system

Abstract

We provide the system. [Solution] A means by which an information processing device generates advertising content, A terminal device that displays advertisements and collects user gaze and facial expression data, The information processing device analyzes the collected data and evaluates the user's emotions to optimize advertising content. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional advertisement evaluation, only limited indicators such as the number of displays and click-through rates can be obtained, and it is difficult to measure the true reaction of users. Therefore, it has been difficult for advertisers to effectively improve advertisement content and provide advertisements most suitable for the target market. It is necessary to improve the decline in advertisement effect caused by such insufficient understanding of user interaction.

Means for Solving the Problems

[0005] This invention provides a system in which an information processing device generates advertising content, and a terminal device displays the advertisement while collecting user gaze and facial expression data. The information processing device analyzes this data, evaluates the user's emotions, and optimizes the advertising content. Furthermore, by redistributing the optimized advertising content to each terminal device, the system aims to continuously improve advertising effectiveness. This system makes it possible to analyze the target market and its trends and select the most effective advertising content.

[0006] An "information processing device" is an electronic device that takes data as input, performs specific processing, and then outputs the results. In this context, it refers to a device that generates advertising content and performs data analysis.

[0007] A "terminal device" is a device that users directly operate to display or input information. In this context, it is a device that displays advertisements and collects user gaze and facial expression data.

[0008] "Advertising content" is a collection of information created to promote a product or service, and includes various forms such as text, videos, and images.

[0009] "Eye-gaze data" refers to information that indicates where a user is focusing their attention, and is collected while an advertisement is being displayed.

[0010] "Facial expression data" refers to information that analyzes a user's facial expressions to show changes in emotion, and is data collected while they are watching advertisements.

[0011] "Emotional assessment" refers to the act of analyzing collected eye-tracking and facial expression data to determine the user's psychological state and emotions.

[0012] "Advertising content optimization" is the process of re-evaluating and modifying the components of content to improve its effectiveness.

[0013] A "target market" refers to the consumer group or market to which a particular product or service is best suited, and the advertising strategy should be concentrated on this market. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]A sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention realizes a system that generates, optimizes, and effectively delivers advertising content using three components: an information processing device (server), a terminal device, and a user. The following describes how each element of this system functions, along with specific examples.

[0036] First, the server uses AI technology to generate advertising content based on initial data provided by advertisers and marketing teams. This generated content is then designed to be optimal by referencing data on past market trends and target consumer preferences.

[0037] Next, the generated advertising content is delivered to each device, and the user views it. The device is equipped with a function to monitor the user's gaze and facial expressions in real time. While viewing the advertisement, the device records where the user's gaze is focused and what kind of facial expressions they are making.

[0038] Here, the user plays the role of viewing the advertisement, and their unintentional, natural reactions become important data for the system. For example, when viewing an advertisement for new sports shoes, the parts that the user showed interest in and, conversely, the parts that they did not, are clearly analyzed.

[0039] The collected gaze and facial expression data is then sent to a server and analyzed by a specialized algorithm. This allows for evaluation of which parts the user showed interest in and which scenes elicited positive reactions. For example, it can identify moments when the user smiled or scenes they were intently watching.

[0040] Finally, based on these evaluation results, the server optimizes the advertising content. Elements that received a high response are highlighted, and elements that received a low response are modified. This optimized content is then sent back to the device and displayed to the user.

[0041] This cycle is repeated, maximizing the effectiveness of advertising and ensuring that the most appropriate ads are delivered to the target market and consumers. This process allows advertisers to develop advertising strategies based on clear data, thereby strengthening their competitiveness in the market.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server uses AI to generate advertising content based on targeting information and campaign objectives provided by advertisers. This includes editing video footage and creating taglines. At this stage, market trends and consumer preference data are utilized to ensure the advertisement has the maximum possible impact.

[0045] Step 2:

[0046] The generated advertising content is sent to the device and displayed to the user. As the user views the advertisement, the device uses its built-in camera and software to track and record the user's gaze and facial expressions in real time.

[0047] Step 3:

[0048] Users watch advertisements and react to them unconsciously. For example, they may spend more time gazing at elements that interest them and smile at scenes they find amusing. These reactions are recorded on the device as eye-tracking and facial expression data.

[0049] Step 4:

[0050] After viewing the advertisement, the device sends the collected eye-tracking and facial expression data to the server. This data is analyzed in real time, enabling rapid reaction analysis.

[0051] Step 5:

[0052] The server uses a dedicated analysis algorithm to analyze the user's gaze and facial expression data in detail and evaluate the user's emotions. It determines where the user's interest was focused and which elements elicited a strong positive response.

[0053] Step 6:

[0054] Based on this analysis, the server restructures the ad content. By strengthening high-rated sections and improving or removing less popular parts, it generates more effective ads.

[0055] Step 7:

[0056] The optimized ad content is sent back to the device and displayed to the user again. The newly generated ad takes the previous data into account and is more tailored to the target user group.

[0057] Step 8:

[0058] This repeated cycle allows advertising effectiveness to continuously improve and enables a highly accurate response to user interests and reactions. Through this process, advertisers can optimize their ads based on data and strengthen their competitiveness in the market.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In recent years, methods that utilize user reaction data to enhance advertising effectiveness have attracted attention. However, conventional systems lack sufficient efficiency in data collection and analysis, as well as in optimizing advertising based on this data, resulting in a limited impact of advertisements on their target markets.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for an information processing device to generate advertising content using an artificial intelligence model based on input data, means for a terminal device to display the advertisement and perform real-time monitoring to acquire user gaze points and facial expression information, and means for the information processing device to analyze the acquired information, evaluate user reactions, and improve the advertising content. As a result, a series of processes from advertising generation to viewing data collection, data analysis, and improvement of advertising content are carried out automatically and effectively, making it possible to significantly improve the impact of advertising on the target market.

[0064] An "information processing device" is a device that receives and analyzes data, and uses AI technology to generate and improve advertising content.

[0065] "Input data" refers to data that includes information necessary for generating advertising content, such as the attributes of target consumers, market trends, and the purpose of the advertisement.

[0066] A "generative artificial intelligence model" is an algorithm or program that automatically generates advertisement designs and content based on provided data.

[0067] A "terminal device" is a device that can display advertising content to a user and provide information about the user's gaze and facial expressions.

[0068] "Advertising content" refers to information generated by AI technology for the purpose of promoting products or services.

[0069] "Point of focus" refers to the specific area that a user looks at when viewing advertising data.

[0070] "Facial expression information" refers to data that shows the emotional reactions a user exhibits while watching an advertisement.

[0071] "Real-time monitoring" is a process that instantly records and analyzes user actions and reactions.

[0072] "Analysis" is the process of evaluating user behavior and emotions from acquired data and extracting information to improve advertising effectiveness.

[0073] "Improvement" refers to making modifications or emphasis on advertising content to make it more attractive and effective based on the analysis results.

[0074] "Target market" refers to the consumer group or sales market that an advertiser specifically targets.

[0075] "Trends" refer to recent trends and shifts in a particular market or consumer behavior.

[0076] This invention is an advertising delivery optimization system composed of three parties: an information processing device (hereinafter referred to as a server), a terminal device that performs display and data acquisition (hereinafter referred to as a terminal), and a user who views the advertisement.

[0077] The server first uses a generative AI model to automatically generate advertising content based on initial data provided by advertisers and marketing teams, such as target consumer attributes, historical market trends, and advertising objectives. Here, Tensorflow® or PyTorch is used as the machine learning framework, and databases such as MySQL® or PostgreSQL are used for data management. The generated advertising content is optimized by the AI ​​by referencing historical data, ensuring that, for example, specific designs and messages resonate with the target users.

[0078] The device not only plays the advertising content delivered to the user, but also plays a role in capturing the user's reactions. It is equipped with a camera with facial recognition capabilities that tracks the user's gaze and facial expressions in real time. This is done using the open-source library OpenCV to record the direction of gaze and changes in facial expressions. For example, it can detect when the user stares at a specific scene in the advertisement for an extended period of time or when they smile.

[0079] Users play a crucial role in providing natural, unintentional responses to advertisements. When a user views an advertisement for new sports shoes, their level of interest in the design and color scheme, as well as their reactions to specific scenes they see, become valuable data.

[0080] After receiving this data, the server uses Python data analysis libraries such as Pandas and Scikit-learn to analyze the user's eye movements and emotional changes. Based on the analysis results, it then improves the advertising content. For example, it might change the ad to emphasize colors or designs that the user showed strong interest in.

[0081] In this process, by providing the AI ​​model with specific prompts such as, "Optimize advertising content for the latest sports shoes targeting young men by analyzing user gaze and facial expression data," a system is created that continuously generates effective advertisements and maximizes advertising effectiveness for the target market.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server receives input data from advertisers, including target audience, historical market data, and advertising objectives. Based on this data, it uses a generative AI model to generate advertising content. Specifically, the AI ​​creates media formats such as text, images, and videos. The output is advertising content optimized for the target audience.

[0085] Step 2:

[0086] The server delivers the generated advertising content to each user's device. The device receives the data via the internet connection and displays the advertisement to the user. The operation here involves data transfer and display in a pre-configured format, and the output is the advertising content played on the user interface.

[0087] Step 3:

[0088] The user views advertisements on their device, exhibiting natural eye movements and facial expressions. The device detects and records the user's gaze and facial expression data in real time through its built-in camera and image processing library. The input is the user's facial expression and gaze data, and the output is a record of this data.

[0089] Step 4:

[0090] The eye-tracking and facial expression data acquired by the device is sent to the server. The server receives this data and performs analysis using a data analysis library. Specifically, the data is processed to analyze the degree of gaze concentration and changes in facial expressions in response to specific scenes. The output is data that represents the user's interest in and preferences for advertisements.

[0091] Step 5:

[0092] The server optimizes advertising content based on the analysis results. It highlights elements that show high interest and modifies or removes elements that show less interest. The input is the analysis results, and the output is the improved advertising content. Specifically, the AI ​​generates content and then runs it again as a prompt to perform any necessary corrections.

[0093] Step 6:

[0094] The server reconstructs the optimized ad content and then redistributes it to each device. This process is repeated to maximize the effectiveness of the ads. The input is the improved ad content, and the output is playback on the user's device.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] Traditional advertising delivery systems have struggled to maximize advertising effectiveness because they cannot optimize ads in real time based on users' interests and emotions. Furthermore, the lack of sufficient dynamic content change functionality on visual display devices has made it difficult to maintain users' interest for extended periods.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for an information processing device to generate advertising content, means for a terminal device to display advertisements and collect user gaze and facial expression data, means for the information processing device to analyze the collected data, evaluate the user's emotions, and optimize the advertising content, and means for dynamically changing the advertising content on a visual display device in real time based on the gaze and facial expressions analyzed by the terminal device. This makes it possible to provide optimized advertising content that keeps the user interested and engaged.

[0100] An "information processing device" is a device that generates advertising content, analyzes collected data to evaluate user sentiment, and optimizes the advertising content.

[0101] A "terminal device" is a device that displays advertisements, collects user eye-tracking and facial expression data, and displays the analysis results in real time.

[0102] A "user" is a person who uses a terminal device to view advertising content, and whose gaze and facial expressions are collected.

[0103] "Eye-gaze and facial expression data" refers to information that quantifies the visual and emotional reactions of users while they are viewing an advertisement.

[0104] "Optimization" is the process of improving advertising content to be most effective for users based on collected data.

[0105] A "visual display device" is a monitor or display device associated with a terminal device that presents advertising content to the user.

[0106] "Dynamic change" refers to the process of adjusting and modifying advertising content in real time based on the user's gaze and facial expression analysis.

[0107] To implement this invention, a system is constructed using an information processing device (server), a terminal device, and a user. First, the server generates advertising content using a generation AI model based on initial data provided by the advertiser. In doing so, past market trends and the preferences of target consumers are taken into consideration.

[0108] The generated advertising content is delivered to a terminal device. The terminal device displays the advertisement to the user using a visual display device such as a smartphone or smart glasses. The terminal device is also equipped with a camera to collect the user's gaze and facial expression data in real time.

[0109] Users watch advertisements and provide natural reactions. The terminal device utilizes eye-tracking and facial expression analysis technologies (e.g., using OpenCV or TensorFlow) to detect which parts of the advertisement the user is interested in.

[0110] The server analyzes gaze and facial expression data transmitted from the terminal device using a dedicated algorithm to evaluate when the user was most interested. Based on these results, the server optimizes the advertising content, emphasizing elements that successfully maintained interest and modifying elements that failed to capture attention.

[0111] The optimized advertising content is then delivered back to the device and displayed to the user. By repeating this cycle, it is possible to maximize the effectiveness of the advertisements.

[0112] For example, if analysis shows that viewers of an advertisement for a new sports product are excessively focused on the product's design, the content can be regenerated to further emphasize that design element. Another example of a prompt input to the generation AI model might be, "Generate new advertising content based on the parts of this advertisement that viewers showed particular interest in."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server receives initial data provided by the advertiser and generates advertising content using a generative AI model. The input data includes market trends and target consumer profiles, which the generative AI model uses to create the content. The output is the generated advertising content.

