results of advertising effectiveness, and the output is visualized data and proposed advertising strategies. Specifically, the server uses a tool to visualize the analysis results and generates materials for the user to use to plan their next advertising strategy.

[0446] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0447] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0448] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0449] [Second embodiment]

[0450] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0451] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0452] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0453] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0454] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0455] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0456] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0457] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0458] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0459] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0460] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0461] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0462] MODE FOR CARRYING OUT THE INVENTION

[0463] This invention is a system that utilizes generative AI models to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location to optimally display advertising.

[0464] Data collection

[0465] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. This data is collected in real time and periodically sent to a server.

[0466] Data transmission and storage

[0467] The device compiles the collected data into packets and sends them to the server via API. The server receives the transmitted data and stores it in a database. The stored data is used for subsequent analysis and optimization of ad display.

[0468] Analyzing data and determining optimal advertising content

[0469] The server analyzes the received data and inputs it into a generative AI model. The generative AI model predicts and determines the optimal advertising content at the current time based on the obtained data. For example, if the analysis results show that there are many young people, it will select advertisements for fashion brands aimed at that demographic.

[0470] Sending and displaying advertising content

[0471] The server sends the selected advertising content to the advertising display device (terminal). The terminal displays the received advertising content in real time. The displayed content is optimized for the target user demographic.

[0472] Measuring the effectiveness of advertising campaigns

[0473] During the advertising campaign, the device collects data again to measure the changes before and after. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as the ad's view rate, increase or decrease in dwell time, and changes in the number of passersby.

[0474] Visualization and proposal of effects

[0475] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of their advertising at a glance. Furthermore, based on the analysis results, the server suggests ways to approach specific target demographics and new advertising strategies.

[0476] Specific examples

[0477] For example, the target is digital signage advertisements installed in large shopping malls.

[0478] The device uses a camera to detect the age group and walking speed of people passing through the mall and sends the data to a server.

[0479] The server analyzes the received data using a generative AI model and detects that there are many young people in the evening hours.

[0480] The server selects advertisements for new fashion brands aimed at young people and sends them to the digital signage.

[0481] The terminal displays the advertisement received from the server in real time.

[0482] During the advertising campaign, the device continuously collects data and transmits it to the server.

[0483] The server analyzes the effectiveness of the advertisement based on the newly collected data and evaluates factors such as increases in viewer ratings and length of stay.

[0484] The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[0485] In this way, the system of the present invention provides an effective advertising strategy through real-time advertising optimization and precise effect measurement.

[0486] The processing flow will be explained below.

[0487] Program processing steps

[0488] Step 1:

[0489] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity.

[0490] Step 2:

[0491] The device collects data and sends it to a server via an API, along with metadata such as time, location, and person attributes.

[0492] Step 3:

[0493] The server receives the data sent from the device and stores it in a database, which is used for subsequent analysis and to optimize the display of advertisements.

[0494] Step 4:

[0495] The server removes noise from the received data and completes missing data, as well as detecting and correcting outliers and completing missing values.

[0496] Step 5:

[0497] The server inputs the preprocessed data into a generative AI model, which predicts optimal advertising content based on the target's attribute data.

[0498] Step 6:

[0499] The server selects the optimal advertising content based on the output of the generative AI model. For example, if it determines that a large number of young people are in the audience, it will select advertisements for fashion brands aimed at that demographic.

[0500] Step 7:

[0501] The server then transmits the selected advertising content to the device, using an encryption protocol to ensure security.

[0502] Step 8:

[0503] The terminal displays the advertising content received from the server on an advertising display device, for example, by projecting advertising videos or images onto a digital signage.

[0504] Step 9:

[0505] During the advertising campaign, the device will again use its cameras and sensors to collect data, again capturing information such as the number of passersby, their attributes, and their behavior.

[0506] Step 10:

[0507] The device sends data during the ad campaign to the server, which also packets the data and sends it via API.

[0508] Step 11:

[0509] The server analyzes the newly received data from the advertising campaign period and evaluates the effectiveness of the advertisement based on factors such as viewer ratings, changes in dwell time, and changes in the number of people passing by.

[0510] Step 12:

[0511] The server visualizes the analysis results in graphs and charts and provides them to the user, who can use them to see the effectiveness of their advertising at a glance.

[0512] Step 13:

[0513] The server then proposes new advertising strategies based on the analysis results, for example, showing effective approaches to specific target demographics.

[0514] Step 14:

[0515] The user checks the proposals sent from the server and plans and implements a new advertising strategy.

[0516] Example 1

[0517] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0518] Conventional advertising display systems mainly display fixed content, making it difficult to display optimal ads in real time based on viewer attributes and behavior. Furthermore, they lacked a mechanism for precisely measuring the effectiveness of advertising campaigns and reflecting this information in subsequent advertising strategies. This made it difficult to maximize advertising effectiveness.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0520] In this invention, the server includes means for collecting installation location data from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for transmitting the determined advertising content to the advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating effectiveness, and means for visualizing the evaluation results in graphs and charts and proposing configurable advertising strategies. This makes it possible to display advertisements based on viewer attributes and behavior in real time, and to precisely measure the effectiveness of the advertising campaign and reflect it in the next advertising strategy.

[0521] A "camera" is a device that captures images of the surrounding area where it is installed and collects visual data.

[0522] A "sensor" is a device that detects physical environmental information (e.g., temperature, light, sound, motion, etc.) and converts it into a digital signal.

[0523] A "terminal" is a device that transmits data collected from cameras and sensors to a server, and also functions as an advertising display device.

[0524] "Server" refers to a central processing unit that receives and stores data sent from the device, analyzes the data using a generative AI model, and determines the optimal advertising content.

[0525] A "generative AI model" is a model that uses machine learning technology to analyze collected data and predict and determine optimal advertising content.

[0526] "Advertising content" refers to the content of the advertisement displayed on the display device (for example, images, videos, text, etc.).

[0527] An "advertising display device" is a device for visually displaying determined advertising content, and generally a display or digital signage is used.

[0528] "Effectiveness measurement" is the process of analyzing data collected during an advertising campaign to evaluate factors such as ad viewership and increase or decrease in time spent on the ad.

[0529] "Evaluation results" refer to indicators and information obtained through analysis of effectiveness measurement data, which indicate the effectiveness of an advertising campaign.

[0530] "Advertising strategy" refers to the specific approach and plan for the next advertising campaign based on the evaluation results.

[0531] "Graphs and charts" are visual tools for visually displaying evaluation results, presenting trends and patterns in the data in an easily understandable format.

[0532] This invention is a system that utilizes generative AI models to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location to optimally display advertising.

[0533] Data collection

[0534] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. This data is collected in real time and periodically sent to a server. Examples of specific hardware used include network cameras (e.g., Hikvision) and motion detection sensors.

[0535] Data transmission and storage

[0536] The device assembles the collected data into packets and sends them to a server via API. The server receives the sent data and stores it in a database. An example of specific software used is data transmission using a RESTful API. The stored data is used for subsequent analysis and optimization of ad display.

[0537] Analyzing data and determining optimal advertising content

[0538] The server analyzes the received data and inputs it into a generative AI model. The generative AI model predicts and determines the optimal advertising content at the current time based on the obtained data. For example, a model built with the TensorFlow library using Python can be used. For example, if the analysis results show that there are many young people, advertisements for fashion brands aimed at that demographic can be selected. As a concrete example, the following prompt sentence can be input:

[0539] "Based on data showing that many young people (aged 18 to 25) visit the shopping mall between 6:00 PM and 9:00 PM, please suggest advertisements for fashion brands that will appeal to them."

[0540] Sending and displaying advertising content

[0541] The server sends the selected advertising content to the advertising display device (terminal). The terminal displays the received advertising content in real time. Examples of specific hardware used include digital signage and large displays. The content displayed is optimized for the target user demographic.

[0542] Measuring the effectiveness of advertising campaigns

[0543] During the advertising campaign, the device collects data again to measure the changes before and after. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as the ad's view rate, increase or decrease in dwell time, and changes in the number of passersby.

[0544] Visualization and proposal of effects

[0545] The server visualizes the analysis results in graphs and charts and provides them to the user. Specific software that can be used includes data visualization tools (e.g., D3.js, Matplotlib). This allows users to see the effectiveness of their advertising at a glance. Furthermore, based on the analysis results, the server suggests approaches to specific target demographics and new advertising strategies.

[0546] Specific examples

[0547] For example, consider digital signage advertisements installed in large shopping malls. The device uses a camera to detect the age group and walking speed of passersby in the mall and sends the data to a server. The server analyzes the received data using a generative AI model and detects that there are many young people in the mall during the evening hours. The server selects advertisements for new fashion brands aimed at young people and sends them to the digital signage. The device displays the advertisements received from the server in real time. During the advertising campaign, the device continuously collects data and sends it to the server. The server analyzes the effectiveness of the advertisements based on the newly collected data and evaluates things like viewer rates and increases in dwell time. The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[0548] In this way, the system of the present invention provides an effective advertising strategy through real-time advertising optimization and precise effect measurement.

[0549] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0550] Step 1: Collect data

[0551] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. Specifically, the camera photographs passersby and uses facial recognition software to estimate their age and gender. Motion detection sensors also measure walking speed and length of stay. This data is collected in real time. The input is raw data from the cameras and sensors, and the output is a dataset of analyzed attribute information and behavioral information.

[0552] Step 2: Sending data

[0553] The device compiles the collected data into a list and sends it to the server via API at regular intervals (e.g., every 5 minutes). Specifically, it constructs an HTTP request and sends the collected data to the server in JSON format. For example, it sends a request to the "POST / data" endpoint. The input is a dataset of collected attribute information and behavioral information, and the output is the response to the HTTP request sent to the server.

[0554] Step 3: Save your data

[0555] The server receives data sent from the device and stores it in a database. Specifically, it analyzes the received data, generates SQL statements to insert into a database (e.g., Amazon RDS MySQL), and executes them. The input is the JSON data sent from the device, and the output is the record stored in the database.

[0556] Step 4: Data analysis

[0557] The server analyzes the data stored in the database and inputs it into the generative AI model. Specifically, it extracts the necessary data from the database, performs data preprocessing, and then inputs it into the generative AI model built using Python and the TensorFlow library. The input is the dataset extracted from the database, and the output is the analysis result of the AI ​​model.

[0558] Step 5: Determine the best ad content

[0559] The server analyzes the output from the generative AI model and determines the optimal advertising content. As a specific example, if data shows a high proportion of young people, it selects an advertisement for a fashion brand aimed at that demographic. In this process, it selects the optimal advertisement from a pre-prepared list of advertisements. The input is the analysis result of the AI ​​model, and the output is the selected advertising content.

[0560] Step 6: Submit your advertising content

[0561] The server sends the selected ad content to the ad display device (terminal). Specifically, it sends the ad content as an HTTP response. For example, it sends a request including an ad file to the "POST / display_ad" endpoint. The input is the selected ad content, and the output is the ad content sent to the terminal.

[0562] Step 7: Displaying the Ad

[0563] The terminal displays the advertising content received from the server in real time. Specifically, it sends commands to play video or image advertisements on the display of the advertising display device. The input is the received advertising content, and the output is the displayed advertising content.

[0564] Step 8: Measure your results

[0565] During the advertising campaign, the device collects data again to measure changes before and after the ad is displayed. Specifically, based on the re-collected data, it measures changes in viewer rate, increase or decrease in stay time, and change in the number of passersby. The input is the data collected during the advertising campaign, and the output is a dataset of the effectiveness measurement results.

[0566] Step 9: Evaluate and visualize the effects

[0567] The server analyzes the measurement results and evaluates the actual impact of the advertisement. Specifically, it uses a data visualization tool to generate graphs and charts and provide them to the user. The input is a dataset of the measurement results, and the output is the visualized evaluation results.

[0568] Step 10: Generate proposals

[0569] The server proposes a new advertising strategy based on the results of the effectiveness measurement. Specifically, it further applies the AI ​​model to generate a specific approach for the next advertising campaign. The input is the analyzed effectiveness measurement results, and the output is a proposal for a new advertising strategy.

[0570] (Application example 1)

[0571] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0572] The modern advertising industry requires the real-time delivery of optimal advertising content to specific target audiences. However, current advertising systems make it difficult to precisely measure advertising effectiveness and develop new advertising strategies based on the results. Furthermore, when displaying advertisements via wearable devices such as smart glasses, there is a lack of technology that can display optimal advertisements based on the user's surrounding environment. Therefore, there is an urgent need to develop an integrated system that can dynamically select and display optimal advertisements under specific conditions and measure their effectiveness.

[0573] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0574] In this invention, the server includes means for collecting data on the installation location from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative model and determining optimal advertising content, means for displaying the advertising content on the display of the smart glasses, means for re-collecting data during the advertising campaign period and evaluating its effectiveness, and means for proposing an advertising strategy based on the results of the effectiveness measurement. This makes it possible to display optimal advertising content to a specific target demographic in real time and precisely measure the effectiveness of the advertising.

[0575] "Cameras and sensors" are optical and sensing devices used to collect data at their locations.

[0576] "Installation location data" refers to information about the environment around the advertisement installation location and information about people's attributes and behavior.

[0577] "Collection means" refers to devices or software that acquire environmental and behavioral data from cameras or sensors.

[0578] A "server" is a computer system that receives collected data and performs analysis and advertising content decisions.

[0579] The "transmission means" is a device or software for assembling collected data into packets and transmitting the data to a server.

[0580] A "generative model" is an artificial intelligence algorithm that analyzes received data and generates or selects advertising content.

[0581] "Advertising content" refers to the content of the advertisement that is optimized and displayed using a generative model.

[0582] An "advertising display device" is a device for visually displaying determined advertising content to a user.

[0583] "Smart glasses" are wearable devices that display information through a built-in display.

[0584] "Data during the advertising campaign" refers to environmental and behavioral data that is continuously collected while the advertisement is displayed.

[0585] The "means for evaluating the effectiveness" refers to a device or software for analyzing the collected data and quantitatively analyzing the impact of the advertisement.

[0586] The "means for proposing advertising strategies" refers to a device or software for planning the next advertising strategy based on the results of effectiveness measurement.

[0587] The present invention relates to a system for displaying advertising content on smart glasses, the implementation of which includes the following steps:

[0588] First, the server installs cameras and sensors at the installation location to collect data on the surrounding environment and people's attributes and behavior. The camera acts as an optical device and captures images in real time. The sensor is a device for detecting attributes such as age, gender, and movement speed. This data is constantly acquired through the collection means.

[0589] The collected data is then sent to a server via API. The sending means is a communication device or software that packages the collected data into packets and sends them to the server. The server receives and stores this data. On the server side, it is recommended to use a database solution such as PostgreSQL.

[0590] A generative AI model analyzes this received data in real time on the server and determines the optimal advertising content. The generative AI model is built on common deep learning frameworks such as TensorFlow and PyTorch. It analyzes user attributes and behavioral patterns and generates advertisements tailored to the target demographic. For example, during times when many young people gather, advertisements for fashion brands appropriate for that demographic are selected.

[0591] The advertising content determined based on the analysis results is sent to the smart glasses, which then display the advertising content in real time within the user's field of view using a display means. Examples of smart glasses that can be used include Google Glass and Microsoft HoloLens.

