system

Abstract

We provide the system. [Solution] Means for collecting user demographic information and past behavioral data, A means of analyzing user sentiment data in real time, A method for integrating analyzed emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors, A method for selecting advertisements based on user emotional state and behavioral data, A means of displaying selected advertisements to users, 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 method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional advertising targeting systems, advertisements have mainly been delivered based on users' demographic information and past behavioral data. This approach has a problem in that it cannot consider users' instantaneous emotional states, making it difficult to achieve immediate and effective advertisement delivery. Since the relevance between users' emotions and behaviors has not been fully utilized, it has been difficult to deliver highly accurate advertisements tailored to users' needs and interests. The present invention aims to solve these problems and realize more personalized advertisement delivery.

Means for Solving the Problems

[0005] The present invention is a system comprising means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to; means for selecting advertisements based on the user's emotional state and behavioral data; and means for displaying the selected advertisements to the user. Furthermore, means for utilizing facial recognition, voice tone analysis, and keyboard input speed are provided to collect user emotional data in real time. This system enables personalized ad delivery based on the user's instantaneous emotional state. In addition, the effectiveness of advertisements can be further enhanced by providing means for setting emotional state thresholds to determine whether a particular emotion leads to purchasing behavior.

[0006] A "user" is an individual or end-user who uses the system.

[0007] "Demographic information" refers to demographic data such as age, gender, and occupation.

[0008] "Past behavioral data" refers to historical information such as a user's browsing history, search history, and purchase history on websites and apps.

[0009] "Emotional data" refers to data that indicates a user's instantaneous emotional state, and is obtained from data such as facial expressions, voice tone, and keyboard input speed.

[0010] "Means of analysis" refers to a combination of hardware and software used to analyze emotional and behavioral data.

[0011] "Means of integration" refer to hardware and software that perform processing to link and unify analyzed emotional data with past behavioral data.

[0012] "Means of selecting advertisements" refers to algorithms and systems that select the most appropriate advertisements based on collected and analyzed data.

[0013] "Means for displaying advertisements" refers to the software and hardware used to display selected advertisements on the user's device.

[0014] "Facial recognition" is a technology that reads emotions from a user's facial expressions.

[0015] "Voice tone analysis" is a technology that analyzes emotions from the tone and intonation of a user's voice.

[0016] "Keyboard input speed" is data used to measure how quickly a user types on a keyboard and to infer their emotional state.

[0017] A "threshold" is a value used as a criterion to determine whether a particular emotional state influences important behaviors such as purchasing decisions. [Brief explanation of the drawing]

[0018] [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 the data processing device and 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] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0020] First, the terms used in the following description will be described.

[0021] In the following embodiments, a signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of the arithmetic unit 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.

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a system that integrates the collection and analysis of user demographic information and past behavioral data, as well as real-time sentiment data analysis, to realize personalized advertising delivery based on this information. The system based on this invention mainly consists of three components: a terminal, a server, and the user.

[0040] System Configuration

[0041] 1. The device collects the user's demographic information and past behavioral data and sends it to the server. The device is also equipped with various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time and send that data to the server.

[0042] 2. The server analyzes the received data and integrates sentiment data with past behavioral data. The server then selects advertisements based on the integrated data and sends those advertisements to the device.

[0043] 3. Users operate their devices and access websites and applications. User behavior and emotions are recorded and analyzed in real time.

[0044] Program processing and specific examples

[0045] When a user accesses a website

[0046] A user accesses a sports goods website.

[0047] The device collects the user's demographic information (e.g., age, gender, occupation) and past behavioral data (e.g., browsing history, purchase history).

[0048] The device sends the collected data to the server.

[0049] The server saves the received data to prepare for future access.

[0050] Real-time collection of emotional data

[0051] A user views a specific product page on a website.

[0052] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[0053] The device sends this emotional data to the server in real time.

[0054] The server uses an AI algorithm to analyze the emotional data it receives and quantifies the user's emotional state (e.g., "Excitement level: 85%").

[0055] Integrating emotions and behaviors and ad selection

[0056] The server integrates emotional data with past behavioral data to analyze the relationship between a user's specific emotional state and their actions.

[0057] The server sets emotional state thresholds and defines rules such as, "If the excitement level is 80% or higher, display a specific advertisement."

[0058] The server can select the most relevant advertisements based on the user's current emotional state and past data (for example, if a user is excited while browsing sports equipment, it will select an advertisement for the latest high-performance running shoes).

[0059] Ad delivery

[0060] The server sends the selected advertisement to the device.

[0061] The device displays advertisements selected by the user (e.g., an ad banner for running shoes appears on the webpage).

[0062] Specific example

[0063] For example, when user A accesses a sports website and views a page for new running shoes, the process proceeds as follows:

[0064] 1. User A accesses the website.

[0065] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[0066] 3. The device sends this data to the server.

[0067] 4. The server analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[0068] 5. The server integrates historical data and analyzes whether "excitement levels" have a high correlation with running shoe purchasing behavior.

[0069] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0070] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to user A.

[0071] Thus, the system of the present invention can deliver optimal advertisements to users in real time by considering the user's instantaneous emotional state and integrating it with past behavioral data. This makes it possible to provide a more personalized experience and maximize the effectiveness of advertisements.

[0072] The following describes the processing flow.

[0073] Step 1:

[0074] Users access websites and apps.

[0075] The device detects user access and records access logs.

[0076] Step 2:

[0077] The device collects the user's demographic information.

[0078] The device reads demographic information such as age, gender, and occupation from the user profile.

[0079] Step 3:

[0080] The device collects data on the user's past behavior.

[0081] The system reads data from a database, including pages and categories the device has previously viewed, as well as its past purchase history.

[0082] Step 4:

[0083] The device sends collected demographic information and behavioral data to the server.

[0084] The device encrypts this information and sends it to the server using a security protocol.

[0085] Step 5:

[0086] The server prepares to collect user sentiment data in real time.

[0087] The server activates the emotion data analysis module and issues instructions to activate the device's camera, microphone, and keyboard input speed sensor.

[0088] Step 6:

[0089] A user views specific content within the site.

[0090] The device monitors the user's browsing behavior and records the viewing status of specific pages or products.

[0091] Step 7:

[0092] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[0093] The device collects this emotional data and formats it so that it can be analyzed in real time.

[0094] Step 8:

[0095] The device sends the collected emotional data to the server.

[0096] The device encrypts emotional data and sends it to the server via a secure communication channel.

[0097] Step 9:

[0098] The server analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[0099] The server outputs the analysis results, for example, "Excitement level: 85%".

[0100] Step 10:

[0101] The server integrates emotional data with past behavioral data.

[0102] The server matches emotional data with past browsing and purchase history to analyze what actions specific emotions lead to.

[0103] Step 11:

[0104] The server sets the threshold for emotional states.

[0105] The server uses the integrated data to create rules, such as "show a specific ad if the excitement level is 80% or higher."

[0106] Step 12:

[0107] The user revisits the website or app.

[0108] The device detects the re-access and reactivates the sensor.

[0109] Step 13:

[0110] The device collects the user's current emotional state in real time.

[0111] The device acquires data in real time from the camera, microphone, keyboard input sensor, etc., and sends it to the server.

[0112] Step 14:

[0113] The server analyzes the current sentiment data.

[0114] The server analyzes the emotional data it receives using an AI algorithm and quantifies it.

[0115] Step 15:

[0116] The server compares current sentiment data with past behavioral data to select the most suitable advertisement.

[0117] The server selects highly relevant ads based on emotional state and behavioral data.

[0118] Step 16:

[0119] The server sends the selected advertising data to the device.

[0120] The server encrypts the advertising data and sends it to the device.

[0121] Step 17:

[0122] The device displays advertisements to the user.

[0123] The device displays a specified advertisement on the user's browsing page and records the user's reaction.

[0124] By sequentially carrying out these processing steps, personalized advertising that reflects the user's emotional state in real time can be achieved.

[0125] (Example 1)

[0126] 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."

[0127] While traditional advertising delivery systems possessed technologies to provide personalized ads based on users' demographic information and past behavioral data, they lacked the ability to select ads considering users' real-time emotional states, resulting in limited advertising effectiveness. Furthermore, the accuracy of emotional data collection and analysis was low, often leading to the display of inappropriate ads. Additionally, security concerns arose regarding the transmission and storage of collected data, necessitating secure data processing.

[0128] 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.

[0129] In this invention, the server includes means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what actions specific emotions lead to; means for selecting advertisements based on the user's emotional state and behavioral data; means for displaying the selected advertisements to the user; means for personalizing the content of the advertisements using a generative AI model; and encryption means for securely transmitting the collected data to the server. This makes it possible to analyze the user's real-time emotional state with high accuracy and display the most suitable advertisements based on the results. Furthermore, it enables secure transmission and storage of data, maximizing advertising effectiveness while protecting user privacy.

[0130] "User demographic information" refers to basic statistical information used to identify an individual, such as age, gender, and occupation.

[0131] "Past behavioral data" refers to information about a user's past behavior, such as their website browsing history, purchase history, and search history.

[0132] "Emotional data" refers to data that indicates a user's real-time emotional state and is collected using methods such as facial expressions, tone of voice, and keyboard input speed.

[0133] "Facial recognition" refers to a technology that uses a camera to analyze the movements of a user's face and identify their emotional state.

[0134] "Voice tone analysis" refers to a technology that uses a microphone to analyze the tone and intonation of a user's voice and identify their emotional state.

[0135] "Keyboard input speed" refers to a method of measuring how fast a user types on a keyboard and analyzing their stress and excitement levels based on that measurement.

[0136] A "generative AI model" refers to an artificial intelligence algorithm that has learned from a large amount of data and generates new advertising content based on input data.

[0137] "Data encryption methods" refer to technologies that encrypt data in order to securely transmit collected data over the internet.

[0138] "Ad personalization" refers to generating and displaying ad content that is optimal for each individual user.

[0139] This invention is a system that delivers personalized advertisements by combining user demographic information, past behavioral data, and real-time sentiment data analysis. The system is mainly composed of three components: a terminal, a server, and the user.

[0140] Hardware and software to be used

[0141] Device: Primarily refers to computer devices such as smartphones and personal computers. These devices are equipped with various sensors, including cameras, microphones, and keyboards.

[0142] Server: A server device capable of high-performance data processing. It has database management systems (e.g., MySQL®, PostgreSQL) and frameworks for running AI models (e.g., TENSORFLOW®, PyTorch) installed.

[0143] Software: We utilize sentiment analysis software (e.g., Kairos, Microsoft® Azure® Face API), voice analysis tools (e.g., Google® Cloud Speech-to-Text, IBM Watson® Speech to Text), and encryption protocols for data transmission (e.g., HTTPS).

[0144] Detailed description of the invention

[0145] Collection of demographic information and behavioral data

[0146] When a user accesses a website or application, their device collects demographic information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.). Demographic information is obtained from the user's profile information, while behavioral data is obtained by analyzing browser cookies and application logs.

[0147] Real-time collection of emotional data

[0148] When a user views a specific product page, various sensors on the device (camera, microphone, keyboard) collect emotional data in real time. The camera performs facial recognition, the microphone analyzes voice tone, and the keyboard measures typing speed. This data is then analyzed using emotional analysis software and voice analysis tools.

[0149] Sending data to the server

[0150] The device sends collected demographic information, historical behavioral data, and real-time sentiment data to the server via a secure protocol (e.g., HTTPS). Communication is encrypted, ensuring data protection.

[0151] Data analysis and ad selection

[0152] The server stores the received data in a database and uses an AI model to analyze the emotional data. Emotional states are quantified (e.g., "Excitement level: 85%") and integrated with past behavioral data. Data analysis tools analyze how specific emotions lead to specific behaviors. Based on set thresholds (e.g., excitement level 80% or higher), the server selects the most suitable advertisement from the advertising database.

[0153] Ad delivery

[0154] The server sends the selected advertisement to the device, and the device displays that advertisement to the user. For example, it may be displayed as a banner ad on a webpage.

[0155] Specific example

[0156] For example, let's consider a scenario where user A accesses a sports website and views a page for new running shoes.

[0157] 1. User A accesses the website and views the running shoes page.

[0158] 2. The device obtains demographic information from the user's profile and analyzes past behavioral data.

[0159] 3. The device captures the user's facial expressions with its camera and detects their state of excitement using emotion analysis software. The microphone analyzes the user's voice tone, and the keyboard input speed is also monitored.

[0160] 4. Encrypt the data collected by the device and send it securely to the server.

[0161] 5. The server analyzes the data, confirms that the excitement level is 80% or higher, and selects the most suitable running shoe advertisement.

[0162] 6. The server sends an advertisement to the device, and the device displays the advertisement to user A.

[0163] Example of a prompt

[0164] "A 30-year-old man was browsing a sports equipment website and showed an excited expression. Please generate an ad for running shoes that would be suitable for this user."

[0165] Thus, the system of the present invention can deliver optimal advertisements to users in real time by considering the user's instantaneous emotional state and integrating it with past behavioral data. This makes it possible to provide a more personalized experience and maximize the effectiveness of advertisements. The above describes a specific embodiment for carrying out the invention.

[0166] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0167] Step 1:

[0168] Users access websites and applications.

[0169] The device collects the user's demographic information (age, gender, occupation). User registration information and profile information are required as input, and demographic information is prepared as output. Specifically, the device retrieves information from the user's initial registration data and web browser cookies.

[0170] Step 2:

[0171] The device collects past behavioral data (browsing history, purchase history). The input is the user's browser history and application log data, and the output is generated as past behavioral data. Specifically, the device analyzes browser cookies and uses log data from the server.

[0172] Step 3:

[0173] The device utilizes facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time. Inputs include camera video, audio, and keyboard input data, and the output is analyzed emotion data. Specifically, the camera captures the user's facial expressions, which are then analyzed by emotion analysis software. The microphone is used to analyze voice tone, and the keyboard input speed is measured.

[0174] Step 4:

[0175] The device collects demographic information, historical behavioral data, and real-time sentiment data, which are then sent to a server via the internet. The input is pre-collected data, and the output is encrypted data to be sent. Specifically, the device uses the HTTPS protocol to encrypt the data and securely transmit it to the server.

[0176] Step 5:

[0177] The server stores the received data in a database. The input includes demographic information, behavioral data, and sentiment data, and the output is the data stored in the database. Specifically, the server uses a database management system to securely store the data.

[0178] Step 6:

[0179] The server analyzes incoming data using an AI model and quantifies the emotional state. The input is stored emotional data, and the output is a quantified emotional state. Specifically, the server runs an AI model (e.g., TensorFlow or PyTorch) and converts the emotional state into a numerical value such as "Excitement Level: 85%".

[0180] Step 7:

[0181] The server integrates emotional data and past behavioral data to analyze how specific emotions lead to certain behaviors. The input consists of quantified emotional and behavioral data, and the output is the analysis of these relationships. Specifically, data analysis tools (e.g., Apache® Hadoop, Spark) are used to integrate and analyze this data.

[0182] Step 8:

[0183] The server selects the most suitable advertisement based on the user's emotional state and behavioral data. The input is the analysis results, and the output is the selected advertisement. Specifically, the server retrieves appropriate advertisements from the ad database based on a set threshold (e.g., excitement level of 80% or higher).

[0184] Step 9:

[0185] The server sends selected advertisements to the device. The input is the selected advertisement data, and the output is a transmission completion status. Specifically, the server encrypts the data again using the HTTPS protocol and sends it to the device.

[0186] Step 10:

[0187] The device displays the received advertisement to the user. The input is the transmitted advertisement data, and the output is the advertisement displayed on the user's screen. Specifically, the device prepares to display the advertisement as a banner ad on a webpage and then displays it to the user.

[0188] (Application Example 1)

[0189] 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."

[0190] Traditional advertising delivery systems only delivered ads based on users' demographic information and past behavioral data, without considering the real-time, fluctuating emotional states of users. This made it difficult to deliver the most relevant ads to users, resulting in decreased advertising effectiveness. Furthermore, there was insufficient technology to accurately collect and analyze real-time emotional data, including users' facial expressions and tone of voice.

[0191] 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.

[0192] In this invention, the server includes means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to; means for selecting advertisements based on the user's emotional state and behavioral data; means for displaying the selected advertisements to the user; means for analyzing the user's facial expressions and voice tone in real time using a smartphone; and means for selecting advertisements to display based on the emotional data analyzed in real time. This makes it possible to deliver optimal advertisements based on user demographic information and past behavioral data, as well as emotional data that changes in real time.

[0193] "Demographic information" refers to information that indicates a user's social and economic attributes, such as age, gender, occupation, and place of residence.

[0194] "Behavioral data" refers to data about a user's digital activities, such as their past website browsing history and purchase history.

[0195] "Emotional data" refers to data that indicates a user's psychological and emotional state, obtained from the user's facial expressions, voice tone, keyboard input speed, and other factors.

[0196] "Facial recognition" is a technology that analyzes the features of a user's face from camera footage to identify their emotional state.

[0197] "Voice tone analysis" is a technology that analyzes the pitch, intensity, and rhythm of a user's voice to infer their emotional state.

[0198] "Keyboard input speed" refers to data that measures the speed at which a user types on a keyboard and is used to estimate their emotional state.

[0199] "Ad selection methods" refer to algorithms and methods that select the most suitable advertisements for a user based on analyzed data.

[0200] A "smartphone" is a portable device equipped with advanced computing and communication capabilities, as well as sensors such as a camera and microphone.

[0201] "Real-time" refers to processing and analyzing data at a time close to the moment when a user's actions or situations occur.

[0202] A "server" is a computer system that collects, analyzes, stores, and distributes data over a network.

[0203] This invention is a system that collects and analyzes user demographic information, past behavioral data, and real-time emotional data to deliver personalized advertisements. This system demonstrates a method for collecting emotional data in real time using a user's smartphone, and selecting and displaying advertisements based on the analysis results.

[0204] System Configuration

[0205] 1. Terminal

[0206] Smartphones are used as a means of collecting users' demographic information and past behavioral data.

[0207] Smartphones are equipped with cameras and microphones, which allow them to analyze the user's facial expressions and voice tone in real time.

[0208] The device sends the collected data to the server.

[0209] 2. Server

[0210] The server analyzes the received demographic information, past behavioral data, and sentiment data.

[0211] Based on the analyzed data, we analyze what kinds of behaviors are linked to specific emotions.

[0212] An algorithm is executed to select advertisements based on the user's emotional state and behavioral data.

[0213] Selected advertisements are sent to the device.

[0214] 3. User

[0215] Users operate their smartphones to access websites and applications.

[0216] User behavior and emotions are recorded and analyzed in real time.

[0217] Program processing

[0218] Terminal processing

[0219] The smartphone's camera captures the user's facial expressions, and the microphone collects their voice tone.

[0220] The collected emotional data, demographic information, and past behavioral data are sent to the server.

[0221] Server Processing

[0222] The server analyzes the received data and integrates emotional and behavioral data.

[0223] Set thresholds for emotional states to determine whether a particular emotion leads to purchasing behavior.

[0224] Based on the results of integrated analysis, the most suitable advertisements are selected for each user.

[0225] The selected advertisements are sent to the device, and the device displays the advertisements to the user.

[0226] Specific hardware and software to be used

[0227] hardware

[0228] Smartphones: Collect user data using cameras and microphones.

[0229] Server: Stores, analyzes, and selects advertisements for data.

[0230] software

[0231] OpenCV: Analyzes a user's facial expressions from smartphone camera footage.

[0232] EmotionRecognition (a virtual emotion recognition library): Recognizes the user's emotional state.

[0233] UserDataProcessor (a virtual user data processing library): Processes user demographic information and past behavioral data.

[0234] AdSelector (virtual ad selection library): Selects ads based on analyzed data.

[0235] Specific example

[0236] For example, when user A opens a shopping app, the process proceeds as follows:

[0237] 1. User A opens a shopping app on their smartphone.

[0238] 2. The smartphone camera captures user A's facial expressions, and the microphone analyzes their voice tone.

[0239] 3. The collected data is sent to the server and analyzed in real time.

[0240] 4. The server integrates user A's current emotional state with past behavioral data and detects that user A is in an excited state.

[0241] 5. The server determines that the excited state will lead to purchasing behavior and selects the most suitable advertisement (for example, a promotion for new sneakers).

[0242] 6. The selected advertisement is sent to the smartphone and displayed to User A.

[0243] Example of a prompt

[0244] "Create a program that recognizes a user's emotional state from their facial expressions and tone of voice when they open a shopping app. Integrate this with their past purchase history and display personalized ads in real time."

[0245] Thus, the present invention takes into account the user's instantaneous emotional state to achieve highly accurate personalized advertising and maximize the effectiveness of the advertisement.

[0246] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0247] Step 1:

[0248] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice tone in real time.