[0116] Step 2:

[0117] The server delivers the advertising content generated in step 1 to the terminal device. The input here is the output from step 1, and the terminal device displays the received advertising content on its visual display device. The output is the advertisement displayed on the terminal device in a format that the user can view.

[0118] Step 3:

[0119] The terminal device collects eye-tracking and facial expression data of users viewing advertisements on a visual display device in real time using its built-in camera. The input for this step is the user's visual response, and the output is eye-tracking and facial expression data that quantifies these responses.

[0120] Step 4:

[0121] The terminal device transmits the gaze and facial expression data collected in step 3 to the server. The transmitted data is the input, and this data serves as basic information for the next processing on the server side. The output is the user's response data transmitted to the server.

[0122] Step 5:

[0123] The server analyzes gaze and facial expression data transmitted from the terminal device using a dedicated algorithm. The input is the output from step 4, and this analysis identifies the moments and elements that the user found interesting. The output is the information identifying the user's interests obtained as a result of the analysis.

[0124] Step 6:

[0125] The server optimizes the ad content based on the analysis results obtained in step 5. The input is the analysis results, and the generative AI model is used to highlight elements that successfully maintained interest and improve elements that did not. The output is the optimized ad content.

[0126] Step 7:

[0127] The server delivers the optimized ad content from step 6 back to the terminal device. The input is the optimized ad content, which is displayed on the terminal device and viewed again by the user. The output is the display of the improved ad content.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention provides a system that integrates an information processing device (server), terminal device, user, and emotion engine to generate, optimize, and effectively deliver advertising content. The specific functions of each element and their interactions are described below.

[0130] First, the server uses AI to generate advertising content based on targeting information and campaign objectives provided by the advertiser. This content generation is based on market trends and consumer preference data, and the generated content is an initial version optimized based on basic data.

[0131] Next, the generated advertising content is delivered to the device and displayed to the user. At this stage, the device uses its built-in camera and sensors to track and record the user's eye movements and facial expressions in real time. The data collected at this stage is stored as the user's initial response.

[0132] Users watch advertisements and unconsciously react to their content. For example, if they are interested in a product, they may linger on that part of the ad, or a smile may appear on their face when they feel reassured. Through these natural reactions, psychological and emotional data about the user is generated.

[0133] The server acquires gaze data and facial expression data sent from the terminal and analyzes them using an emotion engine. This emotion engine analyzes the data to determine the user's emotional state and can identify multiple emotional states (e.g., interest, joy, indifference, etc.).

[0134] As a result, the server transitions to a process of optimizing ad content based on the user's emotional state. In this process, the emotion engine uses the information it has gathered to highlight highly-rated elements and improve or remove elements that received little response. At this stage, the user's past response data is also considered and contributes to future optimizations.

[0135] The optimized ad content is then sent back to the device and displayed to the user in a new form. The improved ads reflect the previous data and are more appealing to the user's interests and emotions.

[0136] Because this series of actions is repeated, the ad content is designed to continuously change in response to the user's interests and emotions, thereby increasing its effectiveness. This system allows advertisers to adopt a data-driven approach and develop highly competitive advertising strategies.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server collects and analyzes data on the target market based on requests from advertisers. This includes age groups, interests, and recent trend information, and uses this information to generate initial ad content using an AI algorithm.

[0140] Step 2:

[0141] The generated advertising content is sent to the device and displayed to the user. The device uses its built-in camera to track the user's gaze and facial expressions while they are viewing the advertisement, and collects this data.

[0142] Step 3:

[0143] Users watch advertisements and react unconsciously to their content. Their gaze lingers on parts that interest them, and their facial expressions change during emotionally charged scenes.

[0144] Step 4:

[0145] The device analyzes the collected eye-tracking and facial expression data in real time and records it as the user's reaction. This data is sent to the server after the advertisement viewing ends.

[0146] Step 5:

[0147] The server uses an emotion engine to analyze the incoming data. Based on the data, it identifies areas of interest and specific emotional states (e.g., joy, surprise) that the user has shown interest in, and evaluates them.

[0148] Step 6:

[0149] The server uses the results of the emotion engine analysis to optimize ad content. By strengthening high-rated elements and adjusting low-rated elements, it reconstructs ads to be more effective for future use.

[0150] Step 7:

[0151] The optimized ad content is sent back to the device and displayed to the user. This improved content is based on previous data, and further improvements can be sought by measuring user responses again.

[0152] Step 8:

[0153] This process is repeated, and the server continuously accumulates data and learns new patterns, thereby continuously improving the effectiveness of the advertisements. Through this iterative process, advertisers can deliver compelling and optimal ads to their target users.

[0154] (Example 2)

[0155] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] Modern advertising delivery systems require advanced personalization to respond to the diversification of target markets and rapid trend changes. However, existing systems have a challenge in that they cannot effectively utilize users' emotional responses in the generation and optimization of advertising content. As a result, advertising effectiveness can be reduced because user interests are not properly captured.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0158] In this invention, the server includes means for an information processing device to utilize a generative AI model to generate advertising content, means for a terminal device to display advertisements and collect user gaze and facial expression data through sensors, and means for the information processing device to apply emotion analysis technology to analyze gaze and facial expression data transmitted from the terminal device, evaluate the user's emotions, and optimize the advertising content. This makes it possible to analyze the user's individual emotional response in real time, effectively optimize the advertising content, and achieve higher advertising effectiveness.

[0159] An "information processing device" is a combination of hardware and software for receiving, analyzing, and processing digital data, and is a device that performs the functions necessary for generating and optimizing advertising content.

[0160] A "generative AI model" is an artificial intelligence technology that analyzes large amounts of data and generates new content based on specified conditions.

[0161] "Terminal device" refers to a computer or smart device that is directly operated by the user, and is a device used for displaying advertising content and collecting user responses.

[0162] A "sensor" is a device that measures physical phenomena (e.g., eye movements or changes in facial expressions) and digitizes that data.

[0163] "Emotional analysis technology" is a technology used to analyze facial expressions, tone of voice, and other characteristics from a user's voice and video data in order to identify their emotional state.

[0164] "Optimization" refers to adjusting processes and deliverables to achieve a given objective under specific conditions, and improving them to obtain maximum effectiveness.

[0165] This invention relates to an advertising delivery system that combines an information processing device, a terminal device, a user, and sentiment analysis technology. The core of the invention lies in the generation of advertising content utilizing AI technology, the collection and analysis of user response data, and the redistribution of optimized advertisements.

[0166] The server, acting as an information processing unit, generates advertising content using a generative AI model based on target information and campaign strategies obtained from advertisers. This process utilizes widely used digital data analysis software as an artificial intelligence framework to generate content that reflects the latest market trends and consumer preference data. For example, it might input a prompt such as, "Generate an advertisement promoting a new beverage for health-conscious people in their 30s," into the AI ​​model to generate the advertisement.

[0167] The terminal acts as a display device placed in front of the user, displaying advertising content transmitted from the server. Furthermore, the terminal incorporates advanced sensor technology to track the user's eye movements and facial expressions in real time, transmitting this response data to the server. Commonly used sensor technologies, such as CMOS-type imaging sensors, are utilized.

[0168] Users view advertisements displayed on their devices and exhibit unconscious reactions. For example, they may watch the advertisement for a longer period when they are interested in a particular product, or their facial expressions may change when they feel pleasure. This data forms the basis for analyzing users' emotional states.

[0169] The server uses emotion analysis technology to analyze eye-tracking and facial expression data from the device to determine the user's emotions. To accurately distinguish between multiple emotional states, it comprehensively analyzes data obtained from audio and video. Based on these analysis results, it optimizes the advertising content according to the user's emotions and delivers the regenerated advertising content back to the user's device. This allows the advertising content to better match the user's individual needs, enabling more effective advertising.

[0170] This system design enables data-driven advertising strategies to be implemented with the information processing unit at the center, contributing to the optimization of marketing and improvement of advertising effectiveness.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The server receives targeting information and campaign objectives provided by the advertiser and forms a prompt. This prompt is then input into a generative AI model to generate promising advertising content. This generative AI model analyzes digital data and generates content based on market trends and consumer preferences. The output is an initial version of the advertising content.

[0174] Step 2:

[0175] The device receives advertising content sent from the server and displays it to the user. Using sensors built into the device, it tracks the user's eye movements and facial expressions in real time. Specifically, an eye-tracking sensor tracks the user's eye movements, and a facial recognition sensor captures changes in facial expressions, collecting user response data. This data is used as input data for the server.

[0176] Step 3:

[0177] The server acquires user gaze data and facial expression data received from the terminal. Using this data as input, it analyzes the user's emotional state using emotion analysis technology. The analysis determines whether the user is showing an emotional state such as interest, pleasure, or indifference towards the advertisement. The output is a dataset reflecting the user's emotions.

[0178] Step 4:

[0179] The server then performs a process to optimize the ad content based on the output data from the sentiment analysis. Specifically, the generative AI model is used again to highlight content elements that received high ratings and improve or remove elements that received low ratings. This results in optimized ad content that better matches the user's emotions.

[0180] Step 5:

[0181] The device receives optimized ad content from the server again and displays it to the user in an improved form. The new content is further personalized based on the user's previous response. This process is repeated, and the ads continue to improve to better meet the user's needs.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] Traditional advertising systems have struggled to accurately understand users' emotions and interests and deliver personalized ads in real time. Furthermore, the accuracy of ad optimization based on users' psychological responses was low, posing challenges in maximizing ad effectiveness.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for an information processing device to automatically generate advertising content, means for a terminal device to present advertisements and acquire the user's gaze and facial expression information, and means for the information processing device to analyze the acquired information and adaptively improve the advertising content by analyzing the user's psychological state. This makes it possible to optimize advertisements in real time based on changes in the user's gaze and facial expressions and to provide personalized promotional information.

[0187] An "information processing device" is a device that receives and processes data, and in particular has the function of generating advertising content and optimizing it based on user responses.

[0188] "Advertising content" refers to a collection of information created to promote products or services to consumers, and in this invention, it is automatically generated and improved based on the user's psychological state.

[0189] A "terminal device" is a device that provides a user interface and acquires information such as the user's gaze and facial expressions, and has the function of transmitting this information to a server.

[0190] "Eye gaze and facial expression information" refers to data about the direction of a user's gaze and instantaneous changes in their facial expression when viewing an advertisement, and is used to capture the user's interest and emotional state.

[0191] "Analysis" is the act of analyzing collected information to derive meaning and patterns, and in this invention, it is a process for understanding the user's psychological state.

[0192] "Psychological state" refers to the internal emotions and feelings a user experiences when encountering an advertisement, with examples including interest, concern, indifference, and a sense of security.

[0193] "Adaptive improvement" refers to the process of changing advertising content to the optimal form in response to user reactions, adjusting information to make it more engaging for users.

[0194] "Personalized promotional information" refers to advertising information that is uniquely created for a specific user based on their responses and interests.

[0195] The system for carrying out this invention consists of an information processing device, a terminal device, and a user. The information processing device (server) utilizes a generative AI model to automatically generate advertising content and adaptively improves the advertisements by analyzing the user's gaze and facial expression information. The terminal device is a device that interacts with the user and uses an eye-tracking sensor and a camera to collect the user's gaze direction and facial expression changes in real time. The user also watches the advertisements and shows unconscious reactions based on their own interests and emotions.

[0196] Specifically, the terminal device uses Tobii Technology's eye-tracking sensors to collect user gaze data. This data is sent to a server in the cloud and analyzed using software such as Python and TensorFlow. Based on the analysis results, the server evaluates the user's psychological state and optimizes the advertising content. The optimized advertising content is then sent back to the terminal device and presented to the user. This allows users to receive personalized promotional information tailored to their interests.

[0197] As a concrete example, let's assume a user is in a fashion shop wearing smart glasses. If the user's gaze lingers on a particular piece of clothing for an extended period, the server might analyze that data and display a message to the user such as, "It seems you're interested in this jacket. We're currently offering a 20% discount on it."

[0198] An example of a prompt to input into the generation AI model is, "Detect the products and reactions the user is focusing on, and create relevant promotional information." This allows the system to provide an advertising experience tailored to the specific needs of the user.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The device uses eye-tracking sensors and a camera to collect gaze and facial expression data while the user is viewing content. This data is used as input to record the user's gaze position and changes in facial expressions. The output data includes information about which parts of the content the user is focusing on.

[0202] Step 2:

[0203] The terminal transmits the collected gaze and facial expression data to the server. In this step, the terminal specifically analyzes the gaze movements and facial expression data in real time, converts it into a digital format, and transmits it to the server. The output data includes gaze and facial expression data in an analyzable format.

[0204] Step 3:

[0205] The server analyzes the received gaze and facial expression data as input. Using software such as Python or TensorFlow, it processes the data to evaluate the user's psychological state. In this process, it performs data calculations to identify the user's level of interest and emotional state, and generates an evaluation result regarding the user's psychological state as output.

[0206] Step 4:

[0207] The server uses a generative AI model to adaptively improve advertising content based on the evaluation results of the user's psychological state. In this step, the evaluation results are converted into prompts, such as "Detect the products the user is focusing on and their reactions, and create relevant promotional information," which are then input into the generative AI model. The output is optimized advertising content.

