system

Abstract

Provide a system. A means for acquiring user data from a database; A means for inputting user data into a generative artificial intelligence model to select and generate appropriate advertisements based on the user's interests; means for delivering the generated advertisement to a user's terminal; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional advertising distribution systems often uniformly distribute generic advertisements without fully considering users' interests and attributes. As a result, users lose interest in the advertisements, reducing their effectiveness. Another issue is that creating advertisements requires a lot of time and resources, making them inefficient. The present invention aims to solve these problems and increase the effectiveness of advertisements by delivering advertisements that are individually optimized for each user. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. A system is provided that includes a means for acquiring user data from a database, a means for inputting the user data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests, and a means for delivering the generated advertisements to the user's terminal. Furthermore, by including a means for extracting advertisements relevant to the user's interests from an advertisement pool and a means for displaying the generated advertisements on the user's terminal, the accuracy and effectiveness of advertisement delivery can be further improved. This allows for the provision of more effective advertisements to users, thereby improving advertisement click rates and purchase rates.

[0006] A "database" is a system that stores user data and allows that data to be searched and retrieved as needed.

[0007] "User data" refers to information including the user's interests, age, place of residence, and other attribute information, as well as behavioral history.

[0008] A "generative artificial intelligence model" is a system that uses machine learning algorithms to analyze data and generate specific results based on input information.

[0009] An "advertising pool" is a collection of multiple advertising contents stored within the system.

[0010] "User interests" refers to information about a particular field or topic that a user is interested in.

[0011] "Advertisement" refers to content that promotes products or services and is distributed to users.

[0012] The "means for selecting and generating appropriate advertisements" refers to a system that has the function of selecting the most relevant advertisements based on user data and generating the advertisement content.

[0013] The "means for delivering the generated advertisement to the user's device" is a system that transmits the selected advertisement to the user's device via digital communication.

[0014] "User terminal" refers to a device such as a smartphone, tablet, or PC that allows a user to connect to the Internet and receive information.

[0015] "Means for displaying advertisements" refers to software or screen interfaces for displaying the delivered advertisements on the screen. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention provides a system that utilizes generative AI and customer data to generate, select, and deliver individually optimized advertisements to users' devices, ensuring that users are only served advertisements that interest them, while enabling companies to increase the effectiveness of their advertisements.

[0038] Program processing (explained in natural language)

[0039] 1. The server retrieves the customer data

[0040] The server receives the user's ID and accesses a database to retrieve relevant user data. For example, the server receives user ID 123 and retrieves data such as the user's interests, age, and location from the database. This data is then used to generate advertisements.

[0041] 2. The server generates the ad using AI

[0042] The server initializes a pre-trained generative AI model and inputs the acquired user data. The generative AI uses this data to analyze the user's interests and select the most relevant ads from the ad pool. For example, if the user is interested in technology and sports, ads related to technology and sports will be selected.

[0043] 3. The server delivers the generated advertisement to the user's device.

[0044] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This is done through digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0045] 4. The user receives the advertisement

[0046] The user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123. This allows the user to view advertisements that match their interests.

[0047] Specific examples

[0048] User A's scenario

[0049] 1. User A:

[0050] Interests: Technology, sports

[0051] Age: 30

[0052] Residence: Tokyo

[0053] 2. Server processing:

[0054] The server retrieves the data for user ID 123 from the database.

[0055] User A's data is as follows: Interests: Technology, Sports, Age: 30, Residence: Tokyo.

[0056] The server inputs this data into the generation AI, which selects the most suitable ad for User A.

[0057] The generated advertisement "Get in shape with new sports equipment!" is delivered to User A's device.

[0058] 3. User terminal processing:

[0059] User A's device receives the advertisement and displays it on the screen.

[0060] This system allows user A to see advertisements that match his or her interests, and allows companies to maximize the effectiveness of their advertising.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The server receives the user ID. Specifically, the server is provided with the user identification information (e.g., user ID 123) that will trigger the advertisement delivery.

[0064] Step 2:

[0065] The server accesses the database. The server retrieves user data (interests, age, place of residence, etc.) corresponding to the user ID from the database. For example, data related to user ID 123 is retrieved.

[0066] Step 3:

[0067] The server formats the user data it acquires. It converts the acquired data into a format that is easy for the generation AI to process. For example, it converts interest categories into a list format.

[0068] Step 4:

[0069] The server initializes the generative AI model. A pre-trained generative AI model (e.g., SimpleAIModel) is loaded and ready for user data input.

[0070] Step 5:

[0071] The server inputs user data into the AI ​​generator, which then passes the formatted user data to the AI ​​generator, which selects ads based on user interests.

[0072] Step 6:

[0073] The server prepares an ad pool, which contains multiple ad content options, from which the generation AI selects the most suitable ad.

[0074] Step 7:

[0075] Generative AI selects advertisements based on user data. It evaluates the relevance of advertisements to users' interests and selects the most appropriate advertisement. For example, an advertisement saying "Get in shape with new sports equipment!" will be selected by users who are interested in technology and sports.

[0076] Step 8:

[0077] The server formats the generated ads and converts the selected ads into a format (e.g., JSON) for delivery to users.

[0078] Step 9:

[0079] The server creates an ad delivery request. The server then creates a request to deliver the generated ad to the user's device. For example, a request is made to deliver an ad 'Get in shape with new sports equipment!' to the device with user ID 123.

[0080] Step 10:

[0081] The server sends the advertisement to the user's device. The server sends a delivery request to the user's device. This request is made using digital communication.

[0082] Step 11:

[0083] The user's device receives the advertisement. The user's device receives the advertisement distribution request from the server and obtains the advertisement content.

[0084] Step 12:

[0085] The user's device displays the advertisement. The device then displays the received advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device.

[0086] Step 13:

[0087] The user views the advertisement. The user checks the advertisement displayed on their device. At this time, the advertisement is optimized to the user's interests, increasing the effectiveness of the advertisement.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] Conventional advertising delivery systems have had difficulty in appropriately selecting, generating, and effectively delivering advertisements based on the interests of each individual user. As a result, they have been unable to provide advertisements that are attractive to users, resulting in reduced advertising effectiveness. Furthermore, there has been a lack of efficient means for generating advertisements that are optimized for each individual user.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for receiving user identification information, means for accessing a database to acquire user data, means for inputting the acquired user data into a generative AI model, analyzing the user's interests, and selecting and generating appropriate advertisements, and means for delivering the generated advertisements to the user's terminal. This enables efficient generation of optimal advertisements based on the individual interests of the user, and effective advertisement delivery.

[0093] "User Identification Information" means unique data used by the system to identify a particular user, such as a user ID or email address.

[0094] A "database" is an information system for systematically collecting, storing, searching, and managing user data.

[0095] "User Data" means information about you, such as your interests, age, and location.

[0096] A "generative AI model" is an artificial intelligence model that is trained using machine learning techniques to generate new data, such as advertisements, from input data.

[0097] "Ad Pool" means the collection of various advertisements available to the system.

[0098] An "advertising delivery request" refers to the commands and data packets created by the server to deliver the generated advertisement to the user's terminal.

[0099] A "terminal" is a device that a user directly operates to receive and display advertisements, such as a smartphone or computer.

[0100] This invention is a system that uses generative AI and user data to generate, select, and deliver individually optimized advertisements to users' devices. This system ensures that users are only served advertisements that interest them, allowing companies to increase the effectiveness of their advertising.

[0101] Overview of the hardware and software used

[0102] The system configuration includes a server, database, generative AI model, and terminal. These elements are combined to achieve the following processing flow:

[0103] 1. Server:

[0104] The server processes the request, receiving the user ID and accessing the database to retrieve the user data, using REST APIs and SQL.

[0105] Generative AI models use pre-trained models (such as GPT-3® and BERT) that take user data as input and generate optimal ads.

[0106] 2. Database:

[0107] The database stores and manages various attribute information such as user interests, age, and place of residence, which is used when generating advertisements.

[0108] 3. Terminal:

[0109] The user's terminal is a device (e.g., a smartphone or PC) that receives the advertisement sent from the server and displays it on the screen.

[0110] Details of data processing and calculation

[0111] The server obtains the user's identification information and retrieves the corresponding user data from the database based on that information. The retrieved data is input into the generative AI model in the following text format:

[0112] Prompt Sentence Examples

[0113] User ID: 123

[0114] Interests: Technology, sports

[0115] Age: 30

[0116] Residence: Tokyo

[0117] The generative AI model analyzes the user's interests based on this prompt. During this process, the following data processing and calculations are performed:

[0118] Scoring relevant ads based on user interests

[0119] Selecting and generating the most relevant ads

[0120] The generated advertisement is delivered to the corresponding user's device via the server. The device receives the advertisement and displays it to the user, thereby delivering the advertisement content.

[0121] Specific examples

[0122] User A scenario:

[0123] 1. User A's interests are "technology" and "sports," his age is "30," and his place of residence is "Tokyo."

[0124] 2. The server retrieves the data for user ID 123 from the database and inputs this data into the generative AI model.

[0125] 3. The model generates the best ad for User A: "Get in shape with new sports equipment!"

[0126] 4. The generated advertisement is delivered to User A's device.

[0127] 5. User A's device receives the advertisement and displays it on the screen.

[0128] According to the above specific flow, the system of the present invention generates and effectively distributes advertisements optimized to the user's interests.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] Receive user identification information

[0132] The server receives the user's identification information (user ID) as a request. This input is sent to the server in the form of an HTTP request, for example. Specifically, the server receives a GET request via the REST API. At this time, the request contains the user ID 123. The input data is obtained as the user ID.

[0133] Step 2:

[0134] Retrieving user data from the database

[0135] The server issues a query to the database based on the received user ID to retrieve data such as the user's interests, age, and place of residence. For example, the SQL query "SELECT FROM users WHERE user_id = 123" is executed. The retrieved data is output in the following format: Interests: Technology, Sports, Age: 30, Place of Residence: Tokyo. The input data is the user ID, and the output data is the user data.

[0136] Step 3:

[0137] Initialize the generative AI model and input customer data

[0138] The server initializes a pre-trained generative AI model (e.g., GPT-3 or BERT) and inputs the acquired user data as a prompt.

[0139] Specifically, the API of the generative AI model is called, and the following prompt is passed: "User ID: 123, Interests: Technology, Sports, Age: 30, Residence: Tokyo." The input data is the user data, and the output data is the start status of the generative AI model.

[0140] Step 4:

[0141] Ad generation and selection using generative AI models

[0142] The generative AI model selects and generates the most relevant ads based on input user data. Specifically, the AI ​​model scores ads from the ad pool and generates an ad such as "Get in shape with new sports equipment!" The input data is the prompt, and the output data is the optimized ad.

[0143] Step 5:

[0144] The generated advertisement is delivered to the user's device.

[0145] The server creates a request to deliver the generated advertisement to the user's device. This request is sent in JSON format, for example, as "User ID: 123, Ad: 'Get in shape with new sports equipment!'". Specifically, the server sends data to the advertisement delivery system using an HTTP POST request. The input data is the optimized advertisement, and the output data is the advertisement delivery status.

[0146] Step 6:

[0147] The device receives the advertisement and displays it on the screen.

[0148] The user's device receives the advertisement sent from the server and displays the advertisement data on the screen. Specifically, the smartphone app analyzes the received JSON data and displays the message "Get in shape with new sports equipment!" on the screen. The input data is the advertisement delivery data, and the output data is the displayed advertisement.

[0149] Step 7:

[0150] User sees the ad

[0151] The user checks the advertisement displayed on the device screen. Specifically, when the user taps on the advertisement, they can move to detailed information or related links. The input data is the displayed advertisement, and the output data is the user's action log.

[0152] (Application example 1)

[0153] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0154] Current advertising delivery systems lack individual optimization based on user interests and behavioral data, resulting in many users receiving ads that are not of interest to them. This has also led to problems with the low effectiveness of advertising, which negatively impacts companies' marketing strategies. Furthermore, virtual stores lack effective methods for delivering ads that reflect users' interests, and improvements in convenience are needed.

[0155] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0156] In this invention, the server includes means for acquiring user data from a database, means for inputting the user data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests, means for delivering the generated advertisements to the user's terminal, means for displaying the generated advertisements on the user's terminal, means for extracting advertisements related to the user's interests from an advertisement pool, and means for customizing advertisements displayed in the virtual store based on the user's interests and behavioral data. This makes it possible to quickly and efficiently deliver advertisements tailored to the user's interests, maximize the effectiveness of advertisements, and improve the customer experience in the virtual store.

[0157] A "database" is a system for storing and managing user data.

[0158] A "means for obtaining user data" is a method or device for collecting information about users from a database.

[0159] A "generative artificial intelligence model" is a machine learning model trained to generate personalized ads based on user data.

[0160] "Inputting user data" refers to the act of supplying user information obtained from a database to a generative artificial intelligence model.

[0161] A "means for selecting and generating relevant advertisements" is a method or device for selecting and generating the most relevant advertisements based on the user's interests.

[0162] "Means for delivering the generated advertisement to the user's terminal" refers to a technology or method for transmitting the advertisement to the user's device, such as a smartphone or computer.

[0163] "Means for displaying advertisements generated on a user's terminal" refers to software and hardware for displaying advertisements on the screen of a user's device.

[0164] A "means for extracting advertisements relevant to a user's interests from an advertisement pool" is a method or device for selecting from a large number of stored advertisements those advertisements that best match the user's interests.

[0165] "Means for customizing advertisements displayed in a virtual store based on user interest and behavior data" refers to a method or device for individually optimizing advertisements displayed in a virtual store based on collected user data.

[0166] The present invention provides a system that utilizes user data acquired from a database, generates individually optimized advertisements using a generative artificial intelligence model, and delivers them to users' terminals. This system operates in the following steps.

[0167] First, the server retrieves user data from a database. The user data includes information such as interests, age, and location. Next, the server inputs the retrieved user data into a generative artificial intelligence model. This model selects and generates appropriate advertisements based on the user's interests.

[0168] The generated advertisements are then sent from the server to the user's device via digital communication. The user's device then displays the received advertisements on its screen. The advertisements are customized based on the user's interests and behavioral data, allowing the user to see advertisements that match their interests.

[0169] Specifically, the server uses the following techniques:

[0170] 1. Database: A system for storing and managing user data. Examples include SQL databases and NoSQL databases.

[0171] 2. Generative AI Models: These are machine learning models trained to generate personalized ads based on user data, typically using deep learning frameworks such as PyTorch and TENSORFLOW®.

[0172] 3. API: An interface for communicating data between services. Specifically, RESTful APIs are often used.

[0173] For example, if the user is a 30-year-old person interested in technology and sports who lives in Tokyo, the following prompt might be sent to the generative AI model:

[0174] Generate the best ads based on user interests. Interests: Technology, Sports, Age: 30, Location: Tokyo

[0175] If the generated advertisement is something like "Get in shape with new sports equipment!", the advertisement is delivered to the user's terminal and displayed on the screen.

[0176] This system makes it easier for users to receive advertisements that match their interests, allowing companies to maximize the effectiveness of their advertisements. In addition, advertisements displayed in virtual stores are customized based on users' interests and behavioral data, which is expected to increase purchasing motivation.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] The server retrieves the user's data from the database.

[0180] Input: User ID

[0181] The server receives the user's ID and uses it to access a database, from which it retrieves data such as the user's interests, age, and place of residence.

[0182] Output: Retrieved user data

[0183] Step 2:

[0184] The server inputs the acquired user data into a generative artificial intelligence model.

[0185] Input: Acquired user data

[0186] The server provides the user's data as input to a generative artificial intelligence model. The model analyzes the user's interests based on this data. For example, if the user is interested in technology and sports, that data is input into the model.

[0187] Output: prompt statement

[0188] Step 3:

[0189] The server uses the generative AI model to generate appropriate advertisements.

[0190] Input: prompt statement

[0191] The generative AI model initialized on the server uses prompt text to generate the most relevant advertisement. For example, based on the input prompt "Generate the best advertisement based on the user's interests. Interests: Technology, Sports, Age: 30, Residence: Tokyo", the generated advertisement "Get in shape with new sports equipment!" is obtained.

[0192] Output: The generated ad

[0193] Step 4:

[0194] The server distributes the generated advertisement to the user's terminal.

[0195] Input: Generated ad, user ID

[0196] The server creates a request to deliver the generated advertisement to the terminal corresponding to the user ID. The server sends the request to the user's terminal via digital communication. For example, the generated advertisement is sent to the terminal with user ID 123.

[0197] Output: Ads delivered to the device

[0198] Step 5:

[0199] The device receives the advertisement and displays it on the screen.