[0249] Input: Camera video data, audio data

[0250] Specific operation: The smartphone's camera captures the user's face, and the microphone records audio. If necessary, lighting conditions and camera focus are automatically adjusted.

[0251] Output: Captured facial expression data and voice tone data

[0252] Step 2:

[0253] The device analyzes captured facial expression data and voice tone data to identify the user's emotional state.

[0254] Input: Facial expression data, voice tone data

[0255] Specific operation: It uses OpenCV to analyze facial features and the EmotionRecognition library to determine emotional states (e.g., joy, sadness, excitement). In speech tone analysis, it analyzes pitch, volume, rhythm, etc., to estimate emotional states.

[0256] Output: Analyzed emotion data (e.g., "Excited state: 85%")

[0257] Step 3:

[0258] The device sends analyzed sentiment data, user demographic information, and past behavioral data to the server.

[0259] Input: Sentimental data, demographic information, historical behavioral data

[0260] Specific actions: Before sending user information to the server in bulk, the data format is prepared (e.g., converted to JSON format). Then, encryption is performed to ensure secure data transfer.

[0261] Output: Integrated data sent to the server

[0262] Step 4:

[0263] The server analyzes demographic information, past behavioral data, and emotional data it receives to determine how specific emotions lead to certain behaviors.

[0264] Input: Demographic information, historical behavioral data, sentiment data

[0265] Specific operation: The server stores the received data in a database and performs analysis using an AI algorithm. A key point is that it analyzes cases where, for example, "excitement" is strongly correlated with "purchasing behavior."

[0266] Output: Analysis results (Example: "People in an excited state are more likely to purchase expensive items")

[0267] Step 5:

[0268] The server selects the most suitable advertisement based on the user's emotional state and behavioral data.

[0269] Input: Analysis results, emotional state, behavioral data

[0270] Specific operation: Based on the analysis results, a selection algorithm is used to choose the most suitable advertisement from multiple options (e.g., "If the user is excited, display an advertisement for sports equipment"). Past behavioral data and demographic information are also taken into account to refine the selection process.

[0271] Output: Selected ad data

[0272] Step 6:

[0273] The server sends the selected advertisement to the device, and the device displays the advertisement to the user.

[0274] Input: Selected ad data

[0275] Specific operation: The server sends the selected advertisement to the device, and the device displays the advertisement. Multiple display formats are possible, such as banner ads, pop-up ads, and video ads, but the advertisement will be displayed in a format that does not detract from the user experience.

[0276] Output: Advertisements displayed on the device

[0277] This allows users to receive personalized ads in real time, improving the effectiveness of those ads.

[0278] 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.

[0279] This invention provides a system that enables more personalized advertising delivery by combining the collection and analysis of user demographic information and past behavioral data with real-time sentiment data analysis. The system based on this invention mainly consists of four components: a terminal, a server, a user, and a sentiment engine.

[0280] System Configuration

[0281] 1. The terminal collects the user's demographic information and past behavior data and transmits this to the server.

[0282] The terminal also has various sensors (such as cameras, microphones, keyboards, etc.) for collecting the user's current emotional state in real time.

[0283] 2. The emotion engine analyzes the collected emotion data and represents the user's specific emotional state numerically (e.g., "excitement level: 85%").

[0284] The emotion engine comprehensively analyzes the emotional state from multiple data sources such as facial expression recognition, voice tone analysis, and keyboard input speed.

[0285] 3. The server analyzes the received data and integrates the emotion data and past behavior data.

[0286] The server further selects an advertisement based on this integrated data and transmits the advertisement to the terminal.

[0287] 4. The user operates the terminal to access websites and applications.

[0288] The user's actions and emotions are recorded and analyzed in real time.

[0289] Program processing and specific examples

[0290] When the user accesses a website

[0291] The user accesses a sports equipment website.

[0292] The terminal collects the user's demographic information (e.g., age, gender, occupation) and past behavior data (e.g., browsing history, purchase history).

[0293] The terminal transmits the data it has collected to the server.

[0294] The server saves the received data in preparation for subsequent accesses.

[0295] Real-time collection of emotion data

[0296] The user views a specific product page on the website.

[0297] The terminal recognizes the user's expression with the camera, analyzes the voice tone with the microphone, and measures the input speed of the keyboard.

[0298] The terminal sends this emotion data to the emotion engine in real time.

[0299] Analysis and recognition of emotion data

[0300] The emotion engine analyzes the received emotion data with an AI algorithm and quantifies the user's emotional state.

[0301] The emotion engine sends the analysis result to the server.

[0302] Integrate the accumulated emotion data and past behavior data, and analyze the impact of specific emotional states on behavior.

[0303] Integration of emotion and behavior and advertisement selection

[0304] The server integrates the emotion data and past behavior data, and analyzes the relevance between the user's specific emotional state and behavior.

[0305] The server sets the threshold of the emotional state (e.g., "When the excitement level is 80% or more, display a specific advertisement") and selects an advertisement.

[0306] The server selects the optimal advertisement and sends the advertisement to the terminal.

[0307] Advertisement delivery

[0308] The server sends the selected advertisement to the device.

[0309] The device displays advertisements to the user (e.g., an ad banner for running shoes appears on a webpage).

[0310] Specific example

[0311] For example, when user A accesses a sports website and views a page for new running shoes, the process proceeds as follows:

[0312] 1. User A accesses the website.

[0313] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[0314] 3. The device sends this data to the server.

[0315] 4. The server analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[0316] 5. The server uses an emotion engine to integrate with past data and analyze that "excitement levels" have a high correlation with running shoe purchasing behavior.

[0317] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0318] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to user A.

[0319] Thus, the system of the present invention utilizes real-time emotional data collected by the emotion engine to achieve more accurate and personalized ad delivery. This allows for the provision of effective ads that reflect the user's emotional state, maximizing the effectiveness of the ads.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] Users access websites and apps.

[0323] The device detects user access and records access logs.

[0324] Step 2:

[0325] The device collects the user's demographic information.

[0326] The device reads demographic information such as age, gender, and occupation from the user profile.

[0327] Step 3:

[0328] The device collects data on the user's past behavior.

[0329] The system reads data from a database, including pages and categories the device has previously viewed, as well as its past purchase history.

[0330] Step 4:

[0331] The device sends collected demographic information and behavioral data to the server.

[0332] The device encrypts this information and sends it to the server using a security protocol.

[0333] Step 5:

[0334] The server prepares to collect user sentiment data in real time.

[0335] The server activates the emotion data analysis module and issues instructions to activate the device's camera, microphone, and keyboard input speed sensor.

[0336] Step 6:

[0337] A user views specific content within the site.

[0338] The device monitors the user's browsing behavior and records the viewing status of specific pages or products.

[0339] Step 7:

[0340] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[0341] The device transmits this emotional data to the emotion engine in real time.

[0342] Step 8:

[0343] The emotion engine analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[0344] The emotion engine outputs analysis results such as "Excitement level: 85%".

[0345] Step 9:

[0346] The emotion engine sends the analysis results to the server.

[0347] The emotion engine encrypts the analysis results and sends them to the server via a secure communication channel.

[0348] Step 10:

[0349] The server integrates emotional data with past behavioral data.

[0350] The server matches emotional data with past browsing and purchase history to analyze what actions specific emotions lead to.

[0351] Step 11:

[0352] The server sets the threshold for emotional states.

[0353] The server uses the integrated data to create rules, such as "show a specific ad if the excitement level is 80% or higher."

[0354] Step 12:

[0355] The user revisits the website or app.

[0356] The device detects the re-access and reactivates the sensor.

[0357] Step 13:

[0358] The device collects the user's current emotional state in real time.

[0359] The device acquires data in real time from the camera, microphone, keyboard input sensor, etc., and sends it to the server.

[0360] Step 14:

[0361] The emotion engine analyzes the current emotional data.

[0362] The emotion engine analyzes the received emotional data using an AI algorithm and quantifies it.

[0363] Step 15:

[0364] The server compares current sentiment data with past behavioral data to select the most suitable advertisement.

[0365] The server selects highly relevant ads based on emotional state and behavioral data.

[0366] Step 16:

[0367] The server sends the selected advertising data to the device.

[0368] The server encrypts the advertising data and sends it to the device.

[0369] Step 17:

[0370] The device displays advertisements to the user.

[0371] The device displays a specified advertisement on the user's browsing page and records the user's reaction.

[0372] By sequentially carrying out these processing steps, personalized advertising that reflects the user's emotional state in real time can be achieved.

[0373] (Example 2)

[0374] 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".

[0375] Traditional advertising delivery systems selected ads based on users' past browsing and purchase history, but they could not consider users' emotional states in real time. Therefore, it was difficult to deliver personalized ads that responded to users' instantaneous interests and psychological states. Furthermore, there was a lack of technology for centralized analysis of collected data and appropriate ad selection based on the results. There was a need to solve these problems and deliver more accurate ads in real time based on users' emotional states and past behavioral data.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user attribute information and past behavioral data, means for analyzing the user's emotional data in real time, and means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to. This makes it possible to select advertisements based on the user's emotional state and behavioral data and to display the selected advertisements to the user in real time.

[0377] "Attribute information" refers to basic profile information such as the user's age, gender, and occupation.

[0378] "Behavioral data" refers to data related to a user's past actions, such as their website browsing history and purchase history.

[0379] "Emotional data" refers to information about the user's emotional state, obtained in real time from factors such as facial expressions, voice tone, and keyboard input speed.

[0380] "Real-time" refers to the process of closely monitoring and analyzing user behavior and emotions and reflecting them immediately.

[0381] "Ad selection" refers to the process of selecting the most suitable advertisement by combining user attribute information, behavioral data, and emotional data.

[0382] "Analysis" refers to the process of analyzing collected emotional and behavioral data using AI algorithms or other methods to derive specific conclusions.

[0383] "Integration" refers to the process of combining different types of data (such as emotional data and behavioral data) and treating them as a single data set.

[0384] A "threshold" refers to a numerical value that serves as a standard for identifying a specific emotional state.

[0385] "Display" refers to the process of presenting selected advertisements on a user's device in a format that the user can see.

[0386] This invention is a system that collects and analyzes user attribute information and past behavioral data, as well as user sentiment data in real time, and delivers personalized advertisements based on that data. The system mainly consists of four components: a server, a terminal, a user, and a sentiment engine.

[0387] System Configuration

[0388] 1. Collection of user behavior data

[0389] The device collects user attribute information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.) when the user accesses a website or application, and sends this information to the server. For example, when a user visits a sports goods website and searches for running shoes, that information is collected at the device.

[0390] 2. Real-time collection of emotional data

[0391] When a user views a specific product page, the device recognizes the user's facial expressions via the camera, analyzes the user's voice tone via the microphone, and measures the keyboard typing speed. This emotional data is sent to an emotion engine in real time. For example, it measures whether the user is excited when viewing a page about running shoes.

[0392] 3. Analysis of emotional data

[0393] The emotion engine uses AI algorithms to analyze collected emotional data and quantify the user's emotional state. For example, it might output a specific numerical value such as "Excitement level: 85%". The analysis results are then sent to the server.

[0394] 4. Data Integration and Ad Selection

[0395] The server integrates received emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors. For example, if a user who has frequently purchased running shoes in the past is determined to be in an "excited state," an advertisement for running shoes will be displayed. The server sets thresholds for emotional states (e.g., "excitement level of 80% or higher") and selects the most appropriate advertisement based on these thresholds.

[0396] 5. Ad delivery

[0397] The selected advertisements are sent from the server to the device, and the device displays the advertisements to the user. For example, an advertisement banner for running shoes is displayed in a prominent position on the webpage.

[0398] Hardware and software used

[0399] Device: A device used to collect and transmit user data and sentiment data (such as a PC, smartphone, or tablet).

[0400] Camera: A sensor used to recognize the user's facial expressions in real time.

[0401] Microphone: A voice input device used to analyze the tone of the user's voice.

[0402] Keyboard: An input device used to measure a user's typing speed.

[0403] Server: A central facility for integrating and analyzing collected data, and for selecting and delivering advertisements.

[0404] Emotion Engine: A software module equipped with AI algorithms for analyzing emotional data.

[0405] Specific example

[0406] For example, when user A accesses a sports website and views a page for new running shoes, the process is as follows:

[0407] 1. User A accesses the website.

[0408] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[0409] 3. The device sends this data to the server.

[0410] 4. The emotion engine analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[0411] 5. The server integrates the results of the emotion engine analysis with past data and analyzes that "excitement" has a high correlation with the purchasing behavior of running shoes.

[0412] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0413] 7. The server sends the selected advertisement to user A's device, and the advertisement is displayed.

[0414] Example of a prompt

[0415] Please build a system that collects and analyzes user attribute information (age, gender, occupation), past behavioral data (browsing history, purchase history), and real-time sentiment data (facial recognition, voice tone, keyboard input speed), and delivers personalized advertisements based on this data.

[0416] This enables highly accurate ad delivery based on users' emotional states and behavioral data. This system not only improves the user experience but also maximizes the effectiveness of the ads.

[0417] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0418] Step 1: Collecting user behavior data

[0419] When a user accesses a website or application, the device obtains user attribute information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.) as input. As a result of this data processing, the collected information is sent to the server. Specifically, the web browser reads the user's profile data and browsing history from cookies, etc., and sends it to the server via an HTTP request.

[0420] Step 2: Collecting real-time sentiment data

[0421] When a user views a specific product page, the device uses its camera to capture the user's facial expressions, its microphone to record the tone of their voice, and its keyboard typing speed to measure the input. As a result of this data processing, the emotional data collected in real time is sent to an emotion engine. Specifically, the device's camera analyzes facial expressions using facial recognition software, the microphone evaluates the tone of voice using voice analysis software, and the keyboard stroke speed monitors the typing motion.

[0422] Step 3: Analysis of emotional data

[0423] The emotion engine receives emotional data collected as input and analyzes it using an AI algorithm. As a result of this data processing, the user's emotional state is quantified (e.g., "Excitement level: 85%"). The analyzed results are sent to the server. Specifically, the emotion engine inputs the received facial image, audio clip, and typing data into a neural network, which then analyzes this data and maps it to a specific emotional state.

[0424] Step 4: Data Integration and Ad Selection

[0425] The server integrates the sentiment data received as input with past behavioral data. As a result of this data processing, it can analyze how specific emotions lead to specific actions. For example, if past data shows a high correlation between "excitement" and the purchase of running shoes, it will select a specific advertisement. Specifically, the server compares past behavioral data stored in the integrated database with real-time sentiment data and recommends advertising campaigns that meet pre-set thresholds.

[0426] Step 5: Ad Delivery

[0427] The server holds the selected advertising data as input and sends it to the device. As a result of this data transfer, the device displays the advertisement to the user. Specifically, the server sends JSON data containing the URL and display instructions of the selected advertisement to the device, and the device's browser parses this data to display the advertisement banner or pop-up.

[0428] (Application Example 2)

[0429] 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 device 14 will be referred to as the "terminal."

[0430] Traditional advertising systems only displayed personalized ads based on users' demographic information and past behavioral data. However, this limited the accuracy and effectiveness of the ads. Therefore, there is a need for a system that reflects users' real-time emotional states and delivers ads with a higher degree of personalization. Another challenge is the lack of a means to display ads in real time using smart devices while users are out and about.

[0431] 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. In this invention, the server includes means for collecting the user's demographic information and past behavioral data, means for analyzing the user's emotional data in real time, and means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to. As a result, the emotional state is reflected in the selection of advertisements, enabling the delivery of optimal advertisements tailored to the user's current situation. Furthermore, by means for collecting the user's real-time emotional data using a smart device and displaying advertisements through a visual display device, it is possible to provide advertisements suitable for the user even when they are out and about.

[0432] "User demographic information" refers to information that indicates personal characteristics such as age, gender, occupation, and region.

[0433] "Past behavioral data" refers to data that describes a user's past behavior, such as their website browsing history, purchase history, and search history.

[0434] "Methods for analyzing user emotional data in real time" refer to methods that use cameras, microphones, keyboard input speed sensors, etc., to analyze the user's facial expressions, voice tone, input speed, etc., in real time and quantify their emotional state.

[0435] "Emotional state" refers to a numerical representation of the emotions a user is feeling at a particular moment, such as excitement, joy, or sadness.

[0436] "Methods for selecting advertisements" refer to methods for selecting the most suitable advertisements that match the user's emotional state and behavioral data, based on integrated data.

[0437] "Means of displaying selected advertisements to users" refers to means of displaying selected advertisements on the devices that users view. This includes, for example, displaying advertisements on smart glasses or smartphone screens.

[0438] A "smart device" is a multi-functional device that users can carry around, and it has built-in features such as a camera, microphone, and sensors. Specific examples include smartphones and smart glasses.

[0439] "Visual display devices" is a general term for display devices that allow users to visually confirm information. Examples include the display screens of head-mounted displays and smart glasses.

[0440] This invention is a system that achieves more personalized advertising delivery by combining the collection and analysis of user demographic information and past behavioral data with real-time sentiment data analysis. This system mainly consists of the following four components: terminal, server, user, and sentiment engine.

[0441] 1. Device functions

[0442] The device collects the user's demographic information and past behavioral data and sends it to a server. It also features various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time. This allows it to capture emotional data such as the user's facial expressions, voice tone, and typing speed.

[0443] The hardware used includes cameras (e.g., Sony IMX586) and microphones (e.g., Intel RealSense D435). This data is then analyzed using software such as OpenCV and NLP toolkits.

[0444] 2. Function of the Emotion Engine

[0445] The emotion engine analyzes collected emotional data and expresses the user's specific emotional state numerically. This engine comprehensively analyzes emotional states from multiple data sources, including facial recognition, voice tone analysis, and keyboard input speed. For example, it uses an AI algorithm to quantify the emotional state as "Excitement Level: 85%".

[0446] 3. Server Functions

[0447] The server analyzes the data received from the device and integrates emotional data with past behavioral data. This allows it to analyze how specific emotions lead to certain behaviors. It also selects the most suitable advertisements based on the user's emotional state and behavioral data and sends those advertisements to the device.

[0448] 4. User actions

[0449] Users operate their devices and access websites and applications. Their actions and emotions are recorded and analyzed in real time. This allows for the delivery of advertisements tailored to the user's current situation.

[0450] Specific example

[0451] For example, when a user accesses a sports goods website, the process proceeds as follows:

[0452] 1. The user accesses the website.

[0453] 2. The device collects the user's past browsing history (e.g., frequently searching for running shoes) and demographic information.

[0454] 3. The device sends this data to the server.

[0455] 4. The server analyzes the user's facial expressions from the camera footage in real time and detects if they are in an "excited state."

[0456] 5. The server uses an emotion engine to integrate with past data and analyze that "excitement levels" have a high correlation with running shoe purchasing behavior.

[0457] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0458] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to the user.

[0459] This system utilizes real-time sentiment data collected by an emotion engine to deliver personalized ads with greater accuracy. This allows for the provision of effective ads that reflect the user's emotional state, maximizing the impact of advertising.

[0460] Example of a prompt

[0461] Design a system that captures a user's gaze, facial expressions, and voice tone in real time while they are wearing smart glasses, and analyzes this data using an emotion engine. The analysis results, along with demographic information and past behavioral data, are sent to a server, and the system then displays the most relevant advertisements in augmented reality (AR) within the user's field of view.

[0462] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0463] Step 1:

[0464] The device collects the user's demographic information and past behavioral data. Input includes the user's age, gender, occupation, region, past browsing history, and purchase history. This information is collected as data and temporarily stored in internal storage to prepare for the next step.

[0465] Step 2:

[0466] The device sends collected demographic information and past behavioral data to the server. A communication protocol (e.g., HTTPS) is used for this transmission. The data is sent in JSON format, which the server stores in a database and uses for analysis.

[0467] Step 3:

[0468] The device collects real-time emotional data from the user. To achieve this, it captures facial expressions with its built-in camera, records voice tone with its microphone, and measures keyboard and touchscreen input speed. The inputs include video data, audio data, and input speed data, all of which are processed in real time.

[0469] Step 4:

[0470] The device sends collected emotional data to the emotion engine in real time. The emotion engine uses AI algorithms (e.g., deep learning models) to analyze facial expressions, voice tone, and input speed data. The analyzed emotional data is quantified (e.g., "Excitement level: 85%") and sent to the next step.

[0471] Step 5:

[0472] The server integrates emotional data with historical behavioral data to analyze how specific emotions lead to certain behaviors. Specifically, it analyzes correlations based on demographic information, historical behavioral data, and real-time emotional data to evaluate the extent to which specific emotional states influence past purchasing behavior. Statistical analysis and machine learning models are used in this process.

[0473] Step 6:

[0474] The server selects advertisements based on the user's emotional state and behavioral data. Here, advertisements corresponding to the emotional state are selected from the database. For example, if the "excitement level" is high, advertisements for sports equipment will be selected. The selected advertisements are then sent to the user in the next step.

