system

Abstract

We provide the system. [Solution] A means of communication for receiving and analyzing user input, An analytical method that analyzes received user data to understand the user's interests and objectives, An ad generation means for generating and delivering ads to the optimal target based on the analysis results, A distribution method for delivering generated advertisements to target users, A learning method for collecting and analyzing feedback from target users to optimize ad delivery, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional advertising distribution method, there is a problem that the selection of the target is insufficient and the effect of the advertisement cannot be maximized. In particular, in the case of individual entrepreneurs and small and medium-sized enterprises, a method for maximizing the use of limited advertising budgets is required. Also, a method for accurately grasping the interests and purposes of the target and efficiently distributing advertisements according to them is required.

Means for Solving the Problems

[0005] The present invention provides a communication means for receiving user input in real time and analyzing that data. It also includes an analysis means for understanding the user's interests and objectives based on the analysis results, and includes an advertising generation means and a distribution means for generating and delivering optimal advertisements. Furthermore, the system solves the aforementioned problems by providing a system that includes a learning means for collecting feedback from target users who have received advertisements and optimizing advertisement delivery based on that feedback.

[0006] "Communication means" refers to an interface for receiving user input data and exchanging data between servers.

[0007] "Analysis means" refers to technologies that process received user data to understand the user's interests and objectives.

[0008] "Ad generation method" refers to the process of designing and creating advertisements that are suitable for the target audience based on analysis results.

[0009] "Delivery method" refers to the system or protocol used to send and display generated advertisements to target users.

[0010] "Learning methods" refer to the process of collecting feedback, analyzing it to improve the effectiveness of ad delivery, and optimizing the system. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5]This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention aims to realize a system that enables sole proprietors and small and medium-sized enterprises to effectively deliver targeted advertising. The configuration and functions of this system are described below.

[0033] First, the user accesses the system's chat interface using their device. The process begins with the user entering information about the product or service they wish to advertise into the interface. The device then transmits the entered information to the server in real time.

[0034] The server analyzes the received user data. This analysis uses a generative AI model and applies algorithms to understand the user's interests and objectives. Based on the analysis results, the server identifies target users and generates optimal advertisements.

[0035] The generated advertisements are delivered from the server to the target user's device. The device then plays the role of displaying the advertisements in a user-friendly format and effectively delivering the information.

[0036] Furthermore, user reactions and feedback are sent to the server via the device. The server aggregates this feedback and evaluates the effectiveness of the advertisement. Based on the feedback, the server improves the AI ​​model and optimizes it for the next ad delivery.

[0037] As a concrete example, consider a local cafe owner who wants to promote a new menu item. The owner enters information about the new product into a chat interface, and this information is sent to a server. The server identifies a target audience of young coffee lovers living in the cafe's neighborhood and generates an advertisement for a discount campaign on the new menu item. This advertisement is delivered to the devices of users with the specified attributes, effectively promoting the new product.

[0038] The defining feature of this system is its ability to dynamically optimize ad delivery based on user feedback. As a result, it provides a system that allows sole proprietors and small businesses to efficiently utilize their advertising budgets.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users access a chat interface using their device and enter information about the products or services they want to advertise. The device receives this input and formats it as data.

[0042] Step 2:

[0043] The device sends formatted user input data to the server. The transmitted data includes details about the advertised products and services.

[0044] Step 3:

[0045] The server analyzes the received data and uses a generated AI model to identify the user's interests and objectives. The server also refers to trend information and historical data stored in the database to identify the target user group.

[0046] Step 4:

[0047] Based on the analysis results, the server generates advertisements targeted at the most suitable users. These advertisements are designed to capture the target audience's interest.

[0048] Step 5:

[0049] The server delivers the generated advertisements to the target user's device. The device displays the advertisements in a format that is easy for the user to view.

[0050] Step 6:

[0051] Users view advertisements and input their reactions and opinions based on their content into their devices. The devices then send this feedback back to the server.

[0052] Step 7:

[0053] The server analyzes the received feedback and evaluates the effectiveness of ad delivery. The server uses this information to update the AI ​​model and further optimize the next ad delivery.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] There is a need to achieve efficient and effective targeted advertising delivery while maximizing the return on investment of advertising. In particular, sole proprietors and small and medium-sized enterprises (SMEs) need to deliver ads to the right audience, evaluate their effectiveness, and make improvements within their limited advertising budgets. Therefore, there is a need for methods that improve the accuracy of ad targeting and enable continuous improvement based on user feedback.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes communication means for receiving and analyzing user input, analysis means for analyzing the received user data to understand the user's interests and objectives, and means for generating prompt sentences from user input using generative artificial intelligence. This enables the generation and delivery of targeted advertisements based on the user's specific needs, and continuous optimization of advertisement content and delivery accuracy through feedback.

[0059] "User input" refers to information that users provide to the chat interface of the advertising system, and it serves as the basic data for generating advertisements.

[0060] "Communication means" refers to the means of sending user input from a terminal to a server, and is a technology that enables accurate data transfer.

[0061] "Analysis means" refers to methods for analyzing received user data and understanding their interests and objectives.

[0062] "Generative artificial intelligence" refers to an AI model that generates prompt text from user input and uses that information to create targeted advertisements.

[0063] A "prompt" is a set of instructions or messages that form the basis of the generated advertisement, and it serves as the starting point for the AI ​​model to create the advertisement content.

[0064] An "advertising generation method" is a means for generating advertisements in a format suitable for a specific target audience based on a prompt message.

[0065] "Delivery method" refers to the means by which generated advertisements are delivered to target users, and it plays a role in determining the appropriate delivery timing and format.

[0066] "Learning methods" refer to technologies used to collect feedback from target users and improve the accuracy of ad delivery through analysis of that feedback.

[0067] This invention is designed as a system for effectively generating and delivering targeted advertisements. Users first access the system's chat interface using their own devices and input information about the products or services they wish to advertise. The system requires a computer and an internet connection as basic hardware.

[0068] The terminal plays the role of transmitting the entered information to the server in real time. The server is a computer server that analyzes user input using artificial intelligence technologies such as OpenAI as a generative AI model. This model understands the user's intent based on the input information and generates prompt messages. These prompt messages serve as a starting point for customizing targeted advertisements.