[0208] Step 5:

[0209] The server sends optimized ad content to the device and presents it to the user. In this step, the device receives the new ad content and performs the specific actions of displaying it on the screen. As output, the user is presented with an adaptively improved ad.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0226] This invention realizes a system that generates, optimizes, and effectively delivers advertising content using three components: an information processing device (server), a terminal device, and a user. The following describes how each element of this system functions, along with specific examples.

[0227] First, the server uses AI technology to generate advertising content based on initial data provided by advertisers and marketing teams. This generated content is then designed to be optimal by referencing data on past market trends and target consumer preferences.

[0228] Next, the generated advertising content is delivered to each device, and the user views it. The device is equipped with a function to monitor the user's gaze and facial expressions in real time. While viewing the advertisement, the device records where the user's gaze is focused and what kind of facial expressions they are making.

[0229] Here, the user plays the role of viewing the advertisement, and their unintentional, natural reactions become important data for the system. For example, when viewing an advertisement for new sports shoes, the parts that the user showed interest in and, conversely, the parts that they did not, are clearly analyzed.

[0230] The collected gaze and facial expression data is then sent to a server and analyzed by a specialized algorithm. This allows for evaluation of which parts the user showed interest in and which scenes elicited positive reactions. For example, it can identify moments when the user smiled or scenes they were intently watching.

[0231] Finally, based on these evaluation results, the server optimizes the advertising content. Elements that received a high response are highlighted, and elements that received a low response are modified. This optimized content is then sent back to the device and displayed to the user.

[0232] This cycle is repeated, maximizing the effectiveness of advertising and ensuring that the most appropriate ads are delivered to the target market and consumers. This process allows advertisers to develop advertising strategies based on clear data, thereby strengthening their competitiveness in the market.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server uses AI to generate advertising content based on targeting information and campaign objectives provided by advertisers. This includes editing video footage and creating taglines. At this stage, market trends and consumer preference data are utilized to ensure the advertisement has the maximum possible impact.

[0236] Step 2:

[0237] The generated advertising content is sent to the device and displayed to the user. As the user views the advertisement, the device uses its built-in camera and software to track and record the user's gaze and facial expressions in real time.

[0238] Step 3:

[0239] Users watch advertisements and react to them unconsciously. For example, they may spend more time gazing at elements that interest them and smile at scenes they find amusing. These reactions are recorded on the device as eye-tracking and facial expression data.

[0240] Step 4:

[0241] After viewing the advertisement, the device sends the collected eye-tracking and facial expression data to the server. This data is analyzed in real time, enabling rapid reaction analysis.

[0242] Step 5:

[0243] The server uses a dedicated analysis algorithm to analyze the user's gaze and facial expression data in detail and evaluate the user's emotions. It determines where the user's interest was focused and which elements elicited a strong positive response.

[0244] Step 6:

[0245] Based on this analysis, the server restructures the ad content. By strengthening high-rated sections and improving or removing less popular parts, it generates more effective ads.

[0246] Step 7:

[0247] The optimized ad content is sent back to the device and displayed to the user again. The newly generated ad takes the previous data into account and is more tailored to the target user group.

[0248] Step 8:

[0249] This repeated cycle allows advertising effectiveness to continuously improve and enables a highly accurate response to user interests and reactions. Through this process, advertisers can optimize their ads based on data and strengthen their competitiveness in the market.

[0250] (Example 1)

[0251] Next, we will describe Example 1. 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."

[0252] In recent years, methods that utilize user reaction data to enhance advertising effectiveness have attracted attention. However, conventional systems lack sufficient efficiency in data collection and analysis, as well as in optimizing advertising based on this data, resulting in a limited impact of advertisements on their target markets.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes means for an information processing device to generate advertising content using an artificial intelligence model based on input data, means for a terminal device to display the advertisement and perform real-time monitoring to acquire user gaze points and facial expression information, and means for the information processing device to analyze the acquired information, evaluate user reactions, and improve the advertising content. As a result, a series of processes from advertising generation to viewing data collection, data analysis, and improvement of advertising content are carried out automatically and effectively, making it possible to significantly improve the impact of advertising on the target market.

[0255] An "information processing device" is a device that receives and analyzes data, and uses AI technology to generate and improve advertising content.

[0256] "Input data" refers to data that includes information necessary for generating advertising content, such as the attributes of target consumers, market trends, and the purpose of the advertisement.

[0257] A "generative artificial intelligence model" is an algorithm or program that automatically generates advertisement designs and content based on provided data.

[0258] A "terminal device" is a device that can display advertising content to a user and provide information about the user's gaze and facial expressions.

[0259] "Advertising content" refers to information generated by AI technology for the purpose of promoting products or services.

[0260] "Point of focus" refers to the specific area that a user looks at when viewing advertising data.

[0261] "Facial expression information" refers to data that shows the emotional reactions a user exhibits while watching an advertisement.

[0262] "Real-time monitoring" is a process that instantly records and analyzes user actions and reactions.

[0263] "Analysis" is the process of evaluating user behavior and emotions from acquired data and extracting information to improve advertising effectiveness.

[0264] "Improvement" refers to making modifications or emphasis on advertising content to make it more attractive and effective based on the analysis results.

[0265] "Target market" refers to the consumer group or sales market that an advertiser specifically targets.

[0266] "Trends" refer to recent trends and shifts in a particular market or consumer behavior.

[0267] This invention is an advertising delivery optimization system composed of three parties: an information processing device (hereinafter referred to as a server), a terminal device that performs display and data acquisition (hereinafter referred to as a terminal), and a user who views the advertisement.

[0268] The server first uses a generative AI model to automatically generate advertising content based on initial data provided by advertisers and marketing teams, such as target consumer attributes, historical market trends, and advertising objectives. Here, TensorFlow or PyTorch is used as the machine learning framework, and databases such as MySQL or PostgreSQL are used for data management. The generated advertising content is optimized by the AI ​​by referencing historical data, ensuring that, for example, specific designs and messages resonate with the target users.

[0269] The device not only plays the advertising content delivered to the user, but also plays a role in capturing the user's reactions. It is equipped with a camera with facial recognition capabilities that tracks the user's gaze and facial expressions in real time. This is done using the open-source library OpenCV to record the direction of gaze and changes in facial expressions. For example, it can detect when the user stares at a specific scene in the advertisement for an extended period of time or when they smile.

[0270] Users play a crucial role in providing natural, unintentional responses to advertisements. When a user views an advertisement for new sports shoes, their level of interest in the design and color scheme, as well as their reactions to specific scenes they see, become valuable data.

[0271] After receiving this data, the server uses Python data analysis libraries such as Pandas and Scikit-learn to analyze the user's eye movements and emotional changes. Based on the analysis results, it then improves the advertising content. For example, it might change the ad to emphasize colors or designs that the user showed strong interest in.

[0272] In this process, by providing the AI ​​model with specific prompts such as, "Optimize advertising content for the latest sports shoes targeting young men by analyzing user gaze and facial expression data," a system is created that continuously generates effective advertisements and maximizes advertising effectiveness for the target market.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The server receives input data from advertisers, including target audience, historical market data, and advertising objectives. Based on this data, it uses a generative AI model to generate advertising content. Specifically, the AI ​​creates media formats such as text, images, and videos. The output is advertising content optimized for the target audience.

[0276] Step 2:

[0277] The server delivers the generated advertising content to each user's device. The device receives the data via the internet connection and displays the advertisement to the user. The operation here involves data transfer and display in a pre-configured format, and the output is the advertising content played on the user interface.

[0278] Step 3:

[0279] The user views an advertisement on the terminal and shows natural eye movement and expressions. The terminal detects and records the user's eye line and expression data in real time through the built-in camera and image processing library. The input is the user's expression and eye line data, and the output is the recording of these data.

[0280] Step 4:

[0281] The eye line and expression data obtained by the terminal are sent to the server. The server receives this data and performs analysis using the data analysis library. As specific data processing, it analyzes the concentration of the eye line and the change of expressions for specific scenes. The output is data representing the user's interest and preference patterns for the advertisement.

[0282] Step 5:

[0283] The server optimizes the advertisement content based on the analysis results. It emphasizes the elements that show high interest and modifies or deletes the elements with low interest. The input is the analysis result, and the output is the improved advertisement content. As a specific operation, the content generated by AI is used as a prompt again to perform the necessary modifications.

[0284] Step 6:

[0285] After the server reconstructs the optimized advertisement content, it redistributes it to each terminal. This process is repeated to maximize the effect of the advertisement. The input is the improved advertisement content, and the output is the playback on the user's terminal.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] Traditional advertising delivery systems have struggled to maximize advertising effectiveness because they cannot optimize ads in real time based on users' interests and emotions. Furthermore, the lack of sufficient dynamic content change functionality on visual display devices has made it difficult to maintain users' interest for extended periods.

[0289] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for an information processing device to generate advertising content, means for a terminal device to display advertisements and collect user gaze and facial expression data, means for the information processing device to analyze the collected data, evaluate the user's emotions, and optimize the advertising content, and means for dynamically changing the advertising content on a visual display device in real time based on the gaze and facial expressions analyzed by the terminal device. This makes it possible to provide optimized advertising content that keeps the user interested and engaged.

[0291] An "information processing device" is a device that generates advertising content, analyzes collected data to evaluate user sentiment, and optimizes the advertising content.

[0292] A "terminal device" is a device that displays advertisements, collects user eye-tracking and facial expression data, and displays the analysis results in real time.

[0293] A "user" is a person who uses a terminal device to view advertising content, and whose gaze and facial expressions are collected.

[0294] "Eye-gaze and facial expression data" refers to information that quantifies the visual and emotional reactions of users while they are viewing an advertisement.

[0295] "Optimization" is the process of improving advertising content to be most effective for users based on collected data.

[0296] A "visual display device" is a monitor or display device associated with a terminal device that presents advertising content to the user.

[0297] "Dynamic change" refers to the process of adjusting and modifying advertising content in real time based on the user's gaze and facial expression analysis.

[0298] To implement this invention, a system is constructed using an information processing device (server), a terminal device, and a user. First, the server generates advertising content using a generation AI model based on initial data provided by the advertiser. In doing so, past market trends and the preferences of target consumers are taken into consideration.

[0299] The generated advertising content is delivered to a terminal device. The terminal device displays the advertisement to the user using a visual display device such as a smartphone or smart glasses. The terminal device is also equipped with a camera to collect the user's gaze and facial expression data in real time.

[0300] Users watch advertisements and provide natural reactions. The terminal device utilizes eye-tracking and facial expression analysis technologies (e.g., using OpenCV or TensorFlow) to detect which parts of the advertisement the user is interested in.

[0301] The server analyzes gaze and facial expression data transmitted from the terminal device using a dedicated algorithm to evaluate when the user was most interested. Based on these results, the server optimizes the advertising content, emphasizing elements that successfully maintained interest and modifying elements that failed to capture attention.

[0302] The optimized advertising content is then delivered back to the device and displayed to the user. By repeating this cycle, it is possible to maximize the effectiveness of the advertisements.

[0303] As a specific example, when it is analyzed that the gaze of a user watching an advertisement for a new sports product is overly concentrated on the design of the product, the content can be regenerated in a form that emphasizes that part of the design more. Also, as an example of a prompt sentence input into the generative AI model, something like "Please generate new advertisement content based on the parts that users who saw this advertisement showed particular interest in." can be considered.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The server receives the initial data provided by the advertiser and generates advertisement content using the generative AI model. The input data includes market trends and the profiles of target consumers, and based on this, the generative AI model creates the content. The output is the generated advertisement content.

[0307] Step 2:

[0308] The server distributes the advertisement content generated in Step 1 to the terminal device. The input here is the output of Step 1, and the terminal device displays the received advertisement content on the visual display device. The output is the advertisement displayed on the terminal device in a form that can be viewed by the user.

[0309] Step 3:

[0310] The terminal device collects the gaze and facial expression data of the user watching the advertisement on the visual display device in real time using the installed camera. The input for this step is the visual user reaction, and the output is the gaze and facial expression data obtained by quantifying these reactions.

[0311] Step 4:

[0312] The terminal device transmits the gaze and facial expression data collected in step 3 to the server. The transmitted data is the input, and this data serves as basic information for the next processing on the server side. The output is the user's response data transmitted to the server.

[0313] Step 5:

[0314] The server analyzes gaze and facial expression data transmitted from the terminal device using a dedicated algorithm. The input is the output from step 4, and this analysis identifies the moments and elements that the user found interesting. The output is the information identifying the user's interests obtained as a result of the analysis.

[0315] Step 6:

[0316] The server optimizes the ad content based on the analysis results obtained in step 5. The input is the analysis results, and the generative AI model is used to highlight elements that successfully maintained interest and improve elements that did not. The output is the optimized ad content.

[0317] Step 7:

[0318] The server delivers the optimized ad content from step 6 back to the terminal device. The input is the optimized ad content, which is displayed on the terminal device and viewed again by the user. The output is the display of the improved ad content.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] This invention provides a system that integrates an information processing device (server), terminal device, user, and emotion engine to generate, optimize, and effectively deliver advertising content. The specific functions of each element and their interactions are described below.