[0200] Input: Ads delivered to the device

[0201] The device receives the advertisement sent from the server. The device displays the advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the user's smartphone screen.

[0202] Output: Ad displayed on screen

[0203] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0204] This invention is a system that combines generative AI and customer data with an emotion engine that recognizes user emotions. This makes it possible to generate, select, and deliver individually optimized advertisements to users' devices. Specifically, it realizes advertisement delivery that takes into account the user's emotional state as well as their interests.

[0205] Program processing (explained in natural language)

[0206] 1. The server retrieves the user data

[0207] The server receives the user ID and accesses a database to retrieve relevant user data, including information about the user's interests, age, location, etc. For example, the server receives user ID 123 and retrieves that user's data from the database.

[0208] 2. The server uses an emotion engine to recognize the user's emotions.

[0209] The server uses an emotion engine to recognize the user's emotions. The emotion engine identifies the user's current emotional state using facial expressions, voice, and text analysis. For example, if the user is using a camera or microphone, that data is used as input.

[0210] 3. The server generates the ad using AI

[0211] The server inputs the acquired user data and the recognized emotional data into the generation AI. Based on this data, the generation AI selects the most appropriate advertisement for the user. For example, if a user is interested in "technology" and "sports" and is currently feeling "joy," a related advertisement will be selected.

[0212] 4. The server delivers the generated ad to the user's device.

[0213] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This advertisement delivery request is made via digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0214] 5. The user's device receives the advertisement

[0215] The user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123.

[0216] Specific examples

[0217] User A's scenario

[0218] 1. User A:

[0219] Interests: Technology, sports

[0220] Age: 30

[0221] Residence: Tokyo

[0222] The server retrieves User A's data from a database and identifies the user's areas of interest (technology, sports), age, and location. The server then uses an emotion engine to recognize User A's current emotional state (e.g., "joy"). Based on this information, the generative AI selects the most suitable ad from the ad pool. For example, an ad might say, "Buy the latest technology product!" or "Get in shape with new sports equipment!"

[0223] Next, the server creates a request to deliver the generated advertisement to User A's device and sends it via digital communication. User A's device receives and displays this advertisement. User A can view advertisements that match his or her interests, increasing the effectiveness of the advertisements.

[0224] This system takes into account not only user interests but also emotions, enabling more personalized ad delivery, thereby improving the user experience and maximizing advertising effectiveness.

[0225] The processing flow will be explained below.

[0226] Step 1:

[0227] The server receives the user ID. Specifically, the server is provided with the user identification information (e.g., user ID 123) that will trigger the advertisement delivery.

[0228] Step 2:

[0229] The server accesses the database. The server retrieves user data (interests, age, place of residence, etc.) corresponding to the user ID from the database. For example, data related to user ID 123 is retrieved.

[0230] Step 3:

[0231] The server formats the user data it acquires. It converts the acquired data into a format that is easy for the generation AI to process. For example, it converts interest categories into a list format.

[0232] Step 4:

[0233] The server initializes the emotion engine, which has the function of analyzing the user's facial expressions, voice, and text to recognize their current emotional state.

[0234] Step 5:

[0235] The user provides their facial expressions and voice to the emotion engine using a camera or microphone. For example, the user's facial expressions and tone of voice can be captured through a webcam or smartphone microphone.

[0236] Step 6:

[0237] The server uses an emotion engine to recognize the user's emotions. The server uses data such as the user's facial expressions, voice, and text analysis as input to identify the user's current emotional state (e.g., "happiness," "sadness," "surprise," etc.).

[0238] Step 7:

[0239] The server initializes the generative AI model. A pre-trained generative AI model (e.g., SimpleAIModel) is loaded and ready to accept user and emotion data.

[0240] Step 8:

[0241] The server inputs user data and recognized emotion data into the generation AI, which then passes the formatted user data and emotion recognition results to the generation AI, which then selects ads based on interests and emotions.

[0242] Step 9:

[0243] The server prepares an ad pool, which contains multiple ad content options, from which the generation AI selects the most suitable ad.

[0244] Step 10:

[0245] Generative AI selects advertisements based on user data and emotional data. Generative AI evaluates the relevance of the user's interests and emotions to select the most appropriate advertisement. For example, if a user is interested in technology and sports and is currently experiencing "joy," an advertisement that matches that will be selected.

[0246] Step 11:

[0247] The server formats the generated ads and converts the selected ads into a format (e.g., JSON) for delivery to users.

[0248] Step 12:

[0249] The server creates an ad delivery request. The server then creates a request to deliver the generated ad to the user's device. For example, the server creates a request to deliver an ad 'Get in shape with new sports equipment!' to the device with user ID 123.

[0250] Step 13:

[0251] The server sends the advertisement to the user's terminal. The server sends a distribution request to the user's terminal. This request is made using digital communication.

[0252] Step 14:

[0253] The user's device receives the advertisement. The user's device receives the advertisement distribution request from the server and acquires the advertisement content.

[0254] Step 15:

[0255] The user's device displays the advertisement. The device displays the received advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device.

[0256] Step 16:

[0257] The user views the ad. The user checks the ad displayed on their device. At this time, the ad is optimized to the user's interests and emotions, increasing the effectiveness of the ad.

[0258] Example 2

[0259] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0260] Conventional ad delivery systems only consider user interests and ignore the user's emotional state, resulting in low personalization and limited advertising effectiveness. Furthermore, no system has been developed that automates the generation and delivery of ads based on user emotions. Therefore, there is a need to improve the accuracy and efficiency of advertising.

[0261] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0262] In this invention, the server includes means for acquiring user data from a database, means for identifying the user's emotions using an emotion recognition engine, means for inputting the user data and emotion data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests and emotions, means for delivering the generated advertisements to the user's terminal, and means for displaying the generated advertisements on the user's terminal, thereby enabling personalized advertisement delivery that takes into account both the user's interests and emotions.

[0263] A "database" is a collection of structured data and a system for efficiently storing, searching, and managing information.

[0264] "User Data" means information about you, including attributes such as your interests, age, and location.

[0265] An "emotion recognition engine" is a system that analyzes and identifies a user's emotions, using data such as facial expressions, voice, and text.

[0266] A "generative artificial intelligence model" is an algorithm that generates information based on given data, particularly for selecting and generating appropriate advertisements.

[0267] An "ad pool" is a resource of advertisements that is used to select appropriate advertisements for a user.

[0268] "User terminal" refers to a device used by a user, including devices such as a PC, smartphone, or tablet.

[0269] "Personalized advertising" refers to advertising that is tailored to a user's specific attributes and status, and is customized based on their interests and emotions.

[0270] This invention is a system that combines a generative AI model with user data and an emotion engine that recognizes user emotions. This allows for the generation, selection, and delivery of individually optimized advertisements to users' devices. Specifically, it realizes ad delivery that takes into account the user's emotional state as well as their interests.

[0271] The server receives the user ID and accesses a database to retrieve relevant user data. This user data may include information such as the user's interests, age, and location. For example, the server receives user ID 123 and retrieves that user's data from the database. The server uses a database management system such as MySQL® to efficiently retrieve this data.

[0272] Next, the server uses an emotion engine to recognize the user's emotions. The emotion engine identifies the user's current emotional state using facial expressions, voice, and text analysis. For example, if the user is using a camera or microphone, that data is used as input. The emotion engine uses the Microsoft® Azure® emotion recognition API, among others.

[0273] The server inputs the acquired user data and recognized emotion data into the generation AI. Based on this data, the generation AI selects and generates the advertisement most suitable for the user. For example, if a user is interested in "technology" and "sports" and is currently feeling "joy," a relevant advertisement will be selected. The generation AI uses OpenAI's (registered trademark) GPT-4 (registered trademark) model.

[0274] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This advertisement delivery request is made via digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0275] Finally, the user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123.

[0276] Specific examples

[0277] User A's scenario

[0278] 1. User A:

[0279] Interests: Technology, sports

[0280] Age: 30

[0281] Residence: Large city

[0282] The server retrieves user A's data from a database and identifies the user's areas of interest (technology, sports), age, and location. The server then uses an emotion engine to recognize user A's current emotional state (e.g., "joy"). Based on this information, the generative AI selects the most suitable ad from the ad pool. For example, an ad might say, "Buy the latest technology product!" or "Get in shape with new sports equipment!"

[0283] Next, the server creates a request to deliver the generated advertisement to User A's device and sends it via digital communication. User A's device receives and displays the advertisement. User A can view advertisements that match his or her interests, increasing the effectiveness of the advertisements. This system takes into account not only the user's interests but also their emotions, enabling more personalized advertisement delivery. This improves the user experience and maximizes advertising effectiveness.

[0284] Prompt Sentence Examples

[0285] Below are some example prompts for generating ads using a generative AI model:

[0286] User attributes:

[0287] Interests: Technology, sports

[0288] Age: 30

[0289] Emotion: Joy

[0290] Create an ad that matches the following attributes:

[0291] By feeding this prompt into OpenAI's GPT-4 model, ads optimized for the user's interests and emotions are generated.

[0292] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0293] Step 1:

[0294] The server retrieves the user data

[0295] The server receives the request and extracts the user ID contained within it. The server receives the request containing the user ID as input. The server then uses a database management system (e.g., MySQL) to query the relevant user data. Specifically, it executes the SQL query SELECT FROM user_data WHERE user_id = ? and obtains the results. The output includes information such as the user's interests, age, and place of residence. For example, the data corresponding to user ID 123 might include the user's interest area being "technology and sports," age being 30, and place of residence being a large city.

[0296] Step 2:

[0297] The server uses an emotion engine to recognize the user's emotions.

[0298] The server collects camera footage and audio data from the user's device. The input includes the user's facial expressions and audio data. The server then calls an emotion recognition API (e.g., emotion recognition API) to analyze this data. Specifically, the server sends the video and audio data as input to the API, and obtains the emotional state, such as "happiness" or "sadness," as output. The output is the emotional data identified as the analysis result. For example, information that the user is feeling "happiness" is output.

[0299] Step 3:

[0300] The server generates ads using AI

[0301] The server formats the acquired user data and emotional data into an input format for the generative AI model. The input includes the user's interests, age, location, and emotional state. The server then sends this formatted data to the generative AI model as a prompt. Specifically, the prompt sentence "Generate an appropriate advertisement for a 30-year-old user who is interested in technology and sports and would be happy" is input to the generative AI model (e.g., generative AI model). Based on this analysis, the generative AI selects and generates the most appropriate advertisement. The output is generated advertising content. For example, the advertising content "Get in shape with new sports equipment!" is generated.

[0302] Step 4:

[0303] The server delivers the generated advertisement to the user's device.

[0304] The server creates a communication request that includes the generated ad content. The input includes the generated ad content and the user ID. Next, the server sends this request to the user's device via an HTTP POST request. Specifically, the server sends the JSON data {"user_id": 123, "ad_content": "Get in shape with new sports equipment!"} to POST / send_ad. The output is a distribution request sent to the user's device.

[0305] Step 5:

[0306] The user's device receives the advertisement

[0307] The user's device receives the HTTP request sent from the server. The input includes the advertising data sent from the server. The user's device then analyzes the received advertising data and displays it on the screen. Specifically, the advertising content "Get in shape with new sports equipment!" is displayed on the user's smartphone or PC screen. The output is that the user is able to view the advertisement.

[0308] This enables personalized ad delivery based on the user's interests and emotions.

[0309] (Application example 2)

[0310] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0311] Conventional ad delivery systems can deliver ads based on a user's interests, but it is difficult to personalize ads that take into account the user's emotional state at that moment. This limits the user experience, making it difficult to maximize advertising effectiveness. Furthermore, the lack of technology to analyze a user's emotional state and deliver ads has made it difficult to provide individually optimized ads.

[0312] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user data from a database, means for recognizing the user's emotions using an emotion recognition engine, means for inputting the user data and emotion data into a generative AI model and selecting and generating appropriate advertisements based on the user's interests and emotional state, and means for delivering the generated advertisements to the user's terminal. This enables more personalized advertisement delivery that takes into account not only the user's interests but also their emotional state.

[0313] A "database" is an information management system that stores information such as user interests, age, and place of residence, and provides the data required by the system.

[0314] An "emotion recognition engine" is a software or hardware component that uses facial expressions, voice, and text analysis to identify a user's current emotional state.

[0315] A "generative artificial intelligence model" is an artificial intelligence system that generates personalized advertisements based on acquired user data and emotional data.

[0316] An "ad pool" is a database or storage system where various advertising content is aggregated.

[0317] "User device" refers to the user's smartphone, computer, tablet, or other electronic device on which the advertisement is displayed.

[0318] "Delivery means" refers to a system that refers to the communication protocol or method for delivering the generated advertisement to the user's terminal.

[0319] "Relevant advertising" refers to advertising that is most effectively delivered based on a user's interests and emotional state.

[0320] "User Data" refers to various personal information obtained from a database, such as a user's interests, age, and place of residence.

[0321] "Emotion Data" refers to information about a user's current emotional state obtained using an emotion recognition engine.

[0322] The present invention is a system that selects and generates appropriate advertisements based on the user's interests and emotional state, and delivers them to the user's terminal, using the following means and processes to achieve this.

[0323] First, the server retrieves user data from the database. This data includes information such as the user's interests, age, and place of residence. For example, data for user ID 123 is retrieved, and it is confirmed that the user is interested in "technology" and "sports," is 30 years old, and lives in Tokyo.

[0324] Next, the server uses an emotion recognition engine to recognize the user's emotions. Emotion recognition utilizes facial expressions, voice, and text analysis to analyze the user's current emotional state based on data from the camera and microphone. For example, the server may recognize that the user is feeling "joy."

[0325] The server then inputs the acquired user data and emotion data into a generative AI model, which then generates personalized advertisements based on this data. For example, the model might generate advertisements such as "Buy the latest technology products!" or "Get in shape with new sports equipment!"

[0326] The generated advertisement is delivered from the server to the user's device. A digital communication protocol is used for delivery. Specifically, a delivery request including the generated advertisement content is created and sent to the device corresponding to the user ID. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the smartphone of user ID 123.

[0327] The device receives the advertisements sent from the server and displays them on the screen, allowing the user to view advertisements that match their interests on the device.

[0328] As a concrete example, consider the scenario of User A. User A is interested in technology and sports, is 30 years old, and lives in Tokyo. The server retrieves this information from the database and, using an emotion recognition engine, recognizes that User A is feeling "joy." Based on this information, a generative artificial intelligence model generates advertisements and delivers them to User A's smartphone. The advertisements include content such as "Buy the latest technology products!" or "Get in shape with new sports equipment!", and User A can view them on his device.

[0329] An example of a prompt sentence could be, "Please generate the optimal advertisement if a 30-year-old user living in Tokyo who is interested in technology and sports is happy." Based on this prompt sentence, the generative AI model can generate the optimal advertisement.

[0330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0331] Step 1:

[0332] The server retrieves user data from a database. The server receives the user ID as input and accesses the database to retrieve relevant user data. Specifically, the server queries the database using user ID 123 and receives a dataset containing information about the user, such as their interests, age, and location.

[0333] (Input) User ID

[0334] (Data processing) Database search

[0335] (Output) A dataset containing user interests, age, location, etc.

[0336] Step 2:

[0337] The server uses an emotion recognition engine to recognize the user's emotions. The emotion recognition engine receives video data from the camera and audio data from the microphone as input and analyzes the user's facial expressions and voice. The server obtains the current emotional state (e.g., "joy") as the output of the emotion recognition engine.

[0338] (Input) Video data, audio data

[0339] (Data processing) Image and audio analysis

[0340] (Output) User's emotional state

[0341] Step 3:

[0342] The server inputs the acquired user data and emotional data into the generative AI model. Based on this data, the generative AI model generates the most suitable advertisement for the user. Specifically, it generates a prompt sentence taking into account the user's interests (technology, sports) and emotional state (joy), and inputs this into the model to generate the advertisement.

[0343] (Input) User data, emotion data

[0344] (Data processing) Prompt sentence generation, advertisement generation using artificial intelligence models

[0345] (Output) Personalized ads

[0346] Step 4:

[0347] The server distributes the generated advertisement to the user's terminal. The server creates a distribution request including the generated advertisement and sends it to the terminal corresponding to the user ID. Specifically, the server creates a request packet including the generated advertisement content and sends it using a digital communication protocol.

[0348] (Input) Generated ad

[0349] (Data processing) Delivery request generation

[0350] (Output) Delivery request packet

[0351] Step 5:

[0352] The user's device receives the advertisement. The device receives the distribution request packet sent from the server and displays the advertisement content on the screen. Specifically, the device receives the advertisement data from the network and displays it on the screen through the application.