[0592] During the advertising campaign, the server again collects data through cameras and sensors to evaluate its effectiveness. Specifically, it analyzes parameters such as ad viewing rate, changes in dwell time, and changes in the number of passersby. The evaluated effectiveness measurement data serves as the basis for proposing the next advertising strategy. Data visualization tools such as Matplotlib and Plotly are suitable for evaluation.

[0593] As a concrete example, the following prompt sentence is input into the generative AI model for a young person in their 20s walking through a busy downtown area during the day.

[0594] Example prompt sentence:

[0595] "Currently, many young people in their 20s walking around busy areas during the day are using smart glasses. Therefore, think about the advertising content that is most suitable for them. Specifically, advertisements for fashion brands and restaurants based on their age group, gender, and speed of movement would be good."

[0596] In this way, the system of the present invention can display optimal advertising content in real time and precisely measure its effectiveness, thereby dramatically improving the accuracy and effectiveness of advertising strategies.

[0597] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0598] Step 1:

[0599] Data on the installation location is collected using cameras and sensors.

[0600] (operation)

[0601] The cameras and sensors connected to the server capture real-time environmental information about the location and the attributes and behavioral data of people around it. Specifically, the cameras capture images, and the sensors detect information such as age, gender, and movement speed.

[0602] (input)

[0603] Physical environment of the installation site, passerby attributes and behavioral data

[0604] (output)

[0605] Raw environmental information and people's attributes and behavior data

[0606] Step 2:

[0607] The collected data is sent to the server.

[0608] (operation)

[0609] The device collects data and sends it to a server via an API, using an internet connection.

[0610] (input)

[0611] Raw data collected in step 1

[0612] (output)

[0613] Environmental information and people data sent to the server

[0614] Step 3:

[0615] A generative AI model is used to analyze the received data and determine the optimal advertising content.

[0616] (operation)

[0617] The server stores the received data in a database and inputs it into a generative AI model for analysis. The generative AI model uses TensorFlow or PyTorch to predict and determine the optimal advertisement based on the input data.

[0618] (input)

[0619] Environmental information and people's attributes and behavior data stored on the server

[0620] (output)

[0621] Optimal advertising content as a result of analysis

[0622] Step 4:

[0623] The determined advertising content is transmitted to the smart glasses.

[0624] (operation)

[0625] The server transmits the determined advertising content to the smart glasses via an API for displaying advertisements.

[0626] (input)

[0627] Ad content determined by generative AI model

[0628] (output)

[0629] Advertising content sent to smart glasses

[0630] Step 5:

[0631] Displaying advertising content on smart glasses.

[0632] (operation)

[0633] The smart glasses then display the received advertising content on their display, with the timing and location of the display controlled in real time.

[0634] (input)

[0635] Advertising content sent to smart glasses

[0636] (output)

[0637] Advertising content displayed in the user's field of view

[0638] Step 6:

[0639] Data will be collected again during the advertising campaign to evaluate its effectiveness.

[0640] (operation)

[0641] During the campaign period, the server will again collect data via cameras and sensors, and analyze that data to evaluate changes in ad viewing rates and dwell times.

[0642] (input)

[0643] Environmental and behavioral data collected again

[0644] (output)

[0645] Advertising effectiveness evaluation results

[0646] Step 7:

[0647] We propose advertising strategies based on the results of effectiveness measurements.

[0648] (operation)

[0649] The server proposes strategies for the next advertising campaign based on the collected and analyzed performance measurement data, and uses visualization tools to display the results in graphs and charts.

[0650] (input)

[0651] Advertising effectiveness evaluation results

[0652] (output)

[0653] Advertisement strategy suggestions and visualized data

[0654] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0655] MODE FOR CARRYING OUT THE INVENTION

[0656] This invention is a system that utilizes a generative AI model and an emotion engine to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location, and recognizing user emotions to optimize advertising display.

[0657] Data collection

[0658] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. At the same time, it uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data. This data is collected in real time and periodically sent to a server.

[0659] Data transmission and storage

[0660] The device compiles the collected data into packets and sends them to a server via API. This data includes metadata such as time, location, person's attributes, behavior, and emotional state. The server receives the transmitted data and stores it in a database. The stored data is used for subsequent analysis and optimization of ad display.

[0661] Analyzing data and determining optimal advertising content

[0662] The server filters out noise from the received data and fills in missing data, then inputs the preprocessed data into the generative AI model and emotion engine. The generative AI model predicts optimal advertising content based on the target's attribute data. The emotion engine then analyzes the user's emotional data and determines the optimal advertising content for the user's current emotional state.

[0663] Sending and displaying advertising content

[0664] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. An encryption protocol is used to ensure security during transmission. The device displays the advertising content received from the server on an advertising display device in real time. For example, a user who is smiling might be shown an advertisement that emphasizes fun.

[0665] Measuring the effectiveness of advertising campaigns

[0666] During the advertising campaign, the device again collects data using cameras and sensors to measure changes before and after the campaign. For example, it compares passerby attributes, length of stay, and user emotion data before and after the campaign. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as viewer ratings, increases or decreases in length of stay, changes in the number of passersby, and changes in user emotion.

[0667] Visualization and proposal of effects

[0668] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of the advertisements at a glance. Furthermore, the server proposes new advertising strategies based on the analysis results. For example, it suggests the type of advertising content that is more effective at a specific time or location.

[0669] Specific examples

[0670] For example, when targeting a digital signage advertisement installed in a large shopping mall, the operation is as follows.

[0671] The device uses a camera to detect the age group and behavioral data of passersby in the mall, and then uses an emotion engine to collect emotional data from the user's facial expressions and voice, and sends this data to a server.

[0672] The server analyzes the received data using a generative AI model and emotion engine, and detects, for example, that there are many young people in the evening hours and that their emotional state is excited.

[0673] The server selects advertisements for new fashion brands that are aimed at young people and match their excitement level, and transmits them to the digital signage.

[0674] The terminal displays the advertisement received from the server in real time.

[0675] During the advertising campaign, the device again collects data and sends it to the server.

[0676] The server analyzes the effectiveness of the advertising campaign based on the newly collected data, evaluating things like viewership rates, increases in dwell time, and changes in user emotions.

[0677] The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[0678] In this way, the system of the present invention provides effective advertising strategies through real-time advertising optimization and precise effectiveness measurement, and by utilizing user emotional data.

[0679] The processing flow will be explained below.

[0680] Program processing steps

[0681] Step 1:

[0682] The device uses cameras and sensors installed at the advertisement location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people around it. It also uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data.

[0683] Step 2:

[0684] The device collects data and sends it to a server via an API, along with metadata such as time, location, person's attributes, behavior, and emotional state.

[0685] Step 3:

[0686] The server receives the data sent from the device and stores it in a database, which is used for subsequent analysis and to optimize the display of advertisements.

[0687] Step 4:

[0688] The server removes noise from the received data and fills in missing data, specifically detecting and correcting outliers and filling in missing values.

[0689] Step 5:

[0690] The server inputs the preprocessed data into a generative AI model, which predicts optimal advertising content based on the target's attribute data.

[0691] Step 6:

[0692] The server also inputs pre-processed emotional data into an emotion engine, which analyzes the user's emotional state and determines emotion-based advertising content.

[0693] Step 7:

[0694] The server combines the output of the generative AI model and the emotion engine to select the most appropriate advertising content for that moment, for example, selecting entertainment advertising for excited young users.

[0695] Step 8:

[0696] The server then transmits the selected advertising content to the device, using an encryption protocol to ensure security.

[0697] Step 9:

[0698] The terminal displays the advertising content received from the server on an advertising display device in real time, for example, by projecting advertising videos or images onto a digital signage.

[0699] Step 10:

[0700] During the advertising campaign, the device will again use its cameras and sensors to collect data and measure changes before and after the campaign, such as comparing passerby attributes, length of stay, and user emotion data before and after the campaign.

[0701] Step 11:

[0702] The device sends data during the ad campaign to the server, which also packets the data and sends it via API.

[0703] Step 12:

[0704] The server analyzes the newly received data from the advertising campaign period and evaluates the effectiveness of the advertisement based on factors such as viewer ratings, changes in time spent on the site, changes in the number of people passing by, and changes in user emotions.

[0705] Step 13:

[0706] The server visualizes the analysis results in graphs and charts and provides them to the user, who can use them to see the effectiveness of their advertising at a glance.

[0707] Step 14:

[0708] The server then suggests new advertising strategies based on the analysis, for example, suggesting the type of advertising content that would be more effective at a particular time or location.

[0709] Step 15:

[0710] The user checks the proposals sent from the server, plans and implements a new advertising strategy, evaluates the proposals, and reflects them in the next advertising campaign.

[0711] With these detailed processing steps, the system of the present invention can provide effective advertising strategies through real-time advertising optimization and precise effectiveness measurement, and by utilizing user sentiment data.

[0712] Example 2

[0713] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0714] Conventional advertising display systems determine advertising content based solely on target user attributes and behavioral data, which limits the effectiveness of advertising. Furthermore, real-time ad optimization and measurement of advertising campaign effectiveness are insufficient, resulting in delays in improving advertising strategies.

[0715] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0716] In this invention, the server includes means for collecting data on the installation location from cameras and sensors, means for analyzing the collected data in real time and detecting user attributes, behavior, and emotional state, means for transmitting the collected data to the server, means for predicting optimal advertising content from the received data using a generative AI model, means for analyzing user emotional data using an emotion engine and determining optimal advertising content, means for transmitting the determined advertising content to an advertising display device and displaying it in real time, means for re-collecting data during the advertising campaign period and measuring and evaluating effectiveness, and means for proposing advertising strategies based on the effectiveness measurement results and visualizing the analysis results, thereby enabling real-time advertising optimization and precise effectiveness measurement.

[0717] A "camera" is a device for capturing images and collecting the data.

[0718] A "sensor" is a device that senses environmental information and collects that data.

[0719] "Installation location data" refers to information about the area around where the advertisement is installed, including information such as people's attributes and behavior.

[0720] "Real-time analysis" refers to the process of processing data as it is collected and obtaining analytical results.

[0721] "User attributes" refers to basic information about individual users, such as age, gender, and occupation.

[0722] "Behavior" refers to the actions and situations that a user can take, such as walking speed and length of stay.

[0723] "Emotional state" refers to the emotion the user is currently feeling (e.g., joy, anger, etc.).

[0724] "Generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate or predict optimal advertising content.

[0725] "Emotion engine" refers to a system for analyzing a user's emotion data and outputting corresponding results.

[0726] "Advertisement display device" refers to a hardware device for displaying determined advertisement content.

[0727] "Campaign Data" means all data collected during the implementation of a particular campaign.

[0728] "Measuring and evaluating effectiveness" refers to the process of quantitatively and qualitatively analyzing the success and impact of an advertising campaign.

[0729] "Proposing an advertising strategy" means showing the direction of future advertising activities and a specific action plan based on the results of effectiveness measurements.

[0730] "Visualizing the analysis results" refers to displaying the results of data analysis in a form that is easy for users to understand (e.g., graphs, charts, etc.).

[0731] This invention is a system that utilizes a generative AI model and an emotion engine to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location, and recognizing user emotions to optimize advertising display.

[0732] Data collection

[0733] The device uses high-resolution cameras and sensors (such as rangefinders and infrared sensors) installed at the advertising location to collect passersby's age, gender, and behavioral data (walking speed, length of stay, etc.) in real time. Furthermore, the device uses a high-performance microphone and emotion recognition software linked to an emotion engine to analyze and collect emotional data from the user's facial expressions and voice. This allows the emotion engine to accurately grasp the user's current emotional state.

[0734] Data transmission and storage

[0735] The device collects data and sends it securely to a server using a REST API. The data packet contains detailed metadata such as time, location, person's attributes, behavior, and emotional state. The transmitted data is received in real time by the server and stored in a cloud database (e.g., Amazon RDS or Google BigQuery).

[0736] Analyzing data and determining optimal advertising content

[0737] The server removes noise from the received data, fills in missing data, and then inputs the preprocessed data into a generative AI model (e.g., OpenAI GPT series) and an emotion engine. The generative AI model predicts optimal advertising content based on the collected attribute data, and the emotion engine analyzes the user's emotional data and determines the optimal advertising content based on the user's current emotional state.

[0738] For example, give the generative AI model the following prompt:

[0739] "Generate the most effective ads for your current audience."

[0740] Sending and displaying advertising content

[0741] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. TLS / SSL is used to ensure data transmission security. The device displays the advertising content received from the server on the advertising display in real time. For example, a music festival advertisement is displayed to a young, smiling user.

[0742] Measuring the effectiveness of advertising campaigns

[0743] During the advertising campaign, the device will again collect data using cameras and sensors to measure changes before and after. The server will analyze the collected effectiveness measurement data and evaluate the actual impact of the advertisement. This evaluation includes multiple parameters such as viewer rate, increase or decrease in dwell time, change in number of passersby, and change in user emotions.

[0744] Visualization and proposal of effects

[0745] The server visualizes the analysis results in graphs and charts (e.g., heat maps and time series graphs) and provides them to the user. Furthermore, it provides specific suggestions for new advertising strategies based on the analysis results. For example, it may suggest that "displaying more ads aimed at younger demographics in the evening will improve effectiveness."

[0746] As described above, the system of the present invention utilizes emotional data to provide an effective advertising strategy through real-time advertising optimization and precise effectiveness measurement.

[0747] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0748] Step 1:

[0749] Data collection

[0750] The devices use high-resolution cameras and sensors installed at the advertising location to collect real-time data from surrounding passersby, including:

[0751] User attributes such as age and gender

[0752] Behavioral data such as walking speed and length of stay

[0753] Using a camera and microphone linked to the emotion engine, emotional data is obtained from the user's facial expressions and voice.

[0754] (input)

[0755] Video, audio and environmental data of passersby

[0756] (output)

[0757] User attribute data, behavioral data, emotional data

[0758] Specific behavior:

[0759] The cameras use facial recognition technology to estimate the age and gender of passersby.

[0760] The sensors aggregate data and analyze pedestrian movement patterns.

[0761] The microphone uses voice analysis technology to capture the user's tone of voice and emotional index.

[0762] Step 2:

[0763] Data transmission and storage

[0764] The device collects data and sends it to the server using a REST API. The data is encrypted using the TLS / SSL protocol, and the server stores the received data in a database.

[0765] (input)

[0766] User attribute data, behavioral data, emotional data

[0767] (output)

[0768] Encrypted data packets, database entries

[0769] Specific behavior:

[0770] The device generates a data packet and sends an HTTP request to the API endpoint.

[0771] The server interprets the received request and inserts it into a database.

[0772] Step 3:

[0773] Analyzing data and determining optimal advertising content

[0774] The server cleanses the data, removes noise, and fills in missing data. It then inputs the data into a generative AI model to predict optimal ad content. Furthermore, an emotion engine analyzes the user's emotional data and determines ad content appropriate for their current emotional state.

[0775] (input)

[0776] Cleansed user attribute data, behavioral data, and emotional data

[0777] (output)

[0778] Optimal advertising content

[0779] Specific behavior:

[0780] The server applies algorithms to remove outliers and impute missing data.

[0781] Enter the following prompt into your generative AI model: "Generate the most effective ad for my current audience."