[0321] First, the server uses AI to generate advertising content based on targeting information and campaign objectives provided by the advertiser. This content generation is based on market trends and consumer preference data, and the generated content is an initial version optimized based on basic data.

[0322] Next, the generated advertising content is delivered to the device and displayed to the user. At this stage, the device uses its built-in camera and sensors to track and record the user's eye movements and facial expressions in real time. The data collected at this stage is stored as the user's initial response.

[0323] Users watch advertisements and unconsciously react to their content. For example, if they are interested in a product, they may linger on that part of the ad, or a smile may appear on their face when they feel reassured. Through these natural reactions, psychological and emotional data about the user is generated.

[0324] The server acquires gaze data and facial expression data sent from the terminal and analyzes them using an emotion engine. This emotion engine analyzes the data to determine the user's emotional state and can identify multiple emotional states (e.g., interest, joy, indifference, etc.).

[0325] As a result, the server transitions to a process of optimizing ad content based on the user's emotional state. In this process, the emotion engine uses the information it has gathered to highlight highly-rated elements and improve or remove elements that received little response. At this stage, the user's past response data is also considered and contributes to future optimizations.

[0326] The optimized ad content is then sent back to the device and displayed to the user in a new form. The improved ads reflect the previous data and are more appealing to the user's interests and emotions.

[0327] Because this series of actions is repeated, the ad content is designed to continuously change in response to the user's interests and emotions, thereby increasing its effectiveness. This system allows advertisers to adopt a data-driven approach and develop highly competitive advertising strategies.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The server collects and analyzes data on the target market based on requests from advertisers. This includes age groups, interests, and recent trend information, and uses this information to generate initial ad content using an AI algorithm.

[0331] Step 2:

[0332] The generated advertising content is sent to the device and displayed to the user. The device uses its built-in camera to track the user's gaze and facial expressions while they are viewing the advertisement, and collects this data.

[0333] Step 3:

[0334] Users watch advertisements and react unconsciously to their content. Their gaze lingers on parts that interest them, and their facial expressions change during emotionally charged scenes.

[0335] Step 4:

[0336] The device analyzes the collected eye-tracking and facial expression data in real time and records it as the user's reaction. This data is sent to the server after the advertisement viewing ends.

[0337] Step 5:

[0338] The server uses an emotion engine to analyze the incoming data. Based on the data, it identifies areas of interest and specific emotional states (e.g., joy, surprise) that the user has shown interest in, and evaluates them.

[0339] Step 6:

[0340] The server uses the results of the emotion engine analysis to optimize ad content. By strengthening high-rated elements and adjusting low-rated elements, it reconstructs ads to be more effective for future use.

[0341] Step 7:

[0342] The optimized ad content is sent back to the device and displayed to the user. This improved content is based on previous data, and further improvements can be sought by measuring user responses again.

[0343] Step 8:

[0344] This process is repeated, and the server continuously accumulates data and learns new patterns, thereby continuously improving the effectiveness of the advertisements. Through this iterative process, advertisers can deliver compelling and optimal ads to their target users.

[0345] (Example 2)

[0346] Next, we will describe Example 2. 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".

[0347] Modern advertising delivery systems require advanced personalization to respond to the diversification of target markets and rapid trend changes. However, existing systems have a challenge in that they cannot effectively utilize users' emotional responses in the generation and optimization of advertising content. As a result, advertising effectiveness can be reduced because user interests are not properly captured.

[0348] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0349] In this invention, the server includes means for an information processing device to utilize a generative AI model to generate advertising content, means for a terminal device to display advertisements and collect user gaze and facial expression data through sensors, and means for the information processing device to apply emotion analysis technology to analyze gaze and facial expression data transmitted from the terminal device, evaluate the user's emotions, and optimize the advertising content. This makes it possible to analyze the user's individual emotional response in real time, effectively optimize the advertising content, and achieve higher advertising effectiveness.

[0350] An "information processing device" is a combination of hardware and software for receiving, analyzing, and processing digital data, and is a device that performs the functions necessary for generating and optimizing advertising content.

[0351] A "generative AI model" is an artificial intelligence technology that analyzes large amounts of data and generates new content based on specified conditions.

[0352] "Terminal device" refers to a computer or smart device that is directly operated by the user, and is a device used for displaying advertising content and collecting user responses.

[0353] A "sensor" is a device that measures physical phenomena (e.g., eye movements or changes in facial expressions) and digitizes that data.

[0354] "Emotional analysis technology" is a technology used to analyze facial expressions, tone of voice, and other characteristics from a user's voice and video data in order to identify their emotional state.

[0355] "Optimization" refers to adjusting processes and deliverables to achieve a given objective under specific conditions, and improving them to obtain maximum effectiveness.

[0356] This invention relates to an advertising delivery system that combines an information processing device, a terminal device, a user, and sentiment analysis technology. The core of the invention lies in the generation of advertising content utilizing AI technology, the collection and analysis of user response data, and the redistribution of optimized advertisements.

[0357] The server, acting as an information processing unit, generates advertising content using a generative AI model based on target information and campaign strategies obtained from advertisers. This process utilizes widely used digital data analysis software as an artificial intelligence framework to generate content that reflects the latest market trends and consumer preference data. For example, it might input a prompt such as, "Generate an advertisement promoting a new beverage for health-conscious people in their 30s," into the AI ​​model to generate the advertisement.

[0358] The terminal acts as a display device placed in front of the user, displaying advertising content transmitted from the server. Furthermore, the terminal incorporates advanced sensor technology to track the user's eye movements and facial expressions in real time, transmitting this response data to the server. Commonly used sensor technologies, such as CMOS-type imaging sensors, are utilized.

[0359] Users view advertisements displayed on their devices and exhibit unconscious reactions. For example, they may watch the advertisement for a longer period when they are interested in a particular product, or their facial expressions may change when they feel pleasure. This data forms the basis for analyzing users' emotional states.

[0360] The server uses emotion analysis technology to analyze eye-tracking and facial expression data from the device to determine the user's emotions. To accurately distinguish between multiple emotional states, it comprehensively analyzes data obtained from audio and video. Based on these analysis results, it optimizes the advertising content according to the user's emotions and delivers the regenerated advertising content back to the user's device. This allows the advertising content to better match the user's individual needs, enabling more effective advertising.

[0361] This system design enables data-driven advertising strategies to be implemented with the information processing unit at the center, contributing to the optimization of marketing and improvement of advertising effectiveness.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The server receives targeting information and campaign objectives provided by the advertiser and forms a prompt. This prompt is then input into a generative AI model to generate promising advertising content. This generative AI model analyzes digital data and generates content based on market trends and consumer preferences. The output is an initial version of the advertising content.

[0365] Step 2:

[0366] The device receives advertising content sent from the server and displays it to the user. Using sensors built into the device, it tracks the user's eye movements and facial expressions in real time. Specifically, an eye-tracking sensor tracks the user's eye movements, and a facial recognition sensor captures changes in facial expressions, collecting user response data. This data is used as input data for the server.

[0367] Step 3:

[0368] The server acquires user gaze data and facial expression data received from the terminal. Using this data as input, it analyzes the user's emotional state using emotion analysis technology. The analysis determines whether the user is showing an emotional state such as interest, pleasure, or indifference towards the advertisement. The output is a dataset reflecting the user's emotions.

[0369] Step 4:

[0370] The server then performs a process to optimize the ad content based on the output data from the sentiment analysis. Specifically, the generative AI model is used again to highlight content elements that received high ratings and improve or remove elements that received low ratings. This results in optimized ad content that better matches the user's emotions.

[0371] Step 5:

[0372] The device receives optimized ad content from the server again and displays it to the user in an improved form. The new content is further personalized based on the user's previous response. This process is repeated, and the ads continue to improve to better meet the user's needs.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] Traditional advertising systems have struggled to accurately understand users' emotions and interests and deliver personalized ads in real time. Furthermore, the accuracy of ad optimization based on users' psychological responses was low, posing challenges in maximizing ad effectiveness.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0377] In this invention, the server includes means for an information processing device to automatically generate advertising content, means for a terminal device to present advertisements and acquire the user's gaze and facial expression information, and means for the information processing device to analyze the acquired information and adaptively improve the advertising content by analyzing the user's psychological state. This makes it possible to optimize advertisements in real time based on changes in the user's gaze and facial expressions and to provide personalized promotional information.

[0378] An "information processing device" is a device that receives and processes data, and in particular has the function of generating advertising content and optimizing it based on user responses.

[0379] "Advertising content" refers to a collection of information created to promote products or services to consumers, and in this invention, it is automatically generated and improved based on the user's psychological state.

[0380] A "terminal device" is a device that provides a user interface and acquires information such as the user's gaze and facial expressions, and has the function of transmitting this information to a server.

[0381] "Eye gaze and facial expression information" refers to data about the direction of a user's gaze and instantaneous changes in their facial expression when viewing an advertisement, and is used to capture the user's interest and emotional state.

[0382] "Analysis" is the act of analyzing collected information to derive meaning and patterns, and in this invention, it is a process for understanding the user's psychological state.

[0383] "Psychological state" refers to the internal emotions and feelings a user experiences when encountering an advertisement, with examples including interest, concern, indifference, and a sense of security.

[0384] "Adaptive improvement" refers to the process of changing advertising content to the optimal form in response to user reactions, adjusting information to make it more engaging for users.

[0385] "Personalized promotional information" refers to advertising information that is uniquely created for a specific user based on their responses and interests.

[0386] The system for carrying out this invention consists of an information processing device, a terminal device, and a user. The information processing device (server) utilizes a generative AI model to automatically generate advertising content and adaptively improves the advertisements by analyzing the user's gaze and facial expression information. The terminal device is a device that interacts with the user and uses an eye-tracking sensor and a camera to collect the user's gaze direction and facial expression changes in real time. The user also watches the advertisements and shows unconscious reactions based on their own interests and emotions.

[0387] Specifically, the terminal device uses Tobii Technology's eye-tracking sensors to collect user gaze data. This data is sent to a server in the cloud and analyzed using software such as Python and TensorFlow. Based on the analysis results, the server evaluates the user's psychological state and optimizes the advertising content. The optimized advertising content is then sent back to the terminal device and presented to the user. This allows users to receive personalized promotional information tailored to their interests.

[0388] As a concrete example, let's assume a user is in a fashion shop wearing smart glasses. If the user's gaze lingers on a particular piece of clothing for an extended period, the server might analyze that data and display a message to the user such as, "It seems you're interested in this jacket. We're currently offering a 20% discount on it."

[0389] An example of a prompt to input into the generation AI model is, "Detect the products and reactions the user is focusing on, and create relevant promotional information." This allows the system to provide an advertising experience tailored to the specific needs of the user.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The device uses eye-tracking sensors and a camera to collect gaze and facial expression data while the user is viewing content. This data is used as input to record the user's gaze position and changes in facial expressions. The output data includes information about which parts of the content the user is focusing on.

[0393] Step 2:

[0394] The terminal transmits the collected gaze and facial expression data to the server. In this step, the terminal specifically analyzes the gaze movements and facial expression data in real time, converts it into a digital format, and transmits it to the server. The output data includes gaze and facial expression data in an analyzable format.

[0395] Step 3:

[0396] The server analyzes the received gaze and facial expression data as input. Using software such as Python or TensorFlow, it processes the data to evaluate the user's psychological state. In this process, it performs data calculations to identify the user's level of interest and emotional state, and generates an evaluation result regarding the user's psychological state as output.

[0397] Step 4:

[0398] The server uses a generative AI model to adaptively improve advertising content based on the evaluation results of the user's psychological state. In this step, the evaluation results are converted into prompts, such as "Detect the products the user is focusing on and their reactions, and create relevant promotional information," which are then input into the generative AI model. The output is optimized advertising content.

[0399] Step 5:

[0400] The server sends optimized ad content to the device and presents it to the user. In this step, the device receives the new ad content and performs the specific actions of displaying it on the screen. As output, the user is presented with an adaptively improved ad.

[0401] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0402] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0403] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0407] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0408] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0409] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0410] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0411] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0412] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0413] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0414] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0415] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0416] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0417] This invention realizes a system that generates, optimizes, and effectively delivers advertising content using three components: an information processing device (server), a terminal device, and a user. The following describes how each element of this system functions, along with specific examples.

[0418] First, the server uses AI technology to generate advertising content based on initial data provided by advertisers and marketing teams. This generated content is then designed to be optimal by referencing data on past market trends and target consumer preferences.

[0419] Next, the generated advertising content is delivered to each device, and the user views it. The device is equipped with a function to monitor the user's gaze and facial expressions in real time. While viewing the advertisement, the device records where the user's gaze is focused and what kind of facial expressions they are making.

[0420] Here, the user plays the role of viewing the advertisement, and their unintentional, natural reactions become important data for the system. For example, when viewing an advertisement for new sports shoes, the parts that the user showed interest in and, conversely, the parts that they did not, are clearly analyzed.

[0421] The collected gaze and facial expression data is then sent to a server and analyzed by a specialized algorithm. This allows for evaluation of which parts the user showed interest in and which scenes elicited positive reactions. For example, it can identify moments when the user smiled or scenes they were intently watching.