[0475] Step 7:

[0476] The server sends the selected advertisement to the device. A communication protocol (e.g., HTTPS) is used for this transmission. The advertisement data is sent in the form of images, text, links, etc.

[0477] Step 8:

[0478] The device displays advertisements to the user. Specifically, advertisements are displayed in the user's field of view using AR (augmented reality) through visual display devices such as smart glasses and smartphones. This display method is visually natural for the user and enables effective ad delivery.

[0479] Example of a prompt

[0480] Design a system that captures a user's gaze, facial expressions, and voice tone in real time while they are wearing smart glasses, and analyzes this data using an emotion engine. The analysis results, along with demographic information and past behavioral data, are sent to a server, and the system then displays the most relevant advertisements in augmented reality (AR) within the user's field of view.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] [Second Embodiment]

[0485] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0486] 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.

[0487] 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).

[0488] 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.

[0489] 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.

[0490] 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).

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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".

[0497] This invention is a system that integrates the collection and analysis of user demographic information and past behavioral data, as well as real-time sentiment data analysis, to realize personalized advertising delivery based on this information. The system based on this invention mainly consists of three components: a terminal, a server, and the user.

[0498] System Configuration

[0499] 1. The device collects the user's demographic information and past behavioral data and sends it to the server. The device is also equipped with various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time and send that data to the server.

[0500] 2. The server analyzes the received data and integrates sentiment data with past behavioral data. The server then selects advertisements based on the integrated data and sends those advertisements to the device.

[0501] 3. Users operate their devices and access websites and applications. User behavior and emotions are recorded and analyzed in real time.

[0502] Program processing and specific examples

[0503] When a user accesses a website

[0504] A user accesses a sports goods website.

[0505] The device collects the user's demographic information (e.g., age, gender, occupation) and past behavioral data (e.g., browsing history, purchase history).

[0506] The device sends the collected data to the server.

[0507] The server saves the received data to prepare for future access.

[0508] Real-time collection of emotional data

[0509] A user views a specific product page on a website.

[0510] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[0511] The device sends this emotional data to the server in real time.

[0512] The server uses an AI algorithm to analyze the emotional data it receives and quantifies the user's emotional state (e.g., "Excitement level: 85%").

[0513] Integrating emotions and behaviors and ad selection

[0514] The server integrates emotional data with past behavioral data to analyze the relationship between a user's specific emotional state and their actions.

[0515] The server sets emotional state thresholds and defines rules such as, "If the excitement level is 80% or higher, display a specific advertisement."

[0516] The server can select the most relevant advertisements based on the user's current emotional state and past data (for example, if a user is excited while browsing sports equipment, it will select an advertisement for the latest high-performance running shoes).

[0517] Ad delivery

[0518] The server sends the selected advertisement to the device.

[0519] The device displays advertisements selected by the user (e.g., an ad banner for running shoes appears on the webpage).

[0520] Specific example

[0521] For example, when user A accesses a sports website and views a page for new running shoes, the process proceeds as follows:

[0522] 1. User A accesses the website.

[0523] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[0524] 3. The device sends this data to the server.

[0525] 4. The server analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[0526] 5. The server integrates historical data and analyzes whether "excitement levels" have a high correlation with running shoe purchasing behavior.

[0527] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0528] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to user A.

[0529] Thus, the system of the present invention can deliver optimal advertisements to users in real time by considering the user's instantaneous emotional state and integrating it with past behavioral data. This makes it possible to provide a more personalized experience and maximize the effectiveness of advertisements.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] Users access websites and apps.

[0533] The device detects user access and records access logs.

[0534] Step 2:

[0535] The device collects the user's demographic information.

[0536] The device reads demographic information such as age, gender, and occupation from the user profile.

[0537] Step 3:

[0538] The device collects data on the user's past behavior.

[0539] The system reads data from a database, including pages and categories the device has previously viewed, as well as its past purchase history.

[0540] Step 4:

[0541] The device sends collected demographic information and behavioral data to the server.

[0542] The device encrypts this information and sends it to the server using a security protocol.

[0543] Step 5:

[0544] The server prepares to collect user sentiment data in real time.

[0545] The server activates the emotion data analysis module and issues instructions to activate the device's camera, microphone, and keyboard input speed sensor.

[0546] Step 6:

[0547] A user views specific content within the site.

[0548] The device monitors the user's browsing behavior and records the viewing status of specific pages or products.

[0549] Step 7:

[0550] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[0551] The device collects this emotional data and formats it so that it can be analyzed in real time.

[0552] Step 8:

[0553] The device sends the collected emotional data to the server.

[0554] The device encrypts emotional data and sends it to the server via a secure communication channel.

[0555] Step 9:

[0556] The server analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[0557] The server outputs the analysis results, for example, "Excitement level: 85%".

[0558] Step 10:

[0559] The server integrates emotional data with past behavioral data.

[0560] The server matches emotional data with past browsing and purchase history to analyze what actions specific emotions lead to.

[0561] Step 11:

[0562] The server sets the threshold for emotional states.

[0563] The server uses the integrated data to create rules, such as "show a specific ad if the excitement level is 80% or higher."

[0564] Step 12:

[0565] The user revisits the website or app.

[0566] The device detects the re-access and reactivates the sensor.

[0567] Step 13:

[0568] The device collects the user's current emotional state in real time.

[0569] The device acquires data in real time from the camera, microphone, keyboard input sensor, etc., and sends it to the server.

[0570] Step 14:

[0571] The server analyzes the current sentiment data.

[0572] The server analyzes the emotional data it receives using an AI algorithm and quantifies it.

[0573] Step 15:

[0574] The server compares current sentiment data with past behavioral data to select the most suitable advertisement.

[0575] The server selects highly relevant ads based on emotional state and behavioral data.

[0576] Step 16:

[0577] The server sends the selected advertising data to the device.

[0578] The server encrypts the advertising data and sends it to the device.

[0579] Step 17:

[0580] The device displays advertisements to the user.

[0581] The device displays a specified advertisement on the user's browsing page and records the user's reaction.

[0582] By sequentially carrying out these processing steps, personalized advertising that reflects the user's emotional state in real time can be achieved.

[0583] (Example 1)

[0584] 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."

[0585] While traditional advertising delivery systems possessed technologies to provide personalized ads based on users' demographic information and past behavioral data, they lacked the ability to select ads considering users' real-time emotional states, resulting in limited advertising effectiveness. Furthermore, the accuracy of emotional data collection and analysis was low, often leading to the display of inappropriate ads. Additionally, security concerns arose regarding the transmission and storage of collected data, necessitating secure data processing.

[0586] 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.

[0587] In this invention, the server includes means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what actions specific emotions lead to; means for selecting advertisements based on the user's emotional state and behavioral data; means for displaying the selected advertisements to the user; means for personalizing the content of the advertisements using a generative AI model; and encryption means for securely transmitting the collected data to the server. This makes it possible to analyze the user's real-time emotional state with high accuracy and display the most suitable advertisements based on the results. Furthermore, it enables secure transmission and storage of data, maximizing advertising effectiveness while protecting user privacy.

[0588] "User demographic information" refers to basic statistical information used to identify an individual, such as the user's age, gender, and occupation.

[0589] "Past behavioral data" refers to information about a user's past behavior, such as their website browsing history, purchase history, and search history.

[0590] "Emotional data" refers to data that indicates a user's real-time emotional state and is collected using methods such as facial expressions, tone of voice, and keyboard input speed.

[0591] "Facial recognition" refers to a technology that uses a camera to analyze the movements of a user's face and identify their emotional state.

[0592] "Voice tone analysis" refers to a technology that uses a microphone to analyze the tone and intonation of a user's voice and identify their emotional state.

[0593] "Keyboard input speed" refers to a method of measuring how fast a user types on a keyboard and analyzing their stress and excitement levels based on that measurement.

[0594] A "generative AI model" refers to an artificial intelligence algorithm that has learned from a large amount of data and generates new advertising content based on input data.

[0595] "Data encryption methods" refer to technologies that encrypt data in order to securely transmit collected data over the internet.

[0596] "Ad personalization" refers to generating and displaying ad content that is optimal for each individual user.

[0597] This invention is a system that delivers personalized advertisements by combining user demographic information, past behavioral data, and real-time sentiment data analysis. The system is mainly composed of three components: a terminal, a server, and the user.

[0598] Hardware and software to be used

[0599] Device: Primarily refers to computer devices such as smartphones and personal computers. These devices are equipped with various sensors, including cameras, microphones, and keyboards.

[0600] Server: A server device capable of high-performance data processing. It has database management systems (e.g., MySQL, PostgreSQL) and frameworks for running AI models (e.g., TensorFlow, PyTorch) installed.

[0601] Software: We utilize sentiment analysis software (e.g., Kairos, Microsoft Azure Face API), speech analysis tools (e.g., Google Cloud Speech-to-Text, IBM Watson Speech to Text), and encryption protocols for data transmission (e.g., HTTPS).

[0602] Detailed description of the invention

[0603] Collection of demographic information and behavioral data

[0604] When a user accesses a website or application, their device collects demographic information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.). Demographic information is obtained from the user's profile information, while behavioral data is obtained by analyzing browser cookies and application logs.

[0605] Real-time collection of emotional data

[0606] When a user views a specific product page, various sensors on the device (camera, microphone, keyboard) collect emotional data in real time. The camera performs facial recognition, the microphone analyzes voice tone, and the keyboard measures typing speed. This data is then analyzed using emotional analysis software and voice analysis tools.

[0607] Sending data to the server

[0608] The device sends collected demographic information, historical behavioral data, and real-time sentiment data to the server via a secure protocol (e.g., HTTPS). Communication is encrypted, ensuring data protection.

[0609] Data analysis and ad selection

[0610] The server stores the received data in a database and uses an AI model to analyze the emotional data. Emotional states are quantified (e.g., "Excitement level: 85%") and integrated with past behavioral data. Data analysis tools analyze how specific emotions lead to specific behaviors. Based on set thresholds (e.g., excitement level 80% or higher), the server selects the most suitable advertisement from the advertising database.

[0611] Ad delivery

[0612] The server sends the selected advertisement to the device, and the device displays that advertisement to the user. For example, it may be displayed as a banner ad on a webpage.

[0613] Specific example

[0614] For example, let's consider a scenario where user A accesses a sports website and views a page for new running shoes.

[0615] 1. User A accesses the website and views the running shoes page.

[0616] 2. The device obtains demographic information from the user's profile and analyzes past behavioral data.

[0617] 3. The device captures the user's facial expressions with its camera and detects their state of excitement using emotion analysis software. The microphone analyzes the user's voice tone, and the keyboard input speed is also monitored.

[0618] 4. Encrypt the data collected by the device and send it securely to the server.

[0619] 5. The server analyzes the data, confirms that the excitement level is 80% or higher, and selects the most suitable running shoe advertisement.

[0620] 6. The server sends an advertisement to the device, and the device displays the advertisement to user A.

[0621] Example of a prompt

[0622] "A 30-year-old man was browsing a sports equipment website and showed an excited expression. Please generate an ad for running shoes that would be suitable for this user."

[0623] Thus, the system of the present invention can deliver optimal advertisements to users in real time by considering the user's instantaneous emotional state and integrating it with past behavioral data. This makes it possible to provide a more personalized experience and maximize the effectiveness of advertisements. The above describes a specific embodiment for carrying out the invention.

[0624] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0625] Step 1:

[0626] Users access websites and applications.

[0627] The device collects the user's demographic information (age, gender, occupation). User registration information and profile information are required as input, and demographic information is prepared as output. Specifically, the device retrieves information from the user's initial registration data and web browser cookies.

[0628] Step 2:

[0629] The device collects past behavioral data (browsing history, purchase history). The input is the user's browser history and application log data, and the output is generated as past behavioral data. Specifically, the device analyzes browser cookies and uses log data from the server.

[0630] Step 3:

[0631] The device utilizes facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time. Inputs include camera video, audio, and keyboard input data, and the output is analyzed emotion data. Specifically, the camera captures the user's facial expressions, which are then analyzed by emotion analysis software. The microphone is used to analyze voice tone, and the keyboard input speed is measured.

[0632] Step 4:

[0633] The device collects demographic information, historical behavioral data, and real-time sentiment data, which are then sent to a server via the internet. The input is pre-collected data, and the output is encrypted data to be sent. Specifically, the device uses the HTTPS protocol to encrypt the data and securely transmit it to the server.

[0634] Step 5:

[0635] The server stores the received data in a database. The input includes demographic information, behavioral data, and sentiment data, and the output is the data stored in the database. Specifically, the server uses a database management system to securely store the data.

[0636] Step 6:

[0637] The server analyzes incoming data using an AI model and quantifies the emotional state. The input is stored emotional data, and the output is a quantified emotional state. Specifically, the server runs an AI model (e.g., TensorFlow or PyTorch) and converts the emotional state into a numerical value such as "Excitement Level: 85%".

[0638] Step 7:

[0639] The server integrates emotional data and past behavioral data to analyze how specific emotions lead to certain behaviors. The input consists of quantified emotional and behavioral data, and the output is the analysis of these relationships. Specifically, data analysis tools (e.g., Apache Hadoop, Spark) are used to integrate and analyze this data.

[0640] Step 8:

[0641] The server selects the most suitable advertisement based on the user's emotional state and behavioral data. The input is the analysis results, and the output is the selected advertisement. Specifically, the server retrieves appropriate advertisements from the ad database based on a set threshold (e.g., excitement level of 80% or higher).

[0642] Step 9:

[0643] The server sends selected advertisements to the device. The input is the selected advertisement data, and the output is a transmission completion status. Specifically, the server encrypts the data again using the HTTPS protocol and sends it to the device.

[0644] Step 10:

[0645] The device displays the received advertisement to the user. The input is the transmitted advertisement data, and the output is the advertisement displayed on the user's screen. Specifically, the device prepares to display the advertisement as a banner ad on a webpage and then displays it to the user.

[0646] (Application Example 1)

[0647] 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 glasses 214 will be referred to as the "terminal."

[0648] Traditional advertising delivery systems only delivered ads based on users' demographic information and past behavioral data, without considering the real-time, fluctuating emotional states of users. This made it difficult to deliver the most relevant ads to users, resulting in decreased advertising effectiveness. Furthermore, there was insufficient technology to accurately collect and analyze real-time emotional data, including users' facial expressions and tone of voice.

[0649] 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.

[0650] In this invention, the server includes means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to; means for selecting advertisements based on the user's emotional state and behavioral data; means for displaying the selected advertisements to the user; means for analyzing the user's facial expressions and voice tone in real time using a smartphone; and means for selecting advertisements to display based on the emotional data analyzed in real time. This makes it possible to deliver optimal advertisements based on user demographic information and past behavioral data, as well as emotional data that changes in real time.

[0651] "Demographic information" refers to information that indicates a user's social and economic attributes, such as age, gender, occupation, and place of residence.

[0652] "Behavioral data" refers to data about a user's digital activities, such as their past website browsing history and purchase history.

[0653] "Emotional data" refers to data that indicates a user's psychological and emotional state, obtained from the user's facial expressions, voice tone, keyboard input speed, and other factors.

[0654] "Facial recognition" is a technology that analyzes the features of a user's face from camera footage to identify their emotional state.

[0655] "Voice tone analysis" is a technology that analyzes the pitch, intensity, and rhythm of a user's voice to infer their emotional state.

[0656] "Keyboard input speed" refers to data that measures the speed at which a user types on a keyboard and is used to estimate their emotional state.

[0657] "Ad selection methods" refer to algorithms and methods that select the most suitable advertisements for a user based on analyzed data.

[0658] A "smartphone" is a portable device equipped with advanced computing and communication capabilities, as well as sensors such as a camera and microphone.

[0659] "Real-time" refers to processing and analyzing data at a time close to the moment when a user's actions or situations occur.

[0660] A "server" is a computer system that collects, analyzes, stores, and distributes data over a network.

[0661] This invention is a system that collects and analyzes user demographic information, past behavioral data, and real-time emotional data to deliver personalized advertisements. This system demonstrates a method for collecting emotional data in real time using a user's smartphone, and selecting and displaying advertisements based on the analysis results.

[0662] System Configuration

[0663] 1. Terminal

[0664] Smartphones are used as a means of collecting users' demographic information and past behavioral data.

[0665] Smartphones are equipped with cameras and microphones, which allow them to analyze the user's facial expressions and voice tone in real time.

[0666] The device sends the collected data to the server.

[0667] 2. Server

[0668] The server analyzes the received demographic information, past behavioral data, and sentiment data.

[0669] Based on the analyzed data, we analyze what kinds of behaviors are linked to specific emotions.

[0670] An algorithm is executed to select advertisements based on the user's emotional state and behavioral data.

[0671] The selected advertisements are sent to the device.

[0672] 3. User

[0673] Users operate their smartphones to access websites and applications.

[0674] User behavior and emotions are recorded and analyzed in real time.

[0675] Program processing

[0676] Terminal processing

[0677] The smartphone's camera captures the user's facial expressions, and the microphone collects their voice tone.

[0678] The collected emotional data, demographic information, and past behavioral data are sent to the server.

[0679] Server Processing

[0680] The server analyzes the received data and integrates emotional and behavioral data.

[0681] Set thresholds for emotional states to determine whether a particular emotion leads to purchasing behavior.

[0682] Based on the results of integrated analysis, the most suitable advertisements are selected for each user.

[0683] The selected advertisements are sent to the device, and the device displays the advertisements to the user.

[0684] Specific hardware and software to be used

[0685] hardware

[0686] Smartphones: Collect user data using cameras and microphones.

[0687] Server: Stores, analyzes, and selects advertisements for data.

[0688] software

[0689] OpenCV: Analyzes a user's facial expressions from smartphone camera footage.

[0690] EmotionRecognition (a virtual emotion recognition library): Recognizes the user's emotional state.

[0691] UserDataProcessor (a virtual user data processing library): Processes user demographic information and past behavioral data.

[0692] AdSelector (virtual ad selection library): Selects ads based on analyzed data.

[0693] Specific example

[0694] For example, when user A opens a shopping app, the process proceeds as follows:

[0695] 1. User A opens a shopping app on their smartphone.

[0696] 2. The smartphone camera captures user A's facial expressions, and the microphone analyzes their voice tone.

[0697] 3. The collected data is sent to the server and analyzed in real time.

[0698] 4. The server integrates user A's current emotional state with past behavioral data and detects that user A is in an excited state.

[0699] 5. The server determines that the excited state will lead to purchasing behavior and selects the most suitable advertisement (for example, a promotion for new sneakers).

[0700] 6. The selected advertisement is sent to the smartphone and displayed to User A.

[0701] Example of a prompt

[0702] "Create a program that recognizes a user's emotional state from their facial expressions and tone of voice when they open a shopping app. Integrate this with their past purchase history and display personalized ads in real time."

[0703] Thus, the present invention takes into account the user's instantaneous emotional state to achieve highly accurate personalized advertising and maximize the effectiveness of the advertisement.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice tone in real time.

[0707] Input: Camera video data, audio data

[0708] Specific operation: The smartphone's camera captures the user's face, and the microphone records audio. If necessary, lighting conditions and camera focus are automatically adjusted.

[0709] Output: Captured facial expression data and voice tone data

[0710] Step 2:

[0711] The device analyzes captured facial expression data and voice tone data to identify the user's emotional state.

[0712] Input: Facial expression data, voice tone data

[0713] Specific operation: It uses OpenCV to analyze facial features and the EmotionRecognition library to determine emotional states (e.g., joy, sadness, excitement). In speech tone analysis, it analyzes pitch, volume, rhythm, etc., to estimate emotional states.

[0714] Output: Analyzed emotion data (e.g., "Excited state: 85%")

[0715] Step 3:

[0716] The device sends analyzed sentiment data, user demographic information, and past behavioral data to the server.

[0717] Input: Sentimental data, demographic information, historical behavioral data

[0718] Specific actions: Before sending user information to the server in bulk, the data format is prepared (e.g., converted to JSON format). Then, encryption is performed to ensure secure data transfer.

[0719] Output: Integrated data sent to the server

[0720] Step 4:

[0721] The server analyzes demographic information, past behavioral data, and emotional data it receives to determine how specific emotions lead to certain behaviors.

[0722] Input: Demographic information, historical behavioral data, sentiment data

[0723] Specific operation: The server stores the received data in a database and performs analysis using an AI algorithm. A key point is that it analyzes cases where, for example, "excitement" is strongly correlated with "purchasing behavior."

[0724] Output: Analysis results (Example: "People in an excited state are more likely to purchase expensive items")

[0725] Step 5:

[0726] The server selects the most suitable advertisement based on the user's emotional state and behavioral data.