[0782] The emotion engine analyzes the emotion data and selects the advertisement that best suits the emotional state.

[0783] Step 4:

[0784] Sending and displaying advertising content

[0785] The server receives optimal advertising content from the generative AI model and emotion engine and sends it to the device, which then displays the received advertising content on the advertising display in real time.

[0786] (input)

[0787] Optimal advertising content

[0788] (output)

[0789] Advertisements displayed on advertising displays

[0790] Specific behavior:

[0791] The server encrypts the content in JSON format and sends it to the terminal.

[0792] The device receives the data and pushes it to the display in real time.

[0793] Step 5:

[0794] Measuring the effectiveness of advertising campaigns

[0795] During the advertising campaign, the device again uses cameras and sensors to collect data and measure changes before and after. The server analyzes this data to assess the actual impact of the ads.

[0796] (input)

[0797] User attribute data, behavioral data, and emotional data during the advertising campaign

[0798] (output)

[0799] Advertising effectiveness measurement results

[0800] Specific behavior:

[0801] The device continuously records the viewing time and length of time that passersby spend there.

[0802] The server uses the before and after data sets to statistically analyze the effectiveness of the advertisement.

[0803] Step 6:

[0804] Visualization and proposal of effects

[0805] The server analyzes the acquired effectiveness measurement data, visualizes the results in graphs and charts, and proposes new advertising strategies based on the analysis results.

[0806] (input)

[0807] Advertising effectiveness measurement results

[0808] (output)

[0809] Visualized analysis results and proposals for new advertising strategies

[0810] Specific behavior:

[0811] The server uses visual tools to visualize the data and displays it in a dashboard for users to view.

[0812] New advertising strategy scenarios are generated from the analysis results and proposed to the user.

[0813] These are the specific processing steps of this system, which enables real-time ad optimization and precise measurement of effectiveness.

[0814] (Application example 2)

[0815] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0816] Conventional advertising display systems simply collect data about the surrounding area of ​​an advertisement and display fixed advertising content based on that data. This means that a single advertisement is not necessarily effective for all users, making it difficult to maximize advertising effectiveness. Furthermore, there are limited means for precisely measuring the effectiveness of advertising campaigns, resulting in a lack of information to utilize for future advertising strategies. The present invention aims to solve these problems by providing a system that displays optimal advertising content in real time based on user emotions and attributes and precisely measures advertising effectiveness.

[0817] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting installation location data from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for analyzing emotional data using an emotion engine and determining advertising content tailored to the user's current emotional state, means for transmitting the determined advertising content to the advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating effectiveness, and means for proposing an advertising strategy based on the effectiveness measurement results. This makes it possible to display optimal advertisements for each user in real time and precisely measure the effectiveness of the advertisements.

[0818] A "camera" is a device that can capture images and has the role of collecting visual data at the location where it is installed.

[0819] A "sensor" is a device that detects physical information (such as movement, temperature, humidity, etc.) and converts it into an electrical signal.

[0820] "Location data" refers to information collected by cameras and sensors about the people and environment around a particular location.

[0821] "Server" means the central computing device that receives, analyzes, generates, manages, and distributes the advertising content collected.

[0822] A "generative AI model" is a module that includes an artificial intelligence algorithm for predicting and generating optimal advertising content based on input data.

[0823] An "emotion engine" refers to an artificial intelligence algorithm or software that analyzes a user's emotional state from their facial expressions, voice, etc.

[0824] "Advertising Content" means information or visual material (e.g., images, videos, text advertisements, etc.) generated or selected for display to users.

[0825] "Advertising display device" refers to a monitor or display for visually displaying optimized advertising content to a user.

[0826] "Effectiveness of advertising campaign" refers to measuring and evaluating changes in user behavior and emotions before, during, and after the advertisement display period.

[0827] "Effectiveness Measurement Results" means statistical information about the effectiveness of advertising analyzed based on data collected during the advertising campaign.

[0828] An "advertising strategy" refers to a plan or policy for maximizing the effectiveness of advertising, and serves as a guideline for setting the content and timing of the next advertisement based on the results of effectiveness measurement.

[0829] This invention is a system that uses cameras and sensors to collect customer data in physical stores, analyzes the data in real time, and displays optimal advertising content. The system is composed of cameras, sensors, a server, and an advertising display device, and incorporates a generative AI model and emotion engine to optimize advertising based on user attributes and emotion data.

[0830] Data collection

[0831] The device uses cameras and sensors installed in the physical store to collect data on customer attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.). It also uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data. This data is sent to a server in real time.

[0832] Data transmission and storage

[0833] The device sends the collected data via a secure API to a server, which stores it in a database for subsequent analysis and ad display optimization.

[0834] Analyzing data and determining optimal advertising content

[0835] The server performs pre-processing after filtering out noise from the received data and filling in missing data. The generative AI model predicts optimal advertising content based on customer attribute data. Furthermore, the emotion engine analyzes the user's emotional data and determines the optimal advertising content for the user's current emotional state.

[0836] Sending and displaying advertising content

[0837] The server transmits the optimal advertising content selected by the generative AI model and emotion engine to the device, which then displays the advertising content received from the server on an advertising display device in real time.

[0838] Measuring the effectiveness of advertising campaigns

[0839] During the advertising campaign, the device again collects data using cameras and sensors and sends it to the server, which analyzes the collected performance measurement data to evaluate the actual impact of the advertisements, based on viewer ratings, increases or decreases in dwell time, changes in foot traffic, and changes in user sentiment.

[0840] Visualization and proposal of effects

[0841] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of their advertising at a glance. Furthermore, the server proposes new advertising strategies based on the analysis results.

[0842] Specific examples

[0843] For example, consider the case of digital signage advertising installed in a large shopping mall. The device uses a camera to detect the age group and behavioral data of passersby in the mall, and then uses an emotion engine to collect emotional data from users' facial expressions and voices, sending this data to a server. The server analyzes the received data using a generative AI model and emotion engine, and detects, for example, that there are many young people in the evening hours and that their emotional state is excited. The server selects an advertisement for a new fashion brand that is targeted to young people and matches their excited state, and sends it to the digital signage. The device displays the advertisement received from the server in real time.

[0844] Specific input prompt examples:

[0845] Analyze your customer's emotional state based on the following image data and show them the most suitable advertisement:

[0846] Image data:<image_data>

[0847] Attribute data: Age 30, Gender female, Time spent 10 minutes

[0848] Analyze and generate advertising content.

[0849] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0850] Step 1:

[0851] The terminal uses cameras and sensors installed in the physical store to collect data on customer attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.). In addition, it uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice to collect emotional data. The input is the collected visual data and audio data, and the output is attribute data and emotional data. Specifically, the camera captures video and the microphone collects audio.

[0852] Step 2:

[0853] The device sends the collected data to the server via a secure API. The input is data collected from the camera and microphone, and the output is packetized data sent to the server through the API. Specifically, the device packs the data into packets and sends them to the server using an encrypted protocol.

[0854] Step 3:

[0855] The server stores the received data in a database. The input is the data sent from the device, and the output is the data stored in the database. Specifically, the server analyzes the data received via the API and stores it in the database in an appropriate format.

[0856] Step 4:

[0857] The server performs preprocessing after removing noise from the received data and completing missing data. The input is raw data stored in a database, and the output is preprocessed data. Specifically, the server uses algorithms to perform noise filtering and completion of missing values.

[0858] Step 5:

[0859] The server uses a generative AI model to predict optimal advertising content based on customer attribute data. The input is preprocessed data, and the output is predicted advertising content. Specifically, the server inputs attribute data into the generative AI model to generate optimal advertising content.

[0860] Step 6:

[0861] The server uses an emotion engine to analyze the user's emotion data and determine the optimal advertising content for the user's current emotional state. The input is emotion data, and the output is emotion-optimized advertising content. Specifically, the emotion data is input into the emotion engine, which selects the optimal advertising content.

[0862] Step 7:

[0863] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. The input is the optimized advertising content, and the output is the advertising data sent to the device. Specifically, the server sends the content data to the device using an encryption protocol.

[0864] Step 8:

[0865] The terminal displays the advertising content received from the server on the advertising display device in real time. The input is advertising data from the server, and the output is advertising content to be displayed on the advertising display device. Specifically, the terminal performs an operation to display the received data on the advertising display device.

[0866] Step 9:

[0867] During the advertising campaign, the device again uses its camera or sensor to collect data and transmits it to the server. The input is new data from the camera or sensor, and the output is data packets sent to the server. Specifically, the device continues to collect data during the campaign and periodically transmits it to the server.

[0868] Step 10:

[0869] The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. The input is the re-collected data, and the output is the analysis results showing the effectiveness of the advertisement. Specifically, the server analyzes parameters such as viewer rate, dwell time, and number of passersby to evaluate the effectiveness of the advertisement.

[0870] Step 11:

[0871] The server visualizes the analysis results in graphs and charts and provides them to the user. It also proposes new advertising strategies based on the analysis results. The input is the analysis results of advertising effectiveness, and the output is visualized data and proposed advertising strategies. Specifically, the server uses a tool to visualize the analysis results and generates materials for the user to use to plan their next advertising strategy.

[0872] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0873] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0874] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0875] [Third embodiment]

[0876] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0877] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0878] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0879] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0880] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0881] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0882] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0883] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0884] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0885] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0886] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0887] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0888] MODE FOR CARRYING OUT THE INVENTION

[0889] This invention is a system that utilizes generative AI models to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location to optimally display advertising.

[0890] Data collection

[0891] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. This data is collected in real time and periodically sent to a server.

[0892] Data transmission and storage

[0893] The device compiles the collected data into packets and sends them to the server via API. The server receives the transmitted data and stores it in a database. The stored data is used for subsequent analysis and optimization of ad display.

[0894] Analyzing data and determining optimal advertising content

[0895] The server analyzes the received data and inputs it into a generative AI model. The generative AI model predicts and determines the optimal advertising content at the current time based on the obtained data. For example, if the analysis results show that there are many young people, it will select advertisements for fashion brands aimed at that demographic.

[0896] Sending and displaying advertising content

[0897] The server sends the selected advertising content to the advertising display device (terminal). The terminal displays the received advertising content in real time. The displayed content is optimized for the target user demographic.

[0898] Measuring the effectiveness of advertising campaigns

[0899] During the advertising campaign, the device collects data again to measure the changes before and after. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as the ad's view rate, increase or decrease in dwell time, and changes in the number of passersby.

[0900] Visualization and proposal of effects

[0901] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of their advertising at a glance. Furthermore, based on the analysis results, the server suggests ways to approach specific target demographics and new advertising strategies.

[0902] Specific examples

[0903] For example, the target is digital signage advertisements installed in large shopping malls.

[0904] The device uses a camera to detect the age group and walking speed of people passing through the mall and sends the data to a server.

[0905] The server analyzes the received data using a generative AI model and detects that there are many young people in the evening hours.

[0906] The server selects advertisements for new fashion brands aimed at young people and sends them to the digital signage.

[0907] The terminal displays the advertisement received from the server in real time.

[0908] During the advertising campaign, the device continuously collects data and transmits it to the server.

[0909] The server analyzes the effectiveness of the advertisement based on the newly collected data and evaluates factors such as increases in viewer ratings and length of stay.

[0910] The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[0911] In this way, the system of the present invention provides an effective advertising strategy through real-time advertising optimization and precise effect measurement.

[0912] The processing flow will be explained below.

[0913] Program processing steps

[0914] Step 1:

[0915] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity.

[0916] Step 2:

[0917] The device collects data and sends it to a server via an API, along with metadata such as time, location, and person attributes.

[0918] Step 3:

[0919] The server receives the data sent from the device and stores it in a database, which is used for subsequent analysis and to optimize the display of advertisements.

[0920] Step 4:

[0921] The server removes noise from the received data and completes missing data, as well as detecting and correcting outliers and completing missing values.

[0922] Step 5:

[0923] The server inputs the preprocessed data into a generative AI model, which predicts optimal advertising content based on the target's attribute data.

[0924] Step 6:

[0925] The server selects the optimal advertising content based on the output of the generative AI model. For example, if it determines that a large number of young people are in the audience, it will select advertisements for fashion brands aimed at that demographic.

[0926] Step 7:

[0927] The server then transmits the selected advertising content to the device, using an encryption protocol to ensure security.

[0928] Step 8:

[0929] The terminal displays the advertising content received from the server on an advertising display device, for example, by projecting advertising videos or images onto a digital signage.

[0930] Step 9:

[0931] During the advertising campaign, the device will again use its cameras and sensors to collect data, again capturing information such as the number of passersby, their attributes, and their behavior.

[0932] Step 10:

[0933] The device sends data during the ad campaign to the server, which also packets the data and sends it via API.

[0934] Step 11:

[0935] The server analyzes the newly received data from the advertising campaign period and evaluates the effectiveness of the advertisement based on factors such as viewer ratings, changes in dwell time, and changes in the number of people passing by.

[0936] Step 12:

[0937] The server visualizes the analysis results in graphs and charts and provides them to the user, who can use them to see the effectiveness of their advertising at a glance.

[0938] Step 13:

[0939] The server then proposes new advertising strategies based on the analysis results, for example, showing effective approaches to specific target demographics.

[0940] Step 14:

[0941] The user checks the proposals sent from the server and plans and implements a new advertising strategy.

[0942] Example 1

[0943] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0944] Conventional advertising display systems mainly display fixed content, making it difficult to display optimal ads in real time based on viewer attributes and behavior. Furthermore, they lacked a mechanism for precisely measuring the effectiveness of advertising campaigns and reflecting this information in subsequent advertising strategies. This made it difficult to maximize advertising effectiveness.

[0945] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0946] In this invention, the server includes means for collecting installation location data from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for transmitting the determined advertising content to the advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating effectiveness, and means for visualizing the evaluation results in graphs and charts and proposing configurable advertising strategies. This makes it possible to display advertisements based on viewer attributes and behavior in real time, and to precisely measure the effectiveness of the advertising campaign and reflect it in the next advertising strategy.

[0947] A "camera" is a device that captures images of the surrounding area where it is installed and collects visual data.

[0948] A "sensor" is a device that detects physical environmental information (e.g., temperature, light, sound, motion, etc.) and converts it into a digital signal.

[0949] A "terminal" is a device that transmits data collected from cameras and sensors to a server, and also functions as an advertising display device.

[0950] "Server" refers to a central processing unit that receives and stores data sent from the device, analyzes the data using a generative AI model, and determines the optimal advertising content.

[0951] A "generative AI model" is a model that uses machine learning technology to analyze collected data and predict and determine optimal advertising content.

[0952] "Advertising content" refers to the content of the advertisement displayed on the display device (for example, images, videos, text, etc.).

[0953] An "advertising display device" is a device for visually displaying determined advertising content, and generally a display or digital signage is used.

[0954] "Effectiveness measurement" is the process of analyzing data collected during an advertising campaign to evaluate factors such as ad viewership and increase or decrease in time spent on the ad.

[0955] "Evaluation results" refer to indicators and information obtained through analysis of effectiveness measurement data, which indicate the effectiveness of an advertising campaign.

[0956] "Advertising strategy" refers to the specific approach and plan for the next advertising campaign based on the evaluation results.

[0957] "Graphs and charts" are visual tools for visually displaying evaluation results, presenting trends and patterns in the data in an easily understandable format.