[0422] Finally, based on these evaluation results, the server optimizes the advertising content. Elements that received a high response are highlighted, and elements that received a low response are modified. This optimized content is then sent back to the device and displayed to the user.

[0423] This cycle is repeated, maximizing the effectiveness of advertising and ensuring that the most appropriate ads are delivered to the target market and consumers. This process allows advertisers to develop advertising strategies based on clear data, thereby strengthening their competitiveness in the market.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The server uses AI to generate advertising content based on targeting information and campaign objectives provided by advertisers. This includes editing video footage and creating taglines. At this stage, market trends and consumer preference data are utilized to ensure the advertisement has the maximum possible impact.

[0427] Step 2:

[0428] The generated advertising content is sent to the device and displayed to the user. As the user views the advertisement, the device uses its built-in camera and software to track and record the user's gaze and facial expressions in real time.

[0429] Step 3:

[0430] Users watch advertisements and react to them unconsciously. For example, they may spend more time gazing at elements that interest them and smile at scenes they find amusing. These reactions are recorded on the device as eye-tracking and facial expression data.

[0431] Step 4:

[0432] After viewing the advertisement, the device sends the collected eye-tracking and facial expression data to the server. This data is analyzed in real time, enabling rapid reaction analysis.

[0433] Step 5:

[0434] The server uses a dedicated analysis algorithm to analyze the user's gaze and facial expression data in detail and evaluate the user's emotions. It determines where the user's interest was focused and which elements elicited a strong positive response.

[0435] Step 6:

[0436] Based on this analysis, the server restructures the ad content. By strengthening high-rated sections and improving or removing less popular parts, it generates more effective ads.

[0437] Step 7:

[0438] The optimized ad content is sent back to the device and displayed to the user again. The newly generated ad takes the previous data into account and is more tailored to the target user group.

[0439] Step 8:

[0440] This repeated cycle allows advertising effectiveness to continuously improve and enables a highly accurate response to user interests and reactions. Through this process, advertisers can optimize their ads based on data and strengthen their competitiveness in the market.

[0441] (Example 1)

[0442] Next, we will describe Example 1. 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."

[0443] In recent years, methods that utilize user reaction data to enhance advertising effectiveness have attracted attention. However, conventional systems lack sufficient efficiency in data collection and analysis, as well as in optimizing advertising based on this data, resulting in a limited impact of advertisements on their target markets.

[0444] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0445] In this invention, the server includes means for an information processing device to generate advertising content using an artificial intelligence model based on input data, means for a terminal device to display the advertisement and perform real-time monitoring to acquire user gaze points and facial expression information, and means for the information processing device to analyze the acquired information, evaluate user reactions, and improve the advertising content. As a result, a series of processes from advertising generation to viewing data collection, data analysis, and improvement of advertising content are carried out automatically and effectively, making it possible to significantly improve the impact of advertising on the target market.

[0446] An "information processing device" is a device that receives and analyzes data, and uses AI technology to generate and improve advertising content.

[0447] "Input data" refers to data that includes information necessary for generating advertising content, such as the attributes of target consumers, market trends, and the purpose of the advertisement.

[0448] A "generative artificial intelligence model" is an algorithm or program that automatically generates advertisement designs and content based on provided data.

[0449] A "terminal device" is a device that can display advertising content to a user and provide information about the user's gaze and facial expressions.

[0450] "Advertising content" refers to information generated by AI technology for the purpose of promoting products or services.

[0451] "Point of focus" refers to the specific area that a user looks at when viewing advertising data.

[0452] "Facial expression information" refers to data that shows the emotional reactions a user exhibits while watching an advertisement.

[0453] "Real-time monitoring" is a process that instantly records and analyzes user actions and reactions.

[0454] "Analysis" is the process of evaluating user behavior and emotions from acquired data and extracting information to improve advertising effectiveness.

[0455] "Improvement" refers to making modifications or emphasis on advertising content to make it more attractive and effective based on the analysis results.

[0456] "Target market" refers to the consumer group or sales market that an advertiser specifically targets.

[0457] "Trends" refer to recent trends and shifts in a particular market or consumer behavior.

[0458] This invention is an advertising delivery optimization system composed of three parties: an information processing device (hereinafter referred to as a server), a terminal device that performs display and data acquisition (hereinafter referred to as a terminal), and a user who views the advertisement.

[0459] The server first uses a generative AI model to automatically generate advertising content based on initial data provided by advertisers and marketing teams, such as target consumer attributes, historical market trends, and advertising objectives. Here, TensorFlow or PyTorch is used as the machine learning framework, and databases such as MySQL or PostgreSQL are used for data management. The generated advertising content is optimized by the AI ​​by referencing historical data, ensuring that, for example, specific designs and messages resonate with the target users.

[0460] The device not only plays the advertising content delivered to the user, but also plays a role in capturing the user's reactions. It is equipped with a camera with facial recognition capabilities that tracks the user's gaze and facial expressions in real time. This is done using the open-source library OpenCV to record the direction of gaze and changes in facial expressions. For example, it can detect when the user stares at a specific scene in the advertisement for an extended period of time or when they smile.

[0461] Users play a crucial role in providing natural, unintentional responses to advertisements. When a user views an advertisement for new sports shoes, their level of interest in the design and color scheme, as well as their reactions to specific scenes they see, become valuable data.

[0462] After receiving this data, the server uses Python data analysis libraries such as Pandas and Scikit-learn to analyze the user's eye movements and emotional changes. Based on the analysis results, it then improves the advertising content. For example, it might change the ad to emphasize colors or designs that the user showed strong interest in.

[0463] In this process, by providing the AI ​​model with specific prompts such as, "Optimize advertising content for the latest sports shoes targeting young men by analyzing user gaze and facial expression data," a system is created that continuously generates effective advertisements and maximizes advertising effectiveness for the target market.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The server receives input data from advertisers, including target audience, historical market data, and advertising objectives. Based on this data, it uses a generative AI model to generate advertising content. Specifically, the AI ​​creates media formats such as text, images, and videos. The output is advertising content optimized for the target audience.

[0467] Step 2:

[0468] The server delivers the generated advertising content to each user's device. The device receives the data via the internet connection and displays the advertisement to the user. The operation here involves data transfer and display in a pre-configured format, and the output is the advertising content played on the user interface.

[0469] Step 3:

[0470] The user views advertisements on their device, exhibiting natural eye movements and facial expressions. The device detects and records the user's gaze and facial expression data in real time through its built-in camera and image processing library. The input is the user's facial expression and gaze data, and the output is a record of this data.

[0471] Step 4:

[0472] The eye-tracking and facial expression data acquired by the device is sent to the server. The server receives this data and performs analysis using a data analysis library. Specifically, the data is processed to analyze the degree of gaze concentration and changes in facial expressions in response to specific scenes. The output is data that represents the user's interest in and preferences for advertisements.

[0473] Step 5:

[0474] The server optimizes advertising content based on the analysis results. It highlights elements that show high interest and modifies or removes elements that show less interest. The input is the analysis results, and the output is the improved advertising content. Specifically, the AI ​​generates content and then runs it again as a prompt to perform any necessary corrections.

[0475] Step 6:

[0476] The server reconstructs the optimized ad content and then redistributes it to each device. This process is repeated to maximize the effectiveness of the ads. The input is the improved ad content, and the output is playback on the user's device.

[0477] (Application Example 1)

[0478] Next, we will explain Application Example 1. In the following explanation, 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."

[0479] Traditional advertising delivery systems have struggled to maximize advertising effectiveness because they cannot optimize ads in real time based on users' interests and emotions. Furthermore, the lack of sufficient dynamic content change functionality on visual display devices has made it difficult to maintain users' interest for extended periods.

[0480] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0481] In this invention, the server includes means for an information processing device to generate advertising content, means for a terminal device to display advertisements and collect user gaze and facial expression data, means for the information processing device to analyze the collected data, evaluate the user's emotions, and optimize the advertising content, and means for dynamically changing the advertising content on a visual display device in real time based on the gaze and facial expressions analyzed by the terminal device. This makes it possible to provide optimized advertising content that keeps the user interested and engaged.

[0482] An "information processing device" is a device that generates advertising content, analyzes collected data to evaluate user sentiment, and optimizes the advertising content.

[0483] A "terminal device" is a device that displays advertisements, collects user eye-tracking and facial expression data, and displays the analysis results in real time.

[0484] A "user" is a person who uses a terminal device to view advertising content, and whose gaze and facial expressions are collected.

[0485] "Eye-gaze and facial expression data" refers to information that quantifies the visual and emotional reactions of users while they are viewing an advertisement.

[0486] "Optimization" is the process of improving advertising content to be most effective for users based on collected data.

[0487] A "visual display device" is a monitor or display device associated with a terminal device that presents advertising content to the user.

[0488] "Dynamic change" refers to the process of adjusting and modifying advertising content in real time based on the user's gaze and facial expression analysis.

[0489] To implement this invention, a system is constructed using an information processing device (server), a terminal device, and a user. First, the server generates advertising content using a generation AI model based on initial data provided by the advertiser. In doing so, past market trends and the preferences of target consumers are taken into consideration.

[0490] The generated advertising content is delivered to a terminal device. The terminal device displays the advertisement to the user using a visual display device such as a smartphone or smart glasses. The terminal device is also equipped with a camera to collect the user's gaze and facial expression data in real time.

[0491] Users watch advertisements and provide natural reactions. The terminal device utilizes eye-tracking and facial expression analysis technologies (e.g., using OpenCV or TensorFlow) to detect which parts of the advertisement the user is interested in.

[0492] The server analyzes gaze and facial expression data transmitted from the terminal device using a dedicated algorithm to evaluate when the user was most interested. Based on these results, the server optimizes the advertising content, emphasizing elements that successfully maintained interest and modifying elements that failed to capture attention.

[0493] The optimized advertising content is then delivered back to the device and displayed to the user. By repeating this cycle, it is possible to maximize the effectiveness of the advertisements.

[0494] For example, if analysis shows that viewers of an advertisement for a new sports product are excessively focused on the product's design, the content can be regenerated to further emphasize that design element. Another example of a prompt input to the generation AI model might be, "Generate new advertising content based on the parts of this advertisement that viewers showed particular interest in."

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The server receives initial data provided by the advertiser and generates advertising content using a generative AI model. The input data includes market trends and target consumer profiles, which the generative AI model uses to create the content. The output is the generated advertising content.

[0498] Step 2:

[0499] The server delivers the advertising content generated in step 1 to the terminal device. The input here is the output from step 1, and the terminal device displays the received advertising content on its visual display device. The output is the advertisement displayed on the terminal device in a format that the user can view.

[0500] Step 3:

[0501] The terminal device collects eye-tracking and facial expression data of users viewing advertisements on a visual display device in real time using its built-in camera. The input for this step is the user's visual response, and the output is eye-tracking and facial expression data that quantifies these responses.

[0502] Step 4:

[0503] The terminal device transmits the gaze and facial expression data collected in step 3 to the server. The transmitted data is the input, and this data serves as basic information for the next processing on the server side. The output is the user's response data transmitted to the server.

[0504] Step 5:

[0505] The server analyzes gaze and facial expression data transmitted from the terminal device using a dedicated algorithm. The input is the output from step 4, and this analysis identifies the moments and elements that the user found interesting. The output is the information identifying the user's interests obtained as a result of the analysis.

[0506] Step 6:

[0507] The server optimizes the ad content based on the analysis results obtained in step 5. The input is the analysis results, and the generative AI model is used to highlight elements that successfully maintained interest and improve elements that did not. The output is the optimized ad content.

[0508] Step 7:

[0509] The server delivers the optimized ad content from step 6 back to the terminal device. The input is the optimized ad content, which is displayed on the terminal device and viewed again by the user. The output is the display of the improved ad content.

[0510] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0511] This invention provides a system that integrates an information processing device (server), terminal device, user, and emotion engine to generate, optimize, and effectively deliver advertising content. The specific functions of each element and their interactions are described below.

[0512] First, the server uses AI to generate advertising content based on targeting information and campaign objectives provided by the advertiser. This content generation is based on market trends and consumer preference data, and the generated content is an initial version optimized based on basic data.

[0513] Next, the generated advertising content is delivered to the device and displayed to the user. At this stage, the device uses its built-in camera and sensors to track and record the user's eye movements and facial expressions in real time. The data collected at this stage is stored as the user's initial response.

[0514] Users watch advertisements and unconsciously react to their content. For example, if they are interested in a product, they may linger on that part of the ad, or a smile may appear on their face when they feel reassured. Through these natural reactions, psychological and emotional data about the user is generated.

[0515] The server acquires gaze data and facial expression data sent from the terminal and analyzes them using an emotion engine. This emotion engine analyzes the data to determine the user's emotional state and can identify multiple emotional states (e.g., interest, joy, indifference, etc.).

[0516] As a result, the server transitions to a process of optimizing ad content based on the user's emotional state. In this process, the emotion engine uses the information it has gathered to highlight highly-rated elements and improve or remove elements that received little response. At this stage, the user's past response data is also considered and contributes to future optimizations.

[0517] The optimized ad content is then sent back to the device and displayed to the user in a new form. The improved ads reflect the previous data and are more appealing to the user's interests and emotions.