[0069] Specifically, the generative AI model generates a prompt such as, "Create a targeted ad to promote our new coffee menu." Based on this instruction, the server identifies target users and determines what kind of ad content is best suited for them. If the user is the owner of a local cafe, the server can target consumers with specific attributes living in the neighborhood (e.g., "coffee lovers" or "young people in their 20s") and generate and deliver ads for discount campaigns on the new menu.

[0070] The generated advertisements are sent to the target user's device using a distribution method. The device receives the advertisement and displays it in a format that is easy for the user to view. For example, visually appealing advertisement formats using vibrant images and catchy slogans may be employed.

[0071] Furthermore, user responses and feedback to advertisements are sent from the device to the server. This feedback data is used by the server to evaluate the effectiveness of the advertisements and to improve future advertising campaigns. The server uses this data to update its AI model and optimize ad delivery. This allows sole proprietors and small businesses to use their advertising budgets efficiently and achieve maximum results.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] Users access the system's chat interface using their own devices. Here, users enter detailed information about the products or services they wish to advertise. This input data is collected as initial information and sent from the interface to the user's device.

[0075] Step 2:

[0076] The terminal transmits user-entered information to the server in real time. The entered information is sent to the server as text data. The server receives this data and prepares for initial analysis.

[0077] Step 3:

[0078] The server analyzes the received user data. This analysis utilizes a generative AI model. Specifically, the server analyzes text data and generates prompts that are optimal for the user's needs and targeted advertisements. These generated prompts form the basis for the next ad generation step.

[0079] Step 4:

[0080] The server uses the generated prompt to identify the target user group and generate ad content. The specific content and messaging of the targeted ad campaign are determined via a generative artificial intelligence model. The ads generated at this stage are prepared as data for delivery.

[0081] Step 5:

[0082] The server uses a delivery method to distribute the generated advertising content to the target users. The advertisement is sent to the target user's device at the appropriate time and in the appropriate format. After delivery, the advertisement is visually presented to the user by the device.

[0083] Step 6:

[0084] User responses and feedback to advertisements are sent to the server via the device. This feedback data is compiled and analyzed on the server as data to evaluate the effectiveness of the advertisements. This allows for an understanding of the actual results of the advertising campaign.

[0085] Step 7:

[0086] The server uses collected feedback data to improve the generated AI model and optimize ad delivery. This process continuously improves targeting accuracy and messaging content for future ad deliveries. This helps users achieve more effective advertising results.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] This invention aims to provide an efficient system that enables sole proprietors and small and medium-sized enterprises to overcome the limitations of conventional advertising methods and deliver advertisements more effectively to their target customers. In particular, it addresses the challenge of maximizing the use of limited advertising budgets by providing a consistent system from ad generation and delivery to optimization through feedback.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes communication means for receiving and processing information from a user terminal, analysis means for analyzing information about the input product or service and identifying potential customer segments, and advertising generation means for automatically generating and transmitting advertisements directed at target customers based on the analysis results. This makes it possible for users to easily create targeted advertisements and effectively reach their target customers.

[0092] A "user terminal" is a device used to access the advertising delivery system and input information, and includes, for example, smartphones and personal computers.

[0093] "Communication means" refers to the function of sending information from the user's terminal to the server and receiving advertisements and feedback from the server to the user's terminal.

[0094] "Analysis means" refers to a function that analyzes information provided by users and processes it to identify potential customer segments.

[0095] An "ad generation method" is a function that handles the process of automatically creating and sending ads that are optimal for the target customer based on analyzed data.

[0096] "Distribution method" refers to the function used to effectively deliver generated advertisements to specific customer segments.

[0097] "Control means" refers to a process for collecting and analyzing received response information and using that information to improve the effectiveness of advertising activities.

[0098] "Generative artificial intelligence" refers to a method that optimizes ad generation and delivery by analyzing data and learning patterns.

[0099] This invention provides a system for effectively delivering targeted advertising to sole proprietors and small and medium-sized enterprises. The system mainly consists of a user terminal, a server, and a generative AI model. A specific example of this system is described below.

[0100] Users access the system interface using user devices such as smartphones or personal computers. Users input information about the products or services they wish to offer and submit it to the platform. The entered information is then transferred to the server via communication means.

[0101] Upon receiving this information, the server analyzes the data using analytical tools. Generative AI models are used in the analysis to identify the most suitable potential customer segment based on the user's information. GPT-4 (registered trademark) is used as an example of a generative AI model. This model generates optimal advertising content using specific prompt sentences.

[0102] Based on the analysis results, the ad generation system activates and automatically creates ads targeted at the identified customers. The generated ads are then sent to the customers' devices in real time via the delivery system.

[0103] After an advertisement is delivered, customer responses are sent back to the server. The server uses control mechanisms to aggregate and analyze this response information. Based on this feedback, advertising activities are continuously optimized.

[0104] For example, if a local florist wants to promote a new bouquet, the user would enter information such as "New spring bouquet, for those in their 20s and 30s, 10% off first purchase" as a prompt. This information is processed by the server and delivered as a customized advertisement to nearby flower-loving customers.

[0105] Example of a prompt:

[0106] Please generate an ad. The target audience is local users in their 20s and 30s who love flowers, and the ad aims to introduce new flower bouquets. Please include a 10% off promotion.

[0107] Thus, the system of the present invention enables users to maximize the effectiveness of their advertising and achieve appropriate approaches to their customers.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user's device receives information about the advertisement content entered by the user. Specifically, it accesses the interface on a smartphone or computer and enters information such as a prompt message: "New spring flower bouquets, for people in their 20s and 30s, 10% off first purchase." The entered information is then prepared to be sent to the server via a communication method.

[0111] Step 2:

[0112] The server processes the prompt messages received from the user terminal using an analysis tool. It analyzes the input prompt messages and applies a generative AI model (e.g., GPT-4) to identify potential customer segments. Specifically, it performs text analysis, and the analysis results generate a profile of the target customer.

[0113] Step 3:

[0114] The server's ad generation mechanism automatically generates ads based on analysis results. It uses a generation AI model to create optimal ads and build specific ad content targeted at specific customers. At this stage, the generated ad text and visual elements are obtained as output.

[0115] Step 4:

[0116] The generated advertisements are sent in real time to the target customers' devices via the server's delivery system. Specifically, the method of ad delivery is determined based on specific location information and past behavioral data. As a result of delivery, the advertisements are displayed to the specified target audience.

[0117] Step 5:

[0118] Customer reactions to advertisements viewed on their devices are sent back to the server. User actions and behavioral data (clicks, dwell time, etc.) are collected as specific inputs and sent to the server via communication means.