[0727] Input: Analysis results, emotional state, behavioral data

[0728] Specific operation: Based on the analysis results, a selection algorithm is used to choose the most suitable advertisement from multiple options (e.g., "If the user is excited, display an advertisement for sports equipment"). Past behavioral data and demographic information are also taken into account to refine the selection process.

[0729] Output: Selected ad data

[0730] Step 6:

[0731] The server sends the selected advertisement to the device, and the device displays the advertisement to the user.

[0732] Input: Selected ad data

[0733] Specific operation: The server sends the selected advertisement to the device, and the device displays the advertisement. Multiple display formats are possible, such as banner ads, pop-up ads, and video ads, but the advertisement will be displayed in a format that does not detract from the user experience.

[0734] Output: Advertisements displayed on the device

[0735] This allows users to receive personalized ads in real time, improving the effectiveness of those ads.

[0736] 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.

[0737] This invention provides a system that enables more personalized advertising delivery by combining the collection and analysis of user demographic information and past behavioral data with real-time sentiment data analysis. The system based on this invention mainly consists of four components: a terminal, a server, a user, and a sentiment engine.

[0738] System Configuration

[0739] 1. The terminal collects the user's demographic information and past behavioral data and sends it to the server.

[0740] The device also features various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time.

[0741] 2. The emotion engine analyzes the collected emotion data and expresses the user's specific emotional state numerically (e.g., "Excitement level: 85%").

[0742] The emotion engine comprehensively analyzes emotional states from multiple data sources, including facial recognition, voice tone analysis, and keyboard input speed.

[0743] 3. The server analyzes the received data and integrates emotional data with past behavioral data.

[0744] The server then uses this integrated data to select advertisements and sends them to the device.

[0745] 4. The user operates the device and accesses websites and applications.

[0746] User behavior and emotions are recorded and analyzed in real time.

[0747] Program processing and specific examples

[0748] When a user accesses a website

[0749] A user accesses a sports goods website.

[0750] The device collects the user's demographic information (e.g., age, gender, occupation) and past behavioral data (e.g., browsing history, purchase history).

[0751] The device sends the collected data to the server.

[0752] The server saves the received data to prepare for future access.

[0753] Real-time collection of emotional data

[0754] A user views a specific product page on a website.

[0755] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[0756] The device transmits this emotional data to the emotion engine in real time.

[0757] Analysis and recognition of emotional data

[0758] The emotion engine analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[0759] The emotion engine sends the analysis results to the server.

[0760] We integrate accumulated emotional data with past behavioral data to analyze the impact of specific emotional states on behavior.

[0761] Integrating emotions and behaviors and ad selection

[0762] The server integrates emotional data with past behavioral data to analyze the relationship between a user's specific emotional state and their actions.

[0763] The server sets emotional state thresholds (e.g., "Show a specific ad if the excitement level is 80% or higher") and selects the ads.

[0764] The server selects the most suitable advertisement and sends it to the device.

[0765] Ad delivery

[0766] The server sends the selected advertisement to the device.

[0767] The device displays advertisements to the user (e.g., an ad banner for running shoes appears on a webpage).

[0768] Specific example

[0769] For example, when user A accesses a sports website and views a page for new running shoes, the process proceeds as follows:

[0770] 1. User A accesses the website.

[0771] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[0772] 3. The device sends this data to the server.

[0773] 4. The server analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[0774] 5. The server uses an emotion engine to integrate with past data and analyze that "excitement levels" have a high correlation with running shoe purchasing behavior.

[0775] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0776] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to user A.

[0777] Thus, the system of the present invention utilizes real-time emotional data collected by the emotion engine to achieve more accurate and personalized ad delivery. This allows for the provision of effective ads that reflect the user's emotional state, maximizing the effectiveness of the ads.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] Users access websites and apps.

[0781] The device detects user access and records access logs.

[0782] Step 2:

[0783] The device collects the user's demographic information.

[0784] The device reads demographic information such as age, gender, and occupation from the user profile.

[0785] Step 3:

[0786] The device collects data on the user's past behavior.

[0787] The system reads data from a database, including pages and categories the device has previously viewed, as well as its past purchase history.

[0788] Step 4:

[0789] The device sends collected demographic information and behavioral data to the server.

[0790] The device encrypts this information and sends it to the server using a security protocol.

[0791] Step 5:

[0792] The server prepares to collect user sentiment data in real time.

[0793] The server activates the emotion data analysis module and issues instructions to activate the device's camera, microphone, and keyboard input speed sensor.

[0794] Step 6:

[0795] A user views specific content within the site.

[0796] The device monitors the user's browsing behavior and records the viewing status of specific pages or products.

[0797] Step 7:

[0798] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[0799] The device transmits this emotional data to the emotion engine in real time.

[0800] Step 8:

[0801] The emotion engine analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[0802] The emotion engine outputs analysis results such as "Excitement level: 85%".

[0803] Step 9:

[0804] The emotion engine sends the analysis results to the server.

[0805] The emotion engine encrypts the analysis results and sends them to the server via a secure communication channel.

[0806] Step 10:

[0807] The server integrates emotional data with past behavioral data.

[0808] The server matches emotional data with past browsing and purchase history to analyze what actions specific emotions lead to.

[0809] Step 11:

[0810] The server sets the threshold for emotional states.

[0811] The server uses the integrated data to create rules, such as "show a specific ad if the excitement level is 80% or higher."

[0812] Step 12:

[0813] The user revisits the website or app.

[0814] The device detects the re-access and reactivates the sensor.

[0815] Step 13:

[0816] The device collects the user's current emotional state in real time.

[0817] The device acquires data in real time from the camera, microphone, keyboard input sensor, etc., and sends it to the server.

[0818] Step 14:

[0819] The emotion engine analyzes the current emotional data.

[0820] The emotion engine analyzes the received emotional data using an AI algorithm and quantifies it.

[0821] Step 15:

[0822] The server compares current sentiment data with past behavioral data to select the most suitable advertisement.

[0823] The server selects highly relevant ads based on emotional state and behavioral data.

[0824] Step 16:

[0825] The server sends the selected advertising data to the device.

[0826] The server encrypts the advertising data and sends it to the device.

[0827] Step 17:

[0828] The device displays advertisements to the user.

[0829] The device displays a specified advertisement on the user's browsing page and records the user's reaction.

[0830] By sequentially carrying out these processing steps, personalized advertising that reflects the user's emotional state in real time can be achieved.

[0831] (Example 2)

[0832] 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".

[0833] Traditional advertising delivery systems selected ads based on users' past browsing and purchase history, but they could not consider users' emotional states in real time. Therefore, it was difficult to deliver personalized ads that responded to users' instantaneous interests and psychological states. Furthermore, there was a lack of technology for centralized analysis of collected data and appropriate ad selection based on the results. There was a need to solve these problems and deliver more accurate ads in real time based on users' emotional states and past behavioral data.

[0834] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user attribute information and past behavioral data, means for analyzing the user's emotional data in real time, and means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to. This makes it possible to select advertisements based on the user's emotional state and behavioral data and to display the selected advertisements to the user in real time.

[0835] "Attribute information" refers to basic profile information such as the user's age, gender, and occupation.

[0836] "Behavioral data" refers to data related to a user's past actions, such as their website browsing history and purchase history.

[0837] "Emotional data" refers to information about the user's emotional state, obtained in real time from factors such as facial expressions, voice tone, and keyboard input speed.

[0838] "Real-time" refers to the process of closely monitoring and analyzing user behavior and emotions and reflecting them immediately.

[0839] "Ad selection" refers to the process of selecting the most suitable advertisement by combining user attribute information, behavioral data, and emotional data.

[0840] "Analysis" refers to the process of analyzing collected emotional and behavioral data using AI algorithms or other methods to derive specific conclusions.

[0841] "Integration" refers to the process of combining different types of data (such as emotional data and behavioral data) and treating them as a single data set.

[0842] A "threshold" refers to a numerical value that serves as a standard for identifying a specific emotional state.

[0843] "Display" refers to the process of presenting selected advertisements on a user's device in a format that the user can see.

[0844] This invention is a system that collects and analyzes user attribute information and past behavioral data, as well as user sentiment data in real time, and delivers personalized advertisements based on that data. The system mainly consists of four components: a server, a terminal, a user, and a sentiment engine.

[0845] System Configuration

[0846] 1. Collection of user behavior data

[0847] The device collects user attribute information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.) when the user accesses a website or application, and sends this information to the server. For example, when a user visits a sports goods website and searches for running shoes, that information is collected at the device.

[0848] 2. Real-time collection of emotional data

[0849] When a user views a specific product page, the device recognizes the user's facial expressions via the camera, analyzes the user's voice tone via the microphone, and measures the keyboard typing speed. This emotional data is sent to an emotion engine in real time. For example, it measures whether the user is excited when viewing a page about running shoes.

[0850] 3. Analysis of emotional data

[0851] The emotion engine uses AI algorithms to analyze collected emotional data and quantify the user's emotional state. For example, it might output a specific numerical value such as "Excitement level: 85%". The analysis results are then sent to the server.

[0852] 4. Data Integration and Ad Selection

[0853] The server integrates received emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors. For example, if a user who has frequently purchased running shoes in the past is determined to be in an "excited state," an advertisement for running shoes will be displayed. The server sets thresholds for emotional states (e.g., "excitement level of 80% or higher") and selects the most appropriate advertisement based on these thresholds.

[0854] 5. Ad delivery

[0855] The selected advertisements are sent from the server to the device, and the device displays the advertisements to the user. For example, an advertisement banner for running shoes is displayed in a prominent position on the webpage.

[0856] Hardware and software used

[0857] Device: A device used to collect and transmit user data and sentiment data (such as a PC, smartphone, or tablet).

[0858] Camera: A sensor used to recognize the user's facial expressions in real time.

[0859] Microphone: A voice input device used to analyze the tone of the user's voice.

[0860] Keyboard: An input device used to measure a user's typing speed.

[0861] Server: A central facility for integrating and analyzing collected data, and for selecting and delivering advertisements.

[0862] Emotion Engine: A software module equipped with AI algorithms for analyzing emotional data.

[0863] Specific example

[0864] For example, when user A accesses a sports website and views a page for new running shoes, the process is as follows:

[0865] 1. User A accesses the website.

[0866] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[0867] 3. The device sends this data to the server.

[0868] 4. The emotion engine analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[0869] 5. The server integrates the results of the emotion engine analysis with past data and analyzes that "excitement" has a high correlation with the purchasing behavior of running shoes.

[0870] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0871] 7. The server sends the selected advertisement to user A's device, and the advertisement is displayed.

[0872] Example of a prompt

[0873] Please build a system that collects and analyzes user attribute information (age, gender, occupation), past behavioral data (browsing history, purchase history), and real-time sentiment data (facial recognition, voice tone, keyboard input speed), and delivers personalized advertisements based on this data.

[0874] This enables highly accurate ad delivery based on users' emotional states and behavioral data. This system not only improves the user experience but also maximizes the effectiveness of the ads.

[0875] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0876] Step 1: Collecting user behavior data

[0877] When a user accesses a website or application, the device obtains user attribute information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.) as input. As a result of this data processing, the collected information is sent to the server. Specifically, the web browser reads the user's profile data and browsing history from cookies, etc., and sends it to the server via an HTTP request.

[0878] Step 2: Collecting real-time sentiment data

[0879] When a user views a specific product page, the device uses its camera to capture the user's facial expressions, its microphone to record the tone of their voice, and its keyboard typing speed to measure the input. As a result of this data processing, the emotional data collected in real time is sent to an emotion engine. Specifically, the device's camera analyzes facial expressions using facial recognition software, the microphone evaluates the tone of voice using voice analysis software, and the keyboard stroke speed monitors the typing motion.

[0880] Step 3: Analysis of emotional data

[0881] The emotion engine receives emotional data collected as input and analyzes it using an AI algorithm. As a result of this data processing, the user's emotional state is quantified (e.g., "Excitement level: 85%"). The analyzed results are sent to the server. Specifically, the emotion engine inputs the received facial image, audio clip, and typing data into a neural network, which then analyzes this data and maps it to a specific emotional state.

[0882] Step 4: Data Integration and Ad Selection

[0883] The server integrates the sentiment data received as input with past behavioral data. As a result of this data processing, it can analyze how specific emotions lead to specific actions. For example, if past data shows a high correlation between "excitement" and the purchase of running shoes, it will select a specific advertisement. Specifically, the server compares past behavioral data stored in the integrated database with real-time sentiment data and recommends advertising campaigns that meet pre-set thresholds.

[0884] Step 5: Ad Delivery

[0885] The server holds the selected advertising data as input and sends it to the terminal. As a result of this data transfer, the terminal displays the advertisement to the user. Specifically, the server sends JSON data containing the URL and display instructions of the selected advertisement to the terminal, and the terminal's browser parses this data to display the advertisement banner or pop-up.

[0886] (Application Example 2)

[0887] 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."

[0888] Traditional advertising systems only displayed personalized ads based on users' demographic information and past behavioral data. However, this limited the accuracy and effectiveness of the ads. Therefore, there is a need for a system that reflects users' real-time emotional states and delivers ads with a higher degree of personalization. Another challenge is the lack of a means to display ads in real time using smart devices while users are out and about.

[0889] 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. In this invention, the server includes means for collecting the user's demographic information and past behavioral data, means for analyzing the user's emotional data in real time, and means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to. As a result, the emotional state is reflected in the selection of advertisements, enabling the delivery of optimal advertisements tailored to the user's current situation. Furthermore, by means for collecting the user's real-time emotional data using a smart device and displaying advertisements through a visual display device, it is possible to provide advertisements suitable for the user even when they are out and about.

[0890] "User demographic information" refers to information that indicates personal characteristics such as age, gender, occupation, and region.

[0891] "Past behavioral data" refers to data that describes a user's past behavior, such as their website browsing history, purchase history, and search history.

[0892] "Methods for analyzing user emotional data in real time" refer to methods that use cameras, microphones, keyboard input speed sensors, etc., to analyze the user's facial expressions, voice tone, input speed, etc., in real time and quantify their emotional state.

[0893] "Emotional state" refers to a numerical representation of the emotions a user is feeling at a particular moment, such as excitement, joy, or sadness.

[0894] "Methods for selecting advertisements" refer to methods for selecting the most suitable advertisements that match the user's emotional state and behavioral data, based on integrated data.

[0895] "Means of displaying selected advertisements to users" refers to means of displaying selected advertisements on the devices that users view. This includes, for example, displaying advertisements on smart glasses or smartphone screens.

[0896] A "smart device" is a multi-functional device that users can carry around, and it has built-in features such as a camera, microphone, and sensors. Specific examples include smartphones and smart glasses.

[0897] "Visual display devices" is a general term for display devices that allow users to visually confirm information. Examples include the display screens of head-mounted displays and smart glasses.

[0898] This invention is a system that achieves more personalized advertising delivery by combining the collection and analysis of user demographic information and past behavioral data with real-time sentiment data analysis. This system mainly consists of the following four components: terminal, server, user, and sentiment engine.

[0899] 1. Device functions

[0900] The device collects the user's demographic information and past behavioral data and sends it to a server. It also features various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time. This allows it to capture emotional data such as the user's facial expressions, voice tone, and typing speed.

[0901] The hardware used includes cameras (e.g., Sony IMX586) and microphones (e.g., Intel RealSense D435). This data is then analyzed using software such as OpenCV and NLP toolkits.

[0902] 2. Function of the Emotion Engine

[0903] The emotion engine analyzes collected emotional data and expresses the user's specific emotional state numerically. This engine comprehensively analyzes emotional states from multiple data sources, including facial recognition, voice tone analysis, and keyboard input speed. For example, it uses an AI algorithm to quantify the emotional state as "Excitement Level: 85%".

[0904] 3. Server Functions

[0905] The server analyzes the data received from the device and integrates emotional data with past behavioral data. This allows it to analyze how specific emotions lead to certain behaviors. It also selects the most suitable advertisements based on the user's emotional state and behavioral data and sends those advertisements to the device.

[0906] 4. User actions

[0907] Users operate their devices and access websites and applications. Their actions and emotions are recorded and analyzed in real time. This allows for the delivery of advertisements tailored to the user's current situation.

[0908] Specific example

[0909] For example, when a user accesses a sports goods website, the process proceeds as follows:

[0910] 1. The user accesses the website.

[0911] 2. The device collects the user's past browsing history (e.g., frequently searching for running shoes) and demographic information.

[0912] 3. The device sends this data to the server.

[0913] 4. The server analyzes the user's facial expressions from the camera footage in real time and detects if they are in an "excited state."

[0914] 5. The server uses an emotion engine to integrate with past data and analyze that "excitement levels" have a high correlation with running shoe purchasing behavior.

[0915] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0916] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to the user.

[0917] This system utilizes real-time sentiment data collected by an emotion engine to deliver personalized ads with greater accuracy. This allows for the provision of effective ads that reflect the user's emotional state, maximizing the impact of advertising.

[0918] Example of a prompt

[0919] Design a system that captures a user's gaze, facial expressions, and voice tone in real time while they are wearing smart glasses, and analyzes this data using an emotion engine. The analysis results, along with demographic information and past behavioral data, are sent to a server, and the system then displays the most relevant advertisements in augmented reality (AR) within the user's field of view.

[0920] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0921] Step 1:

[0922] The device collects the user's demographic information and past behavioral data. Input includes the user's age, gender, occupation, region, past browsing history, and purchase history. This information is collected as data and temporarily stored in internal storage to prepare for the next step.

[0923] Step 2:

[0924] The device sends collected demographic information and past behavioral data to the server. A communication protocol (e.g., HTTPS) is used for this transmission. The data is sent in JSON format, which the server stores in a database and uses for analysis.

[0925] Step 3:

[0926] The device collects real-time emotional data from the user. To achieve this, it captures facial expressions with its built-in camera, records voice tone with its microphone, and measures keyboard and touchscreen input speed. The inputs include video data, audio data, and input speed data, all of which are processed in real time.

[0927] Step 4:

[0928] The device sends collected emotional data to the emotion engine in real time. The emotion engine uses AI algorithms (e.g., deep learning models) to analyze facial expressions, voice tone, and input speed data. The analyzed emotional data is quantified (e.g., "Excitement level: 85%") and sent to the next step.

[0929] Step 5:

[0930] The server integrates emotional data with historical behavioral data to analyze how specific emotions lead to certain behaviors. Specifically, it analyzes correlations based on demographic information, historical behavioral data, and real-time emotional data to evaluate the extent to which specific emotional states influence past purchasing behavior. Statistical analysis and machine learning models are used in this process.

[0931] Step 6:

[0932] The server selects advertisements based on the user's emotional state and behavioral data. Here, advertisements corresponding to the emotional state are selected from the database. For example, if the "excitement level" is high, advertisements for sports equipment will be selected. The selected advertisements are then sent to the user in the next step.

[0933] Step 7:

[0934] The server sends the selected advertisement to the device. A communication protocol (e.g., HTTPS) is used for this transmission. The advertisement data is sent in the form of images, text, links, etc.

[0935] Step 8:

[0936] The device displays advertisements to the user. Specifically, advertisements are displayed in the user's field of view using AR (augmented reality) through visual display devices such as smart glasses and smartphones. This display method is visually natural for the user and enables effective ad delivery.

[0937] Example of a prompt

[0938] Design a system that captures a user's gaze, facial expressions, and voice tone in real time while they are wearing smart glasses, and analyzes this data using an emotion engine. The analysis results, along with demographic information and past behavioral data, are sent to a server, and the system then displays the most relevant advertisements in augmented reality (AR) within the user's field of view.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] [Third Embodiment]

[0943] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0944] 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.

[0945] 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).

[0946] 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.

[0947] 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.

[0948] 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).

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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".

[0955] This invention is a system that integrates the collection and analysis of user demographic information and past behavioral data, as well as real-time sentiment data analysis, to realize personalized advertising delivery based on this information. The system based on this invention mainly consists of three components: a terminal, a server, and the user.

[0956] System Configuration

[0957] 1. The device collects the user's demographic information and past behavioral data and sends it to the server. The device is also equipped with various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time and send that data to the server.

[0958] 2. The server analyzes the received data and integrates sentiment data with past behavioral data. The server then selects advertisements based on the integrated data and sends those advertisements to the device.

[0959] 3. Users operate their devices and access websites and applications. User behavior and emotions are recorded and analyzed in real time.

[0960] Program processing and specific examples

[0961] When a user accesses a website

[0962] A user accesses a sports goods website.

[0963] The device collects the user's demographic information (e.g., age, gender, occupation) and past behavioral data (e.g., browsing history, purchase history).

[0964] The device sends the collected data to the server.

[0965] The server saves the received data to prepare for future access.

[0966] Real-time collection of emotional data

[0967] A user views a specific product page on a website.

[0968] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[0969] The device sends this emotional data to the server in real time.

[0970] The server uses an AI algorithm to analyze the emotional data it receives and quantifies the user's emotional state (e.g., "Excitement level: 85%").