[0958] This invention is a system that utilizes generative AI models to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location to optimally display advertising.

[0959] Data collection

[0960] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. This data is collected in real time and periodically sent to a server. Examples of specific hardware used include network cameras (e.g., Hikvision) and motion detection sensors.

[0961] Data transmission and storage

[0962] The device assembles the collected data into packets and sends them to a server via API. The server receives the sent data and stores it in a database. An example of specific software used is data transmission using a RESTful API. The stored data is used for subsequent analysis and optimization of ad display.

[0963] Analyzing data and determining optimal advertising content

[0964] The server analyzes the received data and inputs it into a generative AI model. The generative AI model predicts and determines the optimal advertising content at the current time based on the obtained data. For example, a model built with the TensorFlow library using Python can be used. For example, if the analysis results show that there are many young people, advertisements for fashion brands aimed at that demographic can be selected. As a concrete example, the following prompt sentence can be input:

[0965] "Based on data showing that many young people (aged 18 to 25) visit the shopping mall between 6:00 PM and 9:00 PM, please suggest advertisements for fashion brands that will appeal to them."

[0966] Sending and displaying advertising content

[0967] The server sends the selected advertising content to the advertising display device (terminal). The terminal displays the received advertising content in real time. Examples of specific hardware used include digital signage and large displays. The content displayed is optimized for the target user demographic.

[0968] Measuring the effectiveness of advertising campaigns

[0969] During the advertising campaign, the device collects data again to measure the changes before and after. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as the ad's view rate, increase or decrease in dwell time, and changes in the number of passersby.

[0970] Visualization and proposal of effects

[0971] The server visualizes the analysis results in graphs and charts and provides them to the user. Specific software that can be used includes data visualization tools (e.g., D3.js, Matplotlib). This allows users to see the effectiveness of their advertising at a glance. Furthermore, based on the analysis results, the server suggests approaches to specific target demographics and new advertising strategies.

[0972] Specific examples

[0973] For example, consider digital signage advertisements installed in large shopping malls. The device uses a camera to detect the age group and walking speed of passersby in the mall and sends the data to a server. The server analyzes the received data using a generative AI model and detects that there are many young people in the mall during the evening hours. The server selects advertisements for new fashion brands aimed at young people and sends them to the digital signage. The device displays the advertisements received from the server in real time. During the advertising campaign, the device continuously collects data and sends it to the server. The server analyzes the effectiveness of the advertisements based on the newly collected data and evaluates things like viewer rates and increases in dwell time. The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[0974] In this way, the system of the present invention provides an effective advertising strategy through real-time advertising optimization and precise effect measurement.

[0975] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0976] Step 1: Collect data

[0977] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. Specifically, the camera photographs passersby and uses facial recognition software to estimate their age and gender. Motion detection sensors also measure walking speed and length of stay. This data is collected in real time. The input is raw data from the cameras and sensors, and the output is a dataset of analyzed attribute information and behavioral information.

[0978] Step 2: Sending data

[0979] The device compiles the collected data into a list and sends it to the server via API at regular intervals (e.g., every 5 minutes). Specifically, it constructs an HTTP request and sends the collected data to the server in JSON format. For example, it sends a request to the "POST / data" endpoint. The input is a dataset of collected attribute information and behavioral information, and the output is the response to the HTTP request sent to the server.

[0980] Step 3: Save your data

[0981] The server receives data sent from the device and stores it in a database. Specifically, it analyzes the received data, generates SQL statements to insert into a database (e.g., Amazon RDS MySQL), and executes them. The input is the JSON data sent from the device, and the output is the record stored in the database.

[0982] Step 4: Data analysis

[0983] The server analyzes the data stored in the database and inputs it into the generative AI model. Specifically, it extracts the necessary data from the database, performs data preprocessing, and then inputs it into the generative AI model built using Python and the TensorFlow library. The input is the dataset extracted from the database, and the output is the analysis result of the AI ​​model.

[0984] Step 5: Determine the best ad content

[0985] The server analyzes the output from the generative AI model and determines the optimal advertising content. As a specific example, if data shows a high proportion of young people, it selects an advertisement for a fashion brand aimed at that demographic. In this process, it selects the optimal advertisement from a pre-prepared list of advertisements. The input is the analysis result of the AI ​​model, and the output is the selected advertising content.

[0986] Step 6: Submit your advertising content

[0987] The server sends the selected ad content to the ad display device (terminal). Specifically, it sends the ad content as an HTTP response. For example, it sends a request including an ad file to the "POST / display_ad" endpoint. The input is the selected ad content, and the output is the ad content sent to the terminal.

[0988] Step 7: Displaying the Ad

[0989] The terminal displays the advertising content received from the server in real time. Specifically, it sends commands to play video or image advertisements on the display of the advertising display device. The input is the received advertising content, and the output is the displayed advertising content.

[0990] Step 8: Measure your results

[0991] During the advertising campaign, the device collects data again to measure changes before and after the ad is displayed. Specifically, based on the re-collected data, it measures changes in viewer rate, increase or decrease in stay time, and change in the number of passersby. The input is the data collected during the advertising campaign, and the output is a dataset of the effectiveness measurement results.

[0992] Step 9: Evaluate and visualize the effects

[0993] The server analyzes the measurement results and evaluates the actual impact of the advertisement. Specifically, it uses a data visualization tool to generate graphs and charts and provide them to the user. The input is a dataset of the measurement results, and the output is the visualized evaluation results.

[0994] Step 10: Generate proposals

[0995] The server proposes a new advertising strategy based on the results of the effectiveness measurement. Specifically, it further applies the AI ​​model to generate a specific approach for the next advertising campaign. The input is the analyzed effectiveness measurement results, and the output is a proposal for a new advertising strategy.

[0996] (Application example 1)

[0997] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0998] The modern advertising industry requires the real-time delivery of optimal advertising content to specific target audiences. However, current advertising systems make it difficult to precisely measure advertising effectiveness and develop new advertising strategies based on the results. Furthermore, when displaying advertisements via wearable devices such as smart glasses, there is a lack of technology that can display optimal advertisements based on the user's surrounding environment. Therefore, there is an urgent need to develop an integrated system that can dynamically select and display optimal advertisements under specific conditions and measure their effectiveness.

[0999] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1000] In this invention, the server includes means for collecting data on the installation location from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative model and determining optimal advertising content, means for displaying the advertising content on the display of the smart glasses, means for re-collecting data during the advertising campaign period and evaluating its effectiveness, and means for proposing an advertising strategy based on the results of the effectiveness measurement. This makes it possible to display optimal advertising content to a specific target demographic in real time and precisely measure the effectiveness of the advertising.

[1001] "Cameras and sensors" are optical and sensing devices used to collect data at their locations.

[1002] "Installation location data" refers to information about the environment around the advertisement installation location and information about people's attributes and behavior.

[1003] "Collection means" refers to devices or software that acquire environmental and behavioral data from cameras or sensors.

[1004] A "server" is a computer system that receives collected data and performs analysis and advertising content decisions.

[1005] The "transmission means" is a device or software for assembling collected data into packets and transmitting the data to a server.

[1006] A "generative model" is an artificial intelligence algorithm that analyzes received data and generates or selects advertising content.

[1007] "Advertising content" refers to the content of the advertisement that is optimized and displayed using a generative model.

[1008] An "advertising display device" is a device for visually displaying determined advertising content to a user.

[1009] "Smart glasses" are wearable devices that display information through a built-in display.

[1010] "Data during the advertising campaign" refers to environmental and behavioral data that is continuously collected while the advertisement is displayed.

[1011] The "means for evaluating the effectiveness" refers to a device or software for analyzing the collected data and quantitatively analyzing the impact of the advertisement.

[1012] The "means for proposing advertising strategies" refers to a device or software for planning the next advertising strategy based on the results of effectiveness measurement.

[1013] The present invention relates to a system for displaying advertising content on smart glasses, the implementation of which includes the following steps:

[1014] First, the server installs cameras and sensors at the installation location to collect data on the surrounding environment and people's attributes and behavior. The camera acts as an optical device and captures images in real time. The sensor is a device for detecting attributes such as age, gender, and movement speed. This data is constantly acquired through the collection means.

[1015] The collected data is then sent to a server via API. The sending means is a communication device or software that packages the collected data into packets and sends them to the server. The server receives and stores this data. On the server side, it is recommended to use a database solution such as PostgreSQL.

[1016] A generative AI model analyzes this received data in real time on the server and determines the optimal advertising content. The generative AI model is built on common deep learning frameworks such as TensorFlow and PyTorch. It analyzes user attributes and behavioral patterns and generates advertisements tailored to the target demographic. For example, during times when many young people gather, advertisements for fashion brands appropriate for that demographic are selected.

[1017] The advertising content determined based on the analysis results is sent to the smart glasses, which then display the advertising content in real time within the user's field of view using a display means. Examples of smart glasses that can be used include Google Glass and Microsoft HoloLens.

[1018] During the advertising campaign, the server again collects data through cameras and sensors to evaluate its effectiveness. Specifically, it analyzes parameters such as ad viewing rate, changes in dwell time, and changes in the number of passersby. The evaluated effectiveness measurement data serves as the basis for proposing the next advertising strategy. Data visualization tools such as Matplotlib and Plotly are suitable for evaluation.

[1019] As a concrete example, the following prompt sentence is input into the generative AI model for a young person in their 20s walking through a busy downtown area during the day.

[1020] Example prompt sentence:

[1021] "Currently, many young people in their 20s walking around busy areas during the day are using smart glasses. Therefore, think about the advertising content that is most suitable for them. Specifically, advertisements for fashion brands and restaurants based on their age group, gender, and speed of movement would be good."

[1022] In this way, the system of the present invention can display optimal advertising content in real time and precisely measure its effectiveness, thereby dramatically improving the accuracy and effectiveness of advertising strategies.

[1023] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1024] Step 1:

[1025] Data on the installation location is collected using cameras and sensors.

[1026] (operation)

[1027] The cameras and sensors connected to the server capture real-time environmental information about the location and the attributes and behavioral data of people around it. Specifically, the cameras capture images, and the sensors detect information such as age, gender, and movement speed.

[1028] (input)

[1029] Physical environment of the installation site, passerby attributes and behavioral data

[1030] (output)

[1031] Raw environmental information and people's attributes and behavior data

[1032] Step 2:

[1033] The collected data is sent to the server.

[1034] (operation)

[1035] The device collects data and sends it to a server via an API, using an internet connection.

[1036] (input)

[1037] Raw data collected in step 1

[1038] (output)

[1039] Environmental information and people data sent to the server

[1040] Step 3:

[1041] A generative AI model is used to analyze the received data and determine the optimal advertising content.

[1042] (operation)

[1043] The server stores the received data in a database and inputs it into a generative AI model for analysis. The generative AI model uses TensorFlow or PyTorch to predict and determine the optimal advertisement based on the input data.

[1044] (input)

[1045] Environmental information and people's attributes and behavior data stored on the server

[1046] (output)

[1047] Optimal advertising content as a result of analysis

[1048] Step 4:

[1049] The determined advertising content is transmitted to the smart glasses.

[1050] (operation)

[1051] The server transmits the determined advertising content to the smart glasses via an API for displaying advertisements.

[1052] (input)

[1053] Ad content determined by generative AI model

[1054] (output)

[1055] Advertising content sent to smart glasses

[1056] Step 5:

[1057] Displaying advertising content on smart glasses.

[1058] (operation)

[1059] The smart glasses then display the received advertising content on their display, with the timing and location of the display controlled in real time.

[1060] (input)

[1061] Advertising content sent to smart glasses

[1062] (output)

[1063] Advertising content displayed in the user's field of view

[1064] Step 6:

[1065] Data will be collected again during the advertising campaign to evaluate its effectiveness.

[1066] (operation)

[1067] During the campaign period, the server will again collect data via cameras and sensors, and analyze that data to evaluate changes in ad viewing rates and dwell times.

[1068] (input)

[1069] Environmental and behavioral data collected again

[1070] (output)

[1071] Advertising effectiveness evaluation results

[1072] Step 7:

[1073] We propose advertising strategies based on the results of effectiveness measurements.

[1074] (operation)

[1075] The server proposes strategies for the next advertising campaign based on the collected and analyzed performance measurement data, and uses visualization tools to display the results in graphs and charts.

[1076] (input)

[1077] Advertising effectiveness evaluation results

[1078] (output)

[1079] Advertisement strategy suggestions and visualized data

[1080] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1081] MODE FOR CARRYING OUT THE INVENTION

[1082] This invention is a system that utilizes a generative AI model and an emotion engine to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location, and recognizing user emotions to optimize advertising display.

[1083] Data collection

[1084] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. At the same time, it uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data. This data is collected in real time and periodically sent to a server.

[1085] Data transmission and storage

[1086] The device compiles the collected data into packets and sends them to a server via API. This data includes metadata such as time, location, person's attributes, behavior, and emotional state. The server receives the transmitted data and stores it in a database. The stored data is used for subsequent analysis and optimization of ad display.

[1087] Analyzing data and determining optimal advertising content

[1088] The server filters out noise from the received data and fills in missing data, then inputs the preprocessed data into the generative AI model and emotion engine. The generative AI model predicts optimal advertising content based on the target's attribute data. The emotion engine then analyzes the user's emotional data and determines the optimal advertising content for the user's current emotional state.

[1089] Sending and displaying advertising content

[1090] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. An encryption protocol is used to ensure security during transmission. The device displays the advertising content received from the server on an advertising display device in real time. For example, a user who is smiling might be shown an advertisement that emphasizes fun.

[1091] Measuring the effectiveness of advertising campaigns

[1092] During the advertising campaign, the device again collects data using cameras and sensors to measure changes before and after the campaign. For example, it compares passerby attributes, length of stay, and user emotion data before and after the campaign. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as viewer ratings, increases or decreases in length of stay, changes in the number of passersby, and changes in user emotion.

[1093] Visualization and proposal of effects

[1094] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of the advertisements at a glance. Furthermore, the server proposes new advertising strategies based on the analysis results. For example, it suggests the type of advertising content that is more effective at a specific time or location.

[1095] Specific examples

[1096] For example, when targeting a digital signage advertisement installed in a large shopping mall, the operation is as follows.

[1097] The device uses a camera to detect the age group and behavioral data of passersby in the mall, and then uses an emotion engine to collect emotional data from the user's facial expressions and voice, and sends this data to a server.

[1098] The server analyzes the received data using a generative AI model and emotion engine, and detects, for example, that there are many young people in the evening hours and that their emotional state is excited.

[1099] The server selects advertisements for new fashion brands that are aimed at young people and match their excitement level, and transmits them to the digital signage.

[1100] The terminal displays the advertisement received from the server in real time.

[1101] During the advertising campaign, the device again collects data and sends it to the server.

[1102] The server analyzes the effectiveness of the advertising campaign based on the newly collected data, evaluating things like viewership rates, increases in dwell time, and changes in user emotions.

[1103] The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[1104] In this way, the system of the present invention provides effective advertising strategies through real-time advertising optimization and precise effectiveness measurement, and by utilizing user emotional data.

[1105] The processing flow will be explained below.

[1106] Program processing steps

[1107] Step 1:

[1108] The device uses cameras and sensors installed at the advertisement location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people around it. It also uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data.