[0119] Step 6:

[0120] The server analyzes collected customer responses using control mechanisms to evaluate the effectiveness of the advertisements. Using the collected data as a medium, it quantifies the advertising's effectiveness and identifies areas for improvement. Based on this analysis, advertising activities are optimized for future campaigns.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention provides a more sophisticated advertising delivery system that incorporates an emotion engine that recognizes user emotions. This system enables sole proprietors and small businesses to highly target and optimize their advertising campaigns.

[0123] The user accesses the chat interface using their device and enters information about the product or service they want to advertise. The device captures this user input and activates an emotion engine. This engine analyzes the user's input to recognize their emotional state and sends it to the server along with the input data.

[0124] The server analyzes the received user data and sentiment data. Using a generative AI model, it builds targeting profiles based on user interests and emotions. Based on these profiles, it generates advertisements that best suit a specific emotional state. For example, if a user is showing positive emotions, it creates advertisements that further enhance those emotions, while for users showing negative emotions, it suggests encouraging advertisements.

[0125] The generated advertisements are delivered from the server to the target user's device, which then displays them in a user-friendly format. After the advertisement is displayed, the user's feedback is analyzed again using an emotion engine to verify the effectiveness of the advertisement content. The server analyzes this feedback and changes in emotion to improve the AI ​​model.

[0126] For example, if an education-related company that holds online seminars uses this system, when a user enters their opinion about the seminar content, the emotion engine recognizes from that opinion whether the user is excited or anxious. The server then uses this emotion information to deliver advertisements encouraging excited users to participate in the next level of the seminar, and encouraging advertisements that reinforce the value of the seminar to anxious users, thereby conducting effective promotion.

[0127] In this way, by using an emotion engine, more precise ad delivery can be achieved than before, enabling sole proprietors and small businesses to reach their target audience cost-effectively and increase sales.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user opens a chat interface using their device and enters details about the product or service they want to advertise. The device receives this input data.

[0131] Step 2:

[0132] The device sends user input information to the emotion engine, which then analyzes the user's emotions based on that information. The emotion engine uses an emotion recognition algorithm to determine whether the emotional state is positive, negative, or neutral.

[0133] Step 3:

[0134] The device sends user data, including analyzed sentiment data, to the server. This user data includes detailed product information and emotional state.

[0135] Step 4:

[0136] The server analyzes the received data and uses a generative AI model to build user profiles. These profiles reflect the user's interests and emotional state and are used for targeting.

[0137] Step 5:

[0138] The server generates optimal advertisements based on the user's profile and their specific emotional state. For example, it creates advertisements that further stimulate purchasing intent for positive users and selects advertisements that provide a sense of security for negative users.

[0139] Step 6:

[0140] The server delivers the generated advertisements to the target user's device. The device then displays the advertisements to the user visually and effectively.

[0141] Step 7:

[0142] Users input their reactions and opinions to the displayed advertisements into their devices. The devices then send this feedback to the server.

[0143] Step 8:

[0144] The server analyzes the feedback and evaluates the user's reaction to the advertisement and their emotional changes. Using this information, the server updates its AI model and optimizes it to improve the accuracy of ad delivery.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] In modern advertising systems, it is difficult to flexibly respond to the diverse emotional states of users and provide advertisements tailored to individual needs. Traditional advertising systems do not take user emotions into consideration, resulting in low targeting accuracy and limited advertising effectiveness.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes communication means for receiving, analyzing, and recognizing user input and emotional state; analysis means for analyzing user data, including the obtained emotional data, to generate a user profile; and advertising generation means for generating targeted advertisements based on the user's emotions using a generation AI model. This enables sophisticated targeting that takes user emotions into account, maximizing the effectiveness of advertising.

[0150] "Communication methods" refer to the processes and technologies necessary to receive information input by users and to analyze that information.

[0151] "Emotional state" refers to data that indicates the user's psychological state and mood, analyzed based on the user's input.

[0152] "Analysis methods" refer to the technologies and processes used to objectively understand users' interests and needs by analyzing acquired user data and sentiment data.

[0153] A "generative AI model" is a model that utilizes artificial intelligence technology to perform analysis based on user data and generate specific outputs.

[0154] "Ad generation methods" refer to technologies and processes for automatically creating optimal advertisements based on user profiles and sentiment data.

[0155] "Delivery method" refers to the technology and processes used to send and display generated advertisements on a user's device in an appropriate format.

[0156] "Learning methods" refer to techniques and technologies that measure the effectiveness of advertisements and use feedback data to improve and optimize the system's accuracy.

[0157] This invention relates to an advertising delivery system that takes user emotions into consideration. This enables advertising campaigns with higher targeting accuracy. The system is composed of three main components: the user, the terminal, and the server.

[0158] First, the user accesses a specific interface using their device and enters information about the product or service they wish to advertise. The device is equipped with a communication method and an emotion engine that receives the user's input and performs sentiment analysis. This emotion engine uses natural language processing technology to understand the user's emotional state from their input and generates data based on that understanding.

[0159] The terminal sends the acquired emotion data and user input to the server. The server uses analytical tools to generate a user profile based on this data. In this process, a generative AI model is utilized to construct a profile based on the user's interests and objectives. An example of a prompt message used by the generative AI model is, "The user is showing positive emotions. Generate an advertisement that corresponds to this emotion."

[0160] The server then generates optimal advertisements based on the user's profile and sentiment data. Here, ad generation tools are used to automatically create ads tailored to the user's different emotional states. This process includes the generation of ad copy by a generative AI model.

[0161] The generated advertisements are delivered from the server to the user via the device. The device utilizes various delivery methods to ensure that the received advertisements are displayed in the most optimal way for the user. Specifically, it presents the advertisements in a format that enhances the user experience, such as through on-screen display or audio playback.

[0162] After an ad is displayed, the device collects user reactions and feedback, and this data is sent to a server. The server analyzes this feedback and uses learning mechanisms in conjunction with the emotion engine to optimize the effectiveness of the ad. In this way, the AI ​​model continues to evolve based on user feedback, and the accuracy of the ads improves.

[0163] This system allows sole proprietors and small and medium-sized enterprises to reach their target audience effectively and at low cost, aiming to increase sales.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user inputs information about the product or service they wish to advertise through their device. This input is captured in text format via the device's communication method. This is the input for processing. The device uses this input information to perform sentiment analysis, recognize the user's emotional state, and generate sentiment data. The output consists of the analyzed sentiment data and the user's input information.