[0353] (Input) Delivery request packet

[0354] (Data processing) Data reception, screen display

[0355] (Output) Display of advertisements on user devices

[0356] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0357] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0358] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0359] [Second embodiment]

[0360] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0361] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0362] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0363] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0364] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0365] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0366] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0367] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0368] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0369] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0370] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0371] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0372] This invention provides a system that utilizes generative AI and customer data to generate, select, and deliver individually optimized advertisements to users' devices, ensuring that users are only served advertisements that interest them, while enabling companies to increase the effectiveness of their advertisements.

[0373] Program processing (explained in natural language)

[0374] 1. The server retrieves the customer data

[0375] The server receives the user's ID and accesses a database to retrieve relevant user data. For example, the server receives user ID 123 and retrieves data such as the user's interests, age, and location from the database. This data is then used to generate advertisements.

[0376] 2. The server generates the ad using AI

[0377] The server initializes a pre-trained generative AI model and inputs the acquired user data. The generative AI uses this data to analyze the user's interests and select the most relevant ads from the ad pool. For example, if the user is interested in technology and sports, ads related to technology and sports will be selected.

[0378] 3. The server delivers the generated advertisement to the user's device.

[0379] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This is done through digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0380] 4. The user receives the advertisement

[0381] The user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123. This allows the user to view advertisements that match their interests.

[0382] Specific examples

[0383] User A's scenario

[0384] 1. User A:

[0385] Interests: Technology, sports

[0386] Age: 30

[0387] Residence: Tokyo

[0388] 2. Server processing:

[0389] The server retrieves the data for user ID 123 from the database.

[0390] User A's data is as follows: Interests: Technology, Sports, Age: 30, Residence: Tokyo.

[0391] The server inputs this data into the generation AI, which selects the most suitable ad for User A.

[0392] The generated advertisement "Get in shape with new sports equipment!" is delivered to User A's device.

[0393] 3. User terminal processing:

[0394] User A's device receives the advertisement and displays it on the screen.

[0395] This system allows user A to see advertisements that match his or her interests, and allows companies to maximize the effectiveness of their advertising.

[0396] The processing flow will be explained below.

[0397] Step 1:

[0398] The server receives the user ID. Specifically, the server is provided with the user identification information (e.g., user ID 123) that will trigger the advertisement delivery.

[0399] Step 2:

[0400] The server accesses the database. The server retrieves user data (interests, age, place of residence, etc.) corresponding to the user ID from the database. For example, data related to user ID 123 is retrieved.

[0401] Step 3:

[0402] The server formats the user data it acquires. It converts the acquired data into a format that is easy for the generation AI to process. For example, it converts interest categories into a list format.

[0403] Step 4:

[0404] The server initializes the generative AI model. A pre-trained generative AI model (e.g., SimpleAIModel) is loaded and ready for user data input.

[0405] Step 5:

[0406] The server inputs user data into the AI ​​generator, which then passes the formatted user data to the AI ​​generator, which selects ads based on user interests.

[0407] Step 6:

[0408] The server prepares an ad pool, which contains multiple ad content options, from which the generation AI selects the most suitable ad.

[0409] Step 7:

[0410] Generative AI selects advertisements based on user data. It evaluates the relevance of advertisements to users' interests and selects the most appropriate advertisement. For example, an advertisement saying "Get in shape with new sports equipment!" will be selected by users who are interested in technology and sports.

[0411] Step 8:

[0412] The server formats the generated ads and converts the selected ads into a format (e.g., JSON) for delivery to users.

[0413] Step 9:

[0414] The server creates an ad delivery request. The server then creates a request to deliver the generated ad to the user's device. For example, a request is made to deliver an ad 'Get in shape with new sports equipment!' to the device with user ID 123.

[0415] Step 10:

[0416] The server sends the advertisement to the user's device. The server sends a delivery request to the user's device. This request is made using digital communication.

[0417] Step 11:

[0418] The user's device receives the advertisement. The user's device receives the advertisement distribution request from the server and obtains the advertisement content.

[0419] Step 12:

[0420] The user's device displays the advertisement. The device then displays the received advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device.

[0421] Step 13:

[0422] The user views the advertisement. The user checks the advertisement displayed on their device. At this time, the advertisement is optimized to the user's interests, increasing the effectiveness of the advertisement.

[0423] Example 1

[0424] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0425] Conventional advertising delivery systems have had difficulty in appropriately selecting, generating, and effectively delivering advertisements based on the interests of each individual user. As a result, they have been unable to provide advertisements that are attractive to users, resulting in reduced advertising effectiveness. Furthermore, there has been a lack of efficient means for generating advertisements that are optimized for each individual user.

[0426] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0427] In this invention, the server includes means for receiving user identification information, means for accessing a database to acquire user data, means for inputting the acquired user data into a generative AI model, analyzing the user's interests, and selecting and generating appropriate advertisements, and means for delivering the generated advertisements to the user's terminal. This enables efficient generation of optimal advertisements based on the individual interests of the user, and effective advertisement delivery.

[0428] "User Identification Information" means unique data used by the system to identify a particular user, such as a user ID or email address.

[0429] A "database" is an information system for systematically collecting, storing, searching, and managing user data.

[0430] "User Data" means information about you, such as your interests, age, and location.

[0431] A "generative AI model" is an artificial intelligence model that is trained using machine learning techniques to generate new data, such as advertisements, from input data.

[0432] "Ad Pool" means the collection of various advertisements available to the system.

[0433] An "advertising delivery request" refers to the commands and data packets created by the server to deliver the generated advertisement to the user's terminal.

[0434] A "terminal" is a device that a user directly operates to receive and display advertisements, such as a smartphone or computer.

[0435] This invention is a system that uses generative AI and user data to generate, select, and deliver individually optimized advertisements to users' devices. This system ensures that users are only served advertisements that interest them, allowing companies to increase the effectiveness of their advertising.

[0436] Overview of the hardware and software used

[0437] The system configuration includes a server, database, generative AI model, and terminal. These elements are combined to achieve the following processing flow:

[0438] 1. Server:

[0439] The server processes the request, receiving the user ID and accessing the database to retrieve the user data, using REST APIs and SQL.

[0440] Generative AI models use pre-trained models (such as GPT-3 and BERT) that take user data as input and generate optimal ads.

[0441] 2. Database:

[0442] The database stores and manages various attribute information such as user interests, age, and place of residence, which is used when generating advertisements.

[0443] 3. Terminal:

[0444] The user's terminal is a device (e.g., a smartphone or PC) that receives the advertisement sent from the server and displays it on the screen.

[0445] Details of data processing and calculation

[0446] The server obtains the user's identification information and retrieves the corresponding user data from the database based on that information. The retrieved data is input into the generative AI model in the following text format:

[0447] Prompt Sentence Examples

[0448] User ID: 123

[0449] Interests: Technology, sports

[0450] Age: 30

[0451] Residence: Tokyo

[0452] The generative AI model analyzes the user's interests based on this prompt. During this process, the following data processing and calculations are performed:

[0453] Scoring relevant ads based on user interests

[0454] Selecting and generating the most relevant ads

[0455] The generated advertisement is delivered to the corresponding user's device via the server. The device receives the advertisement and displays it to the user, thereby delivering the advertisement content.

[0456] Specific examples

[0457] User A scenario:

[0458] 1. User A's interests are "technology" and "sports," his age is "30," and his place of residence is "Tokyo."

[0459] 2. The server retrieves the data for user ID 123 from the database and inputs this data into the generative AI model.

[0460] 3. The model generates the best ad for User A: "Get in shape with new sports equipment!"

[0461] 4. The generated advertisement is delivered to User A's device.

[0462] 5. User A's device receives the advertisement and displays it on the screen.

[0463] According to the above specific flow, the system of the present invention generates and effectively distributes advertisements optimized to the user's interests.

[0464] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0465] Step 1:

[0466] Receive user identification information

[0467] The server receives the user's identification information (user ID) as a request. This input is sent to the server in the form of an HTTP request, for example. Specifically, the server receives a GET request via the REST API. At this time, the request contains the user ID 123. The input data is obtained as the user ID.

[0468] Step 2:

[0469] Retrieving user data from the database

[0470] The server issues a query to the database based on the received user ID to retrieve data such as the user's interests, age, and place of residence. For example, the SQL query "SELECT FROM users WHERE user_id = 123" is executed. The retrieved data is output in the following format: Interests: Technology, Sports, Age: 30, Place of Residence: Tokyo. The input data is the user ID, and the output data is the user data.

[0471] Step 3:

[0472] Initialize the generative AI model and input customer data

[0473] The server initializes a pre-trained generative AI model (e.g., GPT-3 or BERT) and inputs the acquired user data as a prompt.

[0474] Specifically, the API of the generative AI model is called, and the following prompt is passed: "User ID: 123, Interests: Technology, Sports, Age: 30, Residence: Tokyo." The input data is the user data, and the output data is the start status of the generative AI model.

[0475] Step 4:

[0476] Ad generation and selection using generative AI models

[0477] The generative AI model selects and generates the most relevant ads based on input user data. Specifically, the AI ​​model scores ads from the ad pool and generates an ad such as "Get in shape with new sports equipment!" The input data is the prompt, and the output data is the optimized ad.

[0478] Step 5:

[0479] The generated advertisement is delivered to the user's device.

[0480] The server creates a request to deliver the generated advertisement to the user's device. This request is sent in JSON format, for example, as "User ID: 123, Ad: 'Get in shape with new sports equipment!'". Specifically, the server sends data to the advertisement delivery system using an HTTP POST request. The input data is the optimized advertisement, and the output data is the advertisement delivery status.

[0481] Step 6:

[0482] The device receives the advertisement and displays it on the screen.

[0483] The user's device receives the advertisement sent from the server and displays the advertisement data on the screen. Specifically, the smartphone app analyzes the received JSON data and displays the message "Get in shape with new sports equipment!" on the screen. The input data is the advertisement delivery data, and the output data is the displayed advertisement.

[0484] Step 7:

[0485] User sees the ad

[0486] The user checks the advertisement displayed on the device screen. Specifically, when the user taps on the advertisement, they can move to detailed information or related links. The input data is the displayed advertisement, and the output data is the user's action log.

[0487] (Application example 1)

[0488] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0489] Current advertising delivery systems lack individual optimization based on user interests and behavioral data, resulting in many users receiving ads that are not of interest to them. This has also led to problems with the low effectiveness of advertising, which negatively impacts companies' marketing strategies. Furthermore, virtual stores lack effective methods for delivering ads that reflect users' interests, and improvements in convenience are needed.

[0490] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0491] In this invention, the server includes means for acquiring user data from a database, means for inputting the user data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests, means for delivering the generated advertisements to the user's terminal, means for displaying the generated advertisements on the user's terminal, means for extracting advertisements related to the user's interests from an advertisement pool, and means for customizing advertisements displayed in the virtual store based on the user's interests and behavioral data. This makes it possible to quickly and efficiently deliver advertisements tailored to the user's interests, maximize the effectiveness of advertisements, and improve the customer experience in the virtual store.

[0492] A "database" is a system for storing and managing user data.

[0493] A "means for obtaining user data" is a method or device for collecting information about users from a database.

[0494] A "generative artificial intelligence model" is a machine learning model trained to generate personalized ads based on user data.

[0495] "Inputting user data" refers to the act of supplying user information obtained from a database to a generative artificial intelligence model.

[0496] A "means for selecting and generating relevant advertisements" is a method or device for selecting and generating the most relevant advertisements based on the user's interests.

[0497] "Means for delivering the generated advertisement to the user's terminal" refers to a technology or method for transmitting the advertisement to the user's device, such as a smartphone or computer.

[0498] "Means for displaying advertisements generated on a user's terminal" refers to software and hardware for displaying advertisements on the screen of a user's device.

[0499] A "means for extracting advertisements relevant to a user's interests from an advertisement pool" is a method or device for selecting from a large number of stored advertisements those advertisements that best match the user's interests.

[0500] "Means for customizing advertisements displayed in a virtual store based on user interest and behavior data" refers to a method or device for individually optimizing advertisements displayed in a virtual store based on collected user data.

[0501] The present invention provides a system that utilizes user data acquired from a database, generates individually optimized advertisements using a generative artificial intelligence model, and delivers them to users' terminals. This system operates in the following steps.

[0502] First, the server retrieves user data from a database. The user data includes information such as interests, age, and location. Next, the server inputs the retrieved user data into a generative artificial intelligence model. This model selects and generates appropriate advertisements based on the user's interests.

[0503] The generated advertisements are then sent from the server to the user's device via digital communication. The user's device then displays the received advertisements on its screen. The advertisements are customized based on the user's interests and behavioral data, allowing the user to see advertisements that match their interests.

[0504] Specifically, the server uses the following techniques:

[0505] 1. Database: A system for storing and managing user data. Examples include SQL databases and NoSQL databases.

[0506] 2. Generative AI models: These are machine learning models trained to generate personalized ads based on user data, typically using deep learning frameworks such as PyTorch or TensorFlow.

[0507] 3. API: An interface for communicating data between services. Specifically, RESTful APIs are often used.

[0508] For example, if the user is a 30-year-old person interested in technology and sports who lives in Tokyo, the following prompt might be sent to the generative AI model:

[0509] Generate the best ads based on user interests. Interests: Technology, Sports, Age: 30, Location: Tokyo

[0510] If the generated advertisement is something like "Get in shape with new sports equipment!", the advertisement is delivered to the user's terminal and displayed on the screen.

[0511] This system makes it easier for users to receive advertisements that match their interests, allowing companies to maximize the effectiveness of their advertisements. In addition, advertisements displayed in virtual stores are customized based on users' interests and behavioral data, which is expected to increase purchasing motivation.

[0512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0513] Step 1:

[0514] The server retrieves the user's data from the database.

[0515] Input: User ID

[0516] The server receives the user's ID and uses it to access a database, from which it retrieves data such as the user's interests, age, and place of residence.

[0517] Output: Retrieved user data

[0518] Step 2:

[0519] The server inputs the acquired user data into a generative artificial intelligence model.

[0520] Input: Acquired user data

[0521] The server provides the user's data as input to a generative artificial intelligence model. The model analyzes the user's interests based on this data. For example, if the user is interested in technology and sports, that data is input into the model.

[0522] Output: prompt statement

[0523] Step 3:

[0524] The server uses the generative AI model to generate appropriate advertisements.

[0525] Input: prompt statement

[0526] The generative AI model initialized on the server uses prompt text to generate the most relevant advertisement. For example, based on the input prompt "Generate the best advertisement based on the user's interests. Interests: Technology, Sports, Age: 30, Residence: Tokyo", the generated advertisement "Get in shape with new sports equipment!" is obtained.

[0527] Output: The generated ad

[0528] Step 4:

[0529] The server distributes the generated advertisement to the user's terminal.

[0530] Input: Generated ad, user ID

[0531] The server creates a request to deliver the generated advertisement to the terminal corresponding to the user ID. The server sends the request to the user's terminal via digital communication. For example, the generated advertisement is sent to the terminal with user ID 123.

[0532] Output: Ads delivered to the device

[0533] Step 5:

[0534] The device receives the advertisement and displays it on the screen.

[0535] Input: Ads delivered to the device

[0536] The device receives the advertisement sent from the server. The device displays the advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the user's smartphone screen.

[0537] Output: Ad displayed on screen

[0538] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0539] This invention is a system that combines generative AI and customer data with an emotion engine that recognizes user emotions. This makes it possible to generate, select, and deliver individually optimized advertisements to users' devices. Specifically, it realizes advertisement delivery that takes into account the user's emotional state as well as their interests.

[0540] Program processing (explained in natural language)

[0541] 1. The server retrieves the user data

[0542] The server receives the user ID and accesses a database to retrieve relevant user data, including information about the user's interests, age, location, etc. For example, the server receives user ID 123 and retrieves that user's data from the database.

[0543] 2. The server uses an emotion engine to recognize the user's emotions.

[0544] The server uses an emotion engine to recognize the user's emotions. The emotion engine identifies the user's current emotional state using facial expressions, voice, and text analysis. For example, if the user is using a camera or microphone, that data is used as input.

[0545] 3. The server generates the ad using AI

[0546] The server inputs the acquired user data and the recognized emotional data into the generation AI. Based on this data, the generation AI selects the most appropriate advertisement for the user. For example, if a user is interested in "technology" and "sports" and is currently feeling "joy," a related advertisement will be selected.

[0547] 4. The server delivers the generated ad to the user's device.

[0548] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This advertisement delivery request is made via digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0549] 5. The user's device receives the advertisement

[0550] The user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123.