[0971] Integrating emotions and behaviors and ad selection

[0972] The server integrates emotional data with past behavioral data to analyze the relationship between a user's specific emotional state and their actions.

[0973] The server sets emotional state thresholds and defines rules such as, "If the excitement level is 80% or higher, display a specific advertisement."

[0974] The server can select the most relevant advertisements based on the user's current emotional state and past data (for example, if a user is excited while browsing sports equipment, it will select an advertisement for the latest high-performance running shoes).

[0975] Ad delivery

[0976] The server sends the selected advertisement to the device.

[0977] The device displays advertisements selected by the user (e.g., an ad banner for running shoes appears on the webpage).

[0978] Specific example

[0979] For example, when user A accesses a sports website and views a page for new running shoes, the process proceeds as follows:

[0980] 1. User A accesses the website.

[0981] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[0982] 3. The device sends this data to the server.

[0983] 4. The server analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[0984] 5. The server integrates historical data and analyzes whether "excitement levels" have a high correlation with running shoe purchasing behavior.

[0985] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[0986] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to user A.

[0987] Thus, the system of the present invention can deliver optimal advertisements to users in real time by considering the user's instantaneous emotional state and integrating it with past behavioral data. This makes it possible to provide a more personalized experience and maximize the effectiveness of advertisements.

[0988] The following describes the processing flow.

[0989] Step 1:

[0990] Users access websites and apps.

[0991] The device detects user access and records access logs.

[0992] Step 2:

[0993] The device collects the user's demographic information.

[0994] The device reads demographic information such as age, gender, and occupation from the user profile.

[0995] Step 3:

[0996] The device collects data on the user's past behavior.

[0997] The system reads data from a database, including pages and categories the device has previously viewed, as well as its past purchase history.

[0998] Step 4:

[0999] The device sends collected demographic information and behavioral data to the server.

[1000] The device encrypts this information and sends it to the server using a security protocol.

[1001] Step 5:

[1002] The server prepares to collect user sentiment data in real time.

[1003] The server activates the emotion data analysis module and issues instructions to activate the device's camera, microphone, and keyboard input speed sensor.

[1004] Step 6:

[1005] A user views specific content within the site.

[1006] The device monitors the user's browsing behavior and records the viewing status of specific pages or products.

[1007] Step 7:

[1008] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[1009] The device collects this emotional data and formats it so that it can be analyzed in real time.

[1010] Step 8:

[1011] The device sends the collected emotional data to the server.

[1012] The device encrypts emotional data and sends it to the server via a secure communication channel.

[1013] Step 9:

[1014] The server analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[1015] The server outputs the analysis results, for example, "Excitement level: 85%".

[1016] Step 10:

[1017] The server integrates emotional data with past behavioral data.

[1018] The server matches emotional data with past browsing and purchase history to analyze what actions specific emotions lead to.

[1019] Step 11:

[1020] The server sets the threshold for emotional states.

[1021] The server uses the integrated data to create rules, such as "show a specific ad if the excitement level is 80% or higher."

[1022] Step 12:

[1023] The user revisits the website or app.

[1024] The device detects the re-access and reactivates the sensor.

[1025] Step 13:

[1026] The device collects the user's current emotional state in real time.

[1027] The device acquires data in real time from the camera, microphone, keyboard input sensor, etc., and sends it to the server.

[1028] Step 14:

[1029] The server analyzes the current sentiment data.

[1030] The server analyzes the emotional data it receives using an AI algorithm and quantifies it.

[1031] Step 15:

[1032] The server compares current sentiment data with past behavioral data to select the most suitable advertisement.

[1033] The server selects highly relevant ads based on emotional state and behavioral data.

[1034] Step 16:

[1035] The server sends the selected advertising data to the device.

[1036] The server encrypts the advertising data and sends it to the device.

[1037] Step 17:

[1038] The device displays advertisements to the user.

[1039] The device displays a specified advertisement on the user's browsing page and records the user's reaction.

[1040] By sequentially carrying out these processing steps, personalized advertising that reflects the user's emotional state in real time can be achieved.

[1041] (Example 1)

[1042] 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."

[1043] While traditional advertising delivery systems possessed technologies to provide personalized ads based on users' demographic information and past behavioral data, they lacked the ability to select ads considering users' real-time emotional states, resulting in limited advertising effectiveness. Furthermore, the accuracy of emotional data collection and analysis was low, often leading to the display of inappropriate ads. Additionally, security concerns arose regarding the transmission and storage of collected data, necessitating secure data processing.

[1044] 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.

[1045] In this invention, the server includes means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what actions specific emotions lead to; means for selecting advertisements based on the user's emotional state and behavioral data; means for displaying the selected advertisements to the user; means for personalizing the content of the advertisements using a generative AI model; and encryption means for securely transmitting the collected data to the server. This makes it possible to analyze the user's real-time emotional state with high accuracy and display the most suitable advertisements based on the results. Furthermore, it enables secure transmission and storage of data, maximizing advertising effectiveness while protecting user privacy.

[1046] "User demographic information" refers to basic statistical information used to identify an individual, such as age, gender, and occupation.

[1047] "Past behavioral data" refers to information about a user's past behavior, such as their website browsing history, purchase history, and search history.

[1048] "Emotional data" refers to data that indicates a user's real-time emotional state and is collected using methods such as facial expressions, tone of voice, and keyboard input speed.

[1049] "Facial recognition" refers to a technology that uses a camera to analyze the movements of a user's face and identify their emotional state.

[1050] "Voice tone analysis" refers to a technology that uses a microphone to analyze the tone and intonation of a user's voice and identify their emotional state.

[1051] "Keyboard input speed" refers to a method of measuring how fast a user types on a keyboard and analyzing their stress and excitement levels based on that measurement.

[1052] A "generative AI model" refers to an artificial intelligence algorithm that has learned from a large amount of data and generates new advertising content based on input data.

[1053] "Data encryption methods" refer to technologies that encrypt data in order to securely transmit collected data over the internet.

[1054] "Ad personalization" refers to generating and displaying ad content that is optimal for each individual user.

[1055] This invention is a system that delivers personalized advertisements by combining user demographic information, past behavioral data, and real-time sentiment data analysis. The system is mainly composed of three components: a terminal, a server, and the user.

[1056] Hardware and software to be used

[1057] Device: Primarily refers to computer devices such as smartphones and personal computers. These devices are equipped with various sensors, including cameras, microphones, and keyboards.

[1058] Server: A server device capable of high-performance data processing. It has database management systems (e.g., MySQL, PostgreSQL) and frameworks for running AI models (e.g., TensorFlow, PyTorch) installed.

[1059] Software: We utilize sentiment analysis software (e.g., Kairos, Microsoft Azure Face API), speech analysis tools (e.g., Google Cloud Speech-to-Text, IBM Watson Speech to Text), and encryption protocols for data transmission (e.g., HTTPS).

[1060] Detailed description of the invention

[1061] Collection of demographic information and behavioral data

[1062] When a user accesses a website or application, their device collects demographic information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.). Demographic information is obtained from the user's profile information, while behavioral data is obtained by analyzing browser cookies and application logs.

[1063] Real-time collection of emotional data

[1064] When a user views a specific product page, various sensors on the device (camera, microphone, keyboard) collect emotional data in real time. The camera performs facial recognition, the microphone analyzes voice tone, and the keyboard measures typing speed. This data is then analyzed using emotional analysis software and voice analysis tools.

[1065] Sending data to the server

[1066] The device sends collected demographic information, historical behavioral data, and real-time sentiment data to the server via a secure protocol (e.g., HTTPS). Communication is encrypted, ensuring data protection.

[1067] Data analysis and ad selection

[1068] The server stores the received data in a database and uses an AI model to analyze the emotional data. Emotional states are quantified (e.g., "Excitement level: 85%") and integrated with past behavioral data. Data analysis tools analyze how specific emotions lead to specific behaviors. Based on set thresholds (e.g., excitement level 80% or higher), the server selects the most suitable advertisement from the advertising database.

[1069] Ad delivery

[1070] The server sends the selected advertisement to the device, and the device displays that advertisement to the user. For example, it may be displayed as a banner ad on a webpage.

[1071] Specific example

[1072] For example, let's consider a scenario where user A accesses a sports website and views a page for new running shoes.

[1073] 1. User A accesses the website and views the running shoes page.

[1074] 2. The device obtains demographic information from the user's profile and analyzes past behavioral data.

[1075] 3. The device captures the user's facial expressions with its camera and detects their state of excitement using emotion analysis software. The microphone analyzes the user's voice tone, and the keyboard input speed is also monitored.

[1076] 4. Encrypt the data collected by the device and send it securely to the server.

[1077] 5. The server analyzes the data, confirms that the excitement level is 80% or higher, and selects the most suitable running shoe advertisement.

[1078] 6. The server sends an advertisement to the device, and the device displays the advertisement to user A.

[1079] Example of a prompt

[1080] "A 30-year-old man was browsing a sports equipment website and showed an excited expression. Please generate an ad for running shoes that would be suitable for this user."

[1081] Thus, the system of the present invention can deliver optimal advertisements to users in real time by considering the user's instantaneous emotional state and integrating it with past behavioral data. This makes it possible to provide a more personalized experience and maximize the effectiveness of advertisements. The above describes a specific embodiment for carrying out the invention.

[1082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1083] Step 1:

[1084] Users access websites and applications.

[1085] The device collects the user's demographic information (age, gender, occupation). User registration information and profile information are required as input, and demographic information is prepared as output. Specifically, the device retrieves information from the user's initial registration data and web browser cookies.

[1086] Step 2:

[1087] The device collects past behavioral data (browsing history, purchase history). The input is the user's browser history and application log data, and the output is generated as past behavioral data. Specifically, the device analyzes browser cookies and uses log data from the server.

[1088] Step 3:

[1089] The device utilizes facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time. Inputs include camera video, audio, and keyboard input data, and the output is analyzed emotion data. Specifically, the camera captures the user's facial expressions, which are then analyzed by emotion analysis software. The microphone is used to analyze voice tone, and the keyboard input speed is measured.

[1090] Step 4:

[1091] The device collects demographic information, historical behavioral data, and real-time sentiment data, which are then sent to a server via the internet. The input is pre-collected data, and the output is encrypted data to be sent. Specifically, the device uses the HTTPS protocol to encrypt the data and securely transmit it to the server.

[1092] Step 5:

[1093] The server stores the received data in a database. The input includes demographic information, behavioral data, and sentiment data, and the output is the data stored in the database. Specifically, the server uses a database management system to securely store the data.

[1094] Step 6:

[1095] The server analyzes incoming data using an AI model and quantifies the emotional state. The input is stored emotional data, and the output is a quantified emotional state. Specifically, the server runs an AI model (e.g., TensorFlow or PyTorch) and converts the emotional state into a numerical value such as "Excitement Level: 85%".

[1096] Step 7:

[1097] The server integrates emotional data and past behavioral data to analyze how specific emotions lead to certain behaviors. The input consists of quantified emotional and behavioral data, and the output is the analysis of these relationships. Specifically, data analysis tools (e.g., Apache Hadoop, Spark) are used to integrate and analyze this data.

[1098] Step 8:

[1099] The server selects the most suitable advertisement based on the user's emotional state and behavioral data. The input is the analysis results, and the output is the selected advertisement. Specifically, the server retrieves appropriate advertisements from the ad database based on a set threshold (e.g., excitement level of 80% or higher).

[1100] Step 9:

[1101] The server sends selected advertisements to the device. The input is the selected advertisement data, and the output is a transmission completion status. Specifically, the server encrypts the data again using the HTTPS protocol and sends it to the device.

[1102] Step 10:

[1103] The device displays the received advertisement to the user. The input is the transmitted advertisement data, and the output is the advertisement displayed on the user's screen. Specifically, the device prepares to display the advertisement as a banner ad on a webpage and then displays it to the user.

[1104] (Application Example 1)

[1105] 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."

[1106] Traditional advertising delivery systems only delivered ads based on users' demographic information and past behavioral data, without considering the real-time, fluctuating emotional states of users. This made it difficult to deliver the most relevant ads to users, resulting in decreased advertising effectiveness. Furthermore, there was insufficient technology to accurately collect and analyze real-time emotional data, including users' facial expressions and tone of voice.

[1107] 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.

[1108] In this invention, the server includes means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to; means for selecting advertisements based on the user's emotional state and behavioral data; means for displaying the selected advertisements to the user; means for analyzing the user's facial expressions and voice tone in real time using a smartphone; and means for selecting advertisements to display based on the emotional data analyzed in real time. This makes it possible to deliver optimal advertisements based on user demographic information and past behavioral data, as well as emotional data that changes in real time.

[1109] "Demographic information" refers to information that indicates a user's social and economic attributes, such as age, gender, occupation, and place of residence.

[1110] "Behavioral data" refers to data about a user's digital activities, such as their past website browsing history and purchase history.

[1111] "Emotional data" refers to data that indicates a user's psychological and emotional state, obtained from the user's facial expressions, voice tone, keyboard input speed, and other factors.

[1112] "Facial recognition" is a technology that analyzes the features of a user's face from camera footage to identify their emotional state.

[1113] "Voice tone analysis" is a technology that analyzes the pitch, intensity, and rhythm of a user's voice to infer their emotional state.

[1114] "Keyboard input speed" refers to data that measures the speed at which a user types on a keyboard and is used to estimate their emotional state.

[1115] "Ad selection methods" refer to algorithms and methods that select the most suitable advertisements for a user based on analyzed data.

[1116] A "smartphone" is a portable device equipped with advanced computing and communication capabilities, as well as sensors such as a camera and microphone.

[1117] "Real-time" refers to processing and analyzing data at a time close to the moment when a user's actions or situations occur.

[1118] A "server" is a computer system that collects, analyzes, stores, and distributes data over a network.

[1119] This invention is a system that collects and analyzes users' demographic information, past behavioral data, and real-time emotional data to deliver personalized advertisements. This system demonstrates a method for collecting emotional data in real time using a user's smartphone, and selecting and displaying advertisements based on the analysis results.

[1120] System Configuration

[1121] 1. Terminal

[1122] Smartphones are used as a means of collecting users' demographic information and past behavioral data.

[1123] Smartphones are equipped with cameras and microphones, which allow them to analyze the user's facial expressions and voice tone in real time.

[1124] The device sends the collected data to the server.

[1125] 2. Server

[1126] The server analyzes the received demographic information, past behavioral data, and sentiment data.

[1127] Based on the analyzed data, we analyze what kinds of behaviors are linked to specific emotions.

[1128] An algorithm is executed to select advertisements based on the user's emotional state and behavioral data.

[1129] Selected advertisements are sent to the device.

[1130] 3. User

[1131] Users operate their smartphones to access websites and applications.

[1132] User behavior and emotions are recorded and analyzed in real time.

[1133] Program processing

[1134] Terminal processing

[1135] The smartphone's camera captures the user's facial expressions, and the microphone collects their voice tone.

[1136] The collected emotional data, demographic information, and past behavioral data are sent to the server.

[1137] Server Processing

[1138] The server analyzes the received data and integrates emotional and behavioral data.

[1139] Set thresholds for emotional states to determine whether a particular emotion leads to purchasing behavior.

[1140] Based on the results of integrated analysis, the most suitable advertisements are selected for each user.

[1141] The selected advertisements are sent to the device, and the device displays the advertisements to the user.

[1142] Specific hardware and software to be used

[1143] hardware

[1144] Smartphones: Collect user data using cameras and microphones.

[1145] Server: Stores, analyzes, and selects advertisements for data.

[1146] software

[1147] OpenCV: Analyzes a user's facial expressions from smartphone camera footage.

[1148] EmotionRecognition (a virtual emotion recognition library): Recognizes the user's emotional state.

[1149] UserDataProcessor (a virtual user data processing library): Processes user demographic information and past behavioral data.

[1150] AdSelector (virtual ad selection library): Selects ads based on analyzed data.

[1151] Specific example

[1152] For example, when user A opens a shopping app, the process proceeds as follows:

[1153] 1. User A opens a shopping app on their smartphone.

[1154] 2. The smartphone camera captures user A's facial expressions, and the microphone analyzes their voice tone.

[1155] 3. The collected data is sent to the server and analyzed in real time.

[1156] 4. The server integrates user A's current emotional state with past behavioral data and detects that user A is in an excited state.

[1157] 5. The server determines that the excited state will lead to purchasing behavior and selects the most suitable advertisement (for example, a promotion for new sneakers).

[1158] 6. The selected advertisement is sent to the smartphone and displayed to User A.

[1159] Example of a prompt

[1160] "Create a program that recognizes a user's emotional state from their facial expressions and tone of voice when they open a shopping app. Integrate this with their past purchase history and display personalized ads in real time."

[1161] Thus, the present invention takes into account the user's instantaneous emotional state to achieve highly accurate personalized advertising and maximize the effectiveness of the advertisement.

[1162] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1163] Step 1:

[1164] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice tone in real time.

[1165] Input: Camera video data, audio data

[1166] Specific operation: The smartphone's camera captures the user's face, and the microphone records audio. If necessary, lighting conditions and camera focus are automatically adjusted.

[1167] Output: Captured facial expression data and voice tone data

[1168] Step 2:

[1169] The device analyzes captured facial expression data and voice tone data to identify the user's emotional state.

[1170] Input: Facial expression data, voice tone data

[1171] Specific operation: It uses OpenCV to analyze facial features and the EmotionRecognition library to determine emotional states (e.g., joy, sadness, excitement). In speech tone analysis, it analyzes pitch, volume, rhythm, etc., to estimate emotional states.

[1172] Output: Analyzed emotion data (e.g., "Excited state: 85%")

[1173] Step 3:

[1174] The device sends analyzed sentiment data, user demographic information, and past behavioral data to the server.

[1175] Input: Sentimental data, demographic information, historical behavioral data

[1176] Specific actions: Before sending user information to the server in bulk, the data format is prepared (e.g., converted to JSON format). Then, encryption is performed to ensure secure data transfer.

[1177] Output: Integrated data sent to the server

[1178] Step 4:

[1179] The server analyzes demographic information, past behavioral data, and emotional data it receives to determine how specific emotions lead to certain behaviors.

[1180] Input: Demographic information, historical behavioral data, sentiment data

[1181] Specific operation: The server stores the received data in a database and performs analysis using an AI algorithm. A key point is that it analyzes cases where, for example, "excitement" is strongly correlated with "purchasing behavior."

[1182] Output: Analysis results (Example: "People in an excited state are more likely to purchase expensive items")

[1183] Step 5:

[1184] The server selects the most suitable advertisement based on the user's emotional state and behavioral data.

[1185] Input: Analysis results, emotional state, behavioral data

[1186] Specific operation: Based on the analysis results, a selection algorithm is used to choose the most suitable advertisement from multiple options (e.g., "If the user is excited, display an advertisement for sports equipment"). Past behavioral data and demographic information are also taken into account to refine the selection process.

[1187] Output: Selected ad data

[1188] Step 6:

[1189] The server sends the selected advertisement to the device, and the device displays the advertisement to the user.

[1190] Input: Selected ad data

[1191] Specific operation: The server sends selected advertisements to the device, and the device displays those advertisements. Multiple display formats are possible, such as banner ads, pop-up ads, and video ads, but they will be displayed in a format that does not detract from the user experience.

[1192] Output: Advertisements displayed on the device

[1193] This allows users to receive personalized ads in real time, improving the effectiveness of those ads.

[1194] 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.

[1195] This invention provides a system that enables more personalized advertising delivery by combining the collection and analysis of user demographic information and past behavioral data with real-time sentiment data analysis. The system based on this invention mainly consists of four components: a terminal, a server, a user, and a sentiment engine.

[1196] System Configuration

[1197] 1. The terminal collects the user's demographic information and past behavioral data and sends it to the server.

[1198] The device also features various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time.

[1199] 2. The emotion engine analyzes the collected emotion data and expresses the user's specific emotional state numerically (e.g., "Excitement level: 85%").

[1200] The emotion engine comprehensively analyzes emotional states from multiple data sources, including facial recognition, voice tone analysis, and keyboard input speed.

[1201] 3. The server analyzes the received data and integrates emotional data with past behavioral data.

[1202] The server then uses this integrated data to select advertisements and sends them to the device.

[1203] 4. The user operates the device and accesses websites and applications.

[1204] User behavior and emotions are recorded and analyzed in real time.

[1205] Program processing and specific examples

[1206] When a user accesses a website

[1207] A user accesses a sports goods website.

[1208] The device collects the user's demographic information (e.g., age, gender, occupation) and past behavioral data (e.g., browsing history, purchase history).

[1209] The device sends the collected data to the server.

[1210] The server saves the received data to prepare for future access.

[1211] Real-time collection of emotional data

[1212] A user views a specific product page on a website.