[0167] Step 2:

[0168] The terminal sends the generated emotion data and user input information to the server. The input here refers to all the information sent from the terminal. The server receives this data and uses analytical tools to perform data calculations to structure the user data. The output is detailed user profile information necessary for profile generation.

[0169] Step 3:

[0170] The server uses a generative AI model based on the received user profile information to generate targeted advertisements. The input here is user profile information, and the server uses the generative AI model to perform data calculations, such as generating ad content based on the user's interests and emotions. Prompts such as, "The user is showing positive emotions. Generate an ad corresponding to this emotion," are used. The output is customized ad content.

[0171] Step 4:

[0172] The generated advertisement is delivered from the server to the device. The input is the generated advertisement content. The device processes this data to display it in the most optimal format for the user, such as displaying it on the screen or playing audio. The output is the advertisement in a viewable state from the user's perspective.

[0173] Step 5:

[0174] After a user watches an advertisement, the device collects their reactions and feedback. The input is user feedback data, which the device formats for transmission to the server. The output is feedback data used to evaluate the effectiveness of the advertisement.

[0175] Step 6:

[0176] The server receives and analyzes feedback data. The input is feedback information sent from the terminal. The server utilizes an emotion engine to link the feedback with emotion data and executes a learning process to optimize the effectiveness of the advertisement. The output is an analysis result that helps improve ad display.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] Traditional advertising delivery systems have difficulty taking user emotions into account, resulting in insufficient targeting accuracy. Furthermore, the lack of sufficient feedback loops for optimizing advertising effectiveness means that building effective advertising strategies is time-consuming and resource-intensive.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes information communication means for receiving and analyzing user input, emotion analysis means for analyzing the received user information to understand the emotional state, and ad generation means for generating and delivering ads based on the user's emotions. This enables accurate ad delivery that responds to the user's emotions.

[0182] "Information and communication means" refers to a device or system that has the function of receiving user input and analyzing it.

[0183] "Emotional analysis means" refers to a technology or process for analyzing received user information and understanding their emotional state.

[0184] An "advertising generation method" is a mechanism for creating and delivering appropriate advertisements based on user emotions.

[0185] "Display means" refers to a device or technology used to visually present a generated advertisement to a target user.

[0186] "Learning methods" refer to algorithms and methods for collecting and analyzing user feedback to optimize the effectiveness of ad delivery.

[0187] "Adjustment methods" refer to techniques for modifying generated advertisements based on user history information to achieve more precise targeting.

[0188] "Artificial intelligence technology" refers to advanced computing methods used to generate user profiles and improve the accuracy of advertising targeting.

[0189] The system that implements this application performs sentiment analysis on the user's smartphone or other device, and generates and displays advertisements based on that analysis. The server uses the following information and communication means, sentiment analysis means, advertisement generation means, and display means.

[0190] The server first receives input from the user via information and communication means. This input is sent in the form of text, audio data, etc. Next, the sentiment analysis means analyzes this data using a natural language processing library (e.g., Google® NLP API) to understand the user's emotional state. Sentiment recognition AI (e.g., Microsoft® Azure® Emotion API) is used for this sentiment evaluation.

[0191] Based on the acquired sentiment information, the server uses ad generation tools to create advertisements and deliver them in a format suitable for the user's device. This process also incorporates adjustment mechanisms to tailor ad content based on the user's past history. For example, users exhibiting positive sentiments will receive advertisements for corresponding products and services. To support this process, artificial intelligence technology (generative AI models) is used to create user profiles.

[0192] The user's device visually displays advertisements sent from the server through a display mechanism. After the user views the advertisement, feedback is collected through the device and analyzed by the server's learning mechanism. This data contributes to optimizing ad delivery throughout the entire system.

[0193] For example, if a user types "I like this outfit" while using an online shopping app, the sentiment analysis system will capture that feeling of excitement and display advertisements for similar fashion items and accessories. This is expected to further enhance the user's positive response. An example of a prompt used in this system would be: "User review: 'This product is great!' We will analyze this review and suggest relevant positive advertisements."

[0194] Thus, the system of the present invention can provide a highly personalized advertising experience by understanding the user's emotional state and generating targeted advertisements based on that state.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] Users enter their thoughts and reviews on their devices. This input can be in text or voice format, and the device sends this data to a server. The input data is raw data used for sentiment evaluation.

[0198] Step 2:

[0199] The server analyzes the received input data using a natural language processing library (Google NLP API) and uses emotion recognition AI (Microsoft Azure Emotion API) to understand the user's emotional state. This analysis outputs the user's emotional state (positive, negative, etc.) in text format based on the input data.

[0200] Step 3:

[0201] The server uses a generative AI model to generate appropriate ad profiles based on the emotional state it has identified. The input is the user's emotional state, and the output is an ad profile tailored to that state. This profile serves as the foundational data for generating targeted ads.

[0202] Step 4:

[0203] The server creates advertisements using an ad generation mechanism and delivers them to the user's device via a display mechanism. A prompt is used to generate the ad content. The input is an ad profile, and the output is ad data that can be displayed on the device.

[0204] Step 5:

[0205] The user's device visually presents the displayed advertisement to the user. The user views the advertisement, and their subsequent actions and reactions are also recorded by the device. This record becomes feedback data.

[0206] Step 6:

[0207] Feedback data is sent from the terminal to the server, where the server's learning mechanism analyzes it. The input is feedback on user reactions and behaviors, and the output is improvement data to enhance the effectiveness of ad delivery. This optimizes the advertising strategy.

[0208] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] This invention aims to realize a system that enables sole proprietors and small and medium-sized enterprises to effectively deliver targeted advertising. The configuration and functions of this system are described below.

[0225] First, the user accesses the system's chat interface using their device. The process begins with the user entering information about the product or service they wish to advertise into the interface. The device then transmits the entered information to the server in real time.

[0226] The server analyzes the received user data. This analysis uses a generative AI model and applies algorithms to understand the user's interests and objectives. Based on the analysis results, the server identifies target users and generates optimal advertisements.

[0227] The generated advertisements are delivered from the server to the target user's device. The device then plays the role of displaying the advertisements in a user-friendly format and effectively delivering the information.