[0551] Specific examples

[0552] User A's scenario

[0553] 1. User A:

[0554] Interests: Technology, sports

[0555] Age: 30

[0556] Residence: Tokyo

[0557] The server retrieves User A's data from a database and identifies the user's areas of interest (technology, sports), age, and location. The server then uses an emotion engine to recognize User A's current emotional state (e.g., "joy"). Based on this information, the generative AI selects the most suitable ad from the ad pool. For example, an ad might say, "Buy the latest technology product!" or "Get in shape with new sports equipment!"

[0558] Next, the server creates a request to deliver the generated advertisement to User A's device and sends it via digital communication. User A's device receives and displays this advertisement. User A can view advertisements that match his or her interests, increasing the effectiveness of the advertisements.

[0559] This system takes into account not only user interests but also emotions, enabling more personalized ad delivery, thereby improving the user experience and maximizing advertising effectiveness.

[0560] The processing flow will be explained below.

[0561] Step 1:

[0562] The server receives the user ID. Specifically, the server is provided with the user identification information (e.g., user ID 123) that will trigger the advertisement delivery.

[0563] Step 2:

[0564] The server accesses the database. The server retrieves user data (interests, age, place of residence, etc.) corresponding to the user ID from the database. For example, data related to user ID 123 is retrieved.

[0565] Step 3:

[0566] The server formats the user data it acquires. It converts the acquired data into a format that is easy for the generation AI to process. For example, it converts interest categories into a list format.

[0567] Step 4:

[0568] The server initializes the emotion engine, which has the function of analyzing the user's facial expressions, voice, and text to recognize their current emotional state.

[0569] Step 5:

[0570] The user provides their facial expressions and voice to the emotion engine using a camera or microphone. For example, the user's facial expressions and tone of voice can be captured through a webcam or smartphone microphone.

[0571] Step 6:

[0572] The server uses an emotion engine to recognize the user's emotions. The server uses data such as the user's facial expressions, voice, and text analysis as input to identify the user's current emotional state (e.g., "happiness," "sadness," "surprise," etc.).

[0573] Step 7:

[0574] The server initializes the generative AI model. A pre-trained generative AI model (e.g., SimpleAIModel) is loaded and ready to accept user and emotion data.

[0575] Step 8:

[0576] The server inputs user data and recognized emotion data into the generation AI, which then passes the formatted user data and emotion recognition results to the generation AI, which then selects ads based on interests and emotions.

[0577] Step 9:

[0578] The server prepares an ad pool, which contains multiple ad content options, from which the generation AI selects the most suitable ad.

[0579] Step 10:

[0580] Generative AI selects advertisements based on user data and emotional data. Generative AI evaluates the relevance of the user's interests and emotions to select the most appropriate advertisement. For example, if a user is interested in technology and sports and is currently experiencing "joy," an advertisement that matches that will be selected.

[0581] Step 11:

[0582] The server formats the generated ads and converts the selected ads into a format (e.g., JSON) for delivery to users.

[0583] Step 12:

[0584] The server creates an ad delivery request. The server then creates a request to deliver the generated ad to the user's device. For example, the server creates a request to deliver an ad 'Get in shape with new sports equipment!' to the device with user ID 123.

[0585] Step 13:

[0586] The server sends the advertisement to the user's terminal. The server sends a distribution request to the user's terminal. This request is made using digital communication.

[0587] Step 14:

[0588] The user's device receives the advertisement. The user's device receives the advertisement distribution request from the server and acquires the advertisement content.

[0589] Step 15:

[0590] The user's device displays the advertisement. The device displays the received advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device.

[0591] Step 16:

[0592] The user views the ad. The user checks the ad displayed on their device. At this time, the ad is optimized to the user's interests and emotions, increasing the effectiveness of the ad.

[0593] Example 2

[0594] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0595] Conventional ad delivery systems only consider user interests and ignore the user's emotional state, resulting in low personalization and limited advertising effectiveness. Furthermore, no system has been developed that automates the generation and delivery of ads based on user emotions. Therefore, there is a need to improve the accuracy and efficiency of advertising.

[0596] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0597] In this invention, the server includes means for acquiring user data from a database, means for identifying the user's emotions using an emotion recognition engine, means for inputting the user data and emotion data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests and emotions, means for delivering the generated advertisements to the user's terminal, and means for displaying the generated advertisements on the user's terminal, thereby enabling personalized advertisement delivery that takes into account both the user's interests and emotions.

[0598] A "database" is a collection of structured data and a system for efficiently storing, searching, and managing information.

[0599] "User Data" means information about you, including attributes such as your interests, age, and location.

[0600] An "emotion recognition engine" is a system that analyzes and identifies a user's emotions, using data such as facial expressions, voice, and text.

[0601] A "generative artificial intelligence model" is an algorithm that generates information based on given data, particularly for selecting and generating appropriate advertisements.

[0602] An "ad pool" is a resource of advertisements that is used to select appropriate advertisements for a user.

[0603] "User terminal" refers to a device used by a user, including devices such as a PC, smartphone, or tablet.

[0604] "Personalized advertising" refers to advertising that is tailored to a user's specific attributes and status, and is customized based on their interests and emotions.

[0605] This invention is a system that combines a generative AI model with user data and an emotion engine that recognizes user emotions. This allows for the generation, selection, and delivery of individually optimized advertisements to users' devices. Specifically, it realizes ad delivery that takes into account the user's emotional state as well as their interests.

[0606] The server receives the user ID and accesses a database to retrieve relevant user data. This user data may include information such as the user's interests, age, and location. For example, the server receives user ID 123 and retrieves that user's data from the database. The server uses a database management system such as MySQL to efficiently retrieve this data.

[0607] Next, the server uses an emotion engine to recognize the user's emotions. The emotion engine uses facial expressions, voice, and text analysis to identify the user's current emotional state. For example, if the user is using a camera or microphone, that data is used as input. The emotion engine uses the Microsoft Azure emotion recognition API, among others.

[0608] The server inputs the acquired user data and recognized emotion data into the generation AI. Based on this data, the generation AI selects and generates the most appropriate advertisement for the user. For example, if a user is interested in "technology" and "sports" and is currently feeling "joy," a relevant advertisement will be selected. The generation AI uses OpenAI's GPT-4 model.

[0609] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This advertisement delivery request is made via digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0610] Finally, the user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123.

[0611] Specific examples

[0612] User A's scenario

[0613] 1. User A:

[0614] Interests: Technology, sports

[0615] Age: 30

[0616] Residence: Large city

[0617] The server retrieves user A's data from a database and identifies the user's areas of interest (technology, sports), age, and location. The server then uses an emotion engine to recognize user A's current emotional state (e.g., "joy"). Based on this information, the generative AI selects the most suitable ad from the ad pool. For example, an ad might say, "Buy the latest technology product!" or "Get in shape with new sports equipment!"

[0618] Next, the server creates a request to deliver the generated advertisement to User A's device and sends it via digital communication. User A's device receives and displays the advertisement. User A can view advertisements that match his or her interests, increasing the effectiveness of the advertisements. This system takes into account not only the user's interests but also their emotions, enabling more personalized advertisement delivery. This improves the user experience and maximizes advertising effectiveness.

[0619] Prompt Sentence Examples

[0620] Below are some example prompts for generating ads using a generative AI model:

[0621] User attributes:

[0622] Interests: Technology, sports

[0623] Age: 30

[0624] Emotion: Joy

[0625] Create an ad that matches the following attributes:

[0626] By feeding this prompt into OpenAI's GPT-4 model, ads optimized for the user's interests and emotions are generated.

[0627] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0628] Step 1:

[0629] The server retrieves the user data

[0630] The server receives the request and extracts the user ID contained within it. The server receives the request containing the user ID as input. The server then uses a database management system (e.g., MySQL) to query the relevant user data. Specifically, it executes the SQL query SELECT FROM user_data WHERE user_id = ? and obtains the results. The output includes information such as the user's interests, age, and place of residence. For example, the data corresponding to user ID 123 might include the user's interest area being "technology and sports," age being 30, and place of residence being a large city.

[0631] Step 2:

[0632] The server uses an emotion engine to recognize the user's emotions.

[0633] The server collects camera footage and audio data from the user's device. The input includes the user's facial expressions and audio data. The server then calls an emotion recognition API (e.g., emotion recognition API) to analyze this data. Specifically, the server sends the video and audio data as input to the API, and obtains the emotional state, such as "happiness" or "sadness," as output. The output is the emotional data identified as the analysis result. For example, information that the user is feeling "happiness" is output.

[0634] Step 3:

[0635] The server generates ads using AI

[0636] The server formats the acquired user data and emotional data into an input format for the generative AI model. The input includes the user's interests, age, location, and emotional state. The server then sends this formatted data to the generative AI model as a prompt. Specifically, the prompt sentence "Generate an appropriate advertisement for a 30-year-old user who is interested in technology and sports and would be happy" is input to the generative AI model (e.g., generative AI model). Based on this analysis, the generative AI selects and generates the most appropriate advertisement. The output is generated advertising content. For example, the advertising content "Get in shape with new sports equipment!" is generated.

[0637] Step 4:

[0638] The server delivers the generated advertisement to the user's device.

[0639] The server creates a communication request that includes the generated ad content. The input includes the generated ad content and the user ID. Next, the server sends this request to the user's device via an HTTP POST request. Specifically, the server sends the JSON data {"user_id": 123, "ad_content": "Get in shape with new sports equipment!"} to POST / send_ad. The output is a distribution request sent to the user's device.

[0640] Step 5:

[0641] The user's device receives the advertisement

[0642] The user's device receives the HTTP request sent from the server. The input includes the advertising data sent from the server. The user's device then analyzes the received advertising data and displays it on the screen. Specifically, the advertising content "Get in shape with new sports equipment!" is displayed on the user's smartphone or PC screen. The output is that the user is able to view the advertisement.

[0643] This enables personalized ad delivery based on the user's interests and emotions.

[0644] (Application example 2)

[0645] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0646] Conventional ad delivery systems can deliver ads based on a user's interests, but it is difficult to personalize ads that take into account the user's emotional state at that moment. This limits the user experience, making it difficult to maximize advertising effectiveness. Furthermore, the lack of technology to analyze a user's emotional state and deliver ads has made it difficult to provide individually optimized ads.

[0647] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user data from a database, means for recognizing the user's emotions using an emotion recognition engine, means for inputting the user data and emotion data into a generative AI model and selecting and generating appropriate advertisements based on the user's interests and emotional state, and means for delivering the generated advertisements to the user's terminal. This enables more personalized advertisement delivery that takes into account not only the user's interests but also their emotional state.

[0648] A "database" is an information management system that stores information such as user interests, age, and place of residence, and provides the data required by the system.

[0649] An "emotion recognition engine" is a software or hardware component that uses facial expressions, voice, and text analysis to identify a user's current emotional state.

[0650] A "generative artificial intelligence model" is an artificial intelligence system that generates personalized advertisements based on acquired user data and emotional data.

[0651] An "ad pool" is a database or storage system where various advertising content is aggregated.

[0652] "User device" refers to the user's smartphone, computer, tablet, or other electronic device on which the advertisement is displayed.

[0653] "Delivery means" refers to a system that refers to the communication protocol or method for delivering the generated advertisement to the user's terminal.

[0654] "Relevant advertising" refers to advertising that is most effectively delivered based on a user's interests and emotional state.

[0655] "User Data" refers to various personal information obtained from a database, such as a user's interests, age, and place of residence.

[0656] "Emotion Data" refers to information about a user's current emotional state obtained using an emotion recognition engine.

[0657] The present invention is a system that selects and generates appropriate advertisements based on the user's interests and emotional state, and delivers them to the user's terminal, using the following means and processes to achieve this.

[0658] First, the server retrieves user data from the database. This data includes information such as the user's interests, age, and place of residence. For example, data for user ID 123 is retrieved, and it is confirmed that the user is interested in "technology" and "sports," is 30 years old, and lives in Tokyo.

[0659] Next, the server uses an emotion recognition engine to recognize the user's emotions. Emotion recognition utilizes facial expressions, voice, and text analysis to analyze the user's current emotional state based on data from the camera and microphone. For example, the server may recognize that the user is feeling "joy."

[0660] The server then inputs the acquired user data and emotion data into a generative AI model, which then generates personalized advertisements based on this data. For example, the model might generate advertisements such as "Buy the latest technology products!" or "Get in shape with new sports equipment!"

[0661] The generated advertisement is delivered from the server to the user's device. A digital communication protocol is used for delivery. Specifically, a delivery request including the generated advertisement content is created and sent to the device corresponding to the user ID. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the smartphone of user ID 123.

[0662] The device receives the advertisements sent from the server and displays them on the screen, allowing the user to view advertisements that match their interests on the device.

[0663] As a concrete example, consider the scenario of User A. User A is interested in technology and sports, is 30 years old, and lives in Tokyo. The server retrieves this information from the database and, using an emotion recognition engine, recognizes that User A is feeling "joy." Based on this information, a generative artificial intelligence model generates advertisements and delivers them to User A's smartphone. The advertisements include content such as "Buy the latest technology products!" or "Get in shape with new sports equipment!", and User A can view them on his device.

[0664] An example of a prompt sentence could be, "Please generate the optimal advertisement if a 30-year-old user living in Tokyo who is interested in technology and sports is happy." Based on this prompt sentence, the generative AI model can generate the optimal advertisement.

[0665] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0666] Step 1:

[0667] The server retrieves user data from a database. The server receives the user ID as input and accesses the database to retrieve relevant user data. Specifically, the server queries the database using user ID 123 and receives a dataset containing information about the user, such as their interests, age, and location.

[0668] (Input) User ID

[0669] (Data processing) Database search

[0670] (Output) A dataset containing user interests, age, location, etc.

[0671] Step 2:

[0672] The server uses an emotion recognition engine to recognize the user's emotions. The emotion recognition engine receives video data from the camera and audio data from the microphone as input and analyzes the user's facial expressions and voice. The server obtains the current emotional state (e.g., "joy") as the output of the emotion recognition engine.

[0673] (Input) Video data, audio data

[0674] (Data processing) Image and audio analysis

[0675] (Output) User's emotional state

[0676] Step 3:

[0677] The server inputs the acquired user data and emotional data into the generative AI model. Based on this data, the generative AI model generates the most suitable advertisement for the user. Specifically, it generates a prompt sentence taking into account the user's interests (technology, sports) and emotional state (joy), and inputs this into the model to generate the advertisement.

[0678] (Input) User data, emotion data

[0679] (Data processing) Prompt sentence generation, advertisement generation using artificial intelligence models

[0680] (Output) Personalized ads

[0681] Step 4:

[0682] The server distributes the generated advertisement to the user's terminal. The server creates a distribution request including the generated advertisement and sends it to the terminal corresponding to the user ID. Specifically, the server creates a request packet including the generated advertisement content and sends it using a digital communication protocol.

[0683] (Input) Generated ad

[0684] (Data processing) Delivery request generation

[0685] (Output) Delivery request packet

[0686] Step 5:

[0687] The user's device receives the advertisement. The device receives the distribution request packet sent from the server and displays the advertisement content on the screen. Specifically, the device receives the advertisement data from the network and displays it on the screen through the application.

[0688] (Input) Delivery request packet

[0689] (Data processing) Data reception, screen display

[0690] (Output) Display of advertisements on user devices

[0691] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0692] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0693] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0694] [Third embodiment]

[0695] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0696] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0697] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0698] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0699] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0700] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0701] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0702] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0703] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0704] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0705] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0706] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0707] This invention provides a system that utilizes generative AI and customer data to generate, select, and deliver individually optimized advertisements to users' devices, ensuring that users are only served advertisements that interest them, while enabling companies to increase the effectiveness of their advertisements.

[0708] Program processing (explained in natural language)

[0709] 1. The server retrieves the customer data

[0710] The server receives the user's ID and accesses a database to retrieve relevant user data. For example, the server receives user ID 123 and retrieves data such as the user's interests, age, and location from the database. This data is then used to generate advertisements.

[0711] 2. The server generates the ad using AI

[0712] The server initializes a pre-trained generative AI model and inputs the acquired user data. The generative AI uses this data to analyze the user's interests and select the most relevant ads from the ad pool. For example, if the user is interested in technology and sports, ads related to technology and sports will be selected.

[0713] 3. The server delivers the generated advertisement to the user's device.

[0714] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This is done through digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0715] 4. The user receives the advertisement

[0716] The user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123. This allows the user to view advertisements that match their interests.

[0717] Specific examples

[0718] User A's scenario

[0719] 1. User A:

[0720] Interests: Technology, sports

[0721] Age: 30

[0722] Residence: Tokyo

[0723] 2. Server processing:

[0724] The server retrieves the data for user ID 123 from the database.