[1109] Step 2:

[1110] The device collects data and sends it to a server via an API, along with metadata such as time, location, person's attributes, behavior, and emotional state.

[1111] Step 3:

[1112] The server receives the data sent from the device and stores it in a database, which is used for subsequent analysis and to optimize the display of advertisements.

[1113] Step 4:

[1114] The server removes noise from the received data and fills in missing data, specifically detecting and correcting outliers and filling in missing values.

[1115] Step 5:

[1116] The server inputs the preprocessed data into a generative AI model, which predicts optimal advertising content based on the target's attribute data.

[1117] Step 6:

[1118] The server also inputs pre-processed emotional data into an emotion engine, which analyzes the user's emotional state and determines emotion-based advertising content.

[1119] Step 7:

[1120] The server combines the output of the generative AI model and the emotion engine to select the most appropriate advertising content for that moment, for example, selecting entertainment advertising for excited young users.

[1121] Step 8:

[1122] The server then transmits the selected advertising content to the device, using an encryption protocol to ensure security.

[1123] Step 9:

[1124] The terminal displays the advertising content received from the server on an advertising display device in real time, for example, by projecting advertising videos or images onto a digital signage.

[1125] Step 10:

[1126] During the advertising campaign, the device will again use its cameras and sensors to collect data and measure changes before and after the campaign, such as comparing passerby attributes, length of stay, and user emotion data before and after the campaign.

[1127] Step 11:

[1128] The device sends data during the ad campaign to the server, which also packets the data and sends it via API.

[1129] Step 12:

[1130] The server analyzes the newly received data from the advertising campaign period and evaluates the effectiveness of the advertisement based on factors such as viewer ratings, changes in time spent on the site, changes in the number of people passing by, and changes in user emotions.

[1131] Step 13:

[1132] The server visualizes the analysis results in graphs and charts and provides them to the user, who can use them to see the effectiveness of their advertising at a glance.

[1133] Step 14:

[1134] The server then suggests new advertising strategies based on the analysis, for example, suggesting the type of advertising content that would be more effective at a particular time or location.

[1135] Step 15:

[1136] The user checks the proposals sent from the server, plans and implements a new advertising strategy, evaluates the proposals, and reflects them in the next advertising campaign.

[1137] With these detailed processing steps, the system of the present invention can provide effective advertising strategies through real-time advertising optimization and precise effectiveness measurement, and by utilizing user sentiment data.

[1138] Example 2

[1139] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1140] Conventional advertising display systems determine advertising content based solely on target user attributes and behavioral data, which limits the effectiveness of advertising. Furthermore, real-time ad optimization and measurement of advertising campaign effectiveness are insufficient, resulting in delays in improving advertising strategies.

[1141] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1142] In this invention, the server includes means for collecting data on the installation location from cameras and sensors, means for analyzing the collected data in real time and detecting user attributes, behavior, and emotional state, means for transmitting the collected data to the server, means for predicting optimal advertising content from the received data using a generative AI model, means for analyzing user emotional data using an emotion engine and determining optimal advertising content, means for transmitting the determined advertising content to an advertising display device and displaying it in real time, means for re-collecting data during the advertising campaign period and measuring and evaluating effectiveness, and means for proposing advertising strategies based on the effectiveness measurement results and visualizing the analysis results, thereby enabling real-time advertising optimization and precise effectiveness measurement.

[1143] A "camera" is a device for capturing images and collecting the data.

[1144] A "sensor" is a device that senses environmental information and collects that data.

[1145] "Installation location data" refers to information about the area around where the advertisement is installed, including information such as people's attributes and behavior.

[1146] "Real-time analysis" refers to the process of processing data as it is collected and obtaining analytical results.

[1147] "User attributes" refers to basic information about individual users, such as age, gender, and occupation.

[1148] "Behavior" refers to the actions and situations that a user can take, such as walking speed and length of stay.

[1149] "Emotional state" refers to the emotion the user is currently feeling (e.g., joy, anger, etc.).

[1150] "Generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate or predict optimal advertising content.

[1151] "Emotion engine" refers to a system for analyzing a user's emotion data and outputting corresponding results.

[1152] "Advertisement display device" refers to a hardware device for displaying determined advertisement content.

[1153] "Campaign Data" means all data collected during the implementation of a particular campaign.

[1154] "Measuring and evaluating effectiveness" refers to the process of quantitatively and qualitatively analyzing the success and impact of an advertising campaign.

[1155] "Proposing an advertising strategy" means showing the direction of future advertising activities and a specific action plan based on the results of effectiveness measurements.

[1156] "Visualizing the analysis results" refers to displaying the results of data analysis in a form that is easy for users to understand (e.g., graphs, charts, etc.).

[1157] This invention is a system that utilizes a generative AI model and an emotion engine to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location, and recognizing user emotions to optimize advertising display.

[1158] Data collection

[1159] The device uses high-resolution cameras and sensors (such as rangefinders and infrared sensors) installed at the advertising location to collect passersby's age, gender, and behavioral data (walking speed, length of stay, etc.) in real time. Furthermore, the device uses a high-performance microphone and emotion recognition software linked to an emotion engine to analyze and collect emotional data from the user's facial expressions and voice. This allows the emotion engine to accurately grasp the user's current emotional state.

[1160] Data transmission and storage

[1161] The device collects data and sends it securely to a server using a REST API. The data packet contains detailed metadata such as time, location, person's attributes, behavior, and emotional state. The transmitted data is received in real time by the server and stored in a cloud database (e.g., Amazon RDS or Google BigQuery).

[1162] Analyzing data and determining optimal advertising content

[1163] The server removes noise from the received data, fills in missing data, and then inputs the preprocessed data into a generative AI model (e.g., OpenAI GPT series) and an emotion engine. The generative AI model predicts optimal advertising content based on the collected attribute data, and the emotion engine analyzes the user's emotional data and determines the optimal advertising content based on the user's current emotional state.

[1164] For example, give the generative AI model the following prompt:

[1165] "Generate the most effective ads for your current audience."

[1166] Sending and displaying advertising content

[1167] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. TLS / SSL is used to ensure data transmission security. The device displays the advertising content received from the server on the advertising display in real time. For example, a music festival advertisement is displayed to a young, smiling user.

[1168] Measuring the effectiveness of advertising campaigns

[1169] During the advertising campaign, the device will again collect data using cameras and sensors to measure changes before and after. The server will analyze the collected effectiveness measurement data and evaluate the actual impact of the advertisement. This evaluation includes multiple parameters such as viewer rate, increase or decrease in dwell time, change in number of passersby, and change in user emotions.

[1170] Visualization and proposal of effects

[1171] The server visualizes the analysis results in graphs and charts (e.g., heat maps and time series graphs) and provides them to the user. Furthermore, it provides specific suggestions for new advertising strategies based on the analysis results. For example, it may suggest that "displaying more ads aimed at younger demographics in the evening will improve effectiveness."

[1172] As described above, the system of the present invention utilizes emotional data to provide an effective advertising strategy through real-time advertising optimization and precise effectiveness measurement.

[1173] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1174] Step 1:

[1175] Data collection

[1176] The devices use high-resolution cameras and sensors installed at the advertising location to collect real-time data from surrounding passersby, including:

[1177] User attributes such as age and gender

[1178] Behavioral data such as walking speed and length of stay

[1179] Using a camera and microphone linked to the emotion engine, emotional data is obtained from the user's facial expressions and voice.

[1180] (input)

[1181] Video, audio and environmental data of passersby

[1182] (output)

[1183] User attribute data, behavioral data, emotional data

[1184] Specific behavior:

[1185] The cameras use facial recognition technology to estimate the age and gender of passersby.

[1186] The sensors aggregate data and analyze pedestrian movement patterns.

[1187] The microphone uses voice analysis technology to capture the user's tone of voice and emotional index.

[1188] Step 2:

[1189] Data transmission and storage

[1190] The device collects data and sends it to the server using a REST API. The data is encrypted using the TLS / SSL protocol, and the server stores the received data in a database.

[1191] (input)

[1192] User attribute data, behavioral data, emotional data

[1193] (output)

[1194] Encrypted data packets, database entries

[1195] Specific behavior:

[1196] The device generates a data packet and sends an HTTP request to the API endpoint.

[1197] The server interprets the received request and inserts it into a database.

[1198] Step 3:

[1199] Analyzing data and determining optimal advertising content

[1200] The server cleanses the data, removes noise, and fills in missing data. It then inputs the data into a generative AI model to predict optimal ad content. Furthermore, an emotion engine analyzes the user's emotional data and determines ad content appropriate for their current emotional state.

[1201] (input)

[1202] Cleansed user attribute data, behavioral data, and emotional data

[1203] (output)

[1204] Optimal advertising content

[1205] Specific behavior:

[1206] The server applies algorithms to remove outliers and impute missing data.

[1207] Enter the following prompt into your generative AI model: "Generate the most effective ad for my current audience."

[1208] The emotion engine analyzes the emotion data and selects the advertisement that best suits the emotional state.

[1209] Step 4:

[1210] Sending and displaying advertising content

[1211] The server receives optimal advertising content from the generative AI model and emotion engine and sends it to the device, which then displays the received advertising content on the advertising display in real time.

[1212] (input)

[1213] Optimal advertising content

[1214] (output)

[1215] Advertisements displayed on advertising displays

[1216] Specific behavior:

[1217] The server encrypts the content in JSON format and sends it to the terminal.

[1218] The device receives the data and pushes it to the display in real time.

[1219] Step 5:

[1220] Measuring the effectiveness of advertising campaigns

[1221] During the advertising campaign, the device again uses cameras and sensors to collect data and measure changes before and after. The server analyzes this data to assess the actual impact of the ads.

[1222] (input)

[1223] User attribute data, behavioral data, and emotional data during the advertising campaign

[1224] (output)

[1225] Advertising effectiveness measurement results

[1226] Specific behavior:

[1227] The device continuously records the viewing time and length of time that passersby spend there.

[1228] The server uses the before and after data sets to statistically analyze the effectiveness of the advertisement.

[1229] Step 6:

[1230] Visualization and proposal of effects

[1231] The server analyzes the acquired effectiveness measurement data, visualizes the results in graphs and charts, and proposes new advertising strategies based on the analysis results.

[1232] (input)

[1233] Advertising effectiveness measurement results

[1234] (output)

[1235] Visualized analysis results and proposals for new advertising strategies

[1236] Specific behavior:

[1237] The server uses visual tools to visualize the data and displays it in a dashboard for users to view.

[1238] New advertising strategy scenarios are generated from the analysis results and proposed to the user.

[1239] These are the specific processing steps of this system, which enables real-time ad optimization and precise measurement of effectiveness.

[1240] (Application example 2)

[1241] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1242] Conventional advertising display systems simply collect data about the surrounding area of ​​an advertisement and display fixed advertising content based on that data. This means that a single advertisement is not necessarily effective for all users, making it difficult to maximize advertising effectiveness. Furthermore, there are limited means for precisely measuring the effectiveness of advertising campaigns, resulting in a lack of information to utilize for future advertising strategies. The present invention aims to solve these problems by providing a system that displays optimal advertising content in real time based on user emotions and attributes and precisely measures advertising effectiveness.

[1243] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting installation location data from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for analyzing emotional data using an emotion engine and determining advertising content tailored to the user's current emotional state, means for transmitting the determined advertising content to the advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating effectiveness, and means for proposing an advertising strategy based on the effectiveness measurement results. This makes it possible to display optimal advertisements for each user in real time and precisely measure the effectiveness of the advertisements.

[1244] A "camera" is a device that can capture images and has the role of collecting visual data at the location where it is installed.

[1245] A "sensor" is a device that detects physical information (such as movement, temperature, humidity, etc.) and converts it into an electrical signal.

[1246] "Location data" refers to information collected by cameras and sensors about the people and environment around a particular location.

[1247] "Server" means the central computing device that receives, analyzes, generates, manages, and distributes the advertising content collected.

[1248] A "generative AI model" is a module that includes an artificial intelligence algorithm for predicting and generating optimal advertising content based on input data.

[1249] An "emotion engine" refers to an artificial intelligence algorithm or software that analyzes a user's emotional state from their facial expressions, voice, etc.

[1250] "Advertising Content" means information or visual material (e.g., images, videos, text advertisements, etc.) generated or selected for display to users.

[1251] "Advertising display device" refers to a monitor or display for visually displaying optimized advertising content to a user.

[1252] "Effectiveness of advertising campaign" refers to measuring and evaluating changes in user behavior and emotions before, during, and after the advertisement display period.

[1253] "Effectiveness Measurement Results" means statistical information about the effectiveness of advertising analyzed based on data collected during the advertising campaign.

[1254] An "advertising strategy" refers to a plan or policy for maximizing the effectiveness of advertising, and serves as a guideline for setting the content and timing of the next advertisement based on the results of effectiveness measurement.

[1255] This invention is a system that uses cameras and sensors to collect customer data in physical stores, analyzes the data in real time, and displays optimal advertising content. The system is composed of cameras, sensors, a server, and an advertising display device, and incorporates a generative AI model and emotion engine to optimize advertising based on user attributes and emotion data.

[1256] Data collection

[1257] The device uses cameras and sensors installed in the physical store to collect data on customer attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.). It also uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data. This data is sent to a server in real time.

[1258] Data transmission and storage

[1259] The device sends the collected data via a secure API to a server, which stores it in a database for subsequent analysis and ad display optimization.

[1260] Analyzing data and determining optimal advertising content

[1261] The server performs pre-processing after filtering out noise from the received data and filling in missing data. The generative AI model predicts optimal advertising content based on customer attribute data. Furthermore, the emotion engine analyzes the user's emotional data and determines the optimal advertising content for the user's current emotional state.

[1262] Sending and displaying advertising content

[1263] The server transmits the optimal advertising content selected by the generative AI model and emotion engine to the device, which then displays the advertising content received from the server on an advertising display device in real time.

[1264] Measuring the effectiveness of advertising campaigns

[1265] During the advertising campaign, the device again collects data using cameras and sensors and sends it to the server, which analyzes the collected performance measurement data to evaluate the actual impact of the advertisements, based on viewer ratings, increases or decreases in dwell time, changes in foot traffic, and changes in user sentiment.

[1266] Visualization and proposal of effects

[1267] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of their advertising at a glance. Furthermore, the server proposes new advertising strategies based on the analysis results.

[1268] Specific examples

[1269] For example, consider the case of digital signage advertising installed in a large shopping mall. The device uses a camera to detect the age group and behavioral data of passersby in the mall, and then uses an emotion engine to collect emotional data from users' facial expressions and voices, sending this data to a server. The server analyzes the received data using a generative AI model and emotion engine, and detects, for example, that there are many young people in the evening hours and that their emotional state is excited. The server selects an advertisement for a new fashion brand that is targeted to young people and matches their excited state, and sends it to the digital signage. The device displays the advertisement received from the server in real time.

[1270] Specific input prompt examples:

[1271] Analyze your customer's emotional state based on the following image data and show them the most suitable advertisement:

[1272] Image data:<image_data>

[1273] Attribute data: Age 30, Gender female, Time spent 10 minutes

[1274] Analyze and generate advertising content.