[0518] Because this series of actions is repeated, the ad content is designed to continuously change in response to the user's interests and emotions, thereby increasing its effectiveness. This system allows advertisers to adopt a data-driven approach and develop highly competitive advertising strategies.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The server collects and analyzes data on the target market based on requests from advertisers. This includes age groups, interests, and recent trend information, and uses this information to generate initial ad content using an AI algorithm.

[0522] Step 2:

[0523] The generated advertising content is sent to the device and displayed to the user. The device uses its built-in camera to track the user's gaze and facial expressions while they are viewing the advertisement, and collects this data.

[0524] Step 3:

[0525] Users watch advertisements and react unconsciously to their content. Their gaze lingers on parts that interest them, and their facial expressions change during emotionally charged scenes.

[0526] Step 4:

[0527] The device analyzes the collected eye-tracking and facial expression data in real time and records it as the user's reaction. This data is sent to the server after the advertisement viewing ends.

[0528] Step 5:

[0529] The server uses an emotion engine to analyze the incoming data. Based on the data, it identifies areas of interest and specific emotional states (e.g., joy, surprise) that the user has shown interest in, and evaluates them.

[0530] Step 6:

[0531] The server uses the results of the emotion engine analysis to optimize ad content. By strengthening high-rated elements and adjusting low-rated elements, it reconstructs ads to be more effective for future use.

[0532] Step 7:

[0533] The optimized ad content is sent back to the device and displayed to the user. This improved content is based on previous data, and further improvements can be sought by measuring user responses again.

[0534] Step 8:

[0535] This process is repeated, and the server continuously accumulates data and learns new patterns, thereby continuously improving the effectiveness of the advertisements. Through this iterative process, advertisers can deliver compelling and optimal ads to their target users.

[0536] (Example 2)

[0537] Next, we will describe Example 2. 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."

[0538] Modern advertising delivery systems require advanced personalization to respond to the diversification of target markets and rapid trend changes. However, existing systems have a challenge in that they cannot effectively utilize users' emotional responses in the generation and optimization of advertising content. As a result, advertising effectiveness can be reduced because user interests are not properly captured.

[0539] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0540] In this invention, the server includes means for an information processing device to utilize a generative AI model to generate advertising content, means for a terminal device to display advertisements and collect user gaze and facial expression data through sensors, and means for the information processing device to apply emotion analysis technology to analyze gaze and facial expression data transmitted from the terminal device, evaluate the user's emotions, and optimize the advertising content. This makes it possible to analyze the user's individual emotional response in real time, effectively optimize the advertising content, and achieve higher advertising effectiveness.

[0541] An "information processing device" is a combination of hardware and software for receiving, analyzing, and processing digital data, and is a device that performs the functions necessary for generating and optimizing advertising content.

[0542] A "generative AI model" is an artificial intelligence technology that analyzes large amounts of data and generates new content based on specified conditions.

[0543] "Terminal device" refers to a computer or smart device that is directly operated by the user, and is a device used for displaying advertising content and collecting user responses.

[0544] A "sensor" is a device that measures physical phenomena (e.g., eye movements or changes in facial expressions) and digitizes that data.

[0545] "Emotional analysis technology" is a technology used to analyze facial expressions, tone of voice, and other characteristics from a user's voice and video data in order to identify their emotional state.

[0546] "Optimization" refers to adjusting processes and deliverables to achieve a given objective under specific conditions, and improving them to obtain maximum effectiveness.

[0547] This invention relates to an advertising delivery system that combines an information processing device, a terminal device, a user, and sentiment analysis technology. The core of the invention lies in the generation of advertising content utilizing AI technology, the collection and analysis of user response data, and the redistribution of optimized advertisements.

[0548] The server, acting as an information processing unit, generates advertising content using a generative AI model based on target information and campaign strategies obtained from advertisers. This process utilizes widely used digital data analysis software as an artificial intelligence framework to generate content that reflects the latest market trends and consumer preference data. For example, it might input a prompt such as, "Generate an advertisement promoting a new beverage for health-conscious people in their 30s," into the AI ​​model to generate the advertisement.

[0549] The terminal acts as a display device placed in front of the user, displaying advertising content transmitted from the server. Furthermore, the terminal incorporates advanced sensor technology to track the user's eye movements and facial expressions in real time, transmitting this response data to the server. Commonly used sensor technologies, such as CMOS-type imaging sensors, are utilized.

[0550] Users view advertisements displayed on their devices and exhibit unconscious reactions. For example, they may watch the advertisement for a longer period when they are interested in a particular product, or their facial expressions may change when they feel pleasure. This data forms the basis for analyzing users' emotional states.

[0551] The server uses emotion analysis technology to analyze eye-tracking and facial expression data from the device to determine the user's emotions. To accurately distinguish between multiple emotional states, it comprehensively analyzes data obtained from audio and video. Based on these analysis results, it optimizes the advertising content according to the user's emotions and delivers the regenerated advertising content back to the user's device. This allows the advertising content to better match the user's individual needs, enabling more effective advertising.

[0552] This system design enables data-driven advertising strategies to be implemented with the information processing unit at the center, contributing to the optimization of marketing and improvement of advertising effectiveness.

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] The server receives targeting information and campaign objectives provided by the advertiser and forms a prompt. This prompt is then input into a generative AI model to generate promising advertising content. This generative AI model analyzes digital data and generates content based on market trends and consumer preferences. The output is an initial version of the advertising content.

[0556] Step 2:

[0557] The device receives advertising content sent from the server and displays it to the user. Using sensors built into the device, it tracks the user's eye movements and facial expressions in real time. Specifically, an eye-tracking sensor tracks the user's eye movements, and a facial recognition sensor captures changes in facial expressions, collecting user response data. This data is used as input data for the server.

[0558] Step 3:

[0559] The server acquires user gaze data and facial expression data received from the terminal. Using this data as input, it analyzes the user's emotional state using emotion analysis technology. The analysis determines whether the user is showing an emotional state such as interest, pleasure, or indifference towards the advertisement. The output is a dataset reflecting the user's emotions.

[0560] Step 4:

[0561] The server then performs a process to optimize the ad content based on the output data from the sentiment analysis. Specifically, the generative AI model is used again to highlight content elements that received high ratings and improve or remove elements that received low ratings. This results in optimized ad content that better matches the user's emotions.

[0562] Step 5:

[0563] The device receives optimized ad content from the server again and displays it to the user in an improved form. The new content is further personalized based on the user's previous response. This process is repeated, and the ads continue to improve to better meet the user's needs.

[0564] (Application Example 2)

[0565] Next, we will explain application example 2. In the following explanation, 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."

[0566] Traditional advertising systems have struggled to accurately understand users' emotions and interests and deliver personalized ads in real time. Furthermore, the accuracy of ad optimization based on users' psychological responses was low, posing challenges in maximizing ad effectiveness.

[0567] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0568] In this invention, the server includes means for an information processing device to automatically generate advertising content, means for a terminal device to present advertisements and acquire the user's gaze and facial expression information, and means for the information processing device to analyze the acquired information and adaptively improve the advertising content by analyzing the user's psychological state. This makes it possible to optimize advertisements in real time based on changes in the user's gaze and facial expressions and to provide personalized promotional information.

[0569] An "information processing device" is a device that receives and processes data, and in particular has the function of generating advertising content and optimizing it based on user responses.

[0570] "Advertising content" refers to a collection of information created to promote products or services to consumers, and in this invention, it is automatically generated and improved based on the user's psychological state.

[0571] A "terminal device" is a device that provides a user interface and acquires information such as the user's gaze and facial expressions, and has the function of transmitting this information to a server.

[0572] "Eye gaze and facial expression information" refers to data about the direction of a user's gaze and instantaneous changes in their facial expression when viewing an advertisement, and is used to capture the user's interest and emotional state.

[0573] "Analysis" is the act of analyzing collected information to derive meaning and patterns, and in this invention, it is a process for understanding the user's psychological state.

[0574] "Psychological state" refers to the internal emotions and feelings a user experiences when encountering an advertisement, with examples including interest, concern, indifference, and a sense of security.

[0575] "Adaptive improvement" refers to the process of changing advertising content to the optimal form in response to user reactions, adjusting information to make it more engaging for users.

[0576] "Personalized promotional information" refers to advertising information that is uniquely created for a specific user based on their responses and interests.

[0577] The system for carrying out this invention consists of an information processing device, a terminal device, and a user. The information processing device (server) utilizes a generative AI model to automatically generate advertising content and adaptively improves the advertisements by analyzing the user's gaze and facial expression information. The terminal device is a device that interacts with the user and uses an eye-tracking sensor and a camera to collect the user's gaze direction and facial expression changes in real time. The user also watches the advertisements and shows unconscious reactions based on their own interests and emotions.

[0578] Specifically, the terminal device uses Tobii Technology's eye-tracking sensors to collect user gaze data. This data is sent to a server in the cloud and analyzed using software such as Python and TensorFlow. Based on the analysis results, the server evaluates the user's psychological state and optimizes the advertising content. The optimized advertising content is then sent back to the terminal device and presented to the user. This allows users to receive personalized promotional information tailored to their interests.

[0579] As a concrete example, let's assume a user is in a fashion shop wearing smart glasses. If the user's gaze lingers on a particular piece of clothing for an extended period, the server might analyze that data and display a message to the user such as, "It seems you're interested in this jacket. We're currently offering a 20% discount on it."

[0580] An example of a prompt to input into the generation AI model is, "Detect the products and reactions the user is focusing on, and create relevant promotional information." This allows the system to provide an advertising experience tailored to the specific needs of the user.

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The device uses eye-tracking sensors and a camera to collect gaze and facial expression data while the user is viewing content. This data is used as input to record the user's gaze position and changes in facial expressions. The output data includes information about which parts of the content the user is focusing on.

[0584] Step 2:

[0585] The terminal transmits the collected gaze and facial expression data to the server. In this step, the terminal specifically analyzes the gaze movements and facial expression data in real time, converts it into a digital format, and transmits it to the server. The output data includes gaze and facial expression data in an analyzable format.

[0586] Step 3:

[0587] The server analyzes the received gaze and facial expression data as input. Using software such as Python or TensorFlow, it processes the data to evaluate the user's psychological state. In this process, it performs data calculations to identify the user's level of interest and emotional state, and generates an evaluation result regarding the user's psychological state as output.

[0588] Step 4:

[0589] The server uses a generative AI model to adaptively improve advertising content based on the evaluation results of the user's psychological state. In this step, the evaluation results are converted into prompts, such as "Detect the products the user is focusing on and their reactions, and create relevant promotional information," which are then input into the generative AI model. The output is optimized advertising content.

[0590] Step 5:

[0591] The server sends optimized ad content to the device and presents it to the user. In this step, the device receives the new ad content and performs the specific actions of displaying it on the screen. As output, the user is presented with an adaptively improved ad.

[0592] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0593] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0594] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0598] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0599] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0600] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0601] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0602] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0603] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0604] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0605] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0606] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0607] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0608] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0609] This invention realizes a system that generates, optimizes, and effectively delivers advertising content using three components: an information processing device (server), a terminal device, and a user. The following describes how each element of this system functions, along with specific examples.

[0610] First, the server uses AI technology to generate advertising content based on initial data provided by advertisers and marketing teams. This generated content is then designed to be optimal by referencing data on past market trends and target consumer preferences.

[0611] Next, the generated advertising content is delivered to each device, and the user views it. The device is equipped with a function to monitor the user's gaze and facial expressions in real time. While viewing the advertisement, the device records where the user's gaze is focused and what kind of facial expressions they are making.

[0612] Here, the user plays the role of viewing the advertisement, and their unintentional, natural reactions become important data for the system. For example, when viewing an advertisement for new sports shoes, the parts that the user showed interest in and, conversely, the parts that they did not, are clearly analyzed.

[0613] The collected gaze and facial expression data is then sent to a server and analyzed by a specialized algorithm. This allows for evaluation of which parts the user showed interest in and which scenes elicited positive reactions. For example, it can identify moments when the user smiled or scenes they were intently watching.

[0614] Finally, based on these evaluation results, the server optimizes the advertising content. Elements that received a high response are highlighted, and elements that received a low response are modified. This optimized content is then sent back to the device and displayed to the user.

[0615] This cycle is repeated, maximizing the effectiveness of advertising and ensuring that the most appropriate ads are delivered to the target market and consumers. This process allows advertisers to develop advertising strategies based on clear data, thereby strengthening their competitiveness in the market.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The server uses AI to generate advertising content based on targeting information and campaign objectives provided by advertisers. This includes editing video footage and creating taglines. At this stage, market trends and consumer preference data are utilized to ensure the advertisement has the maximum possible impact.

[0619] Step 2:

[0620] The generated advertising content is sent to the device and displayed to the user. As the user views the advertisement, the device uses its built-in camera and software to track and record the user's gaze and facial expressions in real time.

[0621] Step 3:

[0622] Users watch advertisements and react to them unconsciously. For example, they may spend more time gazing at elements that interest them and smile at scenes they find amusing. These reactions are recorded on the device as eye-tracking and facial expression data.