[0725] User A's data is as follows: Interests: Technology, Sports, Age: 30, Residence: Tokyo.

[0726] The server inputs this data into the generation AI, which selects the most suitable ad for User A.

[0727] The generated advertisement "Get in shape with new sports equipment!" is delivered to User A's device.

[0728] 3. User terminal processing:

[0729] User A's device receives the advertisement and displays it on the screen.

[0730] This system allows user A to see advertisements that match his or her interests, and allows companies to maximize the effectiveness of their advertising.

[0731] The processing flow will be explained below.

[0732] Step 1:

[0733] The server receives the user ID. Specifically, the server is provided with the user identification information (e.g., user ID 123) that will trigger the advertisement delivery.

[0734] Step 2:

[0735] The server accesses the database. The server retrieves user data (interests, age, place of residence, etc.) corresponding to the user ID from the database. For example, data related to user ID 123 is retrieved.

[0736] Step 3:

[0737] The server formats the user data it acquires. It converts the acquired data into a format that is easy for the generation AI to process. For example, it converts interest categories into a list format.

[0738] Step 4:

[0739] The server initializes the generative AI model. A pre-trained generative AI model (e.g., SimpleAIModel) is loaded and ready for user data input.

[0740] Step 5:

[0741] The server inputs user data into the AI ​​generator, which then passes the formatted user data to the AI ​​generator, which selects ads based on user interests.

[0742] Step 6:

[0743] The server prepares an ad pool, which contains multiple ad content options, from which the generation AI selects the most suitable ad.

[0744] Step 7:

[0745] Generative AI selects advertisements based on user data. It evaluates the relevance of advertisements to users' interests and selects the most appropriate advertisement. For example, an advertisement saying "Get in shape with new sports equipment!" will be selected by users who are interested in technology and sports.

[0746] Step 8:

[0747] The server formats the generated ads and converts the selected ads into a format (e.g., JSON) for delivery to users.

[0748] Step 9:

[0749] The server creates an ad delivery request. The server then creates a request to deliver the generated ad to the user's device. For example, a request is made to deliver an ad 'Get in shape with new sports equipment!' to the device with user ID 123.

[0750] Step 10:

[0751] The server sends the advertisement to the user's device. The server sends a delivery request to the user's device. This request is made using digital communication.

[0752] Step 11:

[0753] The user's device receives the advertisement. The user's device receives the advertisement distribution request from the server and obtains the advertisement content.

[0754] Step 12:

[0755] The user's device displays the advertisement. The device then displays the received advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device.

[0756] Step 13:

[0757] The user views the advertisement. The user checks the advertisement displayed on their device. At this time, the advertisement is optimized to the user's interests, increasing the effectiveness of the advertisement.

[0758] Example 1

[0759] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0760] Conventional advertising delivery systems have had difficulty in appropriately selecting, generating, and effectively delivering advertisements based on the interests of each individual user. As a result, they have been unable to provide advertisements that are attractive to users, resulting in reduced advertising effectiveness. Furthermore, there has been a lack of efficient means for generating advertisements that are optimized for each individual user.

[0761] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0762] In this invention, the server includes means for receiving user identification information, means for accessing a database to acquire user data, means for inputting the acquired user data into a generative AI model, analyzing the user's interests, and selecting and generating appropriate advertisements, and means for delivering the generated advertisements to the user's terminal. This enables efficient generation of optimal advertisements based on the individual interests of the user, and effective advertisement delivery.

[0763] "User Identification Information" means unique data used by the system to identify a particular user, such as a user ID or email address.

[0764] A "database" is an information system for systematically collecting, storing, searching, and managing user data.

[0765] "User Data" means information about you, such as your interests, age, and location.

[0766] A "generative AI model" is an artificial intelligence model that is trained using machine learning techniques to generate new data, such as advertisements, from input data.

[0767] "Ad Pool" means the collection of various advertisements available to the system.

[0768] An "advertising delivery request" refers to the commands and data packets created by the server to deliver the generated advertisement to the user's terminal.

[0769] A "terminal" is a device that a user directly operates to receive and display advertisements, such as a smartphone or computer.

[0770] This invention is a system that uses generative AI and user data to generate, select, and deliver individually optimized advertisements to users' devices. This system ensures that users are only served advertisements that interest them, allowing companies to increase the effectiveness of their advertising.

[0771] Overview of the hardware and software used

[0772] The system configuration includes a server, database, generative AI model, and terminal. These elements are combined to achieve the following processing flow:

[0773] 1. Server:

[0774] The server processes the request, receiving the user ID and accessing the database to retrieve the user data, using REST APIs and SQL.

[0775] Generative AI models use pre-trained models (such as GPT-3 and BERT) that take user data as input and generate optimal ads.

[0776] 2. Database:

[0777] The database stores and manages various attribute information such as user interests, age, and place of residence, which is used when generating advertisements.

[0778] 3. Terminal:

[0779] The user's terminal is a device (e.g., a smartphone or PC) that receives the advertisement sent from the server and displays it on the screen.

[0780] Details of data processing and calculation

[0781] The server obtains the user's identification information and retrieves the corresponding user data from the database based on that information. The retrieved data is input into the generative AI model in the following text format:

[0782] Prompt Sentence Examples

[0783] User ID: 123

[0784] Interests: Technology, sports

[0785] Age: 30

[0786] Residence: Tokyo

[0787] The generative AI model analyzes the user's interests based on this prompt. During this process, the following data processing and calculations are performed:

[0788] Scoring relevant ads based on user interests

[0789] Selecting and generating the most relevant ads

[0790] The generated advertisement is delivered to the corresponding user's device via the server. The device receives the advertisement and displays it to the user, thereby delivering the advertisement content.

[0791] Specific examples

[0792] User A scenario:

[0793] 1. User A's interests are "technology" and "sports," his age is "30," and his place of residence is "Tokyo."

[0794] 2. The server retrieves the data for user ID 123 from the database and inputs this data into the generative AI model.

[0795] 3. The model generates the best ad for User A: "Get in shape with new sports equipment!"

[0796] 4. The generated advertisement is delivered to User A's device.

[0797] 5. User A's device receives the advertisement and displays it on the screen.

[0798] According to the above specific flow, the system of the present invention generates and effectively distributes advertisements optimized to the user's interests.

[0799] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0800] Step 1:

[0801] Receive user identification information

[0802] The server receives the user's identification information (user ID) as a request. This input is sent to the server in the form of an HTTP request, for example. Specifically, the server receives a GET request via the REST API. At this time, the request contains the user ID 123. The input data is obtained as the user ID.

[0803] Step 2:

[0804] Retrieving user data from the database

[0805] The server issues a query to the database based on the received user ID to retrieve data such as the user's interests, age, and place of residence. For example, the SQL query "SELECT FROM users WHERE user_id = 123" is executed. The retrieved data is output in the following format: Interests: Technology, Sports, Age: 30, Place of Residence: Tokyo. The input data is the user ID, and the output data is the user data.

[0806] Step 3:

[0807] Initialize the generative AI model and input customer data

[0808] The server initializes a pre-trained generative AI model (e.g., GPT-3 or BERT) and inputs the acquired user data as a prompt.

[0809] Specifically, the API of the generative AI model is called, and the following prompt is passed: "User ID: 123, Interests: Technology, Sports, Age: 30, Residence: Tokyo." The input data is the user data, and the output data is the start status of the generative AI model.

[0810] Step 4:

[0811] Ad generation and selection using generative AI models

[0812] The generative AI model selects and generates the most relevant ads based on input user data. Specifically, the AI ​​model scores ads from the ad pool and generates an ad such as "Get in shape with new sports equipment!" The input data is the prompt, and the output data is the optimized ad.

[0813] Step 5:

[0814] The generated advertisement is delivered to the user's device.

[0815] The server creates a request to deliver the generated advertisement to the user's device. This request is sent in JSON format, for example, as "User ID: 123, Ad: 'Get in shape with new sports equipment!'". Specifically, the server sends data to the advertisement delivery system using an HTTP POST request. The input data is the optimized advertisement, and the output data is the advertisement delivery status.

[0816] Step 6:

[0817] The device receives the advertisement and displays it on the screen.

[0818] The user's device receives the advertisement sent from the server and displays the advertisement data on the screen. Specifically, the smartphone app analyzes the received JSON data and displays the message "Get in shape with new sports equipment!" on the screen. The input data is the advertisement delivery data, and the output data is the displayed advertisement.

[0819] Step 7:

[0820] User sees the ad

[0821] The user checks the advertisement displayed on the device screen. Specifically, when the user taps on the advertisement, they can move to detailed information or related links. The input data is the displayed advertisement, and the output data is the user's action log.

[0822] (Application example 1)

[0823] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0824] Current advertising delivery systems lack individual optimization based on user interests and behavioral data, resulting in many users receiving ads that are not of interest to them. This has also led to problems with the low effectiveness of advertising, which negatively impacts companies' marketing strategies. Furthermore, virtual stores lack effective methods for delivering ads that reflect users' interests, and improvements in convenience are needed.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0826] In this invention, the server includes means for acquiring user data from a database, means for inputting the user data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests, means for delivering the generated advertisements to the user's terminal, means for displaying the generated advertisements on the user's terminal, means for extracting advertisements related to the user's interests from an advertisement pool, and means for customizing advertisements displayed in the virtual store based on the user's interests and behavioral data. This makes it possible to quickly and efficiently deliver advertisements tailored to the user's interests, maximize the effectiveness of advertisements, and improve the customer experience in the virtual store.

[0827] A "database" is a system for storing and managing user data.

[0828] A "means for obtaining user data" is a method or device for collecting information about users from a database.

[0829] A "generative artificial intelligence model" is a machine learning model trained to generate personalized ads based on user data.

[0830] "Inputting user data" refers to the act of supplying user information obtained from a database to a generative artificial intelligence model.

[0831] A "means for selecting and generating relevant advertisements" is a method or device for selecting and generating the most relevant advertisements based on the user's interests.

[0832] "Means for delivering the generated advertisement to the user's terminal" refers to a technology or method for transmitting the advertisement to the user's device, such as a smartphone or computer.

[0833] "Means for displaying advertisements generated on a user's terminal" refers to software and hardware for displaying advertisements on the screen of a user's device.

[0834] A "means for extracting advertisements relevant to a user's interests from an advertisement pool" is a method or device for selecting from a large number of stored advertisements those advertisements that best match the user's interests.

[0835] "Means for customizing advertisements displayed in a virtual store based on user interest and behavior data" refers to a method or device for individually optimizing advertisements displayed in a virtual store based on collected user data.

[0836] The present invention provides a system that utilizes user data acquired from a database, generates individually optimized advertisements using a generative artificial intelligence model, and delivers them to users' terminals. This system operates in the following steps.

[0837] First, the server retrieves user data from a database. The user data includes information such as interests, age, and location. Next, the server inputs the retrieved user data into a generative artificial intelligence model. This model selects and generates appropriate advertisements based on the user's interests.

[0838] The generated advertisements are then sent from the server to the user's device via digital communication. The user's device then displays the received advertisements on its screen. The advertisements are customized based on the user's interests and behavioral data, allowing the user to see advertisements that match their interests.

[0839] Specifically, the server uses the following techniques:

[0840] 1. Database: A system for storing and managing user data. Examples include SQL databases and NoSQL databases.

[0841] 2. Generative AI models: These are machine learning models trained to generate personalized ads based on user data, typically using deep learning frameworks such as PyTorch or TensorFlow.

[0842] 3. API: An interface for communicating data between services. Specifically, RESTful APIs are often used.

[0843] For example, if the user is a 30-year-old person interested in technology and sports who lives in Tokyo, the following prompt might be sent to the generative AI model:

[0844] Generate the best ads based on user interests. Interests: Technology, Sports, Age: 30, Location: Tokyo

[0845] If the generated advertisement is something like "Get in shape with new sports equipment!", the advertisement is delivered to the user's terminal and displayed on the screen.

[0846] This system makes it easier for users to receive advertisements that match their interests, allowing companies to maximize the effectiveness of their advertisements. In addition, advertisements displayed in virtual stores are customized based on users' interests and behavioral data, which is expected to increase purchasing motivation.

[0847] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0848] Step 1:

[0849] The server retrieves the user's data from the database.

[0850] Input: User ID

[0851] The server receives the user's ID and uses it to access a database, from which it retrieves data such as the user's interests, age, and place of residence.

[0852] Output: Retrieved user data

[0853] Step 2:

[0854] The server inputs the acquired user data into a generative artificial intelligence model.

[0855] Input: Acquired user data

[0856] The server provides the user's data as input to a generative artificial intelligence model. The model analyzes the user's interests based on this data. For example, if the user is interested in technology and sports, that data is input into the model.

[0857] Output: prompt statement

[0858] Step 3:

[0859] The server uses the generative AI model to generate appropriate advertisements.

[0860] Input: prompt statement

[0861] The generative AI model initialized on the server uses prompt text to generate the most relevant advertisement. For example, based on the input prompt "Generate the best advertisement based on the user's interests. Interests: Technology, Sports, Age: 30, Residence: Tokyo", the generated advertisement "Get in shape with new sports equipment!" is obtained.

[0862] Output: The generated ad

[0863] Step 4:

[0864] The server distributes the generated advertisement to the user's terminal.

[0865] Input: Generated ad, user ID

[0866] The server creates a request to deliver the generated advertisement to the terminal corresponding to the user ID. The server sends the request to the user's terminal via digital communication. For example, the generated advertisement is sent to the terminal with user ID 123.

[0867] Output: Ads delivered to the device

[0868] Step 5:

[0869] The device receives the advertisement and displays it on the screen.

[0870] Input: Ads delivered to the device

[0871] The device receives the advertisement sent from the server. The device displays the advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the user's smartphone screen.

[0872] Output: Ad displayed on screen

[0873] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0874] This invention is a system that combines generative AI and customer data with an emotion engine that recognizes user emotions. This makes it possible to generate, select, and deliver individually optimized advertisements to users' devices. Specifically, it realizes advertisement delivery that takes into account the user's emotional state as well as their interests.

[0875] Program processing (explained in natural language)

[0876] 1. The server retrieves the user data

[0877] The server receives the user ID and accesses a database to retrieve relevant user data, including information about the user's interests, age, location, etc. For example, the server receives user ID 123 and retrieves that user's data from the database.

[0878] 2. The server uses an emotion engine to recognize the user's emotions.

[0879] The server uses an emotion engine to recognize the user's emotions. The emotion engine identifies the user's current emotional state using facial expressions, voice, and text analysis. For example, if the user is using a camera or microphone, that data is used as input.

[0880] 3. The server generates the ad using AI

[0881] The server inputs the acquired user data and the recognized emotional data into the generation AI. Based on this data, the generation AI selects the most appropriate advertisement for the user. For example, if a user is interested in "technology" and "sports" and is currently feeling "joy," a related advertisement will be selected.

[0882] 4. The server delivers the generated ad to the user's device.

[0883] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This advertisement delivery request is made via digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0884] 5. The user's device receives the advertisement

[0885] The user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123.

[0886] Specific examples

[0887] User A's scenario

[0888] 1. User A:

[0889] Interests: Technology, sports

[0890] Age: 30

[0891] Residence: Tokyo

[0892] The server retrieves User A's data from a database and identifies the user's areas of interest (technology, sports), age, and location. The server then uses an emotion engine to recognize User A's current emotional state (e.g., "joy"). Based on this information, the generative AI selects the most suitable ad from the ad pool. For example, an ad might say, "Buy the latest technology product!" or "Get in shape with new sports equipment!"

[0893] Next, the server creates a request to deliver the generated advertisement to User A's device and sends it via digital communication. User A's device receives and displays this advertisement. User A can view advertisements that match his or her interests, increasing the effectiveness of the advertisements.

[0894] This system takes into account not only user interests but also emotions, enabling more personalized ad delivery, thereby improving the user experience and maximizing advertising effectiveness.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] The server receives the user ID. Specifically, the server is provided with the user identification information (e.g., user ID 123) that will trigger the advertisement delivery.

[0898] Step 2:

[0899] The server accesses the database. The server retrieves user data (interests, age, place of residence, etc.) corresponding to the user ID from the database. For example, data related to user ID 123 is retrieved.

[0900] Step 3:

[0901] The server formats the user data it acquires. It converts the acquired data into a format that is easy for the generation AI to process. For example, it converts interest categories into a list format.

[0902] Step 4:

[0903] The server initializes the emotion engine, which has the function of analyzing the user's facial expressions, voice, and text to recognize their current emotional state.