[1213] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[1214] The device transmits this emotional data to the emotion engine in real time.

[1215] Analysis and recognition of emotional data

[1216] The emotion engine analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[1217] The emotion engine sends the analysis results to the server.

[1218] We integrate accumulated emotional data with past behavioral data to analyze the impact of specific emotional states on behavior.

[1219] Integrating emotions and behaviors and ad selection

[1220] The server integrates emotional data with past behavioral data to analyze the relationship between a user's specific emotional state and their actions.

[1221] The server sets emotional state thresholds (e.g., "Show a specific ad if the excitement level is 80% or higher") and selects the ads.

[1222] The server selects the most suitable advertisement and sends it to the device.

[1223] Ad delivery

[1224] The server sends the selected advertisement to the device.

[1225] The device displays advertisements to the user (e.g., an ad banner for running shoes appears on a webpage).

[1226] Specific example

[1227] For example, when user A accesses a sports website and views a page for new running shoes, the process proceeds as follows:

[1228] 1. User A accesses the website.

[1229] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[1230] 3. The device sends this data to the server.

[1231] 4. The server analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[1232] 5. The server uses an emotion engine to integrate with past data and analyze that "excitement levels" have a high correlation with running shoe purchasing behavior.

[1233] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[1234] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to user A.

[1235] Thus, the system of the present invention utilizes real-time emotional data collected by the emotion engine to achieve more accurate and personalized ad delivery. This allows for the provision of effective ads that reflect the user's emotional state, maximizing the effectiveness of the ads.

[1236] The following describes the processing flow.

[1237] Step 1:

[1238] Users access websites and apps.

[1239] The device detects user access and records access logs.

[1240] Step 2:

[1241] The device collects the user's demographic information.

[1242] The device reads demographic information such as age, gender, and occupation from the user profile.

[1243] Step 3:

[1244] The device collects data on the user's past behavior.

[1245] The system reads data from a database, including pages and categories the device has previously viewed, as well as its past purchase history.

[1246] Step 4:

[1247] The device sends collected demographic information and behavioral data to the server.

[1248] The device encrypts this information and sends it to the server using a security protocol.

[1249] Step 5:

[1250] The server prepares to collect user sentiment data in real time.

[1251] The server activates the emotion data analysis module and issues instructions to activate the device's camera, microphone, and keyboard input speed sensor.

[1252] Step 6:

[1253] A user views specific content within the site.

[1254] The device monitors the user's browsing behavior and records the viewing status of specific pages or products.

[1255] Step 7:

[1256] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[1257] The device transmits this emotional data to the emotion engine in real time.

[1258] Step 8:

[1259] The emotion engine analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[1260] The emotion engine outputs analysis results such as "Excitement level: 85%".

[1261] Step 9:

[1262] The emotion engine sends the analysis results to the server.

[1263] The emotion engine encrypts the analysis results and sends them to the server via a secure communication channel.

[1264] Step 10:

[1265] The server integrates emotional data with past behavioral data.

[1266] The server matches emotional data with past browsing and purchase history to analyze what actions specific emotions lead to.

[1267] Step 11:

[1268] The server sets the threshold for emotional states.

[1269] The server uses the integrated data to create rules, such as "show a specific ad if the excitement level is 80% or higher."

[1270] Step 12:

[1271] The user revisits the website or app.

[1272] The device detects the re-access and reactivates the sensor.

[1273] Step 13:

[1274] The device collects the user's current emotional state in real time.

[1275] The device acquires data in real time from the camera, microphone, keyboard input sensor, etc., and sends it to the server.

[1276] Step 14:

[1277] The emotion engine analyzes the current emotional data.

[1278] The emotion engine analyzes the received emotional data using an AI algorithm and quantifies it.

[1279] Step 15:

[1280] The server compares current sentiment data with past behavioral data to select the most suitable advertisement.

[1281] The server selects highly relevant ads based on emotional state and behavioral data.

[1282] Step 16:

[1283] The server sends the selected advertising data to the device.

[1284] The server encrypts the advertising data and sends it to the device.

[1285] Step 17:

[1286] The device displays advertisements to the user.

[1287] The device displays a specified advertisement on the user's browsing page and records the user's reaction.

[1288] By sequentially carrying out these processing steps, personalized advertising that reflects the user's emotional state in real time can be achieved.

[1289] (Example 2)

[1290] 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."

[1291] Traditional advertising delivery systems selected ads based on users' past browsing and purchase history, but they could not consider users' emotional states in real time. Therefore, it was difficult to deliver personalized ads that responded to users' instantaneous interests and psychological states. Furthermore, there was a lack of technology for centralized analysis of collected data and appropriate ad selection based on the results. There was a need to solve these problems and deliver more accurate ads in real time based on users' emotional states and past behavioral data.

[1292] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user attribute information and past behavioral data, means for analyzing the user's emotional data in real time, and means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to. This makes it possible to select advertisements based on the user's emotional state and behavioral data and to display the selected advertisements to the user in real time.

[1293] "Attribute information" refers to basic profile information such as the user's age, gender, and occupation.

[1294] "Behavioral data" refers to data related to a user's past actions, such as their website browsing history and purchase history.

[1295] "Emotional data" refers to information about the user's emotional state, obtained in real time from factors such as facial expressions, voice tone, and keyboard input speed.

[1296] "Real-time" refers to the process of closely monitoring and analyzing user behavior and emotions and reflecting them immediately.

[1297] "Ad selection" refers to the process of selecting the most suitable advertisement by combining user attribute information, behavioral data, and emotional data.

[1298] "Analysis" refers to the process of analyzing collected emotional and behavioral data using AI algorithms or other methods to derive specific conclusions.

[1299] "Integration" refers to the process of combining different types of data (such as emotional data and behavioral data) and treating them as a single data set.

[1300] A "threshold" refers to a numerical value that serves as a standard for identifying a specific emotional state.

[1301] "Display" refers to the process of presenting selected advertisements on a user's device in a format that the user can see.

[1302] This invention is a system that collects and analyzes user attribute information and past behavioral data, as well as user sentiment data in real time, and delivers personalized advertisements based on that data. The system mainly consists of four components: a server, a terminal, a user, and a sentiment engine.

[1303] System Configuration

[1304] 1. Collection of user behavior data

[1305] The device collects user attribute information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.) when the user accesses a website or application, and sends this information to the server. For example, when a user visits a sports goods website and searches for running shoes, that information is collected at the device.

[1306] 2. Real-time collection of emotional data

[1307] When a user views a specific product page, the device recognizes the user's facial expressions via the camera, analyzes the user's voice tone via the microphone, and measures the keyboard typing speed. This emotional data is sent to an emotion engine in real time. For example, it measures whether the user is excited when viewing a page about running shoes.

[1308] 3. Analysis of emotional data

[1309] The emotion engine uses AI algorithms to analyze collected emotional data and quantify the user's emotional state. For example, it might output a specific numerical value such as "Excitement level: 85%". The analysis results are then sent to the server.

[1310] 4. Data Integration and Ad Selection

[1311] The server integrates received emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors. For example, if a user who frequently purchased running shoes in the past is determined to be in an "excited state," an advertisement for running shoes will be displayed. The server sets thresholds for emotional states (e.g., "excitement level of 80% or higher") and selects the most appropriate advertisement based on these thresholds.

[1312] 5. Ad delivery

[1313] The selected advertisements are sent from the server to the device, and the device displays the advertisements to the user. For example, an advertisement banner for running shoes is displayed in a prominent position on the webpage.

[1314] Hardware and software used

[1315] Device: A device used to collect and transmit user data and sentiment data (such as a PC, smartphone, or tablet).

[1316] Camera: A sensor used to recognize the user's facial expressions in real time.

[1317] Microphone: A voice input device used to analyze the tone of the user's voice.

[1318] Keyboard: An input device used to measure a user's typing speed.

[1319] Server: A central facility for integrating and analyzing collected data, and for selecting and delivering advertisements.

[1320] Emotion Engine: A software module equipped with AI algorithms for analyzing emotional data.

[1321] Specific example

[1322] For example, when user A accesses a sports website and views a page for new running shoes, the process is as follows:

[1323] 1. User A accesses the website.

[1324] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[1325] 3. The device sends this data to the server.

[1326] 4. The emotion engine analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[1327] 5. The server integrates the results of the emotion engine analysis with past data and analyzes that "excitement" has a high correlation with the purchasing behavior of running shoes.

[1328] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[1329] 7. The server sends the selected advertisement to user A's device, and the advertisement is displayed.

[1330] Example of a prompt

[1331] Please build a system that collects and analyzes user attribute information (age, gender, occupation), past behavioral data (browsing history, purchase history), and real-time sentiment data (facial expression recognition, voice tone, keyboard input speed), and delivers personalized advertisements based on this data.

[1332] This enables highly accurate ad delivery based on users' emotional states and behavioral data. This system not only improves the user experience but also maximizes the effectiveness of the ads.

[1333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1334] Step 1: Collecting user behavior data

[1335] When a user accesses a website or application, the device obtains user attribute information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.) as input. As a result of this data processing, the collected information is sent to the server. Specifically, the web browser reads the user's profile data and browsing history from cookies, etc., and sends it to the server via an HTTP request.

[1336] Step 2: Collecting real-time sentiment data

[1337] When a user views a specific product page, the device uses its camera to capture the user's facial expressions, its microphone to record the tone of their voice, and its keyboard typing speed to measure the input. As a result of this data processing, the emotional data collected in real time is sent to an emotion engine. Specifically, the device's camera analyzes facial expressions using facial recognition software, the microphone evaluates the tone of voice using voice analysis software, and the keyboard stroke speed monitors the typing motion.

[1338] Step 3: Analysis of emotional data

[1339] The emotion engine receives emotional data collected as input and analyzes it using an AI algorithm. As a result of this data processing, the user's emotional state is quantified (e.g., "Excitement level: 85%"). The analyzed results are sent to the server. Specifically, the emotion engine inputs the received facial image, audio clip, and typing data into a neural network, which then analyzes this data and maps it to a specific emotional state.

[1340] Step 4: Data Integration and Ad Selection

[1341] The server integrates the sentiment data received as input with past behavioral data. As a result of this data processing, it can analyze how specific emotions lead to specific actions. For example, if past data shows a high correlation between "excitement" and the purchase of running shoes, it will select a specific advertisement. Specifically, the server compares past behavioral data stored in the integrated database with real-time sentiment data and recommends advertising campaigns that meet pre-set thresholds.

[1342] Step 5: Ad Delivery

[1343] The server holds the selected advertising data as input and sends it to the terminal. As a result of this data transfer, the terminal displays the advertisement to the user. Specifically, the server sends JSON data containing the URL and display instructions of the selected advertisement to the terminal, and the terminal's browser parses this data to display the advertisement banner or pop-up.

[1344] (Application Example 2)

[1345] 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."

[1346] Traditional advertising systems only displayed personalized ads based on users' demographic information and past behavioral data. However, this limited the accuracy and effectiveness of the ads. Therefore, there is a need for a system that reflects users' real-time emotional states and delivers ads with a higher degree of personalization. Another challenge is the lack of a means to display ads in real time using smart devices while users are out and about.

[1347] 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. In this invention, the server includes means for collecting the user's demographic information and past behavioral data, means for analyzing the user's emotional data in real time, and means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to. As a result, the emotional state is reflected in the selection of advertisements, enabling the delivery of optimal advertisements tailored to the user's current situation. Furthermore, by means for collecting the user's real-time emotional data using a smart device and displaying advertisements through a visual display device, it is possible to provide advertisements suitable for the user even when they are out and about.

[1348] "User demographic information" refers to information that indicates personal characteristics such as age, gender, occupation, and region.

[1349] "Past behavioral data" refers to data that describes a user's past behavior, such as their website browsing history, purchase history, and search history.

[1350] "Methods for analyzing user emotional data in real time" refer to methods that use cameras, microphones, keyboard input speed sensors, etc., to analyze the user's facial expressions, voice tone, input speed, etc., in real time and quantify their emotional state.

[1351] "Emotional state" refers to a numerical representation of the emotions a user is feeling at a particular moment, such as excitement, joy, or sadness.

[1352] "Methods for selecting advertisements" refer to methods for selecting the most suitable advertisements that match the user's emotional state and behavioral data, based on integrated data.

[1353] "Means of displaying selected advertisements to users" refers to means of displaying selected advertisements on the devices that users view. This includes, for example, displaying advertisements on smart glasses or smartphone screens.

[1354] A "smart device" is a multi-functional device that users can carry around, and it has built-in features such as a camera, microphone, and sensors. Specific examples include smartphones and smart glasses.

[1355] "Visual display devices" is a general term for display devices that allow users to visually confirm information. Examples include the display screens of head-mounted displays and smart glasses.

[1356] This invention is a system that achieves more personalized advertising delivery by combining the collection and analysis of user demographic information and past behavioral data with real-time sentiment data analysis. This system mainly consists of the following four components: terminal, server, user, and sentiment engine.

[1357] 1. Device functions

[1358] The device collects the user's demographic information and past behavioral data and sends it to a server. It also features various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time. This allows it to capture emotional data such as the user's facial expressions, voice tone, and typing speed.

[1359] The hardware used includes cameras (e.g., Sony IMX586) and microphones (e.g., Intel RealSense D435). This data is then analyzed using software such as OpenCV and NLP toolkits.

[1360] 2. Function of the Emotion Engine

[1361] The emotion engine analyzes collected emotional data and expresses the user's specific emotional state numerically. This engine comprehensively analyzes emotional states from multiple data sources, including facial recognition, voice tone analysis, and keyboard input speed. For example, it uses an AI algorithm to quantify the emotional state as "Excitement Level: 85%".

[1362] 3. Server Functions

[1363] The server analyzes the data received from the device and integrates emotional data with past behavioral data. This allows it to analyze how specific emotions lead to certain behaviors. It also selects the most suitable advertisements based on the user's emotional state and behavioral data and sends those advertisements to the device.

[1364] 4. User actions

[1365] Users operate their devices and access websites and applications. Their actions and emotions are recorded and analyzed in real time. This allows for the delivery of advertisements tailored to the user's current situation.

[1366] Specific example

[1367] For example, when a user accesses a sports equipment website, the process proceeds as follows:

[1368] 1. The user accesses the website.

[1369] 2. The device collects the user's past browsing history (e.g., frequently searching for running shoes) and demographic information.

[1370] 3. The device sends this data to the server.

[1371] 4. The server analyzes the user's facial expressions from the camera footage in real time and detects if they are in an "excited state."

[1372] 5. The server uses an emotion engine to integrate with past data and analyze that "excitement levels" have a high correlation with running shoe purchasing behavior.

[1373] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[1374] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to the user.

[1375] This system utilizes real-time sentiment data collected by an emotion engine to deliver personalized ads with greater accuracy. This allows for the provision of effective ads that reflect the user's emotional state, maximizing the impact of advertising.

[1376] Example of a prompt

[1377] Design a system that captures a user's gaze, facial expressions, and voice tone in real time while they are wearing smart glasses, and analyzes this data using an emotion engine. The analysis results, along with demographic information and past behavioral data, are sent to a server, and the system then displays the most relevant advertisements in augmented reality (AR) within the user's field of view.

[1378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1379] Step 1:

[1380] The device collects the user's demographic information and past behavioral data. Input includes the user's age, gender, occupation, region, past browsing history, and purchase history. This information is collected as data and temporarily stored in internal storage to prepare for the next step.

[1381] Step 2:

[1382] The device sends collected demographic information and past behavioral data to the server. A communication protocol (e.g., HTTPS) is used for this transmission. The data is sent in JSON format, which the server stores in a database and uses for analysis.

[1383] Step 3:

[1384] The device collects real-time emotional data from the user. To achieve this, it captures facial expressions with its built-in camera, records voice tone with its microphone, and measures keyboard and touchscreen input speed. The inputs include video data, audio data, and input speed data, all of which are processed in real time.

[1385] Step 4:

[1386] The device sends collected emotional data to the emotion engine in real time. The emotion engine uses AI algorithms (e.g., deep learning models) to analyze facial expressions, voice tone, and input speed data. The analyzed emotional data is quantified (e.g., "Excitement level: 85%") and sent to the next step.

[1387] Step 5:

[1388] The server integrates emotional data with historical behavioral data to analyze how specific emotions lead to certain behaviors. Specifically, it analyzes correlations based on demographic information, historical behavioral data, and real-time emotional data to evaluate the extent to which specific emotional states influence past purchasing behavior. Statistical analysis and machine learning models are used in this process.

[1389] Step 6:

[1390] The server selects advertisements based on the user's emotional state and behavioral data. Here, advertisements corresponding to the emotional state are selected from the database. For example, if the "excitement level" is high, advertisements for sports equipment will be selected. The selected advertisements are then sent to the user in the next step.

[1391] Step 7:

[1392] The server sends the selected advertisement to the device. A communication protocol (e.g., HTTPS) is used for this transmission. The advertisement data is sent in the form of images, text, links, etc.

[1393] Step 8:

[1394] The device displays advertisements to the user. Specifically, advertisements are displayed in the user's field of view using AR (augmented reality) through visual display devices such as smart glasses and smartphones. This display method is visually natural for the user and enables effective ad delivery.

[1395] Example of a prompt

[1396] Design a system that captures a user's gaze, facial expressions, and voice tone in real time while they are wearing smart glasses, and analyzes this data using an emotion engine. The analysis results, along with demographic information and past behavioral data, are sent to a server, and the system then displays the most relevant advertisements in augmented reality (AR) within the user's field of view.

[1397] 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.

[1398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[1399] 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.

[1400] [Fourth Embodiment]

[1401] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1402] 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.

[1403] 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).

[1404] 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.

[1405] 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.

[1406] 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).

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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.

[1413] 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".

[1414] This invention is a system that integrates the collection and analysis of user demographic information and past behavioral data, as well as real-time sentiment data analysis, to realize personalized advertising delivery based on this information. The system based on this invention mainly consists of three components: a terminal, a server, and the user.

[1415] System Configuration

[1416] 1. The device collects the user's demographic information and past behavioral data and sends it to the server. The device is also equipped with various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time and send that data to the server.

[1417] 2. The server analyzes the received data and integrates sentiment data with past behavioral data. The server then selects advertisements based on the integrated data and sends those advertisements to the device.

[1418] 3. Users operate their devices and access websites and applications. User behavior and emotions are recorded and analyzed in real time.

[1419] Program processing and specific examples

[1420] When a user accesses a website

[1421] A user accesses a sports goods website.

[1422] The device collects the user's demographic information (e.g., age, gender, occupation) and past behavioral data (e.g., browsing history, purchase history).

[1423] The device sends the collected data to the server.

[1424] The server saves the received data to prepare for future access.

[1425] Real-time collection of emotional data

[1426] A user views a specific product page on a website.

[1427] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[1428] The device sends this emotional data to the server in real time.

[1429] The server uses an AI algorithm to analyze the emotional data it receives and quantifies the user's emotional state (e.g., "Excitement level: 85%").

[1430] Integrating emotions and behaviors and ad selection

[1431] The server integrates emotional data with past behavioral data to analyze the relationship between a user's specific emotional state and their actions.

[1432] The server sets emotional thresholds and defines rules such as "show a specific ad if the excitement level is 80% or higher."

[1433] The server can select the most relevant advertisements based on the user's current emotional state and past data (for example, if a user is excited while browsing sports equipment, it will select an advertisement for the latest high-performance running shoes).

[1434] Ad delivery

[1435] The server sends the selected advertisement to the device.

[1436] The device displays advertisements selected by the user (e.g., an ad banner for running shoes appears on the webpage).

[1437] Specific example

[1438] For example, when user A accesses a sports website and views a page for new running shoes, the process proceeds as follows:

[1439] 1. User A accesses the website.

[1440] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[1441] 3. The device sends this data to the server.

[1442] 4. The server analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[1443] 5. The server integrates historical data and analyzes whether "excitement levels" have a high correlation with running shoe purchasing behavior.

[1444] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[1445] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to user A.

[1446] Thus, the system of the present invention can deliver optimal advertisements to users in real time by considering the user's instantaneous emotional state and integrating it with past behavioral data. This makes it possible to provide a more personalized experience and maximize the effectiveness of advertisements.

[1447] The following describes the processing flow.

[1448] Step 1:

[1449] Users access websites and apps.

[1450] The device detects user access and records access logs.

[1451] Step 2:

[1452] The device collects the user's demographic information.

[1453] The device reads demographic information such as age, gender, and occupation from the user profile.

[1454] Step 3:

[1455] The device collects data on the user's past behavior.

[1456] The system reads data from a database, including pages and categories the device has previously viewed, as well as its past purchase history.

[1457] Step 4:

[1458] The device sends collected demographic information and behavioral data to the server.

[1459] The device encrypts this information and sends it to the server using a security protocol.