[0228] Furthermore, user reactions and feedback are sent to the server via the device. The server aggregates this feedback and evaluates the effectiveness of the advertisement. Based on the feedback, the server improves the AI ​​model and optimizes it for the next ad delivery.

[0229] As a concrete example, consider a local cafe owner who wants to promote a new menu item. The owner enters information about the new product into a chat interface, and this information is sent to a server. The server identifies a target audience of young coffee lovers living in the cafe's neighborhood and generates an advertisement for a discount campaign on the new menu item. This advertisement is delivered to the devices of users with the specified attributes, effectively promoting the new product.

[0230] The defining feature of this system is its ability to dynamically optimize ad delivery based on user feedback. As a result, it provides a system that allows sole proprietors and small businesses to efficiently utilize their advertising budgets.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] Users access a chat interface using their device and enter information about the products or services they want to advertise. The device receives this input and formats it as data.

[0234] Step 2:

[0235] The device sends formatted user input data to the server. The transmitted data includes details about the advertised products and services.

[0236] Step 3:

[0237] The server analyzes the received data and uses a generated AI model to identify the user's interests and objectives. The server also refers to trend information and historical data stored in the database to identify the target user group.

[0238] Step 4:

[0239] The server generates advertisements tailored to the most suitable target users based on the analysis results. These advertisements are designed to capture the target audience's interest.

[0240] Step 5:

[0241] The server delivers the generated advertisements to the target user's device. The device displays the advertisements in a format that is easy for the user to view.

[0242] Step 6:

[0243] Users view advertisements and input their reactions and opinions based on their content into their devices. The devices then send this feedback back to the server.

[0244] Step 7:

[0245] The server analyzes the received feedback and evaluates the effectiveness of ad delivery. The server uses this information to update the AI ​​model and further optimize future ad delivery.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] There is a need to achieve efficient and effective targeted advertising delivery while maximizing the return on investment of advertising. In particular, sole proprietors and small and medium-sized enterprises (SMEs) need to deliver ads to the right audience, evaluate their effectiveness, and make improvements within their limited advertising budgets. Therefore, there is a need for methods that improve the accuracy of ad targeting and enable continuous improvement based on user feedback.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes communication means for receiving and analyzing user input, analysis means for analyzing the received user data to understand the user's interests and objectives, and means for generating prompt sentences from user input using generative artificial intelligence. This enables the generation and delivery of targeted advertisements based on the user's specific needs, and continuous optimization of advertisement content and delivery accuracy through feedback.

[0251] "User input" refers to information that users provide to the chat interface of an advertising system, and it serves as the basic data for generating advertisements.

[0252] "Communication means" refers to the means of sending user input from a terminal to a server, and is a technology that enables accurate data transfer.

[0253] "Analysis means" refers to methods for analyzing received user data and understanding their interests and objectives.

[0254] "Generative artificial intelligence" refers to an AI model that generates prompt text from user input and uses that information to create targeted advertisements.

[0255] A "prompt" is a set of instructions or messages that form the basis of the generated advertisement, and it serves as the starting point for the AI ​​model to create the advertisement content.

[0256] An "advertising generation method" is a means for generating advertisements in a format suitable for a specific target audience based on a prompt message.

[0257] "Delivery method" refers to the means by which generated advertisements are delivered to target users, and it plays a role in determining the appropriate delivery timing and format.

[0258] "Learning methods" refer to technologies used to collect feedback from target users and improve the accuracy of ad delivery through analysis of that feedback.

[0259] This invention is designed as a system for effectively generating and delivering targeted advertisements. Users first access the system's chat interface using their own devices and input information about the products or services they wish to advertise. The system requires a computer and an internet connection as basic hardware.

[0260] The terminal plays the role of transmitting the entered information to the server in real time. The server is a computer server that analyzes user input using artificial intelligence technologies such as OpenAI as a generative AI model. This model understands the user's intent based on the input information and generates prompt messages. These prompt messages serve as a starting point for customizing targeted advertisements.

[0261] Specifically, the generative AI model generates a prompt such as, "Create a targeted ad to promote our new coffee menu." Based on this instruction, the server identifies target users and determines what kind of ad content is best suited for them. If the user is the owner of a local cafe, the server can target consumers with specific attributes living in the neighborhood (e.g., "coffee lovers" or "young people in their 20s") and generate and deliver ads for discount campaigns on the new menu.

[0262] The generated advertisements are sent to the target user's device using a delivery method. The device receives the advertisement and displays it in a format that is easy for the user to view. For example, visually appealing ad formats using vibrant images and catchy slogans may be employed.

[0263] Furthermore, user responses and feedback to advertisements are sent from the device to the server. This feedback data is used by the server to evaluate the effectiveness of the advertisements and to improve future advertising campaigns. The server uses this data to update its AI model and optimize ad delivery. This allows sole proprietors and small businesses to use their advertising budgets efficiently and achieve maximum results.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] Users access the system's chat interface using their own devices. Here, users enter detailed information about the products or services they wish to advertise. This input data is collected as initial information and sent from the interface to the user's device.

[0267] Step 2:

[0268] The terminal transmits user-entered information to the server in real time. The entered information is sent to the server as text data. The server receives this data and prepares for initial analysis.

[0269] Step 3:

[0270] The server analyzes the received user data. This analysis utilizes a generative AI model. Specifically, the server analyzes text data and generates prompts that are optimal for the user's needs and targeted advertisements. These generated prompts form the basis for the next ad generation step.

[0271] Step 4:

[0272] The server uses the generated prompt to identify the target user group and generate ad content. The specific content and messaging of the targeted ad campaign are determined via a generative artificial intelligence model. The ads generated at this stage are prepared as data for delivery.

[0273] Step 5:

[0274] The server uses a delivery method to distribute the generated advertising content to the target users. The advertisement is sent to the target user's device at the appropriate time and in the appropriate format. After delivery, the advertisement is visually presented to the user by the device.

[0275] Step 6:

[0276] User responses and feedback to advertisements are sent to the server via the device. This feedback data is compiled and analyzed on the server as data to evaluate the effectiveness of the advertisements. This allows for an understanding of the actual results of the advertising campaign.

[0277] Step 7:

[0278] The server uses the collected feedback data to improve the generated AI model and optimize ad delivery. Through this process, the targeting accuracy and messaging content for the next ad delivery are continuously improved. As a result, users are supported to obtain more effective ad results.