[1275] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1276] Step 1:

[1277] The terminal uses cameras and sensors installed in the physical store to collect data on customer attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.). In addition, it uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice to collect emotional data. The input is the collected visual data and audio data, and the output is attribute data and emotional data. Specifically, the camera captures video and the microphone collects audio.

[1278] Step 2:

[1279] The device sends the collected data to the server via a secure API. The input is data collected from the camera and microphone, and the output is packetized data sent to the server through the API. Specifically, the device packs the data into packets and sends them to the server using an encrypted protocol.

[1280] Step 3:

[1281] The server stores the received data in a database. The input is the data sent from the device, and the output is the data stored in the database. Specifically, the server analyzes the data received via the API and stores it in the database in an appropriate format.

[1282] Step 4:

[1283] The server performs preprocessing after removing noise from the received data and completing missing data. The input is raw data stored in a database, and the output is preprocessed data. Specifically, the server uses algorithms to perform noise filtering and completion of missing values.

[1284] Step 5:

[1285] The server uses a generative AI model to predict optimal advertising content based on customer attribute data. The input is preprocessed data, and the output is predicted advertising content. Specifically, the server inputs attribute data into the generative AI model to generate optimal advertising content.

[1286] Step 6:

[1287] The server uses an emotion engine to analyze the user's emotion data and determine the optimal advertising content for the user's current emotional state. The input is emotion data, and the output is emotion-optimized advertising content. Specifically, the emotion data is input into the emotion engine, which selects the optimal advertising content.

[1288] Step 7:

[1289] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. The input is the optimized advertising content, and the output is the advertising data sent to the device. Specifically, the server sends the content data to the device using an encryption protocol.

[1290] Step 8:

[1291] The terminal displays the advertising content received from the server on the advertising display device in real time. The input is advertising data from the server, and the output is advertising content to be displayed on the advertising display device. Specifically, the terminal performs an operation to display the received data on the advertising display device.

[1292] Step 9:

[1293] During the advertising campaign, the device again uses its camera or sensor to collect data and transmits it to the server. The input is new data from the camera or sensor, and the output is data packets sent to the server. Specifically, the device continues to collect data during the campaign and periodically transmits it to the server.

[1294] Step 10:

[1295] The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. The input is the re-collected data, and the output is the analysis results showing the effectiveness of the advertisement. Specifically, the server analyzes parameters such as viewer rate, dwell time, and number of passersby to evaluate the effectiveness of the advertisement.

[1296] Step 11:

[1297] The server visualizes the analysis results in graphs and charts and provides them to the user. It also proposes new advertising strategies based on the analysis results. The input is the analysis results of advertising effectiveness, and the output is visualized data and proposed advertising strategies. Specifically, the server uses a tool to visualize the analysis results and generates materials for the user to use to plan their next advertising strategy.

[1298] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1299] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1300] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1301] [Fourth embodiment]

[1302] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1303] 7, a 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.

[1304] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[1305] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1306] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1307] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1308] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1309] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1310] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1311] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[1312] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1313] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1314] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1315] MODE FOR CARRYING OUT THE INVENTION

[1316] This invention is a system that utilizes generative AI models to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location to optimally display advertising.

[1317] Data collection

[1318] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. This data is collected in real time and periodically sent to a server.

[1319] Data transmission and storage

[1320] The device compiles the collected data into packets and sends them to the server via API. The server receives the transmitted data and stores it in a database. The stored data is used for subsequent analysis and optimization of ad display.

[1321] Analyzing data and determining optimal advertising content

[1322] The server analyzes the received data and inputs it into a generative AI model. The generative AI model predicts and determines the optimal advertising content at the current time based on the obtained data. For example, if the analysis results show that there are many young people, it will select advertisements for fashion brands aimed at that demographic.

[1323] Sending and displaying advertising content

[1324] The server sends the selected advertising content to the advertising display device (terminal). The terminal displays the received advertising content in real time. The displayed content is optimized for the target user demographic.

[1325] Measuring the effectiveness of advertising campaigns

[1326] During the advertising campaign, the device collects data again to measure the changes before and after. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as the ad's view rate, increase or decrease in dwell time, and changes in the number of passersby.

[1327] Visualization and proposal of effects

[1328] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of their advertising at a glance. Furthermore, based on the analysis results, the server suggests ways to approach specific target demographics and new advertising strategies.

[1329] Specific examples

[1330] For example, the target is digital signage advertisements installed in large shopping malls.

[1331] The device uses a camera to detect the age group and walking speed of people passing through the mall and sends the data to a server.

[1332] The server analyzes the received data using a generative AI model and detects that there are many young people in the evening hours.

[1333] The server selects advertisements for new fashion brands aimed at young people and sends them to the digital signage.

[1334] The terminal displays the advertisement received from the server in real time.

[1335] During the advertising campaign, the device continuously collects data and transmits it to the server.

[1336] The server analyzes the effectiveness of the advertisement based on the newly collected data and evaluates factors such as increases in viewer ratings and length of stay.

[1337] The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[1338] In this way, the system of the present invention provides an effective advertising strategy through real-time advertising optimization and precise effect measurement.

[1339] The processing flow will be explained below.

[1340] Program processing steps

[1341] Step 1:

[1342] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity.

[1343] Step 2:

[1344] The device collects data and sends it to a server via an API, along with metadata such as time, location, and person attributes.

[1345] Step 3:

[1346] The server receives the data sent from the device and stores it in a database, which is used for subsequent analysis and to optimize the display of advertisements.

[1347] Step 4:

[1348] The server removes noise from the received data and completes missing data, as well as detecting and correcting outliers and completing missing values.

[1349] Step 5:

[1350] The server inputs the preprocessed data into a generative AI model, which predicts optimal advertising content based on the target's attribute data.

[1351] Step 6:

[1352] The server selects the optimal advertising content based on the output of the generative AI model. For example, if it determines that a large number of young people are in the audience, it will select advertisements for fashion brands aimed at that demographic.

[1353] Step 7:

[1354] The server then transmits the selected advertising content to the device, using an encryption protocol to ensure security.

[1355] Step 8:

[1356] The terminal displays the advertising content received from the server on an advertising display device, for example, by projecting advertising videos or images onto a digital signage.

[1357] Step 9:

[1358] During the advertising campaign, the device will again use its cameras and sensors to collect data, again capturing information such as the number of passersby, their attributes, and their behavior.

[1359] Step 10:

[1360] The device sends data during the ad campaign to the server, which also packets the data and sends it via API.

[1361] Step 11:

[1362] The server analyzes the newly received data from the advertising campaign period and evaluates the effectiveness of the advertisement based on factors such as viewer ratings, changes in dwell time, and changes in the number of people passing by.

[1363] Step 12:

[1364] The server visualizes the analysis results in graphs and charts and provides them to the user, who can use them to see the effectiveness of their advertising at a glance.

[1365] Step 13:

[1366] The server then proposes new advertising strategies based on the analysis results, for example, showing effective approaches to specific target demographics.

[1367] Step 14:

[1368] The user checks the proposals sent from the server and plans and implements a new advertising strategy.

[1369] Example 1

[1370] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1371] Conventional advertising display systems mainly display fixed content, making it difficult to display optimal ads in real time based on viewer attributes and behavior. Furthermore, they lacked a mechanism for precisely measuring the effectiveness of advertising campaigns and reflecting this information in subsequent advertising strategies. This made it difficult to maximize advertising effectiveness.

[1372] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1373] In this invention, the server includes means for collecting installation location data from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for transmitting the determined advertising content to the advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating effectiveness, and means for visualizing the evaluation results in graphs and charts and proposing configurable advertising strategies. This makes it possible to display advertisements based on viewer attributes and behavior in real time, and to precisely measure the effectiveness of the advertising campaign and reflect it in the next advertising strategy.

[1374] A "camera" is a device that captures images of the surrounding area where it is installed and collects visual data.

[1375] A "sensor" is a device that detects physical environmental information (e.g., temperature, light, sound, motion, etc.) and converts it into a digital signal.

[1376] A "terminal" is a device that transmits data collected from cameras and sensors to a server, and also functions as an advertising display device.

[1377] "Server" refers to a central processing unit that receives and stores data sent from the device, analyzes the data using a generative AI model, and determines the optimal advertising content.

[1378] A "generative AI model" is a model that uses machine learning technology to analyze collected data and predict and determine optimal advertising content.

[1379] "Advertising content" refers to the content of the advertisement displayed on the display device (for example, images, videos, text, etc.).

[1380] An "advertising display device" is a device for visually displaying determined advertising content, and generally a display or digital signage is used.

[1381] "Effectiveness measurement" is the process of analyzing data collected during an advertising campaign to evaluate factors such as ad viewership and increase or decrease in time spent on the ad.

[1382] "Evaluation results" refer to indicators and information obtained through analysis of effectiveness measurement data, which indicate the effectiveness of an advertising campaign.

[1383] "Advertising strategy" refers to the specific approach and plan for the next advertising campaign based on the evaluation results.

[1384] "Graphs and charts" are visual tools for visually displaying evaluation results, presenting trends and patterns in the data in an easily understandable format.

[1385] This invention is a system that utilizes generative AI models to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location to optimally display advertising.

[1386] Data collection

[1387] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. This data is collected in real time and periodically sent to a server. Examples of specific hardware used include network cameras (e.g., Hikvision) and motion detection sensors.

[1388] Data transmission and storage

[1389] The device assembles the collected data into packets and sends them to a server via API. The server receives the sent data and stores it in a database. An example of specific software used is data transmission using a RESTful API. The stored data is used for subsequent analysis and optimization of ad display.

[1390] Analyzing data and determining optimal advertising content

[1391] The server analyzes the received data and inputs it into a generative AI model. The generative AI model predicts and determines the optimal advertising content at the current time based on the obtained data. For example, a model built with the TensorFlow library using Python can be used. For example, if the analysis results show that there are many young people, advertisements for fashion brands aimed at that demographic can be selected. As a concrete example, the following prompt sentence can be input:

[1392] "Based on data showing that many young people (aged 18 to 25) visit the shopping mall between 6:00 PM and 9:00 PM, please suggest advertisements for fashion brands that will appeal to them."

[1393] Sending and displaying advertising content

[1394] The server sends the selected advertising content to the advertising display device (terminal). The terminal displays the received advertising content in real time. Examples of specific hardware used include digital signage and large displays. The content displayed is optimized for the target user demographic.

[1395] Measuring the effectiveness of advertising campaigns

[1396] During the advertising campaign, the device collects data again to measure the changes before and after. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as the ad's view rate, increase or decrease in dwell time, and changes in the number of passersby.

[1397] Visualization and proposal of effects

[1398] The server visualizes the analysis results in graphs and charts and provides them to the user. Specific software that can be used includes data visualization tools (e.g., D3.js, Matplotlib). This allows users to see the effectiveness of their advertising at a glance. Furthermore, based on the analysis results, the server suggests approaches to specific target demographics and new advertising strategies.

[1399] Specific examples

[1400] For example, consider digital signage advertisements installed in large shopping malls. The device uses a camera to detect the age group and walking speed of passersby in the mall and sends the data to a server. The server analyzes the received data using a generative AI model and detects that there are many young people in the mall during the evening hours. The server selects advertisements for new fashion brands aimed at young people and sends them to the digital signage. The device displays the advertisements received from the server in real time. During the advertising campaign, the device continuously collects data and sends it to the server. The server analyzes the effectiveness of the advertisements based on the newly collected data and evaluates things like viewer rates and increases in dwell time. The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[1401] In this way, the system of the present invention provides an effective advertising strategy through real-time advertising optimization and precise effect measurement.

[1402] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1403] Step 1: Collect data

[1404] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. Specifically, the camera photographs passersby and uses facial recognition software to estimate their age and gender. Motion detection sensors also measure walking speed and length of stay. This data is collected in real time. The input is raw data from the cameras and sensors, and the output is a dataset of analyzed attribute information and behavioral information.

[1405] Step 2: Sending data

[1406] The device compiles the collected data into a list and sends it to the server via API at regular intervals (e.g., every 5 minutes). Specifically, it constructs an HTTP request and sends the collected data to the server in JSON format. For example, it sends a request to the "POST / data" endpoint. The input is a dataset of collected attribute information and behavioral information, and the output is the response to the HTTP request sent to the server.

[1407] Step 3: Save your data

[1408] The server receives data sent from the device and stores it in a database. Specifically, it analyzes the received data, generates SQL statements to insert into a database (e.g., Amazon RDS MySQL), and executes them. The input is the JSON data sent from the device, and the output is the record stored in the database.

[1409] Step 4: Data analysis

[1410] The server analyzes the data stored in the database and inputs it into the generative AI model. Specifically, it extracts the necessary data from the database, performs data preprocessing, and then inputs it into the generative AI model built using Python and the TensorFlow library. The input is the dataset extracted from the database, and the output is the analysis result of the AI ​​model.

[1411] Step 5: Determine the best ad content

[1412] The server analyzes the output from the generative AI model and determines the optimal advertising content. As a specific example, if data shows a high proportion of young people, it selects an advertisement for a fashion brand aimed at that demographic. In this process, it selects the optimal advertisement from a pre-prepared list of advertisements. The input is the analysis result of the AI ​​model, and the output is the selected advertising content.

[1413] Step 6: Submit your advertising content

[1414] The server sends the selected ad content to the ad display device (terminal). Specifically, it sends the ad content as an HTTP response. For example, it sends a request including an ad file to the "POST / display_ad" endpoint. The input is the selected ad content, and the output is the ad content sent to the terminal.

[1415] Step 7: Displaying the Ad

[1416] The terminal displays the advertising content received from the server in real time. Specifically, it sends commands to play video or image advertisements on the display of the advertising display device. The input is the received advertising content, and the output is the displayed advertising content.

[1417] Step 8: Measure your results

[1418] During the advertising campaign, the device collects data again to measure changes before and after the ad is displayed. Specifically, based on the re-collected data, it measures changes in viewer rate, increase or decrease in stay time, and change in the number of passersby. The input is the data collected during the advertising campaign, and the output is a dataset of the effectiveness measurement results.

[1419] Step 9: Evaluate and visualize the effects

[1420] The server analyzes the measurement results and evaluates the actual impact of the advertisement. Specifically, it uses a data visualization tool to generate graphs and charts and provide them to the user. The input is a dataset of the measurement results, and the output is the visualized evaluation results.

[1421] Step 10: Generate proposals

[1422] The server proposes a new advertising strategy based on the results of the effectiveness measurement. Specifically, it further applies the AI ​​model to generate a specific approach for the next advertising campaign. The input is the analyzed effectiveness measurement results, and the output is a proposal for a new advertising strategy.

[1423] (Application example 1)

[1424] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1425] The modern advertising industry requires the real-time delivery of optimal advertising content to specific target audiences. However, current advertising systems make it difficult to precisely measure advertising effectiveness and develop new advertising strategies based on the results. Furthermore, when displaying advertisements via wearable devices such as smart glasses, there is a lack of technology that can display optimal advertisements based on the user's surrounding environment. Therefore, there is an urgent need to develop an integrated system that can dynamically select and display optimal advertisements under specific conditions and measure their effectiveness.

[1426] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1427] In this invention, the server includes means for collecting data on the installation location from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative model and determining optimal advertising content, means for displaying the advertising content on the display of the smart glasses, means for re-collecting data during the advertising campaign period and evaluating its effectiveness, and means for proposing an advertising strategy based on the results of the effectiveness measurement. This makes it possible to display optimal advertising content to a specific target demographic in real time and precisely measure the effectiveness of the advertising.