[1460] Step 5:

[1461] The server prepares to collect user sentiment data in real time.

[1462] The server activates the emotion data analysis module and issues instructions to activate the device's camera, microphone, and keyboard input speed sensor.

[1463] Step 6:

[1464] A user views specific content within the site.

[1465] The device monitors the user's browsing behavior and records the viewing status of specific pages or products.

[1466] Step 7:

[1467] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[1468] The device collects this emotional data and formats it so that it can be analyzed in real time.

[1469] Step 8:

[1470] The device sends the collected emotional data to the server.

[1471] The device encrypts emotional data and sends it to the server via a secure communication channel.

[1472] Step 9:

[1473] The server analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[1474] The server outputs the analysis results, for example, "Excitement level: 85%".

[1475] Step 10:

[1476] The server integrates emotional data with past behavioral data.

[1477] The server matches emotional data with past browsing and purchase history to analyze what actions specific emotions lead to.

[1478] Step 11:

[1479] The server sets the threshold for emotional states.

[1480] The server uses the integrated data to create rules, such as "show a specific ad if the excitement level is 80% or higher."

[1481] Step 12:

[1482] The user revisits the website or app.

[1483] The device detects the re-access and reactivates the sensor.

[1484] Step 13:

[1485] The device collects the user's current emotional state in real time.

[1486] The device acquires data in real time from the camera, microphone, keyboard input sensor, etc., and sends it to the server.

[1487] Step 14:

[1488] The server analyzes the current sentiment data.

[1489] The server analyzes the emotional data it receives using an AI algorithm and quantifies it.

[1490] Step 15:

[1491] The server compares current sentiment data with past behavioral data to select the most suitable advertisement.

[1492] The server selects highly relevant ads based on emotional state and behavioral data.

[1493] Step 16:

[1494] The server sends the selected advertising data to the device.

[1495] The server encrypts the advertising data and sends it to the device.

[1496] Step 17:

[1497] The device displays advertisements to the user.

[1498] The device displays a specified advertisement on the user's browsing page and records the user's reaction.

[1499] By sequentially carrying out these processing steps, personalized advertising that reflects the user's emotional state in real time can be achieved.

[1500] (Example 1)

[1501] 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".

[1502] While traditional advertising delivery systems possessed technologies to provide personalized ads based on users' demographic information and past behavioral data, they lacked the ability to select ads considering users' real-time emotional states, resulting in limited advertising effectiveness. Furthermore, the accuracy of emotional data collection and analysis was low, often leading to the display of inappropriate ads. Additionally, security concerns arose regarding the transmission and storage of collected data, necessitating secure data processing.

[1503] 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.

[1504] In this invention, the server includes means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what actions specific emotions lead to; means for selecting advertisements based on the user's emotional state and behavioral data; means for displaying the selected advertisements to the user; means for personalizing the content of the advertisements using a generative AI model; and encryption means for securely transmitting the collected data to the server. This makes it possible to analyze the user's real-time emotional state with high accuracy and display the most suitable advertisements based on the results. Furthermore, it enables secure transmission and storage of data, maximizing advertising effectiveness while protecting user privacy.

[1505] "User demographic information" refers to basic statistical information used to identify an individual, such as age, gender, and occupation.

[1506] "Past behavioral data" refers to information about a user's past behavior, such as their website browsing history, purchase history, and search history.

[1507] "Emotional data" refers to data that indicates a user's real-time emotional state and is collected using methods such as facial expressions, tone of voice, and keyboard input speed.

[1508] "Facial recognition" refers to a technology that uses a camera to analyze the movements of a user's face and identify their emotional state.

[1509] "Voice tone analysis" refers to a technology that uses a microphone to analyze the tone and intonation of a user's voice and identify their emotional state.

[1510] "Keyboard input speed" refers to a method of measuring how fast a user types on a keyboard and analyzing their stress and excitement levels based on that measurement.

[1511] A "generative AI model" refers to an artificial intelligence algorithm that has learned from a large amount of data and generates new advertising content based on input data.

[1512] "Data encryption methods" refer to technologies that encrypt data in order to securely transmit collected data over the internet.

[1513] "Ad personalization" refers to generating and displaying ad content that is optimal for each individual user.

[1514] This invention is a system that delivers personalized advertisements by combining user demographic information, past behavioral data, and real-time sentiment data analysis. The system is mainly composed of three components: a terminal, a server, and the user.

[1515] Hardware and software to be used

[1516] Device: Primarily refers to computer devices such as smartphones and personal computers. These devices are equipped with various sensors, including cameras, microphones, and keyboards.

[1517] Server: A server device capable of high-performance data processing. It has database management systems (e.g., MySQL, PostgreSQL) and frameworks for running AI models (e.g., TensorFlow, PyTorch) installed.

[1518] Software: We utilize sentiment analysis software (e.g., Kairos, Microsoft Azure Face API), speech analysis tools (e.g., Google Cloud Speech-to-Text, IBM Watson Speech to Text), and encryption protocols for data transmission (e.g., HTTPS).

[1519] Detailed description of the invention

[1520] Collection of demographic information and behavioral data

[1521] When a user accesses a website or application, their device collects demographic information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.). Demographic information is obtained from the user's profile information, while behavioral data is obtained by analyzing browser cookies and application logs.

[1522] Real-time collection of emotional data

[1523] When a user views a specific product page, various sensors on the device (camera, microphone, keyboard) collect emotional data in real time. The camera performs facial recognition, the microphone analyzes voice tone, and the keyboard measures typing speed. This data is then analyzed using emotional analysis software and voice analysis tools.

[1524] Sending data to the server

[1525] The device sends collected demographic information, historical behavioral data, and real-time sentiment data to the server via a secure protocol (e.g., HTTPS). Communication is encrypted, ensuring data protection.

[1526] Data analysis and ad selection

[1527] The server stores the received data in a database and uses an AI model to analyze the emotional data. Emotional states are quantified (e.g., "Excitement level: 85%") and integrated with past behavioral data. Data analysis tools analyze how specific emotions lead to specific behaviors. Based on set thresholds (e.g., excitement level 80% or higher), the server selects the most suitable advertisement from the advertising database.

[1528] Ad delivery

[1529] The server sends the selected advertisement to the device, and the device displays that advertisement to the user. For example, it may be displayed as a banner ad on a webpage.

[1530] Specific example

[1531] For example, let's consider a scenario where user A accesses a sports website and views a page for new running shoes.

[1532] 1. User A accesses the website and views the running shoes page.

[1533] 2. The device obtains demographic information from the user's profile and analyzes past behavioral data.

[1534] 3. The device captures the user's facial expressions with its camera and detects their state of excitement using emotion analysis software. The microphone analyzes the user's voice tone, and the keyboard input speed is also monitored.

[1535] 4. Encrypt the data collected by the device and send it securely to the server.

[1536] 5. The server analyzes the data, confirms that the excitement level is 80% or higher, and selects the most suitable running shoe advertisement.

[1537] 6. The server sends an advertisement to the device, and the device displays the advertisement to user A.

[1538] Example of a prompt

[1539] "A 30-year-old man was browsing a sports equipment website and showed an excited expression. Please generate an ad for running shoes that would be suitable for this user."

[1540] Thus, the system of the present invention can deliver optimal advertisements to users in real time by considering the user's instantaneous emotional state and integrating it with past behavioral data. This makes it possible to provide a more personalized experience and maximize the effectiveness of advertisements. The above describes a specific embodiment for carrying out the invention.

[1541] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1542] Step 1:

[1543] Users access websites and applications.

[1544] The device collects the user's demographic information (age, gender, occupation). User registration information and profile information are required as input, and demographic information is prepared as output. Specifically, the device retrieves information from the user's initial registration data and web browser cookies.

[1545] Step 2:

[1546] The device collects past behavioral data (browsing history, purchase history). The input is the user's browser history and application log data, and the output is generated as past behavioral data. Specifically, the device analyzes browser cookies and uses log data from the server.

[1547] Step 3:

[1548] The device utilizes facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time. Inputs include camera video, audio, and keyboard input data, and the output is analyzed emotion data. Specifically, the camera captures the user's facial expressions, which are then analyzed by emotion analysis software. The microphone is used to analyze voice tone, and the keyboard input speed is measured.

[1549] Step 4:

[1550] The device collects demographic information, historical behavioral data, and real-time sentiment data, which are then sent to a server via the internet. The input is pre-collected data, and the output is encrypted data to be sent. Specifically, the device uses the HTTPS protocol to encrypt the data and securely transmit it to the server.

[1551] Step 5:

[1552] The server stores the received data in a database. The input includes demographic information, behavioral data, and sentiment data, and the output is the data stored in the database. Specifically, the server uses a database management system to securely store the data.

[1553] Step 6:

[1554] The server analyzes incoming data using an AI model and quantifies the emotional state. The input is stored emotional data, and the output is a quantified emotional state. Specifically, the server runs an AI model (e.g., TensorFlow or PyTorch) and converts the emotional state into a numerical value such as "Excitement Level: 85%".

[1555] Step 7:

[1556] The server integrates emotional data and past behavioral data to analyze how specific emotions lead to certain behaviors. The input consists of quantified emotional and behavioral data, and the output is the analysis of these relationships. Specifically, data analysis tools (e.g., Apache Hadoop, Spark) are used to integrate and analyze this data.

[1557] Step 8:

[1558] The server selects the most suitable advertisement based on the user's emotional state and behavioral data. The input is the analysis results, and the output is the selected advertisement. Specifically, the server retrieves appropriate advertisements from the ad database based on a set threshold (e.g., excitement level of 80% or higher).

[1559] Step 9:

[1560] The server sends selected advertisements to the device. The input is the selected advertisement data, and the output is a transmission completion status. Specifically, the server encrypts the data again using the HTTPS protocol and sends it to the device.

[1561] Step 10:

[1562] The device displays the received advertisement to the user. The input is the transmitted advertisement data, and the output is the advertisement displayed on the user's screen. Specifically, the device prepares to display the advertisement as a banner ad on a webpage and then displays it to the user.

[1563] (Application Example 1)

[1564] 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".

[1565] Traditional advertising delivery systems only delivered ads based on users' demographic information and past behavioral data, without considering the real-time, fluctuating emotional states of users. This made it difficult to deliver the most relevant ads to users, resulting in decreased advertising effectiveness. Furthermore, there was insufficient technology to accurately collect and analyze real-time emotional data, including users' facial expressions and tone of voice.

[1566] 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.

[1567] In this invention, the server includes means for collecting user demographic information and past behavioral data; means for analyzing user emotional data in real time; means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to; means for selecting advertisements based on the user's emotional state and behavioral data; means for displaying the selected advertisements to the user; means for analyzing the user's facial expressions and voice tone in real time using a smartphone; and means for selecting advertisements to display based on the emotional data analyzed in real time. This makes it possible to deliver optimal advertisements based on user demographic information and past behavioral data, as well as emotional data that changes in real time.

[1568] "Demographic information" refers to information that indicates a user's social and economic attributes, such as age, gender, occupation, and place of residence.

[1569] "Behavioral data" refers to data about a user's digital activities, such as their past website browsing history and purchase history.

[1570] "Emotional data" refers to data that indicates a user's psychological and emotional state, obtained from the user's facial expressions, voice tone, keyboard input speed, and other factors.

[1571] "Facial recognition" is a technology that analyzes the features of a user's face from camera footage to identify their emotional state.

[1572] "Voice tone analysis" is a technology that analyzes the pitch, intensity, and rhythm of a user's voice to infer their emotional state.

[1573] "Keyboard input speed" refers to data that measures the speed at which a user types on a keyboard and is used to estimate their emotional state.

[1574] "Ad selection methods" refer to algorithms and methods that select the most suitable advertisements for a user based on analyzed data.

[1575] A "smartphone" is a portable device equipped with advanced computing and communication capabilities, as well as sensors such as a camera and microphone.

[1576] "Real-time" refers to processing and analyzing data at a time close to the moment when a user's actions or situations occur.

[1577] A "server" is a computer system that collects, analyzes, stores, and distributes data over a network.

[1578] This invention is a system that collects and analyzes user demographic information, past behavioral data, and real-time emotional data to deliver personalized advertisements. This system demonstrates a method for collecting emotional data in real time using a user's smartphone, and selecting and displaying advertisements based on the analysis results.

[1579] System Configuration

[1580] 1. Terminal

[1581] Smartphones are used as a means of collecting users' demographic information and past behavioral data.

[1582] Smartphones are equipped with cameras and microphones, which allow them to analyze the user's facial expressions and voice tone in real time.

[1583] The device sends the collected data to the server.

[1584] 2. Server

[1585] The server analyzes the received demographic information, past behavioral data, and sentiment data.

[1586] Based on the analyzed data, we analyze what kinds of behaviors are linked to specific emotions.

[1587] An algorithm is executed to select advertisements based on the user's emotional state and behavioral data.

[1588] Selected advertisements are sent to the device.

[1589] 3. User

[1590] Users operate their smartphones to access websites and applications.

[1591] User behavior and emotions are recorded and analyzed in real time.

[1592] Program processing

[1593] Terminal processing

[1594] The smartphone's camera captures the user's facial expressions, and the microphone collects their voice tone.

[1595] The collected emotional data, demographic information, and past behavioral data are sent to the server.

[1596] Server Processing

[1597] The server analyzes the received data and integrates emotional and behavioral data.

[1598] Set thresholds for emotional states to determine whether a particular emotion leads to purchasing behavior.

[1599] Based on the results of integrated analysis, the most suitable advertisements are selected for each user.

[1600] The selected advertisements are sent to the device, and the device displays the advertisements to the user.

[1601] Specific hardware and software to be used

[1602] hardware

[1603] Smartphones: Collect user data using cameras and microphones.

[1604] Server: Stores, analyzes, and selects advertisements for data.

[1605] software

[1606] OpenCV: Analyzes a user's facial expressions from smartphone camera footage.

[1607] EmotionRecognition (a virtual emotion recognition library): Recognizes the user's emotional state.

[1608] UserDataProcessor (a virtual user data processing library): Processes user demographic information and past behavioral data.

[1609] AdSelector (virtual ad selection library): Selects ads based on analyzed data.

[1610] Specific example

[1611] For example, when user A opens a shopping app, the process proceeds as follows:

[1612] 1. User A opens a shopping app on their smartphone.

[1613] 2. The smartphone camera captures user A's facial expressions, and the microphone analyzes their voice tone.

[1614] 3. The collected data is sent to the server and analyzed in real time.

[1615] 4. The server integrates user A's current emotional state with past behavioral data and detects that user A is in an excited state.

[1616] 5. The server determines that the excited state will lead to purchasing behavior and selects the most suitable advertisement (for example, a promotion for new sneakers).

[1617] 6. The selected advertisement is sent to the smartphone and displayed to User A.

[1618] Example of a prompt

[1619] "Create a program that recognizes a user's emotional state from their facial expressions and tone of voice when they open a shopping app. Integrate this with their past purchase history and display personalized ads in real time."

[1620] Thus, the present invention takes into account the user's instantaneous emotional state to achieve highly accurate personalized advertising and maximize the effectiveness of the advertisement.

[1621] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1622] Step 1:

[1623] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice tone in real time.

[1624] Input: Camera video data, audio data

[1625] Specific operation: The smartphone's camera captures the user's face, and the microphone records audio. If necessary, lighting conditions and camera focus are automatically adjusted.

[1626] Output: Captured facial expression data and voice tone data

[1627] Step 2:

[1628] The device analyzes captured facial expression data and voice tone data to identify the user's emotional state.

[1629] Input: Facial expression data, voice tone data

[1630] Specific operation: It uses OpenCV to analyze facial features and the EmotionRecognition library to determine emotional states (e.g., joy, sadness, excitement). In speech tone analysis, it analyzes pitch, volume, rhythm, etc., to estimate emotional states.

[1631] Output: Analyzed emotion data (e.g., "Excited state: 85%")

[1632] Step 3:

[1633] The device sends analyzed sentiment data, user demographic information, and past behavioral data to the server.

[1634] Input: Sentimental data, demographic information, historical behavioral data

[1635] Specific actions: Before sending user information to the server in bulk, the data format is prepared (e.g., converted to JSON format). Then, encryption is performed to ensure secure data transfer.

[1636] Output: Integrated data sent to the server

[1637] Step 4:

[1638] The server analyzes demographic information, past behavioral data, and emotional data it receives to determine how specific emotions lead to certain behaviors.

[1639] Input: Demographic information, historical behavioral data, sentiment data

[1640] Specific operation: The server stores the received data in a database and performs analysis using an AI algorithm. A key point is that it analyzes cases where, for example, "excitement" is strongly correlated with "purchasing behavior."

[1641] Output: Analysis results (Example: "People in an excited state are more likely to purchase expensive items")

[1642] Step 5:

[1643] The server selects the most suitable advertisement based on the user's emotional state and behavioral data.

[1644] Input: Analysis results, emotional state, behavioral data

[1645] Specific operation: Based on the analysis results, a selection algorithm is used to choose the most suitable advertisement from multiple options (e.g., "If the user is excited, display an advertisement for sports equipment"). Past behavioral data and demographic information are also taken into account to refine the selection process.

[1646] Output: Selected ad data

[1647] Step 6:

[1648] The server sends the selected advertisement to the device, and the device displays the advertisement to the user.

[1649] Input: Selected ad data

[1650] Specific operation: The server sends the selected advertisement to the device, and the device displays the advertisement. Multiple display formats are possible, such as banner ads, pop-up ads, and video ads, but the advertisement will be displayed in a format that does not detract from the user experience.

[1651] Output: Advertisements displayed on the device

[1652] This allows users to receive personalized ads in real time, improving the effectiveness of those ads.

[1653] 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.

[1654] This invention provides a system that enables more personalized advertising delivery by combining the collection and analysis of user demographic information and past behavioral data with real-time sentiment data analysis. The system based on this invention mainly consists of four components: a terminal, a server, a user, and a sentiment engine.

[1655] System Configuration

[1656] 1. The terminal collects the user's demographic information and past behavioral data and sends it to the server.

[1657] The device also features various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time.

[1658] 2. The emotion engine analyzes the collected emotion data and expresses the user's specific emotional state numerically (e.g., "Excitement level: 85%").

[1659] The emotion engine comprehensively analyzes emotional states from multiple data sources, including facial recognition, voice tone analysis, and keyboard input speed.

[1660] 3. The server analyzes the received data and integrates emotional data with past behavioral data.

[1661] The server then uses this integrated data to select advertisements and sends them to the device.

[1662] 4. The user operates the device and accesses websites and applications.

[1663] User behavior and emotions are recorded and analyzed in real time.

[1664] Program processing and specific examples

[1665] When a user accesses a website

[1666] A user accesses a sports goods website.

[1667] The device collects the user's demographic information (e.g., age, gender, occupation) and past behavioral data (e.g., browsing history, purchase history).

[1668] The device sends the collected data to the server.

[1669] The server saves the received data to prepare for future access.

[1670] Real-time collection of emotional data

[1671] A user views a specific product page on a website.

[1672] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[1673] The device transmits this emotional data to the emotion engine in real time.

[1674] Analysis and recognition of emotional data

[1675] The emotion engine analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[1676] The emotion engine sends the analysis results to the server.

[1677] We integrate accumulated emotional data with past behavioral data to analyze the impact of specific emotional states on behavior.

[1678] Integrating emotions and behaviors and ad selection

[1679] The server integrates emotional data with past behavioral data to analyze the relationship between a user's specific emotional state and their actions.

[1680] The server sets emotional state thresholds (e.g., "Show a specific ad if the excitement level is 80% or higher") and selects the ads.

[1681] The server selects the most suitable advertisement and sends it to the device.

[1682] Ad delivery

[1683] The server sends the selected advertisement to the device.

[1684] The device displays advertisements to the user (e.g., an ad banner for running shoes appears on a webpage).

[1685] Specific example

[1686] For example, when user A accesses a sports website and views a page for new running shoes, the process proceeds as follows:

[1687] 1. User A accesses the website.

[1688] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[1689] 3. The device sends this data to the server.

[1690] 4. The server analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[1691] 5. The server uses an emotion engine to integrate with past data and analyze that "excitement levels" have a high correlation with running shoe purchasing behavior.

[1692] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[1693] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to user A.

[1694] Thus, the system of the present invention utilizes real-time emotional data collected by the emotion engine to achieve more accurate and personalized ad delivery. This allows for the provision of effective ads that reflect the user's emotional state, maximizing the effectiveness of the ads.

[1695] The following describes the processing flow.

[1696] Step 1:

[1697] Users access websites and apps.

[1698] The device detects user access and records access logs.

[1699] Step 2:

[1700] The device collects the user's demographic information.

[1701] The device reads demographic information such as age, gender, and occupation from the user profile.

[1702] Step 3:

[1703] The device collects data on the user's past behavior.