[0279] (Application Example 1)

[0280] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0281] An object of the present invention is to provide an efficient system for individual business owners and small and medium-sized enterprises to distribute ads to target customers more effectively beyond the limitations of conventional advertising methods. In particular, it is an issue to consistently perform from ad generation to distribution and optimization by feedback, and to maximize the use of a limited advertising budget.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0283] In this invention, the server includes communication means for receiving information from a user terminal and processing it, analysis means for analyzing information on an input product or service and identifying potential customer segments, and ad generation means for automatically generating an ad for target customers based on the analysis result and transmitting it. As a result, it becomes possible for users to easily create targeted ads and effectively reach target customers.

[0284] The "user terminal" is a device for accessing an ad delivery system and inputting information, and includes, for example, a smartphone or a personal computer. <000901><000902>The "communication means" refers to a function for transmitting information from a user terminal to the server and receiving ads and feedback from the server to the user terminal.

[0286] The "analysis means" refers to a function that analyzes the information provided by the user and performs processing to identify potential customer segments.

[0287] The "advertisement generation means" is a function responsible for the process of automatically creating and transmitting the most suitable advertisement for target customers based on the analyzed data.

[0288] The "delivery means" refers to a function for effectively delivering the generated advertisement to the identified customer segments.

[0289] The "control means" indicates a process of collecting and analyzing the received response information and improving the effectiveness of the advertising campaign based on it.

[0290] The "generation AI" refers to a method that realizes the optimization of advertisement generation and delivery by analyzing data and learning patterns.

[0291] This invention provides a system for effectively delivering targeted advertisements to individual business owners and small and medium-sized enterprises. The system is mainly composed of a user terminal, a server, and a generation AI model. Specific embodiments of this system will be described below.

[0292] The user accesses the interface of the system using a user terminal such as a smartphone or a personal computer. The user inputs information about the product or service to be provided and transmits it to the platform. The input information is transferred to the server via the communication means.

[0293] After receiving this information, the server analyzes the data using the analysis means. The generation AI model is utilized for the analysis to identify the most suitable potential customer segments from the user's information. GPT-4 is used as an example of the generation AI model. This model generates the most suitable advertisement content using a specific prompt sentence.

[0294] Based on the analysis results, the ad generation system activates and automatically creates ads targeted at the identified customers. The generated ads are then sent to the customers' devices in real time via the delivery system.

[0295] After an advertisement is delivered, customer responses are sent back to the server. The server uses control mechanisms to aggregate and analyze this response information. Based on this feedback, advertising activities are continuously optimized.

[0296] For example, if a local florist wants to promote a new bouquet, the user would enter information such as "New spring bouquet, for those in their 20s and 30s, 10% off first purchase" as a prompt. This information is processed by the server and delivered as a customized advertisement to nearby flower-loving customers.

[0297] Example of a prompt:

[0298] Please generate an ad. The target audience is local users in their 20s and 30s who love flowers, and the ad aims to introduce new flower bouquets. Please include a 10% off promotion.

[0299] Thus, the system of the present invention enables users to maximize the effectiveness of their advertising and achieve appropriate approaches to their customers.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The user's device receives information about the advertisement content entered by the user. Specifically, it accesses the interface on a smartphone or computer and enters information such as a prompt message: "New spring flower bouquets, for people in their 20s and 30s, 10% off first purchase." The entered information is then prepared to be sent to the server via a communication method.

[0303] Step 2:

[0304] The server processes the prompt text received from the user terminal by means of analysis. To analyze the input prompt text and identify the potential customer layer, a generative AI model (e.g., GPT-4) is applied. As a specific data processing, text analysis is performed, and as a result of the analysis, a profile of the target customer is generated.

[0305] Step 3:

[0306] The advertisement generation means of the server automatically generates an advertisement based on the analysis result. Using a generative AI model, an optimal advertisement is created, and specific advertisement content for the target customer is constructed. At this stage, the generated advertisement text and visual elements are obtained as output.

[0307] Step 4:

[0308] The generated advertisement is transmitted in real time to the target customer's terminal through the server's distribution means. Specifically, based on specific location information and past behavior data, a method for delivering the advertisement is determined. As a result of the distribution, the advertisement is displayed to the designated target layer.

[0309] Step 5:

[0310] The reaction of the customer who viewed the advertisement on the terminal is sent back to the server again. The user's operations and behavior data (such as clicks, dwell time, etc.) are collected as specific inputs and transmitted to the server through the communication means.

[0311] Step 6:

[0312] The server analyzes the collected customer reactions by means of control and evaluates the effectiveness of the advertisement. Using the collected data as a medium, the effectiveness of the advertisement is quantified and improvement points are identified. Based on this analysis, the advertising activity is optimized for subsequent times.

[0313] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0314] This invention provides a more sophisticated advertising delivery system that incorporates an emotion engine that recognizes user emotions. This system enables sole proprietors and small businesses to highly target and optimize their advertising campaigns.

[0315] The user accesses the chat interface using their device and enters information about the product or service they want to advertise. The device captures this user input and activates an emotion engine. This engine analyzes the user's input to recognize their emotional state and sends it to the server along with the input data.

[0316] The server analyzes the received user data and sentiment data. Using a generative AI model, it builds targeting profiles based on user interests and emotions. Based on these profiles, it generates advertisements that best suit a specific emotional state. For example, if a user is showing positive emotions, it creates advertisements that further enhance those emotions, while for users showing negative emotions, it suggests encouraging advertisements.

[0317] The generated advertisements are delivered from the server to the target user's device, which then displays them in a user-friendly format. After the advertisement is displayed, the user's feedback is analyzed again using an emotion engine to verify the effectiveness of the advertisement content. The server analyzes this feedback and changes in emotion to improve the AI ​​model.

[0318] For example, if an education-related company that holds online seminars uses this system, when a user enters their opinion about the seminar content, the emotion engine recognizes from that opinion whether the user is excited or anxious. The server then uses this emotion information to deliver advertisements encouraging excited users to participate in the next level of the seminar, and encouraging advertisements that reinforce the value of the seminar to anxious users, thereby conducting effective promotion.

[0319] In this way, by using an emotion engine, more precise ad delivery can be achieved than before, enabling sole proprietors and small businesses to reach their target audience cost-effectively and increase sales.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user opens a chat interface using their device and enters details about the product or service they want to advertise. The device receives this input data.

[0323] Step 2:

[0324] The device sends user input information to the emotion engine, which then analyzes the user's emotions based on that information. The emotion engine uses an emotion recognition algorithm to determine whether the emotional state is positive, negative, or neutral.