[0623] Step 4:

[0624] After viewing the advertisement, the device sends the collected eye-tracking and facial expression data to the server. This data is analyzed in real time, enabling rapid reaction analysis.

[0625] Step 5:

[0626] The server uses a dedicated analysis algorithm to analyze the user's gaze and facial expression data in detail and evaluate the user's emotions. It determines where the user's interest was focused and which elements elicited a strong positive response.

[0627] Step 6:

[0628] Based on this analysis, the server restructures the ad content. By strengthening high-rated sections and improving or removing less popular parts, it generates more effective ads.

[0629] Step 7:

[0630] The optimized ad content is sent back to the device and displayed to the user again. The newly generated ad takes the previous data into account and is more tailored to the target user group.

[0631] Step 8:

[0632] This repeated cycle allows advertising effectiveness to continuously improve and enables a highly accurate response to user interests and reactions. Through this process, advertisers can optimize their ads based on data and strengthen their competitiveness in the market.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] In recent years, methods that utilize user reaction data to enhance advertising effectiveness have attracted attention. However, conventional systems lack sufficient efficiency in data collection and analysis, as well as in optimizing advertising based on this data, resulting in a limited impact of advertisements on their target markets.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes means for an information processing device to generate advertising content using an artificial intelligence model based on input data, means for a terminal device to display the advertisement and perform real-time monitoring to acquire user gaze points and facial expression information, and means for the information processing device to analyze the acquired information, evaluate user reactions, and improve the advertising content. As a result, a series of processes from advertising generation to viewing data collection, data analysis, and improvement of advertising content are carried out automatically and effectively, making it possible to significantly improve the impact of advertising on the target market.

[0638] An "information processing device" is a device that receives and analyzes data, and uses AI technology to generate and improve advertising content.

[0639] "Input data" refers to data that includes information necessary for generating advertising content, such as the attributes of target consumers, market trends, and the purpose of the advertisement.

[0640] A "generative artificial intelligence model" is an algorithm or program that automatically generates advertisement designs and content based on provided data.

[0641] A "terminal device" is a device that can display advertising content to a user and provide information about the user's gaze and facial expressions.

[0642] "Advertising content" refers to information generated by AI technology for the purpose of promoting products or services.

[0643] "Point of focus" refers to the specific area that a user looks at when viewing advertising data.

[0644] "Facial expression information" refers to data that shows the emotional reactions a user exhibits while watching an advertisement.

[0645] "Real-time monitoring" is a process that instantly records and analyzes user actions and reactions.

[0646] "Analysis" is the process of evaluating user behavior and emotions from acquired data and extracting information to improve advertising effectiveness.

[0647] "Improvement" refers to making modifications or emphasis on advertising content to make it more attractive and effective based on the analysis results.

[0648] "Target market" refers to the consumer group or sales market that an advertiser specifically targets.

[0649] "Trends" refer to recent trends and shifts in a particular market or consumer behavior.

[0650] This invention is an advertising delivery optimization system composed of three parties: an information processing device (hereinafter referred to as a server), a terminal device that performs display and data acquisition (hereinafter referred to as a terminal), and a user who views the advertisement.

[0651] The server first uses a generative AI model to automatically generate advertising content based on initial data provided by advertisers and marketing teams, such as target consumer attributes, historical market trends, and advertising objectives. Here, TensorFlow or PyTorch is used as the machine learning framework, and databases such as MySQL or PostgreSQL are used for data management. The generated advertising content is optimized by the AI ​​by referencing historical data, ensuring that, for example, specific designs and messages resonate with the target users.

[0652] The device not only plays the advertising content delivered to the user, but also plays a role in capturing the user's reactions. It is equipped with a camera with facial recognition capabilities that tracks the user's gaze and facial expressions in real time. This is done using the open-source library OpenCV to record the direction of gaze and changes in facial expressions. For example, it can detect when the user stares at a specific scene in the advertisement for an extended period of time or when they smile.

[0653] Users play a crucial role in providing natural, unintentional responses to advertisements. When a user views an advertisement for new sports shoes, their level of interest in the design and color scheme, as well as their reactions to specific scenes they see, become valuable data.

[0654] After receiving this data, the server uses Python data analysis libraries such as Pandas and Scikit-learn to analyze the user's eye movements and emotional changes. Based on the analysis results, it then improves the advertising content. For example, it might change the ad to emphasize colors or designs that the user showed strong interest in.

[0655] In this process, by providing the AI ​​model with specific prompts such as, "Optimize advertising content for the latest sports shoes targeting young men by analyzing user gaze and facial expression data," a system is created that continuously generates effective advertisements and maximizes advertising effectiveness for the target market.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The server receives input data from advertisers, including target audience, historical market data, and advertising objectives. Based on this data, it uses a generative AI model to generate advertising content. Specifically, the AI ​​creates media formats such as text, images, and videos. The output is advertising content optimized for the target audience.

[0659] Step 2:

[0660] The server delivers the generated advertising content to each user's device. The device receives the data via the internet connection and displays the advertisement to the user. The operation here involves data transfer and display in a pre-configured format, and the output is the advertising content played on the user interface.

[0661] Step 3:

[0662] The user views advertisements on their device, exhibiting natural eye movements and facial expressions. The device detects and records the user's gaze and facial expression data in real time through its built-in camera and image processing library. The input is the user's facial expression and gaze data, and the output is a record of this data.

[0663] Step 4:

[0664] The eye-tracking and facial expression data acquired by the device is sent to the server. The server receives this data and performs analysis using a data analysis library. Specifically, the data is processed to analyze the degree of gaze concentration and changes in facial expressions in response to specific scenes. The output is data that represents the user's interest in and preferences for advertisements.

[0665] Step 5:

[0666] The server optimizes advertising content based on the analysis results. It highlights elements that show high interest and modifies or removes elements that show less interest. The input is the analysis results, and the output is the improved advertising content. Specifically, the AI ​​generates content and then runs it again as a prompt to perform any necessary corrections.

[0667] Step 6:

[0668] The server reconstructs the optimized ad content and then redistributes it to each device. This process is repeated to maximize the effectiveness of the ads. The input is the improved ad content, and the output is playback on the user's device.

[0669] (Application Example 1)

[0670] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] Traditional advertising delivery systems have struggled to maximize advertising effectiveness because they cannot optimize ads in real time based on users' interests and emotions. Furthermore, the lack of sufficient dynamic content change functionality on visual display devices has made it difficult to maintain users' interest for extended periods.

[0672] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0673] In this invention, the server includes means for an information processing device to generate advertising content, means for a terminal device to display advertisements and collect user gaze and facial expression data, means for the information processing device to analyze the collected data, evaluate the user's emotions, and optimize the advertising content, and means for dynamically changing the advertising content on a visual display device in real time based on the gaze and facial expressions analyzed by the terminal device. This makes it possible to provide optimized advertising content that keeps the user interested and engaged.

[0674] An "information processing device" is a device that generates advertising content, analyzes collected data to evaluate user sentiment, and optimizes the advertising content.

[0675] A "terminal device" is a device that displays advertisements, collects user eye-tracking and facial expression data, and displays the analysis results in real time.

[0676] A "user" is a person who uses a terminal device to view advertising content, and whose gaze and facial expressions are collected.

[0677] "Eye-gaze and facial expression data" refers to information that quantifies the visual and emotional reactions of users while they are viewing an advertisement.

[0678] "Optimization" is the process of improving advertising content to be most effective for users based on collected data.

[0679] A "visual display device" is a monitor or display device associated with a terminal device that presents advertising content to the user.

[0680] "Dynamic change" refers to the process of adjusting and modifying advertising content in real time based on the user's gaze and facial expression analysis.

[0681] To implement this invention, a system is constructed using an information processing device (server), a terminal device, and a user. First, the server generates advertising content using a generation AI model based on initial data provided by the advertiser. In doing so, past market trends and the preferences of target consumers are taken into consideration.

[0682] The generated advertising content is delivered to a terminal device. The terminal device displays the advertisement to the user using a visual display device such as a smartphone or smart glasses. The terminal device is also equipped with a camera to collect the user's gaze and facial expression data in real time.

[0683] Users watch advertisements and provide natural reactions. The terminal device utilizes eye-tracking and facial expression analysis technologies (e.g., using OpenCV or TensorFlow) to detect which parts of the advertisement the user is interested in.

[0684] The server analyzes gaze and facial expression data transmitted from the terminal device using a dedicated algorithm to evaluate when the user was most interested. Based on these results, the server optimizes the advertising content, emphasizing elements that successfully maintained interest and modifying elements that failed to capture attention.

[0685] The optimized advertising content is then delivered back to the device and displayed to the user. By repeating this cycle, it is possible to maximize the effectiveness of the advertisements.

[0686] For example, if analysis shows that viewers of an advertisement for a new sports product are excessively focused on the product's design, the content can be regenerated to further emphasize that design element. Another example of a prompt input to the generation AI model might be, "Generate new advertising content based on the parts of this advertisement that viewers showed particular interest in."

[0687] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0688] Step 1:

[0689] The server receives initial data provided by the advertiser and generates advertising content using a generative AI model. The input data includes market trends and target consumer profiles, which the generative AI model uses to create the content. The output is the generated advertising content.

[0690] Step 2:

[0691] The server delivers the advertising content generated in step 1 to the terminal device. The input here is the output from step 1, and the terminal device displays the received advertising content on its visual display device. The output is the advertisement displayed on the terminal device in a format that the user can view.

[0692] Step 3:

[0693] The terminal device collects eye-tracking and facial expression data of users viewing advertisements on a visual display device in real time using its built-in camera. The input for this step is the user's visual response, and the output is eye-tracking and facial expression data that quantifies these responses.

[0694] Step 4:

[0695] The terminal device transmits the gaze and facial expression data collected in step 3 to the server. The transmitted data is the input, and this data serves as basic information for the next processing on the server side. The output is the user's response data transmitted to the server.

[0696] Step 5:

[0697] The server analyzes gaze and facial expression data transmitted from the terminal device using a dedicated algorithm. The input is the output from step 4, and this analysis identifies the moments and elements that the user found interesting. The output is the information identifying the user's interests obtained as a result of the analysis.

[0698] Step 6:

[0699] The server optimizes the ad content based on the analysis results obtained in step 5. The input is the analysis results, and the generative AI model is used to highlight elements that successfully maintained interest and improve elements that did not. The output is the optimized ad content.

[0700] Step 7:

[0701] The server delivers the optimized ad content from step 6 back to the terminal device. The input is the optimized ad content, which is displayed on the terminal device and viewed again by the user. The output is the display of the improved ad content.

[0702] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0703] This invention provides a system that integrates an information processing device (server), terminal device, user, and emotion engine to generate, optimize, and effectively deliver advertising content. The specific functions of each element and their interactions are described below.

[0704] First, the server uses AI to generate advertising content based on targeting information and campaign objectives provided by the advertiser. This content generation is based on market trends and consumer preference data, and the generated content is an initial version optimized based on basic data.

[0705] Next, the generated advertising content is delivered to the device and displayed to the user. At this stage, the device uses its built-in camera and sensors to track and record the user's eye movements and facial expressions in real time. The data collected at this stage is stored as the user's initial response.

[0706] Users watch advertisements and unconsciously react to their content. For example, if they are interested in a product, they may linger on that part of the ad, or a smile may appear on their face when they feel reassured. Through these natural reactions, psychological and emotional data about the user is generated.

[0707] The server acquires gaze data and facial expression data sent from the terminal and analyzes them using an emotion engine. This emotion engine analyzes the data to determine the user's emotional state and can identify multiple emotional states (e.g., interest, joy, indifference, etc.).

[0708] As a result, the server transitions to a process of optimizing ad content based on the user's emotional state. In this process, the emotion engine uses the information it has gathered to highlight highly-rated elements and improve or remove elements that received little response. At this stage, the user's past response data is also considered and contributes to future optimizations.

[0709] The optimized ad content is then sent back to the device and displayed to the user in a new form. The improved ads reflect the previous data and are more appealing to the user's interests and emotions.

[0710] Because this series of actions is repeated, the ad content is designed to continuously change in response to the user's interests and emotions, thereby increasing its effectiveness. This system allows advertisers to adopt a data-driven approach and develop highly competitive advertising strategies.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The server collects and analyzes data on the target market based on requests from advertisers. This includes age groups, interests, and recent trend information, and uses this information to generate initial ad content using an AI algorithm.

[0714] Step 2:

[0715] The generated advertising content is sent to the device and displayed to the user. The device uses its built-in camera to track the user's gaze and facial expressions while they are viewing the advertisement, and collects this data.

[0716] Step 3:

[0717] Users watch advertisements and react unconsciously to their content. Their gaze lingers on parts that interest them, and their facial expressions change during emotionally charged scenes.

[0718] Step 4:

[0719] The device analyzes the collected eye-tracking and facial expression data in real time and records it as the user's reaction. This data is sent to the server after the advertisement viewing ends.

[0720] Step 5:

[0721] The server uses an emotion engine to analyze the incoming data. Based on the data, it identifies areas of interest and specific emotional states (e.g., joy, surprise) that the user has shown interest in, and evaluates them.