[1428] "Cameras and sensors" are optical and sensing devices used to collect data at their locations.

[1429] "Installation location data" refers to information about the environment around the advertisement installation location and information about people's attributes and behavior.

[1430] "Collection means" refers to devices or software that acquire environmental and behavioral data from cameras or sensors.

[1431] A "server" is a computer system that receives collected data and performs analysis and advertising content decisions.

[1432] The "transmission means" is a device or software for assembling collected data into packets and transmitting the data to a server.

[1433] A "generative model" is an artificial intelligence algorithm that analyzes received data and generates or selects advertising content.

[1434] "Advertising content" refers to the content of the advertisement that is optimized and displayed using a generative model.

[1435] An "advertising display device" is a device for visually displaying determined advertising content to a user.

[1436] "Smart glasses" are wearable devices that display information through a built-in display.

[1437] "Data during the advertising campaign" refers to environmental and behavioral data that is continuously collected while the advertisement is displayed.

[1438] The "means for evaluating the effectiveness" refers to a device or software for analyzing the collected data and quantitatively analyzing the impact of the advertisement.

[1439] The "means for proposing advertising strategies" refers to a device or software for planning the next advertising strategy based on the results of effectiveness measurement.

[1440] The present invention relates to a system for displaying advertising content on smart glasses, the implementation of which includes the following steps:

[1441] First, the server installs cameras and sensors at the installation location to collect data on the surrounding environment and people's attributes and behavior. The camera acts as an optical device and captures images in real time. The sensor is a device for detecting attributes such as age, gender, and movement speed. This data is constantly acquired through the collection means.

[1442] The collected data is then sent to a server via API. The sending means is a communication device or software that packages the collected data into packets and sends them to the server. The server receives and stores this data. On the server side, it is recommended to use a database solution such as PostgreSQL.

[1443] A generative AI model analyzes this received data in real time on the server and determines the optimal advertising content. The generative AI model is built on common deep learning frameworks such as TensorFlow and PyTorch. It analyzes user attributes and behavioral patterns and generates advertisements tailored to the target demographic. For example, during times when many young people gather, advertisements for fashion brands appropriate for that demographic are selected.

[1444] The advertising content determined based on the analysis results is sent to the smart glasses, which then display the advertising content in real time within the user's field of view using a display means. Examples of smart glasses that can be used include Google Glass and Microsoft HoloLens.

[1445] During the advertising campaign, the server again collects data through cameras and sensors to evaluate its effectiveness. Specifically, it analyzes parameters such as ad viewing rate, changes in dwell time, and changes in the number of passersby. The evaluated effectiveness measurement data serves as the basis for proposing the next advertising strategy. Data visualization tools such as Matplotlib and Plotly are suitable for evaluation.

[1446] As a concrete example, the following prompt sentence is input into the generative AI model for a young person in their 20s walking through a busy downtown area during the day.

[1447] Example prompt sentence:

[1448] "Currently, many young people in their 20s walking around busy areas during the day are using smart glasses. Therefore, think about the advertising content that is most suitable for them. Specifically, advertisements for fashion brands and restaurants based on their age group, gender, and speed of movement would be good."

[1449] In this way, the system of the present invention can display optimal advertising content in real time and precisely measure its effectiveness, thereby dramatically improving the accuracy and effectiveness of advertising strategies.

[1450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1451] Step 1:

[1452] Data on the installation location is collected using cameras and sensors.

[1453] (operation)

[1454] The cameras and sensors connected to the server capture real-time environmental information about the location and the attributes and behavioral data of people around it. Specifically, the cameras capture images, and the sensors detect information such as age, gender, and movement speed.

[1455] (input)

[1456] Physical environment of the installation site, passerby attributes and behavioral data

[1457] (output)

[1458] Raw environmental information and people's attributes and behavior data

[1459] Step 2:

[1460] The collected data is sent to the server.

[1461] (operation)

[1462] The device collects data and sends it to a server via an API, using an internet connection.

[1463] (input)

[1464] Raw data collected in step 1

[1465] (output)

[1466] Environmental information and people data sent to the server

[1467] Step 3:

[1468] A generative AI model is used to analyze the received data and determine the optimal advertising content.

[1469] (operation)

[1470] The server stores the received data in a database and inputs it into a generative AI model for analysis. The generative AI model uses TensorFlow or PyTorch to predict and determine the optimal advertisement based on the input data.

[1471] (input)

[1472] Environmental information and people's attributes and behavior data stored on the server

[1473] (output)

[1474] Optimal advertising content as a result of analysis

[1475] Step 4:

[1476] The determined advertising content is transmitted to the smart glasses.

[1477] (operation)

[1478] The server transmits the determined advertising content to the smart glasses via an API for displaying advertisements.

[1479] (input)

[1480] Ad content determined by generative AI model

[1481] (output)

[1482] Advertising content sent to smart glasses

[1483] Step 5:

[1484] Displaying advertising content on smart glasses.

[1485] (operation)

[1486] The smart glasses then display the received advertising content on their display, with the timing and location of the display controlled in real time.

[1487] (input)

[1488] Advertising content sent to smart glasses

[1489] (output)

[1490] Advertising content displayed in the user's field of view

[1491] Step 6:

[1492] Data will be collected again during the advertising campaign to evaluate its effectiveness.

[1493] (operation)

[1494] During the campaign period, the server will again collect data via cameras and sensors, and analyze that data to evaluate changes in ad viewing rates and dwell times.

[1495] (input)

[1496] Environmental and behavioral data collected again

[1497] (output)

[1498] Advertising effectiveness evaluation results

[1499] Step 7:

[1500] We propose advertising strategies based on the results of effectiveness measurements.

[1501] (operation)

[1502] The server proposes strategies for the next advertising campaign based on the collected and analyzed performance measurement data, and uses visualization tools to display the results in graphs and charts.

[1503] (input)

[1504] Advertising effectiveness evaluation results

[1505] (output)

[1506] Advertisement strategy suggestions and visualized data

[1507] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1508] MODE FOR CARRYING OUT THE INVENTION

[1509] This invention is a system that utilizes a generative AI model and an emotion engine to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location, and recognizing user emotions to optimize advertising display.

[1510] Data collection

[1511] The device uses cameras and sensors installed at the advertising location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people in the vicinity. At the same time, it uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data. This data is collected in real time and periodically sent to a server.

[1512] Data transmission and storage

[1513] The device compiles the collected data into packets and sends them to a server via API. This data includes metadata such as time, location, person's attributes, behavior, and emotional state. The server receives the transmitted data and stores it in a database. The stored data is used for subsequent analysis and optimization of ad display.

[1514] Analyzing data and determining optimal advertising content

[1515] The server filters out noise from the received data and fills in missing data, then inputs the preprocessed data into the generative AI model and emotion engine. The generative AI model predicts optimal advertising content based on the target's attribute data. The emotion engine then analyzes the user's emotional data and determines the optimal advertising content for the user's current emotional state.

[1516] Sending and displaying advertising content

[1517] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. An encryption protocol is used to ensure security during transmission. The device displays the advertising content received from the server on an advertising display device in real time. For example, a user who is smiling might be shown an advertisement that emphasizes fun.

[1518] Measuring the effectiveness of advertising campaigns

[1519] During the advertising campaign, the device again collects data using cameras and sensors to measure changes before and after the campaign. For example, it compares passerby attributes, length of stay, and user emotion data before and after the campaign. The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. This evaluation is based on multiple parameters, such as viewer ratings, increases or decreases in length of stay, changes in the number of passersby, and changes in user emotion.

[1520] Visualization and proposal of effects

[1521] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of the advertisements at a glance. Furthermore, the server proposes new advertising strategies based on the analysis results. For example, it suggests the type of advertising content that is more effective at a specific time or location.

[1522] Specific examples

[1523] For example, when targeting a digital signage advertisement installed in a large shopping mall, the operation is as follows.

[1524] The device uses a camera to detect the age group and behavioral data of passersby in the mall, and then uses an emotion engine to collect emotional data from the user's facial expressions and voice, and sends this data to a server.

[1525] The server analyzes the received data using a generative AI model and emotion engine, and detects, for example, that there are many young people in the evening hours and that their emotional state is excited.

[1526] The server selects advertisements for new fashion brands that are aimed at young people and match their excitement level, and transmits them to the digital signage.

[1527] The terminal displays the advertisement received from the server in real time.

[1528] During the advertising campaign, the device again collects data and sends it to the server.

[1529] The server analyzes the effectiveness of the advertising campaign based on the newly collected data, evaluating things like viewership rates, increases in dwell time, and changes in user emotions.

[1530] The server visualizes the evaluation results and provides the user with specific suggestions for the next advertising campaign.

[1531] In this way, the system of the present invention provides effective advertising strategies through real-time advertising optimization and precise effectiveness measurement, and by utilizing user emotional data.

[1532] The processing flow will be explained below.

[1533] Program processing steps

[1534] Step 1:

[1535] The device uses cameras and sensors installed at the advertisement location to collect data on the attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.) of people around it. It also uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data.

[1536] Step 2:

[1537] The device collects data and sends it to a server via an API, along with metadata such as time, location, person's attributes, behavior, and emotional state.

[1538] Step 3:

[1539] The server receives the data sent from the device and stores it in a database, which is used for subsequent analysis and to optimize the display of advertisements.

[1540] Step 4:

[1541] The server removes noise from the received data and fills in missing data, specifically detecting and correcting outliers and filling in missing values.

[1542] Step 5:

[1543] The server inputs the preprocessed data into a generative AI model, which predicts optimal advertising content based on the target's attribute data.

[1544] Step 6:

[1545] The server also inputs pre-processed emotional data into an emotion engine, which analyzes the user's emotional state and determines emotion-based advertising content.

[1546] Step 7:

[1547] The server combines the output of the generative AI model and the emotion engine to select the most appropriate advertising content for that moment, for example, selecting entertainment advertising for excited young users.

[1548] Step 8:

[1549] The server then transmits the selected advertising content to the device, using an encryption protocol to ensure security.

[1550] Step 9:

[1551] The terminal displays the advertising content received from the server on an advertising display device in real time, for example, by projecting advertising videos or images onto a digital signage.

[1552] Step 10:

[1553] During the advertising campaign, the device will again use its cameras and sensors to collect data and measure changes before and after the campaign, such as comparing passerby attributes, length of stay, and user emotion data before and after the campaign.

[1554] Step 11:

[1555] The device sends data during the ad campaign to the server, which also packets the data and sends it via API.

[1556] Step 12:

[1557] The server analyzes the newly received data from the advertising campaign period and evaluates the effectiveness of the advertisement based on factors such as viewer ratings, changes in time spent on the site, changes in the number of people passing by, and changes in user emotions.

[1558] Step 13:

[1559] The server visualizes the analysis results in graphs and charts and provides them to the user, who can use them to see the effectiveness of their advertising at a glance.

[1560] Step 14:

[1561] The server then suggests new advertising strategies based on the analysis, for example, suggesting the type of advertising content that would be more effective at a particular time or location.

[1562] Step 15:

[1563] The user checks the proposals sent from the server, plans and implements a new advertising strategy, evaluates the proposals, and reflects them in the next advertising campaign.

[1564] With these detailed processing steps, the system of the present invention can provide effective advertising strategies through real-time advertising optimization and precise effectiveness measurement, and by utilizing user sentiment data.

[1565] Example 2

[1566] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1567] Conventional advertising display systems determine advertising content based solely on target user attributes and behavioral data, which limits the effectiveness of advertising. Furthermore, real-time ad optimization and measurement of advertising campaign effectiveness are insufficient, resulting in delays in improving advertising strategies.

[1568] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1569] In this invention, the server includes means for collecting data on the installation location from cameras and sensors, means for analyzing the collected data in real time and detecting user attributes, behavior, and emotional state, means for transmitting the collected data to the server, means for predicting optimal advertising content from the received data using a generative AI model, means for analyzing user emotional data using an emotion engine and determining optimal advertising content, means for transmitting the determined advertising content to an advertising display device and displaying it in real time, means for re-collecting data during the advertising campaign period and measuring and evaluating effectiveness, and means for proposing advertising strategies based on the effectiveness measurement results and visualizing the analysis results, thereby enabling real-time advertising optimization and precise effectiveness measurement.

[1570] A "camera" is a device for capturing images and collecting the data.

[1571] A "sensor" is a device that senses environmental information and collects that data.

[1572] "Installation location data" refers to information about the area around where the advertisement is installed, including information such as people's attributes and behavior.

[1573] "Real-time analysis" refers to the process of processing data as it is collected and obtaining analytical results.

[1574] "User attributes" refers to basic information about individual users, such as age, gender, and occupation.

[1575] "Behavior" refers to the actions and situations that a user can take, such as walking speed and length of stay.

[1576] "Emotional state" refers to the emotion the user is currently feeling (e.g., joy, anger, etc.).

[1577] "Generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate or predict optimal advertising content.

[1578] "Emotion engine" refers to a system for analyzing a user's emotion data and outputting corresponding results.

[1579] "Advertisement display device" refers to a hardware device for displaying determined advertisement content.

[1580] "Campaign Data" means all data collected during the implementation of a particular campaign.

[1581] "Measuring and evaluating effectiveness" refers to the process of quantitatively and qualitatively analyzing the success and impact of an advertising campaign.

[1582] "Proposing an advertising strategy" means showing the direction of future advertising activities and a specific action plan based on the results of effectiveness measurements.

[1583] "Visualizing the analysis results" refers to displaying the results of data analysis in a form that is easy for users to understand (e.g., graphs, charts, etc.).

[1584] This invention is a system that utilizes a generative AI model and an emotion engine to display optimal advertising content in real time and precisely measure the effectiveness of advertising campaigns. This system includes multiple means and processes for collecting and analyzing data around the advertising placement location, and recognizing user emotions to optimize advertising display.

[1585] Data collection

[1586] The device uses high-resolution cameras and sensors (such as rangefinders and infrared sensors) installed at the advertising location to collect passersby's age, gender, and behavioral data (walking speed, length of stay, etc.) in real time. Furthermore, the device uses a high-performance microphone and emotion recognition software linked to an emotion engine to analyze and collect emotional data from the user's facial expressions and voice. This allows the emotion engine to accurately grasp the user's current emotional state.

[1587] Data transmission and storage

[1588] The device collects data and sends it securely to a server using a REST API. The data packet contains detailed metadata such as time, location, person's attributes, behavior, and emotional state. The transmitted data is received in real time by the server and stored in a cloud database (e.g., Amazon RDS or Google BigQuery).

[1589] Analyzing data and determining optimal advertising content

[1590] The server removes noise from the received data, fills in missing data, and then inputs the preprocessed data into a generative AI model (e.g., OpenAI GPT series) and an emotion engine. The generative AI model predicts optimal advertising content based on the collected attribute data, and the emotion engine analyzes the user's emotional data and determines the optimal advertising content based on the user's current emotional state.

[1591] For example, give the generative AI model the following prompt:

[1592] "Generate the most effective ads for your current audience."

[1593] Sending and displaying advertising content

[1594] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. TLS / SSL is used to ensure data transmission security. The device displays the advertising content received from the server on the advertising display in real time. For example, a music festival advertisement is displayed to a young, smiling user.