[1704] The system reads data from a database, including pages and categories the device has previously viewed, as well as its past purchase history.

[1705] Step 4:

[1706] The device sends collected demographic information and behavioral data to the server.

[1707] The device encrypts this information and sends it to the server using a security protocol.

[1708] Step 5:

[1709] The server prepares to collect user sentiment data in real time.

[1710] The server activates the emotion data analysis module and issues instructions to activate the device's camera, microphone, and keyboard input speed sensor.

[1711] Step 6:

[1712] A user views specific content within the site.

[1713] The device monitors the user's browsing behavior and records the viewing status of specific pages or products.

[1714] Step 7:

[1715] The device uses its camera to recognize the user's facial expressions, its microphone to analyze their voice tone, and its keyboard input speed to measure their typing speed.

[1716] The device transmits this emotional data to the emotion engine in real time.

[1717] Step 8:

[1718] The emotion engine analyzes the received emotional data using an AI algorithm to quantify the user's emotional state.

[1719] The emotion engine outputs analysis results such as "Excitement level: 85%".

[1720] Step 9:

[1721] The emotion engine sends the analysis results to the server.

[1722] The emotion engine encrypts the analysis results and sends them to the server via a secure communication channel.

[1723] Step 10:

[1724] The server integrates emotional data with past behavioral data.

[1725] The server matches emotional data with past browsing and purchase history to analyze what actions specific emotions lead to.

[1726] Step 11:

[1727] The server sets the threshold for emotional states.

[1728] The server uses the integrated data to create rules, such as "show a specific ad if the excitement level is 80% or higher."

[1729] Step 12:

[1730] The user revisits the website or app.

[1731] The device detects the re-access and reactivates the sensor.

[1732] Step 13:

[1733] The device collects the user's current emotional state in real time.

[1734] The device acquires data in real time from the camera, microphone, keyboard input sensor, etc., and sends it to the server.

[1735] Step 14:

[1736] The emotion engine analyzes the current emotional data.

[1737] The emotion engine analyzes the received emotional data using an AI algorithm and quantifies it.

[1738] Step 15:

[1739] The server compares current sentiment data with past behavioral data to select the most suitable advertisement.

[1740] The server selects highly relevant ads based on emotional state and behavioral data.

[1741] Step 16:

[1742] The server sends the selected advertising data to the device.

[1743] The server encrypts the advertising data and sends it to the device.

[1744] Step 17:

[1745] The device displays advertisements to the user.

[1746] The device displays a specified advertisement on the user's browsing page and records the user's reaction.

[1747] By sequentially carrying out these processing steps, personalized advertising that reflects the user's emotional state in real time can be achieved.

[1748] (Example 2)

[1749] 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".

[1750] Traditional advertising delivery systems selected ads based on users' past browsing and purchase history, but they could not consider users' emotional states in real time. Therefore, it was difficult to deliver personalized ads that responded to users' instantaneous interests and psychological states. Furthermore, there was a lack of technology for centralized analysis of collected data and appropriate ad selection based on the results. There was a need to solve these problems and deliver more accurate ads in real time based on users' emotional states and past behavioral data.

[1751] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user attribute information and past behavioral data, means for analyzing the user's emotional data in real time, and means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to. This makes it possible to select advertisements based on the user's emotional state and behavioral data and to display the selected advertisements to the user in real time.

[1752] "Attribute information" refers to basic profile information such as the user's age, gender, and occupation.

[1753] "Behavioral data" refers to data related to a user's past actions, such as their website browsing history and purchase history.

[1754] "Emotional data" refers to information about the user's emotional state, obtained in real time from factors such as facial expressions, voice tone, and keyboard input speed.

[1755] "Real-time" refers to the process of closely monitoring and analyzing user behavior and emotions and reflecting them immediately.

[1756] "Ad selection" refers to the process of selecting the most suitable advertisement by combining user attribute information, behavioral data, and emotional data.

[1757] "Analysis" refers to the process of analyzing collected emotional and behavioral data using AI algorithms or other methods to derive specific conclusions.

[1758] "Integration" refers to the process of combining different types of data (such as emotional data and behavioral data) and treating them as a single data set.

[1759] A "threshold" refers to a numerical value that serves as a standard for identifying a specific emotional state.

[1760] "Display" refers to the process of presenting selected advertisements on a user's device in a format that the user can see.

[1761] This invention is a system that collects and analyzes user attribute information and past behavioral data, as well as user sentiment data in real time, and delivers personalized advertisements based on that data. The system mainly consists of four components: a server, a terminal, a user, and a sentiment engine.

[1762] System Configuration

[1763] 1. Collection of user behavior data

[1764] The device collects user attribute information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.) when the user accesses a website or application, and sends this information to the server. For example, when a user visits a sports goods website and searches for running shoes, that information is collected at the device.

[1765] 2. Real-time collection of emotional data

[1766] When a user views a specific product page, the device recognizes the user's facial expressions via the camera, analyzes the user's voice tone via the microphone, and measures the keyboard typing speed. This emotional data is sent to an emotion engine in real time. For example, it measures whether the user is excited when viewing a page about running shoes.

[1767] 3. Analysis of emotional data

[1768] The emotion engine uses AI algorithms to analyze collected emotional data and quantify the user's emotional state. For example, it might output a specific numerical value such as "Excitement level: 85%". The analysis results are then sent to the server.

[1769] 4. Data Integration and Ad Selection

[1770] The server integrates received emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors. For example, if a user who has frequently purchased running shoes in the past is determined to be in an "excited state," an advertisement for running shoes will be displayed. The server sets thresholds for emotional states (e.g., "excitement level of 80% or higher") and selects the most appropriate advertisement based on these thresholds.

[1771] 5. Ad delivery

[1772] The selected advertisements are sent from the server to the device, and the device displays the advertisements to the user. For example, an advertisement banner for running shoes is displayed in a prominent position on the webpage.

[1773] Hardware and software used

[1774] Device: A device used to collect and transmit user data and sentiment data (such as a PC, smartphone, or tablet).

[1775] Camera: A sensor used to recognize the user's facial expressions in real time.

[1776] Microphone: A voice input device used to analyze the tone of the user's voice.

[1777] Keyboard: An input device used to measure a user's typing speed.

[1778] Server: A central facility for integrating and analyzing collected data, and for selecting and delivering advertisements.

[1779] Emotion Engine: A software module equipped with AI algorithms for analyzing emotional data.

[1780] Specific example

[1781] For example, when user A accesses a sports website and views a page for new running shoes, the process is as follows:

[1782] 1. User A accesses the website.

[1783] 2. The device collects user A's previous browsing history (including frequent searches for running shoes in the past) and demographic information.

[1784] 3. The device sends this data to the server.

[1785] 4. The emotion engine analyzes user A's facial expressions from the camera footage in real time and detects that the user is in an "excited state."

[1786] 5. The server integrates the results of the emotion engine analysis with past data and analyzes that "excitement" has a high correlation with the purchasing behavior of running shoes.

[1787] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[1788] 7. The server sends the selected advertisement to user A's device, and the advertisement is displayed.

[1789] Example of a prompt

[1790] Please build a system that collects and analyzes user attribute information (age, gender, occupation), past behavioral data (browsing history, purchase history), and real-time sentiment data (facial expression recognition, voice tone, keyboard input speed), and delivers personalized advertisements based on this data.

[1791] This enables highly accurate ad delivery based on users' emotional states and behavioral data. This system not only improves the user experience but also maximizes the effectiveness of the ads.

[1792] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1793] Step 1: Collecting user behavior data

[1794] When a user accesses a website or application, the device obtains user attribute information (age, gender, occupation, etc.) and past behavioral data (browsing history, purchase history, etc.) as input. As a result of this data processing, the collected information is sent to the server. Specifically, the web browser reads the user's profile data and browsing history from cookies, etc., and sends it to the server via an HTTP request.

[1795] Step 2: Collecting real-time sentiment data

[1796] When a user views a specific product page, the device uses its camera to capture the user's facial expressions, its microphone to record the tone of their voice, and its keyboard typing speed to measure the input. As a result of this data processing, the emotional data collected in real time is sent to an emotion engine. Specifically, the device's camera analyzes facial expressions using facial recognition software, the microphone evaluates the tone of voice using voice analysis software, and the keyboard stroke speed monitors the typing motion.

[1797] Step 3: Analysis of emotional data

[1798] The emotion engine receives emotional data collected as input and analyzes it using an AI algorithm. As a result of this data processing, the user's emotional state is quantified (e.g., "Excitement level: 85%"). The analyzed results are sent to the server. Specifically, the emotion engine inputs the received facial image, audio clip, and typing data into a neural network, which then analyzes this data and maps it to a specific emotional state.

[1799] Step 4: Data Integration and Ad Selection

[1800] The server integrates the sentiment data received as input with past behavioral data. As a result of this data processing, it can analyze how specific emotions lead to specific actions. For example, if past data shows a high correlation between "excitement" and the purchase of running shoes, it will select a specific advertisement. Specifically, the server compares past behavioral data stored in the integrated database with real-time sentiment data and recommends advertising campaigns that meet pre-set thresholds.

[1801] Step 5: Ad Delivery

[1802] The server holds the selected advertising data as input and sends it to the terminal. As a result of this data transfer, the terminal displays the advertisement to the user. Specifically, the server sends JSON data containing the URL and display instructions of the selected advertisement to the terminal, and the terminal's browser parses this data to display the advertisement banner or pop-up.

[1803] (Application Example 2)

[1804] 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".

[1805] Traditional advertising systems only displayed personalized ads based on users' demographic information and past behavioral data. However, this limited the accuracy and effectiveness of the ads. Therefore, there is a need for a system that reflects users' real-time emotional states and delivers ads with a higher degree of personalization. Another challenge is the lack of a means to display ads in real time using smart devices while users are out and about.

[1806] 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. In this invention, the server includes means for collecting the user's demographic information and past behavioral data, means for analyzing the user's emotional data in real time, and means for integrating the analyzed emotional data and past behavioral data to analyze what kind of behavior a particular emotion leads to. As a result, the emotional state is reflected in the selection of advertisements, enabling the delivery of optimal advertisements tailored to the user's current situation. Furthermore, by means for collecting the user's real-time emotional data using a smart device and displaying advertisements through a visual display device, it is possible to provide advertisements suitable for the user even when they are out and about.

[1807] "User demographic information" refers to information that indicates personal characteristics such as age, gender, occupation, and region.

[1808] "Past behavioral data" refers to data that describes a user's past behavior, such as their website browsing history, purchase history, and search history.

[1809] "Methods for analyzing user emotional data in real time" refer to methods that use cameras, microphones, keyboard input speed sensors, etc., to analyze the user's facial expressions, voice tone, input speed, etc., in real time and quantify their emotional state.

[1810] "Emotional state" refers to a numerical representation of the emotions a user is feeling at a particular moment, such as excitement, joy, or sadness.

[1811] "Methods for selecting advertisements" refer to methods for selecting the most suitable advertisements that match the user's emotional state and behavioral data, based on integrated data.

[1812] "Means of displaying selected advertisements to users" refers to means of displaying selected advertisements on the devices that users view. This includes, for example, displaying advertisements on smart glasses or smartphone screens.

[1813] A "smart device" is a multi-functional device that users can carry around, and it has built-in features such as a camera, microphone, and sensors. Specific examples include smartphones and smart glasses.

[1814] "Visual display devices" is a general term for display devices that allow users to visually confirm information. Examples include the display screens of head-mounted displays and smart glasses.

[1815] This invention is a system that achieves more personalized advertising delivery by combining the collection and analysis of user demographic information and past behavioral data with real-time sentiment data analysis. This system mainly consists of the following four components: terminal, server, user, and sentiment engine.

[1816] 1. Device functions

[1817] The device collects the user's demographic information and past behavioral data and sends it to a server. It also features various sensors (camera, microphone, keyboard, etc.) to collect the user's current emotional state in real time. This allows it to capture emotional data such as the user's facial expressions, voice tone, and typing speed.

[1818] The hardware used includes cameras (e.g., Sony IMX586) and microphones (e.g., Intel RealSense D435). This data is then analyzed using software such as OpenCV and NLP toolkits.

[1819] 2. Function of the Emotion Engine

[1820] The emotion engine analyzes collected emotional data and expresses the user's specific emotional state numerically. This engine comprehensively analyzes emotional states from multiple data sources, including facial recognition, voice tone analysis, and keyboard input speed. For example, it uses an AI algorithm to quantify the emotional state as "Excitement Level: 85%".

[1821] 3. Server Functions

[1822] The server analyzes the data received from the device and integrates emotional data with past behavioral data. This allows it to analyze how specific emotions lead to certain behaviors. It also selects the most suitable advertisements based on the user's emotional state and behavioral data and sends those advertisements to the device.

[1823] 4. User actions

[1824] Users operate their devices and access websites and applications. Their actions and emotions are recorded and analyzed in real time. This allows for the delivery of advertisements tailored to the user's current situation.

[1825] Specific example

[1826] For example, when a user accesses a sports equipment website, the process proceeds as follows:

[1827] 1. The user accesses the website.

[1828] 2. The device collects the user's past browsing history (e.g., frequently searching for running shoes) and demographic information.

[1829] 3. The device sends this data to the server.

[1830] 4. The server analyzes the user's facial expressions from the camera footage in real time and detects if they are in an "excited state."

[1831] 5. The server uses an emotion engine to integrate with past data and analyze that "excitement levels" have a high correlation with running shoe purchasing behavior.

[1832] 6. Confirm that the server's excitement threshold is 80% or higher, and then select an advertisement for running shoes.

[1833] 7. The server sends the selected advertisement to the device, and the device displays the advertisement to the user.

[1834] This system utilizes real-time sentiment data collected by an emotion engine to deliver personalized ads with greater accuracy. This allows for the provision of effective ads that reflect the user's emotional state, maximizing the impact of advertising.

[1835] Example of a prompt

[1836] Design a system that captures a user's gaze, facial expressions, and voice tone in real time while they are wearing smart glasses, and analyzes this data using an emotion engine. The analysis results, along with demographic information and past behavioral data, are sent to a server, and the system then displays the most relevant advertisements in augmented reality (AR) within the user's field of view.

[1837] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1838] Step 1:

[1839] The device collects the user's demographic information and past behavioral data. Input includes the user's age, gender, occupation, region, past browsing history, and purchase history. This information is collected as data and temporarily stored in internal storage to prepare for the next step.

[1840] Step 2:

[1841] The device sends collected demographic information and past behavioral data to the server. A communication protocol (e.g., HTTPS) is used for this transmission. The data is sent in JSON format, which the server stores in a database and uses for analysis.

[1842] Step 3:

[1843] The device collects real-time emotional data from the user. To achieve this, it captures facial expressions with its built-in camera, records voice tone with its microphone, and measures keyboard and touchscreen input speed. The inputs include video data, audio data, and input speed data, all of which are processed in real time.

[1844] Step 4:

[1845] The device sends collected emotional data to the emotion engine in real time. The emotion engine uses AI algorithms (e.g., deep learning models) to analyze facial expressions, voice tone, and input speed data. The analyzed emotional data is quantified (e.g., "Excitement level: 85%") and sent to the next step.

[1846] Step 5:

[1847] The server integrates emotional data with historical behavioral data to analyze how specific emotions lead to certain behaviors. Specifically, it analyzes correlations based on demographic information, historical behavioral data, and real-time emotional data to evaluate the extent to which specific emotional states influence past purchasing behavior. Statistical analysis and machine learning models are used in this process.

[1848] Step 6:

[1849] The server selects advertisements based on the user's emotional state and behavioral data. Here, advertisements corresponding to the emotional state are selected from the database. For example, if the "excitement level" is high, advertisements for sports equipment will be selected. The selected advertisements are then sent to the user in the next step.

[1850] Step 7:

[1851] The server sends the selected advertisement to the device. A communication protocol (e.g., HTTPS) is used for this transmission. The advertisement data is sent in the form of images, text, links, etc.

[1852] Step 8:

[1853] The device displays advertisements to the user. Specifically, advertisements are displayed in the user's field of view using AR (augmented reality) through visual display devices such as smart glasses and smartphones. This display method is visually natural for the user and enables effective ad delivery.

[1854] Example of a prompt

[1855] Design a system that captures a user's gaze, facial expressions, and voice tone in real time while they are wearing smart glasses, and analyzes this data using an emotion engine. The analysis results, along with demographic information and past behavioral data, are sent to a server, and the system then displays the most relevant advertisements in augmented reality (AR) within the user's field of view.

[1856] 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.

[1857] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[1858] 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.

[1859] 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.

[1860] 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.

[1861] 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.

[1862] 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.

[1863] 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.

[1864] 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."

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] 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.

[1873] 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.

[1874] 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.

[1875] 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.

[1876] 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.

[1877] The following is further disclosed regarding the embodiments described above.

[1878] (Claim 1)

[1879] Means for collecting user demographic information and past behavioral data,

[1880] A means of analyzing user sentiment data in real time,

[1881] A method for integrating analyzed emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors,

[1882] A method for selecting advertisements based on user emotional state and behavioral data,

[1883] A means of displaying selected advertisements to users,

[1884] A system that includes this.

[1885] (Claim 2)

[1886] The system according to claim 1, comprising means for utilizing facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time.

[1887] (Claim 3)

[1888] The system according to claim 1, comprising means for setting an emotional state threshold to determine whether a particular emotion leads to purchasing behavior.

[1889] "Example 1"

[1890] (Claim 1)

[1891] Means for collecting user demographic information and past behavioral data,

[1892] A means of analyzing user sentiment data in real time,

[1893] A method for integrating analyzed emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors,

[1894] A method for selecting advertisements based on user emotional state and behavioral data,

[1895] A means of displaying selected advertisements to users,

[1896] A method for personalizing ad content using generative AI models,

[1897] Encryption methods for securely transmitting collected data to the server,

[1898] A system that includes this.

[1899] (Claim 2)

[1900] The system according to claim 1, comprising means for utilizing facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time.

[1901] (Claim 3)

[1902] The system according to claim 1, further comprising means for setting an emotional state threshold to determine whether a particular emotion leads to purchasing behavior.

[1903] "Application Example 1"

[1904] (Claim 1)

[1905] Means for collecting user demographic information and past behavioral data,

[1906] A means of analyzing user sentiment data in real time,

[1907] A method for integrating analyzed emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors,

[1908] A method for selecting advertisements based on user emotional state and behavioral data,

[1909] A means of displaying selected advertisements to users,

[1910] A method for analyzing a user's facial expressions and voice tone in real time using a smartphone,

[1911] The means selected to display ads based on sentiment data analyzed in real time,

[1912] A system that includes this.

[1913] (Claim 2)

[1914] The system according to claim 1, comprising means for utilizing facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time.

[1915] (Claim 3)

[1916] The system according to claim 1, comprising means for setting an emotional state threshold to determine whether a particular emotion leads to purchasing behavior.

[1917] "Example 2 of combining an emotion engine"

[1918] (Claim 1)

[1919] Means for collecting user attribute information and past behavioral data,

[1920] A means of analyzing user sentiment data in real time,

[1921] A method for integrating analyzed emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors,

[1922] A method for selecting advertisements based on user emotional state and behavioral data,

[1923] A means of displaying selected advertisements to users,

[1924] A system that includes this.

[1925] (Claim 2)

[1926] The system according to claim 1, comprising means for utilizing facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time.

[1927] (Claim 3)

[1928] The system according to claim 1, comprising means for setting an emotional state threshold to determine whether a particular emotion leads to purchasing behavior.

[1929] "Application example 2 when combining with an emotional engine"

[1930] (Claim 1)

[1931] Means for collecting user demographic information and past behavioral data,

[1932] A means of analyzing user sentiment data in real time,

[1933] A method for integrating analyzed emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors,

[1934] A method for selecting advertisements based on user emotional state and behavioral data,

[1935] A means of displaying selected advertisements to users,

[1936] A means of collecting real-time emotional data of users using smart devices and displaying advertisements through a visual display device,

[1937] A system that includes this.

[1938] (Claim 2)

[1939] The system according to claim 1, comprising means for utilizing facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time.

[1940] (Claim 3)

[1941] The system according to claim 1, comprising means for setting an emotional state threshold to determine whether a particular emotion leads to purchasing behavior. [Explanation of Symbols]

[1942] 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. Means for collecting user demographic information and past behavioral data, A means of analyzing user sentiment data in real time, A method for integrating analyzed emotional data with past behavioral data to analyze how specific emotions lead to certain behaviors, A method for selecting advertisements based on user emotional state and behavioral data, A means of displaying selected advertisements to users, A system that includes this.

2. The system according to claim 1, comprising means for utilizing facial recognition, voice tone analysis, and keyboard input speed to collect user emotion data in real time.

3. The system according to claim 1, further comprising means for setting an emotional state threshold to determine whether a particular emotion leads to purchasing behavior.

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