[0325] Step 3:

[0326] The device sends user data, including analyzed sentiment data, to the server. This user data includes detailed product information and emotional state.

[0327] Step 4:

[0328] The server analyzes the received data and uses a generative AI model to build user profiles. These profiles reflect the user's interests and emotional state and are used for targeting.

[0329] Step 5:

[0330] The server generates optimal advertisements based on the user's profile and their specific emotional state. For example, it creates advertisements that further stimulate purchasing intent for positive users and selects advertisements that provide a sense of security for negative users.

[0331] Step 6:

[0332] The server delivers the generated advertisements to the target user's device. The device then displays the advertisements to the user visually and effectively.

[0333] Step 7:

[0334] Users input their reactions and opinions to the displayed advertisements into their devices. The devices then send this feedback to the server.

[0335] Step 8:

[0336] The server analyzes the feedback and evaluates the user's reaction to the advertisement and their emotional changes. Using this information, the server updates its AI model and optimizes it to improve the accuracy of ad delivery.

[0337] (Example 2)

[0338] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0339] In modern advertising systems, it is difficult to flexibly respond to the diverse emotional states of users and provide advertisements tailored to individual needs. Traditional advertising systems do not take user emotions into consideration, resulting in low targeting accuracy and limited advertising effectiveness.

[0340] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0341] In this invention, the server includes communication means for receiving, analyzing, and recognizing user input and emotional state; analysis means for analyzing user data, including the obtained emotional data, to generate a user profile; and advertising generation means for generating targeted advertisements based on the user's emotions using a generation AI model. This enables sophisticated targeting that takes user emotions into account, maximizing the effectiveness of advertising.

[0342] "Communication methods" refer to the processes and technologies necessary to receive information input by users and to analyze that information.

[0343] "Emotional state" refers to data that indicates the user's psychological state and mood, analyzed based on the user's input.

[0344] "Analysis methods" refer to the technologies and processes used to objectively understand users' interests and needs by analyzing acquired user data and sentiment data.

[0345] A "generative AI model" is a model that utilizes artificial intelligence technology to perform analysis based on user data and generate specific outputs.

[0346] "Ad generation methods" refer to technologies and processes for automatically creating optimal advertisements based on user profiles and sentiment data.

[0347] "Delivery method" refers to the technology and processes used to send and display generated advertisements on a user's device in an appropriate format.

[0348] "Learning methods" refer to techniques and technologies that measure the effectiveness of advertisements and use feedback data to improve and optimize the system's accuracy.

[0349] This invention relates to an advertising delivery system that takes user emotions into consideration. This enables advertising campaigns with higher targeting accuracy. The system is composed of three main components: the user, the terminal, and the server.

[0350] First, the user accesses a specific interface using their device and enters information about the product or service they wish to advertise. The device is equipped with a communication method and an emotion engine that receives the user's input and performs sentiment analysis. This emotion engine uses natural language processing technology to understand the user's emotional state from their input and generates data based on that understanding.

[0351] The terminal sends the acquired emotion data and user input to the server. The server uses analytical tools to generate a user profile based on this data. In this process, a generative AI model is utilized to construct a profile based on the user's interests and objectives. An example of a prompt message used by the generative AI model is, "The user is showing positive emotions. Generate an advertisement that corresponds to this emotion."

[0352] The server then generates optimal advertisements based on the user's profile and sentiment data. Here, ad generation tools are used to automatically create ads tailored to the user's different emotional states. This process includes the generation of ad copy by a generative AI model.

[0353] The generated advertisements are delivered from the server to the user via the device. The device utilizes various delivery methods to ensure that the received advertisements are displayed in the most optimal way for the user. Specifically, it presents the advertisements in a format that enhances the user experience, such as through on-screen display or audio playback.

[0354] After an ad is displayed, the device collects user reactions and feedback, and this data is sent to a server. The server analyzes this feedback and uses learning mechanisms in conjunction with the emotion engine to optimize the effectiveness of the ad. In this way, the AI ​​model continues to evolve based on user feedback, and the accuracy of the ads improves.

[0355] This system allows sole proprietors and small and medium-sized enterprises to reach their target audience effectively and at low cost, aiming to increase sales.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The user inputs information about the product or service they wish to advertise through their device. This input is captured in text format via the device's communication method. This is the input for processing. The device uses this input information to perform sentiment analysis, recognize the user's emotional state, and generate sentiment data. The output consists of the analyzed sentiment data and the user's input information.

[0359] Step 2:

[0360] The terminal sends the generated emotion data and user input information to the server. The input here refers to all the information sent from the terminal. The server receives this data and uses analytical tools to perform data calculations to structure the user data. The output is detailed user profile information necessary for profile generation.

[0361] Step 3:

[0362] The server uses a generative AI model based on the received user profile information to generate targeted advertisements. The input here is user profile information, and the server uses the generative AI model to perform data calculations, such as generating ad content based on the user's interests and emotions. Prompts such as, "The user is showing positive emotions. Generate an ad corresponding to this emotion," are used. The output is customized ad content.

[0363] Step 4:

[0364] The generated advertisement is delivered from the server to the device. The input is the generated advertisement content. The device processes this data to display it in the most optimal format for the user, such as displaying it on the screen or playing audio. The output is the advertisement in a viewable state from the user's perspective.

[0365] Step 5:

[0366] After a user watches an advertisement, the device collects their reactions and feedback. The input is user feedback data, which the device formats for transmission to the server. The output is feedback data used to evaluate the effectiveness of the advertisement.

[0367] Step 6:

[0368] The server receives and analyzes feedback data. The input is feedback information sent from the terminal. The server utilizes an emotion engine to link the feedback with emotion data and executes a learning process to optimize the effectiveness of the advertisement. The output is an analysis result that helps improve ad display.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] Traditional advertising delivery systems have difficulty taking user emotions into account, resulting in insufficient targeting accuracy. Furthermore, the lack of sufficient feedback loops for optimizing advertising effectiveness means that building effective advertising strategies is time-consuming and resource-intensive.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes information communication means for receiving and analyzing user input, emotion analysis means for analyzing the received user information to understand the emotional state, and ad generation means for generating and delivering ads based on the user's emotions. This enables accurate ad delivery that responds to the user's emotions.

[0374] "Information and communication means" refers to a device or system that has the function of receiving user input and analyzing it.