[0722] Step 6:

[0723] The server uses the results of the emotion engine analysis to optimize ad content. By strengthening high-rated elements and adjusting low-rated elements, it reconstructs ads to be more effective for future use.

[0724] Step 7:

[0725] The optimized ad content is sent back to the device and displayed to the user. This improved content is based on previous data, and further improvements can be sought by measuring user responses again.

[0726] Step 8:

[0727] This process is repeated, and the server continuously accumulates data and learns new patterns, thereby continuously improving the effectiveness of the advertisements. Through this iterative process, advertisers can deliver compelling and optimal ads to their target users.

[0728] (Example 2)

[0729] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0730] Modern advertising delivery systems require advanced personalization to respond to the diversification of target markets and rapid trend changes. However, existing systems have a challenge in that they cannot effectively utilize users' emotional responses in the generation and optimization of advertising content. As a result, advertising effectiveness can be reduced because user interests are not properly captured.

[0731] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0732] In this invention, the server includes means for an information processing device to utilize a generative AI model to generate advertising content, means for a terminal device to display advertisements and collect user gaze and facial expression data through sensors, and means for the information processing device to apply emotion analysis technology to analyze gaze and facial expression data transmitted from the terminal device, evaluate the user's emotions, and optimize the advertising content. This makes it possible to analyze the user's individual emotional response in real time, effectively optimize the advertising content, and achieve higher advertising effectiveness.

[0733] An "information processing device" is a combination of hardware and software for receiving, analyzing, and processing digital data, and is a device that performs the functions necessary for generating and optimizing advertising content.

[0734] A "generative AI model" is an artificial intelligence technology that analyzes large amounts of data and generates new content based on specified conditions.

[0735] "Terminal device" refers to a computer or smart device that is directly operated by the user, and is a device used for displaying advertising content and collecting user responses.

[0736] A "sensor" is a device that measures physical phenomena (e.g., eye movements or changes in facial expressions) and digitizes that data.

[0737] "Emotional analysis technology" is a technology used to analyze facial expressions, tone of voice, and other characteristics from a user's voice and video data in order to identify their emotional state.

[0738] "Optimization" refers to adjusting processes and deliverables to achieve a given objective under specific conditions, and improving them to obtain maximum effectiveness.

[0739] This invention relates to an advertising delivery system that combines an information processing device, a terminal device, a user, and sentiment analysis technology. The core of the invention lies in the generation of advertising content utilizing AI technology, the collection and analysis of user response data, and the redistribution of optimized advertisements.

[0740] The server, acting as an information processing unit, generates advertising content using a generative AI model based on target information and campaign strategies obtained from advertisers. This process utilizes widely used digital data analysis software as an artificial intelligence framework to generate content that reflects the latest market trends and consumer preference data. For example, it might input a prompt such as, "Generate an advertisement promoting a new beverage for health-conscious people in their 30s," into the AI ​​model to generate the advertisement.

[0741] The terminal acts as a display device placed in front of the user, displaying advertising content transmitted from the server. Furthermore, the terminal incorporates advanced sensor technology to track the user's eye movements and facial expressions in real time, transmitting this response data to the server. Commonly used sensor technologies, such as CMOS-type imaging sensors, are utilized.

[0742] Users view advertisements displayed on their devices and exhibit unconscious reactions. For example, they may watch the advertisement for a longer period when they are interested in a particular product, or their facial expressions may change when they feel pleasure. This data forms the basis for analyzing users' emotional states.

[0743] The server uses emotion analysis technology to analyze eye-tracking and facial expression data from the device to determine the user's emotions. To accurately distinguish between multiple emotional states, it comprehensively analyzes data obtained from audio and video. Based on these analysis results, it optimizes the advertising content according to the user's emotions and delivers the regenerated advertising content back to the user's device. This allows the advertising content to better match the user's individual needs, enabling more effective advertising.

[0744] This system design enables data-driven advertising strategies to be implemented with the information processing unit at the center, contributing to the optimization of marketing and improvement of advertising effectiveness.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] The server receives targeting information and campaign objectives provided by the advertiser and forms a prompt. This prompt is then input into a generative AI model to generate promising advertising content. This generative AI model analyzes digital data and generates content based on market trends and consumer preferences. The output is an initial version of the advertising content.

[0748] Step 2:

[0749] The device receives advertising content sent from the server and displays it to the user. Using sensors built into the device, it tracks the user's eye movements and facial expressions in real time. Specifically, an eye-tracking sensor tracks the user's eye movements, and a facial recognition sensor captures changes in facial expressions, collecting user response data. This data is used as input data for the server.

[0750] Step 3:

[0751] The server acquires user gaze data and facial expression data received from the terminal. Using this data as input, it analyzes the user's emotional state using emotion analysis technology. The analysis determines whether the user is showing an emotional state such as interest, pleasure, or indifference towards the advertisement. The output is a dataset reflecting the user's emotions.

[0752] Step 4:

[0753] The server then performs a process to optimize the ad content based on the output data from the sentiment analysis. Specifically, the generative AI model is used again to highlight content elements that received high ratings and improve or remove elements that received low ratings. This results in optimized ad content that better matches the user's emotions.

[0754] Step 5:

[0755] The device receives optimized ad content from the server again and displays it to the user in an improved form. The new content is further personalized based on the user's previous response. This process is repeated, and the ads continue to improve to better meet the user's needs.

[0756] (Application Example 2)

[0757] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] Traditional advertising systems have struggled to accurately understand users' emotions and interests and deliver personalized ads in real time. Furthermore, the accuracy of ad optimization based on users' psychological responses was low, posing challenges in maximizing ad effectiveness.

[0759] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0760] In this invention, the server includes means for an information processing device to automatically generate advertising content, means for a terminal device to present advertisements and acquire the user's gaze and facial expression information, and means for the information processing device to analyze the acquired information and adaptively improve the advertising content by analyzing the user's psychological state. This makes it possible to optimize advertisements in real time based on changes in the user's gaze and facial expressions and to provide personalized promotional information.

[0761] An "information processing device" is a device that receives and processes data, and in particular has the function of generating advertising content and optimizing it based on user responses.

[0762] "Advertising content" refers to a collection of information created to promote products or services to consumers, and in this invention, it is automatically generated and improved based on the user's psychological state.

[0763] A "terminal device" is a device that provides a user interface and acquires information such as the user's gaze and facial expressions, and has the function of transmitting this information to a server.

[0764] "Eye gaze and facial expression information" refers to data about the direction of a user's gaze and instantaneous changes in their facial expression when viewing an advertisement, and is used to capture the user's interest and emotional state.

[0765] "Analysis" is the act of analyzing collected information to derive meaning and patterns, and in this invention, it is a process for understanding the user's psychological state.

[0766] "Psychological state" refers to the internal emotions and feelings a user experiences when encountering an advertisement, with examples including interest, concern, indifference, and a sense of security.

[0767] "Adaptive improvement" refers to the process of changing advertising content to the optimal form in response to user reactions, adjusting information to make it more engaging for users.

[0768] "Personalized promotional information" refers to advertising information that is uniquely created for a specific user based on their responses and interests.

[0769] The system for carrying out this invention consists of an information processing device, a terminal device, and a user. The information processing device (server) utilizes a generative AI model to automatically generate advertising content and adaptively improves the advertisements by analyzing the user's gaze and facial expression information. The terminal device is a device that interacts with the user and uses an eye-tracking sensor and a camera to collect the user's gaze direction and facial expression changes in real time. The user also watches the advertisements and shows unconscious reactions based on their own interests and emotions.

[0770] Specifically, the terminal device uses Tobii Technology's eye-tracking sensors to collect user gaze data. This data is sent to a server in the cloud and analyzed using software such as Python and TensorFlow. Based on the analysis results, the server evaluates the user's psychological state and optimizes the advertising content. The optimized advertising content is then sent back to the terminal device and presented to the user. This allows users to receive personalized promotional information tailored to their interests.

[0771] As a concrete example, let's assume a user is in a fashion shop wearing smart glasses. If the user's gaze lingers on a particular piece of clothing for an extended period, the server might analyze that data and display a message to the user such as, "It seems you're interested in this jacket. We're currently offering a 20% discount on it."

[0772] An example of a prompt to input into the generation AI model is, "Detect the products and reactions the user is focusing on, and create relevant promotional information." This allows the system to provide an advertising experience tailored to the specific needs of the user.

[0773] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0774] Step 1:

[0775] The device uses eye-tracking sensors and a camera to collect gaze and facial expression data while the user is viewing content. This data is used as input to record the user's gaze position and changes in facial expressions. The output data includes information about which parts of the content the user is focusing on.

[0776] Step 2:

[0777] The terminal transmits the collected gaze and facial expression data to the server. In this step, the terminal specifically analyzes the gaze movements and facial expression data in real time, converts it into a digital format, and transmits it to the server. The output data includes gaze and facial expression data in an analyzable format.

[0778] Step 3:

[0779] The server analyzes the received gaze and facial expression data as input. Using software such as Python or TensorFlow, it processes the data to evaluate the user's psychological state. In this process, it performs data calculations to identify the user's level of interest and emotional state, and generates an evaluation result regarding the user's psychological state as output.

[0780] Step 4:

[0781] The server uses a generative AI model to adaptively improve advertising content based on the evaluation results of the user's psychological state. In this step, the evaluation results are converted into prompts, such as "Detect the products the user is focusing on and their reactions, and create relevant promotional information," which are then input into the generative AI model. The output is optimized advertising content.

[0782] Step 5:

[0783] The server sends optimized ad content to the device and presents it to the user. In this step, the device receives the new ad content and performs the specific actions of displaying it on the screen. As output, the user is presented with an adaptively improved ad.

[0784] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0785] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0786] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0787] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0788] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0789] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0790] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0791] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0792] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0793] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0794] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0795] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0796] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0797] 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.

[0798] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0799] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0800] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0801] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0802] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0803] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0804] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0805] The following is further disclosed regarding the embodiments described above.

[0806] (Claim 1)

[0807] A means by which an information processing device generates advertising content,

[0808] A terminal device that displays advertisements and collects user gaze and facial expression data,

[0809] The information processing device analyzes the collected data and evaluates the user's emotions to optimize advertising content.

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means for evaluating the effectiveness of generated advertising content and redistributing optimized advertising content to each terminal device.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising means for an information processing device to analyze the target market and trends for advertising placement and to select the most effective advertising content.

[0815] "Example 1"

[0816] (Claim 1)

[0817] A means by which an information processing device generates advertising content using an artificial intelligence model based on input data,

[0818] A means for a terminal device to display advertisements and perform real-time monitoring to acquire the user's gaze point and facial expression information,

[0819] The information processing device analyzes the acquired information, evaluates user reactions, and improves the advertising content.

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, further comprising means for continuously improving the advertisement content based on the analysis results and retransmitting the improved advertisement content to each terminal device.

[0823] (Claim 3)

[0824] The system according to claim 1, further comprising means for an information processing device to analyze the target market and trends of an advertisement and select the most effective advertisement content.

[0825] "Application Example 1"

[0826] (Claim 1)

[0827] A means by which an information processing device generates advertising content,

[0828] A terminal device that displays advertisements and collects user gaze and facial expression data,

[0829] The information processing device analyzes the collected data and evaluates the user's emotions to optimize advertising content.

[0830] A means of dynamically changing the advertisement content on a visual display device in real time based on gaze and facial expressions analyzed by a terminal device,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for evaluating the effectiveness of generated advertising content and redistributing optimized advertising content to each terminal device.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for an information processing device to analyze the target market and trends for advertising placement and to select the most effective advertising content.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A means by which an information processing device uses a generative AI model to generate advertising content,

[0839] A terminal device that displays advertisements and collects user gaze and facial expression data through sensors,

[0840] An information processing device applies emotion analysis technology to analyze gaze data and facial expression data transmitted from a terminal device, and a means of evaluating the user's emotions and optimizing advertising content.

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for delivering optimized advertising content to each terminal device in an improved form based on a regenerated prompt message.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for an information processing device to analyze the target market and trends for advertising placement and propose the most effective advertising content using a generating AI model.

[0846] "Application example 2 when combining with an emotional engine"

[0847] (Claim 1)

[0848] A means by which an information processing device automatically generates advertising content,

[0849] A terminal device that displays advertisements and acquires the user's gaze and facial expression information,

[0850] The information processing device analyzes the acquired information and the user's psychological state to adaptively improve the advertising content.

[0851] A means for generating personalized promotional information based on the user's gaze time and facial expression changes,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for providing improved advertising content to each terminal device again and for presenting information related to items that the user is paying attention to.

[0855] (Claim 3)

[0856] The system according to claim 1, further comprising means for an information processing device to analyze the target market for advertising placement and its fluctuations, and to select the optimal advertising content. [Explanation of Symbols]

[0857] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means by which an information processing device generates advertising content, A terminal device that displays advertisements and collects user gaze and facial expression data, The information processing device analyzes the collected data and evaluates the user's emotions to optimize advertising content. A system that includes this.

2. The system according to claim 1, further comprising means for evaluating the effectiveness of generated advertising content and redistributing optimized advertising content to each terminal device.

3. The system according to claim 1, further comprising means for an information processing device to analyze the target market and trends for advertising placement and to select the most effective advertising content.

Images