[0904] Step 5:

[0905] The user provides their facial expressions and voice to the emotion engine using a camera or microphone. For example, the user's facial expressions and tone of voice can be captured through a webcam or smartphone microphone.

[0906] Step 6:

[0907] The server uses an emotion engine to recognize the user's emotions. The server uses data such as the user's facial expressions, voice, and text analysis as input to identify the user's current emotional state (e.g., "happiness," "sadness," "surprise," etc.).

[0908] Step 7:

[0909] The server initializes the generative AI model. A pre-trained generative AI model (e.g., SimpleAIModel) is loaded and ready to accept user and emotion data.

[0910] Step 8:

[0911] The server inputs user data and recognized emotion data into the generation AI, which then passes the formatted user data and emotion recognition results to the generation AI, which then selects ads based on interests and emotions.

[0912] Step 9:

[0913] The server prepares an ad pool, which contains multiple ad content options, from which the generation AI selects the most suitable ad.

[0914] Step 10:

[0915] Generative AI selects advertisements based on user data and emotional data. Generative AI evaluates the relevance of the user's interests and emotions to select the most appropriate advertisement. For example, if a user is interested in technology and sports and is currently experiencing "joy," an advertisement that matches that will be selected.

[0916] Step 11:

[0917] The server formats the generated ads and converts the selected ads into a format (e.g., JSON) for delivery to users.

[0918] Step 12:

[0919] The server creates an ad delivery request. The server then creates a request to deliver the generated ad to the user's device. For example, the server creates a request to deliver an ad 'Get in shape with new sports equipment!' to the device with user ID 123.

[0920] Step 13:

[0921] The server sends the advertisement to the user's terminal. The server sends a distribution request to the user's terminal. This request is made using digital communication.

[0922] Step 14:

[0923] The user's device receives the advertisement. The user's device receives the advertisement distribution request from the server and acquires the advertisement content.

[0924] Step 15:

[0925] The user's device displays the advertisement. The device displays the received advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device.

[0926] Step 16:

[0927] The user views the ad. The user checks the ad displayed on their device. At this time, the ad is optimized to the user's interests and emotions, increasing the effectiveness of the ad.

[0928] Example 2

[0929] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0930] Conventional ad delivery systems only consider user interests and ignore the user's emotional state, resulting in low personalization and limited advertising effectiveness. Furthermore, no system has been developed that automates the generation and delivery of ads based on user emotions. Therefore, there is a need to improve the accuracy and efficiency of advertising.

[0931] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0932] In this invention, the server includes means for acquiring user data from a database, means for identifying the user's emotions using an emotion recognition engine, means for inputting the user data and emotion data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests and emotions, means for delivering the generated advertisements to the user's terminal, and means for displaying the generated advertisements on the user's terminal, thereby enabling personalized advertisement delivery that takes into account both the user's interests and emotions.

[0933] A "database" is a collection of structured data and a system for efficiently storing, searching, and managing information.

[0934] "User Data" means information about you, including attributes such as your interests, age, and location.

[0935] An "emotion recognition engine" is a system that analyzes and identifies a user's emotions, using data such as facial expressions, voice, and text.

[0936] A "generative artificial intelligence model" is an algorithm that generates information based on given data, particularly for selecting and generating appropriate advertisements.

[0937] An "ad pool" is a resource of advertisements that is used to select appropriate advertisements for a user.

[0938] "User terminal" refers to a device used by a user, including devices such as a PC, smartphone, or tablet.

[0939] "Personalized advertising" refers to advertising that is tailored to a user's specific attributes and status, and is customized based on their interests and emotions.

[0940] This invention is a system that combines a generative AI model with user data and an emotion engine that recognizes user emotions. This allows for the generation, selection, and delivery of individually optimized advertisements to users' devices. Specifically, it realizes ad delivery that takes into account the user's emotional state as well as their interests.

[0941] The server receives the user ID and accesses a database to retrieve relevant user data. This user data may include information such as the user's interests, age, and location. For example, the server receives user ID 123 and retrieves that user's data from the database. The server uses a database management system such as MySQL to efficiently retrieve this data.

[0942] Next, the server uses an emotion engine to recognize the user's emotions. The emotion engine uses facial expressions, voice, and text analysis to identify the user's current emotional state. For example, if the user is using a camera or microphone, that data is used as input. The emotion engine uses the Microsoft Azure emotion recognition API, among others.

[0943] The server inputs the acquired user data and recognized emotion data into the generation AI. Based on this data, the generation AI selects and generates the most appropriate advertisement for the user. For example, if a user is interested in "technology" and "sports" and is currently feeling "joy," a relevant advertisement will be selected. The generation AI uses OpenAI's GPT-4 model.

[0944] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This advertisement delivery request is made via digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[0945] Finally, the user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123.

[0946] Specific examples

[0947] User A's scenario

[0948] 1. User A:

[0949] Interests: Technology, sports

[0950] Age: 30

[0951] Residence: Large city

[0952] The server retrieves user A's data from a database and identifies the user's areas of interest (technology, sports), age, and location. The server then uses an emotion engine to recognize user A's current emotional state (e.g., "joy"). Based on this information, the generative AI selects the most suitable ad from the ad pool. For example, an ad might say, "Buy the latest technology product!" or "Get in shape with new sports equipment!"

[0953] Next, the server creates a request to deliver the generated advertisement to User A's device and sends it via digital communication. User A's device receives and displays the advertisement. User A can view advertisements that match his or her interests, increasing the effectiveness of the advertisements. This system takes into account not only the user's interests but also their emotions, enabling more personalized advertisement delivery. This improves the user experience and maximizes advertising effectiveness.

[0954] Prompt Sentence Examples

[0955] Below are some example prompts for generating ads using a generative AI model:

[0956] User attributes:

[0957] Interests: Technology, sports

[0958] Age: 30

[0959] Emotion: Joy

[0960] Create an ad that matches the following attributes:

[0961] By feeding this prompt into OpenAI's GPT-4 model, ads optimized for the user's interests and emotions are generated.

[0962] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0963] Step 1:

[0964] The server retrieves the user data

[0965] The server receives the request and extracts the user ID contained within it. The server receives the request containing the user ID as input. The server then uses a database management system (e.g., MySQL) to query the relevant user data. Specifically, it executes the SQL query SELECT FROM user_data WHERE user_id = ? and obtains the results. The output includes information such as the user's interests, age, and place of residence. For example, the data corresponding to user ID 123 might include the user's interest area being "technology and sports," age being 30, and place of residence being a large city.

[0966] Step 2:

[0967] The server uses an emotion engine to recognize the user's emotions.

[0968] The server collects camera footage and audio data from the user's device. The input includes the user's facial expressions and audio data. The server then calls an emotion recognition API (e.g., emotion recognition API) to analyze this data. Specifically, the server sends the video and audio data as input to the API, and obtains the emotional state, such as "happiness" or "sadness," as output. The output is the emotional data identified as the analysis result. For example, information that the user is feeling "happiness" is output.

[0969] Step 3:

[0970] The server generates ads using AI

[0971] The server formats the acquired user data and emotional data into an input format for the generative AI model. The input includes the user's interests, age, location, and emotional state. The server then sends this formatted data to the generative AI model as a prompt. Specifically, the prompt sentence "Generate an appropriate advertisement for a 30-year-old user who is interested in technology and sports and would be happy" is input to the generative AI model (e.g., generative AI model). Based on this analysis, the generative AI selects and generates the most appropriate advertisement. The output is generated advertising content. For example, the advertising content "Get in shape with new sports equipment!" is generated.

[0972] Step 4:

[0973] The server delivers the generated advertisement to the user's device.

[0974] The server creates a communication request that includes the generated ad content. The input includes the generated ad content and the user ID. Next, the server sends this request to the user's device via an HTTP POST request. Specifically, the server sends the JSON data {"user_id": 123, "ad_content": "Get in shape with new sports equipment!"} to POST / send_ad. The output is a distribution request sent to the user's device.

[0975] Step 5:

[0976] The user's device receives the advertisement

[0977] The user's device receives the HTTP request sent from the server. The input includes the advertising data sent from the server. The user's device then analyzes the received advertising data and displays it on the screen. Specifically, the advertising content "Get in shape with new sports equipment!" is displayed on the user's smartphone or PC screen. The output is that the user is able to view the advertisement.

[0978] This enables personalized ad delivery based on the user's interests and emotions.

[0979] (Application example 2)

[0980] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0981] Conventional ad delivery systems can deliver ads based on a user's interests, but it is difficult to personalize ads that take into account the user's emotional state at that moment. This limits the user experience, making it difficult to maximize advertising effectiveness. Furthermore, the lack of technology to analyze a user's emotional state and deliver ads has made it difficult to provide individually optimized ads.

[0982] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user data from a database, means for recognizing the user's emotions using an emotion recognition engine, means for inputting the user data and emotion data into a generative AI model and selecting and generating appropriate advertisements based on the user's interests and emotional state, and means for delivering the generated advertisements to the user's terminal. This enables more personalized advertisement delivery that takes into account not only the user's interests but also their emotional state.

[0983] A "database" is an information management system that stores information such as user interests, age, and place of residence, and provides the data required by the system.

[0984] An "emotion recognition engine" is a software or hardware component that uses facial expressions, voice, and text analysis to identify a user's current emotional state.

[0985] A "generative artificial intelligence model" is an artificial intelligence system that generates personalized advertisements based on acquired user data and emotional data.

[0986] An "ad pool" is a database or storage system where various advertising content is aggregated.

[0987] "User device" refers to the user's smartphone, computer, tablet, or other electronic device on which the advertisement is displayed.

[0988] "Delivery means" refers to a system that refers to the communication protocol or method for delivering the generated advertisement to the user's terminal.

[0989] "Relevant advertising" refers to advertising that is most effectively delivered based on a user's interests and emotional state.

[0990] "User Data" refers to various personal information obtained from a database, such as a user's interests, age, and place of residence.

[0991] "Emotion Data" refers to information about a user's current emotional state obtained using an emotion recognition engine.

[0992] The present invention is a system that selects and generates appropriate advertisements based on the user's interests and emotional state, and delivers them to the user's terminal, using the following means and processes to achieve this.

[0993] First, the server retrieves user data from the database. This data includes information such as the user's interests, age, and place of residence. For example, data for user ID 123 is retrieved, and it is confirmed that the user is interested in "technology" and "sports," is 30 years old, and lives in Tokyo.

[0994] Next, the server uses an emotion recognition engine to recognize the user's emotions. Emotion recognition utilizes facial expressions, voice, and text analysis to analyze the user's current emotional state based on data from the camera and microphone. For example, the server may recognize that the user is feeling "joy."

[0995] The server then inputs the acquired user data and emotion data into a generative AI model, which then generates personalized advertisements based on this data. For example, the model might generate advertisements such as "Buy the latest technology products!" or "Get in shape with new sports equipment!"

[0996] The generated advertisement is delivered from the server to the user's device. A digital communication protocol is used for delivery. Specifically, a delivery request including the generated advertisement content is created and sent to the device corresponding to the user ID. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the smartphone of user ID 123.

[0997] The device receives the advertisements sent from the server and displays them on the screen, allowing the user to view advertisements that match their interests on the device.

[0998] As a concrete example, consider the scenario of User A. User A is interested in technology and sports, is 30 years old, and lives in Tokyo. The server retrieves this information from the database and, using an emotion recognition engine, recognizes that User A is feeling "joy." Based on this information, a generative artificial intelligence model generates advertisements and delivers them to User A's smartphone. The advertisements include content such as "Buy the latest technology products!" or "Get in shape with new sports equipment!", and User A can view them on his device.

[0999] An example of a prompt sentence could be, "Please generate the optimal advertisement if a 30-year-old user living in Tokyo who is interested in technology and sports is happy." Based on this prompt sentence, the generative AI model can generate the optimal advertisement.

[1000] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1001] Step 1:

[1002] The server retrieves user data from a database. The server receives the user ID as input and accesses the database to retrieve relevant user data. Specifically, the server queries the database using user ID 123 and receives a dataset containing information about the user, such as their interests, age, and location.

[1003] (Input) User ID

[1004] (Data processing) Database search

[1005] (Output) A dataset containing user interests, age, location, etc.

[1006] Step 2:

[1007] The server uses an emotion recognition engine to recognize the user's emotions. The emotion recognition engine receives video data from the camera and audio data from the microphone as input and analyzes the user's facial expressions and voice. The server obtains the current emotional state (e.g., "joy") as the output of the emotion recognition engine.

[1008] (Input) Video data, audio data

[1009] (Data processing) Image and audio analysis

[1010] (Output) User's emotional state

[1011] Step 3:

[1012] The server inputs the acquired user data and emotional data into the generative AI model. Based on this data, the generative AI model generates the most suitable advertisement for the user. Specifically, it generates a prompt sentence taking into account the user's interests (technology, sports) and emotional state (joy), and inputs this into the model to generate the advertisement.

[1013] (Input) User data, emotion data

[1014] (Data processing) Prompt sentence generation, advertisement generation using artificial intelligence models

[1015] (Output) Personalized ads

[1016] Step 4:

[1017] The server distributes the generated advertisement to the user's terminal. The server creates a distribution request including the generated advertisement and sends it to the terminal corresponding to the user ID. Specifically, the server creates a request packet including the generated advertisement content and sends it using a digital communication protocol.

[1018] (Input) Generated ad

[1019] (Data processing) Delivery request generation

[1020] (Output) Delivery request packet

[1021] Step 5:

[1022] The user's device receives the advertisement. The device receives the distribution request packet sent from the server and displays the advertisement content on the screen. Specifically, the device receives the advertisement data from the network and displays it on the screen through the application.

[1023] (Input) Delivery request packet

[1024] (Data processing) Data reception, screen display

[1025] (Output) Display of advertisements on user devices

[1026] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1027] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1028] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1029] [Fourth embodiment]

[1030] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1031] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1033] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1034] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1035] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1037] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1038] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1039] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1041] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1043] This invention provides a system that utilizes generative AI and customer data to generate, select, and deliver individually optimized advertisements to users' devices, ensuring that users are only served advertisements that interest them, while enabling companies to increase the effectiveness of their advertisements.

[1044] Program processing (explained in natural language)

[1045] 1. The server retrieves the customer data

[1046] The server receives the user's ID and accesses a database to retrieve relevant user data. For example, the server receives user ID 123 and retrieves data such as the user's interests, age, and location from the database. This data is then used to generate advertisements.

[1047] 2. The server generates the ad using AI

[1048] The server initializes a pre-trained generative AI model and inputs the acquired user data. The generative AI uses this data to analyze the user's interests and select the most relevant ads from the ad pool. For example, if the user is interested in technology and sports, ads related to technology and sports will be selected.

[1049] 3. The server delivers the generated advertisement to the user's device.

[1050] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This is done through digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[1051] 4. The user receives the advertisement

[1052] The user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123. This allows the user to view advertisements that match their interests.

[1053] Specific examples

[1054] User A's scenario

[1055] 1. User A:

[1056] Interests: Technology, sports

[1057] Age: 30

[1058] Residence: Tokyo

[1059] 2. Server processing:

[1060] The server retrieves the data for user ID 123 from the database.

[1061] User A's data is as follows: Interests: Technology, Sports, Age: 30, Residence: Tokyo.

[1062] The server inputs this data into the generation AI, which selects the most suitable ad for User A.

[1063] The generated advertisement "Get in shape with new sports equipment!" is delivered to User A's device.

[1064] 3. User terminal processing:

[1065] User A's device receives the advertisement and displays it on the screen.

[1066] This system allows user A to see advertisements that match his or her interests, and allows companies to maximize the effectiveness of their advertising.

[1067] The processing flow will be explained below.

[1068] Step 1:

[1069] The server receives the user ID. Specifically, the server is provided with the user identification information (e.g., user ID 123) that will trigger the advertisement delivery.

[1070] Step 2:

[1071] The server accesses the database. The server retrieves user data (interests, age, place of residence, etc.) corresponding to the user ID from the database. For example, data related to user ID 123 is retrieved.

[1072] Step 3:

[1073] The server formats the user data it acquires. It converts the acquired data into a format that is easy for the generation AI to process. For example, it converts interest categories into a list format.

[1074] Step 4:

[1075] The server initializes the generative AI model. A pre-trained generative AI model (e.g., SimpleAIModel) is loaded and ready for user data input.

[1076] Step 5:

[1077] The server inputs user data into the AI ​​generator, which then passes the formatted user data to the AI ​​generator, which selects ads based on user interests.

[1078] Step 6:

[1079] The server prepares an ad pool, which contains multiple ad content options, from which the generation AI selects the most suitable ad.