[1595] Measuring the effectiveness of advertising campaigns

[1596] During the advertising campaign, the device will again collect data using cameras and sensors to measure changes before and after. The server will analyze the collected effectiveness measurement data and evaluate the actual impact of the advertisement. This evaluation includes multiple parameters such as viewer rate, increase or decrease in dwell time, change in number of passersby, and change in user emotions.

[1597] Visualization and proposal of effects

[1598] The server visualizes the analysis results in graphs and charts (e.g., heat maps and time series graphs) and provides them to the user. Furthermore, it provides specific suggestions for new advertising strategies based on the analysis results. For example, it may suggest that "displaying more ads aimed at younger demographics in the evening will improve effectiveness."

[1599] As described above, the system of the present invention utilizes emotional data to provide an effective advertising strategy through real-time advertising optimization and precise effectiveness measurement.

[1600] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1601] Step 1:

[1602] Data collection

[1603] The devices use high-resolution cameras and sensors installed at the advertising location to collect real-time data from surrounding passersby, including:

[1604] User attributes such as age and gender

[1605] Behavioral data such as walking speed and length of stay

[1606] Using a camera and microphone linked to the emotion engine, emotional data is obtained from the user's facial expressions and voice.

[1607] (input)

[1608] Video, audio and environmental data of passersby

[1609] (output)

[1610] User attribute data, behavioral data, emotional data

[1611] Specific behavior:

[1612] The cameras use facial recognition technology to estimate the age and gender of passersby.

[1613] The sensors aggregate data and analyze pedestrian movement patterns.

[1614] The microphone uses voice analysis technology to capture the user's tone of voice and emotional index.

[1615] Step 2:

[1616] Data transmission and storage

[1617] The device collects data and sends it to the server using a REST API. The data is encrypted using the TLS / SSL protocol, and the server stores the received data in a database.

[1618] (input)

[1619] User attribute data, behavioral data, emotional data

[1620] (output)

[1621] Encrypted data packets, database entries

[1622] Specific behavior:

[1623] The device generates a data packet and sends an HTTP request to the API endpoint.

[1624] The server interprets the received request and inserts it into a database.

[1625] Step 3:

[1626] Analyzing data and determining optimal advertising content

[1627] The server cleanses the data, removes noise, and fills in missing data. It then inputs the data into a generative AI model to predict optimal ad content. Furthermore, an emotion engine analyzes the user's emotional data and determines ad content appropriate for their current emotional state.

[1628] (input)

[1629] Cleansed user attribute data, behavioral data, and emotional data

[1630] (output)

[1631] Optimal advertising content

[1632] Specific behavior:

[1633] The server applies algorithms to remove outliers and impute missing data.

[1634] Enter the following prompt into your generative AI model: "Generate the most effective ad for my current audience."

[1635] The emotion engine analyzes the emotion data and selects the advertisement that best suits the emotional state.

[1636] Step 4:

[1637] Sending and displaying advertising content

[1638] The server receives optimal advertising content from the generative AI model and emotion engine and sends it to the device, which then displays the received advertising content on the advertising display in real time.

[1639] (input)

[1640] Optimal advertising content

[1641] (output)

[1642] Advertisements displayed on advertising displays

[1643] Specific behavior:

[1644] The server encrypts the content in JSON format and sends it to the terminal.

[1645] The device receives the data and pushes it to the display in real time.

[1646] Step 5:

[1647] Measuring the effectiveness of advertising campaigns

[1648] During the advertising campaign, the device again uses cameras and sensors to collect data and measure changes before and after. The server analyzes this data to assess the actual impact of the ads.

[1649] (input)

[1650] User attribute data, behavioral data, and emotional data during the advertising campaign

[1651] (output)

[1652] Advertising effectiveness measurement results

[1653] Specific behavior:

[1654] The device continuously records the viewing time and length of time that passersby spend there.

[1655] The server uses the before and after data sets to statistically analyze the effectiveness of the advertisement.

[1656] Step 6:

[1657] Visualization and proposal of effects

[1658] The server analyzes the acquired effectiveness measurement data, visualizes the results in graphs and charts, and proposes new advertising strategies based on the analysis results.

[1659] (input)

[1660] Advertising effectiveness measurement results

[1661] (output)

[1662] Visualized analysis results and proposals for new advertising strategies

[1663] Specific behavior:

[1664] The server uses visual tools to visualize the data and displays it in a dashboard for users to view.

[1665] New advertising strategy scenarios are generated from the analysis results and proposed to the user.

[1666] These are the specific processing steps of this system, which enables real-time ad optimization and precise measurement of effectiveness.

[1667] (Application example 2)

[1668] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1669] Conventional advertising display systems simply collect data about the surrounding area of ​​an advertisement and display fixed advertising content based on that data. This means that a single advertisement is not necessarily effective for all users, making it difficult to maximize advertising effectiveness. Furthermore, there are limited means for precisely measuring the effectiveness of advertising campaigns, resulting in a lack of information to utilize for future advertising strategies. The present invention aims to solve these problems by providing a system that displays optimal advertising content in real time based on user emotions and attributes and precisely measures advertising effectiveness.

[1670] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting installation location data from cameras and sensors, means for transmitting the collected data to the server, means for analyzing the received data using a generative AI model and determining optimal advertising content, means for analyzing emotional data using an emotion engine and determining advertising content tailored to the user's current emotional state, means for transmitting the determined advertising content to the advertising display device, means for displaying the advertising content on the advertising display device, means for re-collecting data during the advertising campaign period and evaluating effectiveness, and means for proposing an advertising strategy based on the effectiveness measurement results. This makes it possible to display optimal advertisements for each user in real time and precisely measure the effectiveness of the advertisements.

[1671] A "camera" is a device that can capture images and has the role of collecting visual data at the location where it is installed.

[1672] A "sensor" is a device that detects physical information (such as movement, temperature, humidity, etc.) and converts it into an electrical signal.

[1673] "Location data" refers to information collected by cameras and sensors about the people and environment around a particular location.

[1674] "Server" means the central computing device that receives, analyzes, generates, manages, and distributes the advertising content collected.

[1675] A "generative AI model" is a module that includes an artificial intelligence algorithm for predicting and generating optimal advertising content based on input data.

[1676] An "emotion engine" refers to an artificial intelligence algorithm or software that analyzes a user's emotional state from their facial expressions, voice, etc.

[1677] "Advertising Content" means information or visual material (e.g., images, videos, text advertisements, etc.) generated or selected for display to users.

[1678] "Advertising display device" refers to a monitor or display for visually displaying optimized advertising content to a user.

[1679] "Effectiveness of advertising campaign" refers to measuring and evaluating changes in user behavior and emotions before, during, and after the advertisement display period.

[1680] "Effectiveness Measurement Results" means statistical information about the effectiveness of advertising analyzed based on data collected during the advertising campaign.

[1681] An "advertising strategy" refers to a plan or policy for maximizing the effectiveness of advertising, and serves as a guideline for setting the content and timing of the next advertisement based on the results of effectiveness measurement.

[1682] This invention is a system that uses cameras and sensors to collect customer data in physical stores, analyzes the data in real time, and displays optimal advertising content. The system is composed of cameras, sensors, a server, and an advertising display device, and incorporates a generative AI model and emotion engine to optimize advertising based on user attributes and emotion data.

[1683] Data collection

[1684] The device uses cameras and sensors installed in the physical store to collect data on customer attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.). It also uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice and collect emotional data. This data is sent to a server in real time.

[1685] Data transmission and storage

[1686] The device sends the collected data via a secure API to a server, which stores it in a database for subsequent analysis and ad display optimization.

[1687] Analyzing data and determining optimal advertising content

[1688] The server performs pre-processing after filtering out noise from the received data and filling in missing data. The generative AI model predicts optimal advertising content based on customer attribute data. Furthermore, the emotion engine analyzes the user's emotional data and determines the optimal advertising content for the user's current emotional state.

[1689] Sending and displaying advertising content

[1690] The server transmits the optimal advertising content selected by the generative AI model and emotion engine to the device, which then displays the advertising content received from the server on an advertising display device in real time.

[1691] Measuring the effectiveness of advertising campaigns

[1692] During the advertising campaign, the device again collects data using cameras and sensors and sends it to the server, which analyzes the collected performance measurement data to evaluate the actual impact of the advertisements, based on viewer ratings, increases or decreases in dwell time, changes in foot traffic, and changes in user sentiment.

[1693] Visualization and proposal of effects

[1694] The server visualizes the analysis results in graphs and charts and provides them to the user, allowing the user to see the effectiveness of their advertising at a glance. Furthermore, the server proposes new advertising strategies based on the analysis results.

[1695] Specific examples

[1696] For example, consider the case of digital signage advertising installed in a large shopping mall. The device uses a camera to detect the age group and behavioral data of passersby in the mall, and then uses an emotion engine to collect emotional data from users' facial expressions and voices, sending this data to a server. The server analyzes the received data using a generative AI model and emotion engine, and detects, for example, that there are many young people in the evening hours and that their emotional state is excited. The server selects an advertisement for a new fashion brand that is targeted to young people and matches their excited state, and sends it to the digital signage. The device displays the advertisement received from the server in real time.

[1697] Specific input prompt examples:

[1698] Analyze your customer's emotional state based on the following image data and show them the most suitable advertisement:

[1699] Image data:<image_data>

[1700] Attribute data: Age 30, Gender female, Time spent 10 minutes

[1701] Analyze and generate advertising content.

[1702] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1703] Step 1:

[1704] The terminal uses cameras and sensors installed in the physical store to collect data on customer attributes (age, gender, etc.) and behavior (walking speed, length of stay, etc.). In addition, it uses cameras and microphones linked to an emotion engine to analyze the user's facial expressions and voice to collect emotional data. The input is the collected visual data and audio data, and the output is attribute data and emotional data. Specifically, the camera captures video and the microphone collects audio.

[1705] Step 2:

[1706] The device sends the collected data to the server via a secure API. The input is data collected from the camera and microphone, and the output is packetized data sent to the server through the API. Specifically, the device packs the data into packets and sends them to the server using an encrypted protocol.

[1707] Step 3:

[1708] The server stores the received data in a database. The input is the data sent from the device, and the output is the data stored in the database. Specifically, the server analyzes the data received via the API and stores it in the database in an appropriate format.

[1709] Step 4:

[1710] The server performs preprocessing after removing noise from the received data and completing missing data. The input is raw data stored in a database, and the output is preprocessed data. Specifically, the server uses algorithms to perform noise filtering and completion of missing values.

[1711] Step 5:

[1712] The server uses a generative AI model to predict optimal advertising content based on customer attribute data. The input is preprocessed data, and the output is predicted advertising content. Specifically, the server inputs attribute data into the generative AI model to generate optimal advertising content.

[1713] Step 6:

[1714] The server uses an emotion engine to analyze the user's emotion data and determine the optimal advertising content for the user's current emotional state. The input is emotion data, and the output is emotion-optimized advertising content. Specifically, the emotion data is input into the emotion engine, which selects the optimal advertising content.

[1715] Step 7:

[1716] The server sends the optimal advertising content selected by the generative AI model and emotion engine to the device. The input is the optimized advertising content, and the output is the advertising data sent to the device. Specifically, the server sends the content data to the device using an encryption protocol.

[1717] Step 8:

[1718] The terminal displays the advertising content received from the server on the advertising display device in real time. The input is advertising data from the server, and the output is advertising content to be displayed on the advertising display device. Specifically, the terminal performs an operation to display the received data on the advertising display device.

[1719] Step 9:

[1720] During the advertising campaign, the device again uses its camera or sensor to collect data and transmits it to the server. The input is new data from the camera or sensor, and the output is data packets sent to the server. Specifically, the device continues to collect data during the campaign and periodically transmits it to the server.

[1721] Step 10:

[1722] The server analyzes the collected effectiveness measurement data and evaluates the actual impact of the advertisement. The input is the re-collected data, and the output is the analysis results showing the effectiveness of the advertisement. Specifically, the server analyzes parameters such as viewer rate, dwell time, and number of passersby to evaluate the effectiveness of the advertisement.

[1723] Step 11:

[1724] The server visualizes the analysis results in graphs and charts and provides them to the user. It also proposes new advertising strategies based on the analysis results. The input is the analysis results of advertising effectiveness, and the output is visualized data and proposed advertising strategies. Specifically, the server uses a tool to visualize the analysis results and generates materials for the user to use to plan their next advertising strategy.

[1725] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1726] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1727] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1728] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1729] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1730] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1731] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1732] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1733] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1734] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1735] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1736] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1737] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1738] 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.

[1739] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1740] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1741] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1742] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1743] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1744] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1745] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1746] The following is further disclosed regarding the above embodiment.

[1747] (Claim 1)

[1748] A means of collecting data on the installation location from cameras and sensors,

[1749] means for transmitting the collected data to a server;

[1750] means for analyzing the received data using a generative model to determine optimal advertising content;

[1751] means for transmitting the determined advertisement content to the advertisement display device;

[1752] means for displaying advertising content on an advertising display device;

[1753] A means of collecting data again during the advertising campaign to evaluate its effectiveness;

[1754] A means to propose advertising strategies based on the results of effectiveness measurement,

[1755] A system including:

[1756] (Claim 2)

[1757] 10. The system of claim 1, wherein the generative model analyzes data in real time to determine optimal advertising content.

[1758] (Claim 3)

[1759] The system of claim 1, wherein the camera or sensor detects the attributes and behavior of the subject.

[1760] "Example 1"

[1761] (Claim 1)

[1762] A means of collecting data on the installation location from cameras and sensors,

[1763] means for transmitting the collected data to a server;

[1764] a means for analyzing the received data using a generative AI model to determine optimal advertising content;

[1765] means for transmitting the determined advertisement content to the advertisement display device;

[1766] means for displaying advertising content on an advertising display device;

[1767] A means of collecting data again during the advertising campaign to evaluate its effectiveness;

[1768] A means to visualize the evaluation results in graphs and charts and propose configurable advertising strategies,

[1769] A system including:

[1770] (Claim 2)

[1771] 10. The system of claim 1, wherein the generative AI model analyzes data in real time to determine optimal advertising content.

[1772] (Claim 3)

[1773] The system of claim 1, wherein a camera or sensor detects the attributes and behavior of a subject and transmits the results as image data.

[1774] "Application Example 1"

[1775] (Claim 1)

[1776] A means of collecting data on the installation location from cameras and sensors,

[1777] means for transmitting the collected data to a server;

[1778] means for analyzing the received data using a generative model to determine optimal advertising content;

[1779] means for transmitting the determined advertisement content to the advertisement ...

Claims

1. A means of collecting data on the installation location from cameras and sensors, means for transmitting the collected data to a server; means for analyzing the received data using a generative model to determine optimal advertising content; means for transmitting the determined advertisement content to the advertisement display device; means for displaying advertising content on an advertising display device; A means of collecting data again during the advertising campaign to evaluate its effectiveness; A means to propose advertising strategies based on the results of effectiveness measurement, A system including:

2. The system of claim 1 , wherein the generative model analyzes data in real time to determine optimal advertising content.

3. The system of claim 1 , wherein a camera or sensor detects attributes or behaviors of a subject.

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