[1080] Step 7:

[1081] Generative AI selects advertisements based on user data. It evaluates the relevance of advertisements to users' interests and selects the most appropriate advertisement. For example, an advertisement saying "Get in shape with new sports equipment!" will be selected by users who are interested in technology and sports.

[1082] Step 8:

[1083] The server formats the generated ads and converts the selected ads into a format (e.g., JSON) for delivery to users.

[1084] Step 9:

[1085] The server creates an ad delivery request. The server then creates a request to deliver the generated ad to the user's device. For example, a request is made to deliver an ad 'Get in shape with new sports equipment!' to the device with user ID 123.

[1086] Step 10:

[1087] The server sends the advertisement to the user's device. The server sends a delivery request to the user's device. This request is made using digital communication.

[1088] Step 11:

[1089] The user's device receives the advertisement. The user's device receives the advertisement distribution request from the server and obtains the advertisement content.

[1090] Step 12:

[1091] The user's device displays the advertisement. The device then displays the received advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device.

[1092] Step 13:

[1093] The user views the advertisement. The user checks the advertisement displayed on their device. At this time, the advertisement is optimized to the user's interests, increasing the effectiveness of the advertisement.

[1094] Example 1

[1095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1096] Conventional advertising delivery systems have had difficulty in appropriately selecting, generating, and effectively delivering advertisements based on the interests of each individual user. As a result, they have been unable to provide advertisements that are attractive to users, resulting in reduced advertising effectiveness. Furthermore, there has been a lack of efficient means for generating advertisements that are optimized for each individual user.

[1097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1098] In this invention, the server includes means for receiving user identification information, means for accessing a database to acquire user data, means for inputting the acquired user data into a generative AI model, analyzing the user's interests, and selecting and generating appropriate advertisements, and means for delivering the generated advertisements to the user's terminal. This enables efficient generation of optimal advertisements based on the individual interests of the user, and effective advertisement delivery.

[1099] "User Identification Information" means unique data used by the system to identify a particular user, such as a user ID or email address.

[1100] A "database" is an information system for systematically collecting, storing, searching, and managing user data.

[1101] "User Data" means information about you, such as your interests, age, and location.

[1102] A "generative AI model" is an artificial intelligence model that is trained using machine learning techniques to generate new data, such as advertisements, from input data.

[1103] "Ad Pool" means the collection of various advertisements available to the system.

[1104] An "advertising delivery request" refers to the commands and data packets created by the server to deliver the generated advertisement to the user's terminal.

[1105] A "terminal" is a device that a user directly operates to receive and display advertisements, such as a smartphone or computer.

[1106] This invention is a system that uses generative AI and user data to generate, select, and deliver individually optimized advertisements to users' devices. This system ensures that users are only served advertisements that interest them, allowing companies to increase the effectiveness of their advertising.

[1107] Overview of the hardware and software used

[1108] The system configuration includes a server, database, generative AI model, and terminal. These elements are combined to achieve the following processing flow:

[1109] 1. Server:

[1110] The server processes the request, receiving the user ID and accessing the database to retrieve the user data, using REST APIs and SQL.

[1111] Generative AI models use pre-trained models (such as GPT-3 and BERT) that take user data as input and generate optimal ads.

[1112] 2. Database:

[1113] The database stores and manages various attribute information such as user interests, age, and place of residence, which is used when generating advertisements.

[1114] 3. Terminal:

[1115] The user's terminal is a device (e.g., a smartphone or PC) that receives the advertisement sent from the server and displays it on the screen.

[1116] Details of data processing and calculation

[1117] The server obtains the user's identification information and retrieves the corresponding user data from the database based on that information. The retrieved data is input into the generative AI model in the following text format:

[1118] Prompt Sentence Examples

[1119] User ID: 123

[1120] Interests: Technology, sports

[1121] Age: 30

[1122] Residence: Tokyo

[1123] The generative AI model analyzes the user's interests based on this prompt. During this process, the following data processing and calculations are performed:

[1124] Scoring relevant ads based on user interests

[1125] Selecting and generating the most relevant ads

[1126] The generated advertisement is delivered to the corresponding user's device via the server. The device receives the advertisement and displays it to the user, thereby delivering the advertisement content.

[1127] Specific examples

[1128] User A scenario:

[1129] 1. User A's interests are "technology" and "sports," his age is "30," and his place of residence is "Tokyo."

[1130] 2. The server retrieves the data for user ID 123 from the database and inputs this data into the generative AI model.

[1131] 3. The model generates the best ad for User A: "Get in shape with new sports equipment!"

[1132] 4. The generated advertisement is delivered to User A's device.

[1133] 5. User A's device receives the advertisement and displays it on the screen.

[1134] According to the above specific flow, the system of the present invention generates and effectively distributes advertisements optimized to the user's interests.

[1135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1136] Step 1:

[1137] Receive user identification information

[1138] The server receives the user's identification information (user ID) as a request. This input is sent to the server in the form of an HTTP request, for example. Specifically, the server receives a GET request via the REST API. At this time, the request contains the user ID 123. The input data is obtained as the user ID.

[1139] Step 2:

[1140] Retrieving user data from the database

[1141] The server issues a query to the database based on the received user ID to retrieve data such as the user's interests, age, and place of residence. For example, the SQL query "SELECT FROM users WHERE user_id = 123" is executed. The retrieved data is output in the following format: Interests: Technology, Sports, Age: 30, Place of Residence: Tokyo. The input data is the user ID, and the output data is the user data.

[1142] Step 3:

[1143] Initialize the generative AI model and input customer data

[1144] The server initializes a pre-trained generative AI model (e.g., GPT-3 or BERT) and inputs the acquired user data as a prompt.

[1145] Specifically, the API of the generative AI model is called, and the following prompt is passed: "User ID: 123, Interests: Technology, Sports, Age: 30, Residence: Tokyo." The input data is the user data, and the output data is the start status of the generative AI model.

[1146] Step 4:

[1147] Ad generation and selection using generative AI models

[1148] The generative AI model selects and generates the most relevant ads based on input user data. Specifically, the AI ​​model scores ads from the ad pool and generates an ad such as "Get in shape with new sports equipment!" The input data is the prompt, and the output data is the optimized ad.

[1149] Step 5:

[1150] The generated advertisement is delivered to the user's device.

[1151] The server creates a request to deliver the generated advertisement to the user's device. This request is sent in JSON format, for example, as "User ID: 123, Ad: 'Get in shape with new sports equipment!'". Specifically, the server sends data to the advertisement delivery system using an HTTP POST request. The input data is the optimized advertisement, and the output data is the advertisement delivery status.

[1152] Step 6:

[1153] The device receives the advertisement and displays it on the screen.

[1154] The user's device receives the advertisement sent from the server and displays the advertisement data on the screen. Specifically, the smartphone app analyzes the received JSON data and displays the message "Get in shape with new sports equipment!" on the screen. The input data is the advertisement delivery data, and the output data is the displayed advertisement.

[1155] Step 7:

[1156] User sees the ad

[1157] The user checks the advertisement displayed on the device screen. Specifically, when the user taps on the advertisement, they can move to detailed information or related links. The input data is the displayed advertisement, and the output data is the user's action log.

[1158] (Application example 1)

[1159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1160] Current advertising delivery systems lack individual optimization based on user interests and behavioral data, resulting in many users receiving ads that are not of interest to them. This has also led to problems with the low effectiveness of advertising, which negatively impacts companies' marketing strategies. Furthermore, virtual stores lack effective methods for delivering ads that reflect users' interests, and improvements in convenience are needed.

[1161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1162] In this invention, the server includes means for acquiring user data from a database, means for inputting the user data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests, means for delivering the generated advertisements to the user's terminal, means for displaying the generated advertisements on the user's terminal, means for extracting advertisements related to the user's interests from an advertisement pool, and means for customizing advertisements displayed in the virtual store based on the user's interests and behavioral data. This makes it possible to quickly and efficiently deliver advertisements tailored to the user's interests, maximize the effectiveness of advertisements, and improve the customer experience in the virtual store.

[1163] A "database" is a system for storing and managing user data.

[1164] A "means for obtaining user data" is a method or device for collecting information about users from a database.

[1165] A "generative artificial intelligence model" is a machine learning model trained to generate personalized ads based on user data.

[1166] "Inputting user data" refers to the act of supplying user information obtained from a database to a generative artificial intelligence model.

[1167] A "means for selecting and generating relevant advertisements" is a method or device for selecting and generating the most relevant advertisements based on the user's interests.

[1168] "Means for delivering the generated advertisement to the user's terminal" refers to a technology or method for transmitting the advertisement to the user's device, such as a smartphone or computer.

[1169] "Means for displaying advertisements generated on a user's terminal" refers to software and hardware for displaying advertisements on the screen of a user's device.

[1170] A "means for extracting advertisements relevant to a user's interests from an advertisement pool" is a method or device for selecting from a large number of stored advertisements those advertisements that best match the user's interests.

[1171] "Means for customizing advertisements displayed in a virtual store based on user interest and behavior data" refers to a method or device for individually optimizing advertisements displayed in a virtual store based on collected user data.

[1172] The present invention provides a system that utilizes user data acquired from a database, generates individually optimized advertisements using a generative artificial intelligence model, and delivers them to users' terminals. This system operates in the following steps.

[1173] First, the server retrieves user data from a database. The user data includes information such as interests, age, and location. Next, the server inputs the retrieved user data into a generative artificial intelligence model. This model selects and generates appropriate advertisements based on the user's interests.

[1174] The generated advertisements are then sent from the server to the user's device via digital communication. The user's device then displays the received advertisements on its screen. The advertisements are customized based on the user's interests and behavioral data, allowing the user to see advertisements that match their interests.

[1175] Specifically, the server uses the following techniques:

[1176] 1. Database: A system for storing and managing user data. Examples include SQL databases and NoSQL databases.

[1177] 2. Generative AI models: These are machine learning models trained to generate personalized ads based on user data, typically using deep learning frameworks such as PyTorch or TensorFlow.

[1178] 3. API: An interface for communicating data between services. Specifically, RESTful APIs are often used.

[1179] For example, if the user is a 30-year-old person interested in technology and sports who lives in Tokyo, the following prompt might be sent to the generative AI model:

[1180] Generate the best ads based on user interests. Interests: Technology, Sports, Age: 30, Location: Tokyo

[1181] If the generated advertisement is something like "Get in shape with new sports equipment!", the advertisement is delivered to the user's terminal and displayed on the screen.

[1182] This system makes it easier for users to receive advertisements that match their interests, allowing companies to maximize the effectiveness of their advertisements. In addition, advertisements displayed in virtual stores are customized based on users' interests and behavioral data, which is expected to increase purchasing motivation.

[1183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1184] Step 1:

[1185] The server retrieves the user's data from the database.

[1186] Input: User ID

[1187] The server receives the user's ID and uses it to access a database, from which it retrieves data such as the user's interests, age, and place of residence.

[1188] Output: Retrieved user data

[1189] Step 2:

[1190] The server inputs the acquired user data into a generative artificial intelligence model.

[1191] Input: Acquired user data

[1192] The server provides the user's data as input to a generative artificial intelligence model. The model analyzes the user's interests based on this data. For example, if the user is interested in technology and sports, that data is input into the model.

[1193] Output: prompt statement

[1194] Step 3:

[1195] The server uses the generative AI model to generate appropriate advertisements.

[1196] Input: prompt statement

[1197] The generative AI model initialized on the server uses prompt text to generate the most relevant advertisement. For example, based on the input prompt "Generate the best advertisement based on the user's interests. Interests: Technology, Sports, Age: 30, Residence: Tokyo", the generated advertisement "Get in shape with new sports equipment!" is obtained.

[1198] Output: The generated ad

[1199] Step 4:

[1200] The server distributes the generated advertisement to the user's terminal.

[1201] Input: Generated ad, user ID

[1202] The server creates a request to deliver the generated advertisement to the terminal corresponding to the user ID. The server sends the request to the user's terminal via digital communication. For example, the generated advertisement is sent to the terminal with user ID 123.

[1203] Output: Ads delivered to the device

[1204] Step 5:

[1205] The device receives the advertisement and displays it on the screen.

[1206] Input: Ads delivered to the device

[1207] The device receives the advertisement sent from the server. The device displays the advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the user's smartphone screen.

[1208] Output: Ad displayed on screen

[1209] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1210] This invention is a system that combines generative AI and customer data with an emotion engine that recognizes user emotions. This makes it possible to generate, select, and deliver individually optimized advertisements to users' devices. Specifically, it realizes advertisement delivery that takes into account the user's emotional state as well as their interests.

[1211] Program processing (explained in natural language)

[1212] 1. The server retrieves the user data

[1213] The server receives the user ID and accesses a database to retrieve relevant user data, including information about the user's interests, age, location, etc. For example, the server receives user ID 123 and retrieves that user's data from the database.

[1214] 2. The server uses an emotion engine to recognize the user's emotions.

[1215] The server uses an emotion engine to recognize the user's emotions. The emotion engine identifies the user's current emotional state using facial expressions, voice, and text analysis. For example, if the user is using a camera or microphone, that data is used as input.

[1216] 3. The server generates the ad using AI

[1217] The server inputs the acquired user data and the recognized emotional data into the generation AI. Based on this data, the generation AI selects the most appropriate advertisement for the user. For example, if a user is interested in "technology" and "sports" and is currently feeling "joy," a related advertisement will be selected.

[1218] 4. The server delivers the generated ad to the user's device.

[1219] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This advertisement delivery request is made via digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[1220] 5. The user's device receives the advertisement

[1221] The user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123.

[1222] Specific examples

[1223] User A's scenario

[1224] 1. User A:

[1225] Interests: Technology, sports

[1226] Age: 30

[1227] Residence: Tokyo

[1228] The server retrieves User A's data from a database and identifies the user's areas of interest (technology, sports), age, and location. The server then uses an emotion engine to recognize User A's current emotional state (e.g., "joy"). Based on this information, the generative AI selects the most suitable ad from the ad pool. For example, an ad might say, "Buy the latest technology product!" or "Get in shape with new sports equipment!"

[1229] Next, the server creates a request to deliver the generated advertisement to User A's device and sends it via digital communication. User A's device receives and displays this advertisement. User A can view advertisements that match his or her interests, increasing the effectiveness of the advertisements.

[1230] This system takes into account not only user interests but also emotions, enabling more personalized ad delivery, thereby improving the user experience and maximizing advertising effectiveness.

[1231] The processing flow will be explained below.

[1232] Step 1:

[1233] The server receives the user ID. Specifically, the server is provided with the user identification information (e.g., user ID 123) that will trigger the advertisement delivery.

[1234] Step 2:

[1235] The server accesses the database. The server retrieves user data (interests, age, place of residence, etc.) corresponding to the user ID from the database. For example, data related to user ID 123 is retrieved.

[1236] Step 3:

[1237] The server formats the user data it acquires. It converts the acquired data into a format that is easy for the generation AI to process. For example, it converts interest categories into a list format.

[1238] Step 4:

[1239] The server initializes the emotion engine, which has the function of analyzing the user's facial expressions, voice, and text to recognize their current emotional state.

[1240] Step 5:

[1241] The user provides their facial expressions and voice to the emotion engine using a camera or microphone. For example, the user's facial expressions and tone of voice can be captured through a webcam or smartphone microphone.

[1242] Step 6:

[1243] The server uses an emotion engine to recognize the user's emotions. The server uses data such as the user's facial expressions, voice, and text analysis as input to identify the user's current emotional state (e.g., "happiness," "sadness," "surprise," etc.).

[1244] Step 7:

[1245] The server initializes the generative AI model. A pre-trained generative AI model (e.g., SimpleAIModel) is loaded and ready to accept user and emotion data.

[1246] Step 8:

[1247] The server inputs user data and recognized emotion data into the generation AI, which then passes the formatted user data and emotion recognition results to the generation AI, which then selects ads based on interests and emotions.

[1248] Step 9:

[1249] The server prepares an ad pool, which contains multiple ad content options, from which the generation AI selects the most suitable ad.

[1250] Step 10:

[1251] Generative AI selects advertisements based on user data and emotional data. Generative AI evaluates the relevance of the user's interests and emotions to select the most appropriate advertisement. For example, if a user is interested in technology and sports and is currently experiencing "joy," an advertisement that matches that will be selected.

[1252] Step 11:

[1253] The server formats the generated ads and converts the selected ads into a format (e.g., JSON) for delivery to users.

[1254] Step 12:

[1255] The server creates an ad delivery request. The server then creates a request to deliver the generated ad to the user's device. For example, the server creates a request to deliver an ad 'Get in shape with new sports equipment!' to the device with user ID 123.

[1256] Step 13:

[1257] The server sends the advertisement to the user's terminal. The server sends a distribution request to the user's terminal. This request is made using digital communication.

[1258] Step 14:

[1259] The user's device receives the advertisement. The user's device receives the advertisement distribution request from the server and acquires the advertisement content.

[1260] Step 15:

[1261] The user's device displays the advertisement. The device displays the received advertisement on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device.

[1262] Step 16:

[1263] The user views the ad. The user checks the ad displayed on their device. At this time, the ad is optimized to the user's interests and emotions, increasing the effectiveness of the ad.

[1264] Example 2

[1265] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1266] Conventional ad delivery systems only consider user interests and ignore the user's emotional state, resulting in low personalization and limited advertising effectiveness. Furthermore, no system has been developed that automates the generation and delivery of ads based on user emotions. Therefore, there is a need to improve the accuracy and efficiency of advertising.

[1267] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1268] In this invention, the server includes means for acquiring user data from a database, means for identifying the user's emotions using an emotion recognition engine, means for inputting the user data and emotion data into a generative artificial intelligence model and selecting and generating appropriate advertisements based on the user's interests and emotions, means for delivering the generated advertisements to the user's terminal, and means for displaying the generated advertisements on the user's terminal, thereby enabling personalized advertisement delivery that takes into account both the user's interests and emotions.

[1269] A "database" is a collection of structured data and a system for efficiently storing, searching, and managing information.

[1270] "User Data" means information about you, including attributes such as your interests, age, and location.

[1271] An "emotion recognition engine" is a system that analyzes and identifies a user's emotions, using data such as facial expressions, voice, and text.

[1272] A "generative artificial intelligence model" is an algorithm that generates information based on given data, particularly for selecting and generating appropriate advertisements.

[1273] An "ad pool" is a resource of advertisements that is used to select appropriate advertisements for a user.

[1274] "User terminal" refers to a device used by a user, including devices such as a PC, smartphone, or tablet.

[1275] "Personalized advertising" refers to advertising that is tailored to a user's specific attributes and status, and is customized based on their interests and emotions.

[1276] This invention is a system that combines a generative AI model with user data and an emotion engine that recognizes user emotions. This allows for the generation, selection, and delivery of individually optimized advertisements to users' devices. Specifically, it realizes ad delivery that takes into account the user's emotional state as well as their interests.

[1277] The server receives the user ID and accesses a database to retrieve relevant user data. This user data may include information such as the user's interests, age, and location. For example, the server receives user ID 123 and retrieves that user's data from the database. The server uses a database management system such as MySQL to efficiently retrieve this data.

[1278] Next, the server uses an emotion engine to recognize the user's emotions. The emotion engine uses facial expressions, voice, and text analysis to identify the user's current emotional state. For example, if the user is using a camera or microphone, that data is used as input. The emotion engine uses the Microsoft Azure emotion recognition API, among others.

[1279] The server inputs the acquired user data and recognized emotion data into the generation AI. Based on this data, the generation AI selects and generates the most appropriate advertisement for the user. For example, if a user is interested in "technology" and "sports" and is currently feeling "joy," a relevant advertisement will be selected. The generation AI uses OpenAI's GPT-4 model.

[1280] The server then makes a request to deliver the generated advertisement to the device corresponding to the user ID. This advertisement delivery request is made via digital communication. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the device with user ID 123.

[1281] Finally, the user's device receives the advertisement sent from the server and displays it on the screen. For example, an advertisement saying "Get in shape with new sports equipment!" is displayed on the device of user ID 123.

[1282] Specific examples

[1283] User A's scenario

[1284] 1. User A:

[1285] Interests: Technology, sports

[1286] Age: 30

[1287] Residence: Large city

[1288] The server retrieves user A's data from a database and identifies the user's areas of interest (technology, sports), age, and location. The server then uses an emotion engine to recognize user A's current emotional state (e.g., "joy"). Based on this information, the generative AI selects the most suitable ad from the ad pool. For example, an ad might say, "Buy the latest technology product!" or "Get in shape with new sports equipment!"

[1289] Next, the server creates a request to deliver the generated advertisement to User A's device and sends it via digital communication. User A's device receives and displays the advertisement. User A can view advertisements that match his or her interests, increasing the effectiveness of the advertisements. This system takes into account not only the user's interests but also their emotions, enabling more personalized advertisement delivery. This improves the user experience and maximizes advertising effectiveness.

[1290] Prompt Sentence Examples

[1291] Below are some example prompts for generating ads using a generative AI model:

[1292] User attributes:

[1293] Interests: Technology, sports

[1294] Age: 30

[1295] Emotion: Joy

[1296] Create an ad that matches the following attributes:

[1297] By feeding this prompt into OpenAI's GPT-4 model, ads optimized for the user's interests and emotions are generated.

[1298] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1299] Step 1:

[1300] The server retrieves the user data

[1301] The server receives the request and extracts the user ID contained within it. The server receives the request containing the user ID as input. The server then uses a database management system (e.g., MySQL) to query the relevant user data. Specifically, it executes the SQL query SELECT FROM user_data WHERE user_id = ? and obtains the results. The output includes information such as the user's interests, age, and place of residence. For example, the data corresponding to user ID 123 might include the user's interest area being "technology and sports," age being 30, and place of residence being a large city.

[1302] Step 2:

[1303] The server uses an emotion engine to recognize the user's emotions.

[1304] The server collects camera footage and audio data from the user's device. The input includes the user's facial expressions and audio data. The server then calls an emotion recognition API (e.g., emotion recognition API) to analyze this data. Specifically, the server sends the video and audio data as input to the API, and obtains the emotional state, such as "happiness" or "sadness," as output. The output is the emotional data identified as the analysis result. For example, information that the user is feeling "happiness" is output.

[1305] Step 3:

[1306] The server generates ads using AI

[1307] The server formats the acquired user data and emotional data into an input format for the generative AI model. The input includes the user's interests, age, location, and emotional state. The server then sends this formatted data to the generative AI model as a prompt. Specifically, the prompt sentence "Generate an appropriate advertisement for a 30-year-old user who is interested in technology and sports and would be happy" is input to the generative AI model (e.g., generative AI model). Based on this analysis, the generative AI selects and generates the most appropriate advertisement. The output is generated advertising content. For example, the advertising content "Get in shape with new sports equipment!" is generated.

[1308] Step 4:

[1309] The server delivers the generated advertisement to the user's device.

[1310] The server creates a communication request that includes the generated ad content. The input includes the generated ad content and the user ID. Next, the server sends this request to the user's device via an HTTP POST request. Specifically, the server sends the JSON data {"user_id": 123, "ad_content": "Get in shape with new sports equipment!"} to POST / send_ad. The output is a distribution request sent to the user's device.

[1311] Step 5:

[1312] The user's device receives the advertisement

[1313] The user's device receives the HTTP request sent from the server. The input includes the advertising data sent from the server. The user's device then analyzes the received advertising data and displays it on the screen. Specifically, the advertising content "Get in shape with new sports equipment!" is displayed on the user's smartphone or PC screen. The output is that the user is able to view the advertisement.

[1314] This enables personalized ad delivery based on the user's interests and emotions.

[1315] (Application example 2)

[1316] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1317] Conventional ad delivery systems can deliver ads based on a user's interests, but it is difficult to personalize ads that take into account the user's emotional state at that moment. This limits the user experience, making it difficult to maximize advertising effectiveness. Furthermore, the lack of technology to analyze a user's emotional state and deliver ads has made it difficult to provide individually optimized ads.

[1318] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user data from a database, means for recognizing the user's emotions using an emotion recognition engine, means for inputting the user data and emotion data into a generative AI model and selecting and generating appropriate advertisements based on the user's interests and emotional state, and means for delivering the generated advertisements to the user's terminal. This enables more personalized advertisement delivery that takes into account not only the user's interests but also their emotional state.

[1319] A "database" is an information management system that stores information such as user interests, age, and place of residence, and provides the data required by the system.

[1320] An "emotion recognition engine" is a software or hardware component that uses facial expressions, voice, and text analysis to identify a user's current emotional state.

[1321] A "generative artificial intelligence model" is an artificial intelligence system that generates personalized advertisements based on acquired user data and emotional data.

[1322] An "ad pool" is a database or storage system where various advertising content is aggregated.

[1323] "User device" refers to the user's smartphone, computer, tablet, or other electronic device on which the advertisement is displayed.

[1324] "Delivery means" refers to a system that refers to the communication protocol or method for delivering the generated advertisement to the user's terminal.

[1325] "Relevant advertising" refers to advertising that is most effectively delivered based on a user's interests and emotional state.

[1326] "User Data" refers to various personal information obtained from a database, such as a user's interests, age, and place of residence.

[1327] "Emotion Data" refers to information about a user's current emotional state obtained using an emotion recognition engine.

[1328] The present invention is a system that selects and generates appropriate advertisements based on the user's interests and emotional state, and delivers them to the user's terminal, using the following means and processes to achieve this.

[1329] First, the server retrieves user data from the database. This data includes information such as the user's interests, age, and place of residence. For example, data for user ID 123 is retrieved, and it is confirmed that the user is interested in "technology" and "sports," is 30 years old, and lives in Tokyo.

[1330] Next, the server uses an emotion recognition engine to recognize the user's emotions. Emotion recognition utilizes facial expressions, voice, and text analysis to analyze the user's current emotional state based on data from the camera and microphone. For example, the server may recognize that the user is feeling "joy."

[1331] The server then inputs the acquired user data and emotion data into a generative AI model, which then generates personalized advertisements based on this data. For example, the model might generate advertisements such as "Buy the latest technology products!" or "Get in shape with new sports equipment!"

[1332] The generated advertisement is delivered from the server to the user's device. A digital communication protocol is used for delivery. Specifically, a delivery request including the generated advertisement content is created and sent to the device corresponding to the user ID. For example, an advertisement saying "Get in shape with new sports equipment!" is sent to the smartphone of user ID 123.

[1333] The device receives the advertisements sent from the server and displays them on the screen, allowing the user to view advertisements that match their interests on the device.

[1334] As a concrete example, consider the scenario of User A. User A is interested in technology and sports, is 30 years old, and lives in Tokyo. The server retrieves this information from the database and, using an emotion recognition engine, recognizes that User A is feeling "joy." Based on this information, a generative artificial intelligence model generates advertisements and delivers them to User A's smartphone. The advertisements include content such as "Buy the latest technology products!" or "Get in shape with new sports equipment!", and User A can view them on his device.

[1335] An example of a prompt sentence could be, "Please generate the optimal advertisement if a 30-year-old user living in Tokyo who is interested in technology and sports is happy." Based on this prompt sentence, the generative AI model can generate the optimal advertisement.

[1336] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1337] Step 1:

[1338] The server retrieves user data from a database. The server receives the user ID as input and accesses the database to retrieve relevant user data. Specifically, the server queries the database using user ID 123 and receives a dataset containing information about the user, such as their interests, age, and location.

[1339] (Input) User ID

[1340] (Data processing) Database search

[1341] (Output) A dataset containing user interests, age, location, etc.

[1342] Step 2:

[1343] The server uses an emotion recognition engine to recognize the user's emotions. The emotion recognition engine receives video data from the camera and audio data from the microphone as input and analyzes the user's facial expressions and voice. The server obtains the current emotional state (e.g., "joy") as the output of the emotion recognition engine.

[1344] (Input) Video data, audio data

[1345] (Data processing) Image and audio analysis

[1346] (Output) User's emotional state

[1347] Step 3:

[1348] The server inputs the acquired user data and emotional data into the generative AI model. Based on this data, the generative AI model generates the most suitable advertisement for the user. Specifically, it generates a prompt sentence taking into account the user's interests (technology, sports) and emotional state (joy), and inputs this into the model to generate the advertisement.

[1349] (Input) User data, emotion data

[1350] (Data processing) Prompt sentence generation, advertisement generation using artificial intelligence models

[1351] (Output) Personalized ads

[1352] Step 4:

[1353] The server distributes the generated advertisement to the user's terminal. The server creates a distribution request including the generated advertisement and sends it to the terminal corresponding to the user ID. Specifically, the server creates a request packet including the generated advertisement content and sends it using a digital communication protocol.

[1354] (Input) Generated ad

[1355] (Data processing) Delivery request generation

[1356] (Output) Delivery request packet

[1357] Step 5:

[1358] The user's device receives the advertisement. The device receives the distribution request packet sent from the server and displays the advertisement content on the screen. Specifically, the device receives the advertisement data from the network and displays it on the screen through the application.

[1359] (Input) Delivery request packet

[1360] (Data processing) Data reception, screen display

[1361] (Output) Display of advertisements on user devices

[1362] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1363] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1364] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1365] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1366] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1367] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1368] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1369] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1370] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1371] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1372] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1373] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1374] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1375] 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.

[1376] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1377] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1378] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1379] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1380] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1381] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1382] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1383] The following is further disclosed regarding the above embodiment.

[1384] (Claim 1)

[1385] A means for retrieving user data from a database;

[1386] A means for inputting user data into a generative artificial intelligence model to select and generate appropriate advertisements based on the user's interests;

[1387] means for delivering the generated advertisement to a user's terminal;

[1388] A system including:

[1389] (Claim 2)

[1390] 10. The system of claim 1, further comprising means for extracting advertisements relevant to the user's interests from an advertisement pool.

[1391] (Claim 3)

[1392] 10. The system of claim 1, further comprising means for displaying the generated advertisement on the user's terminal.

[1393] "Example 1"

[1394] (Claim 1)

[1395] means for receiving user identification information;

[1396] a means for accessing the database to obtain user data;

[1397] A means for inputting the acquired user data into a generating AI model, analyzing the user's interests, and selecting and generating appropriate advertisements;

[1398] means for delivering the generated advertisement to a user's terminal;

[1399] A system including:

[1400] (Claim 2)

[1401] 10. The system of claim 1, further comprising means for extracting advertisements relevant to the user's interests from an advertisement pool.

[1402] (Claim 3)

[1403] 10. The system of claim 1, further comprising means for displaying the generated advertisement on the user's terminal.

[1404] "Application Example 1"

[1405] (Claim 1)

[1406] A means for retrieving user data from a database;

[1407] A means for inputting user data into a generative artificial intelligence model to select and generate appropriate advertisements based on the user's interests;

[1408] means for delivering the generated advertisement to a user's terminal;

[1409] means for displaying the advertisement generated on the user's terminal;

[1410] A system including:

[1411] (Claim 2)

[1412] 10. The system of claim 1, further comprising means for extracting advertisements relevant to the user's interests from an advertisement pool.

[1413] (Claim 3)

[1414] 10. The system of claim 1, further comprising means for customizing advertisements displayed within the virtual store based on user interest and behavior data.

[1415] "Example 2: Combining Emotion Engines"

[1416] (Claim 1)

[1417] A means for retrieving user data from a database;

[1418] a means for identifying a user's emotion using an emotion recognition engine;

[1419] A means for inputting user data and emotion data into a generative artificial intelligence model, and selecting and generating appropriate advertisements based on the user's interests and emotions;

[1420] means for delivering the generated advertisement to a user's terminal;

[1421] means for displaying the generated advertisement on a user's terminal;

[1422] A system including:

[1423] (Claim 2)

[1424] 10. The system of claim 1, further comprising means for extracting advertisements from the advertisement pool that are relevant to the user's interests and emotions.

[1425] (Claim 3)

[1426] 10. The system of claim 1, further comprising means for collecting emotion data from a user terminal.

[1427] "Application example 2 when combining emotion engines"

[1428] (Claim 1)

[1429] A means for retrieving user data from a database;

[1430] means for recognizing a user's emotion using an emotion recognition engine;

[1431] A means for inputting user data and emotional data into a generative artificial intelligence model to select and generate appropriate advertisements based on the user's interests and emotional state;

[1432] means for delivering the generated advertisement to a user's terminal;

[1433] A system including:

[1434] (Claim 2)

[1435] 10. The system of claim 1, further comprising means for extracting advertisements relevant to the user's interests from an advertisement pool.

[1436] (Claim 3)

[1437] 10. The system of claim 1, further comprising means for displaying the generated advertisement on the user's terminal. [Explanation of symbols]

[1438] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for retrieving user data from a database; A means for inputting user data into a generative artificial intelligence model to select and generate appropriate advertisements based on the user's interests; means for delivering the generated advertisement to a user's terminal; A system including:

2. 10. The system of claim 1, further comprising means for extracting advertisements relevant to a user's interests from an advertisement pool.

3. 10. The system of claim 1, further comprising means for displaying the generated advertisement on the user's terminal.

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