[0375] "Emotional analysis means" refers to a technology or process for analyzing received user information and understanding their emotional state.

[0376] An "advertising generation method" is a mechanism for creating and delivering appropriate advertisements based on user emotions.

[0377] "Display means" refers to a device or technology used to visually present a generated advertisement to a target user.

[0378] "Learning methods" refer to algorithms and methods for collecting and analyzing user feedback to optimize the effectiveness of ad delivery.

[0379] "Adjustment methods" refer to techniques for modifying generated advertisements based on user history information to achieve more precise targeting.

[0380] "Artificial intelligence technology" refers to advanced computing methods used to generate user profiles and improve the accuracy of advertising targeting.

[0381] The system that implements this application performs sentiment analysis on the user's smartphone or other device, and generates and displays advertisements based on that analysis. The server uses the following information and communication means, sentiment analysis means, advertisement generation means, and display means.

[0382] The server first receives input from the user via information and communication means. This input is sent in the form of text, audio data, etc. Next, the sentiment analysis means analyzes this data using a natural language processing library (e.g., Google NLP API) to understand the user's emotional state. Sentiment recognition AI (e.g., Microsoft Azure Emotion API) is used for this sentiment evaluation.

[0383] Based on the acquired sentiment information, the server uses ad generation tools to create advertisements and deliver them in a format suitable for the user's device. This process also incorporates adjustment mechanisms to tailor ad content based on the user's past history. For example, users exhibiting positive sentiments will receive advertisements for corresponding products and services. To support this process, artificial intelligence technology (generative AI models) is used to create user profiles.

[0384] The user's device visually displays advertisements sent from the server through a display mechanism. After the user views the advertisement, feedback is collected through the device and analyzed by the server's learning mechanism. This data contributes to optimizing ad delivery throughout the entire system.

[0385] For example, if a user types "I like this outfit" while using an online shopping app, the sentiment analysis system will capture that feeling of excitement and display advertisements for similar fashion items and accessories. This is expected to further enhance the user's positive response. An example of a prompt used in this system would be: "User review: 'This product is great!' We will analyze this review and suggest relevant positive advertisements."

[0386] Thus, the system of the present invention can provide a highly personalized advertising experience by understanding the user's emotional state and generating targeted advertisements based on that state.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] Users enter their thoughts and reviews on their devices. This input can be in text or voice format, and the device sends this data to a server. The input data is raw data used for sentiment evaluation.

[0390] Step 2:

[0391] The server analyzes the received input data using a natural language processing library (Google NLP API) and uses emotion recognition AI (Microsoft Azure Emotion API) to understand the user's emotional state. This analysis outputs the user's emotional state (positive, negative, etc.) in text format based on the input data.

[0392] Step 3:

[0393] The server uses a generative AI model to generate appropriate ad profiles based on the emotional state it has identified. The input is the user's emotional state, and the output is an ad profile tailored to that state. This profile serves as the foundational data for generating targeted ads.

[0394] Step 4:

[0395] The server creates advertisements using an ad generation mechanism and delivers them to the user's device via a display mechanism. A prompt is used to generate the ad content. The input is an ad profile, and the output is ad data that can be displayed on the device.

[0396] Step 5:

[0397] The user's device visually presents the displayed advertisement to the user. The user views the advertisement, and their subsequent actions and reactions are also recorded by the device. This record becomes feedback data.

[0398] Step 6:

[0399] Feedback data is sent from the terminal to the server, where the server's learning mechanism analyzes it. The input is feedback on user reactions and behaviors, and the output is improvement data to enhance the effectiveness of ad delivery. This optimizes the advertising strategy.

[0400] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0401] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0402] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0406] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0407] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0408] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0409] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0410] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0411] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0412] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0413] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0414] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0415] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0416] This invention aims to realize a system that enables sole proprietors and small and medium-sized enterprises to effectively deliver targeted advertising. The configuration and functions of this system are described below.

[0417] First, the user accesses the system's chat interface using their device. The process begins with the user entering information about the product or service they wish to advertise into the interface. The device then transmits the entered information to the server in real time.

[0418] The server analyzes the received user data. This analysis uses a generative AI model and applies algorithms to understand the user's interests and objectives. Based on the analysis results, the server identifies target users and generates optimal advertisements.

[0419] The generated advertisements are delivered from the server to the target user's device. The device then plays the role of displaying the advertisements in a user-friendly format and effectively delivering the information.

[0420] Furthermore, user reactions and feedback are sent to the server via the device. The server aggregates this feedback and evaluates the effectiveness of the advertisement. Based on the feedback, the server improves the AI ​​model and optimizes it for the next ad delivery.

[0421] As a concrete example, consider a local cafe owner who wants to promote a new menu item. The owner enters information about the new product into a chat interface, and this information is sent to a server. The server identifies a target audience of young coffee lovers living in the cafe's neighborhood and generates an advertisement for a discount campaign on the new menu item. This advertisement is delivered to the devices of users with the specified attributes, effectively promoting the new product.

[0422] The defining feature of this system is its ability to dynamically optimize ad delivery based on user feedback. As a result, it provides a system that allows sole proprietors and small businesses to efficiently utilize their advertising budgets.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] Users access a chat interface using their device and enter information about the products or services they want to advertise. The device receives this input and formats it as data.

[0426] Step 2:

[0427] The device sends formatted user input data to the server. The transmitted data includes details about the advertised products and services.

[0428] Step 3:

[0429] The server analyzes the received data and uses a generated AI model to identify the user's interests and objectives. The server also refers to trend information and historical data stored in the database to identify the target user group.

[0430] Step 4:

[0431] The server generates advertisements tailored to the most suitable target users based on the analysis results. These advertisements are designed to capture the target audience's interest.

[0432] Step 5:

[0433] The server delivers the generated advertisements to the target user's device. The device displays the advertisements in a format that is easy for the user to view.

[0434] Step 6:

[0435] Users view advertisements and input their reactions and opinions based on their content into their devices. The devices then send this feedback back to the server.

[0436] Step 7:

[0437] The server analyzes the received feedback and evaluates the effectiveness of ad delivery. The server uses this information to update the AI ​​model and further optimize future ad delivery.

[0438] (Example 1)

[0439] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0440] There is a need to achieve efficient and effective targeted advertising delivery while maximizing the return on investment of advertising. In particular, sole proprietors and small and medium-sized enterprises (SMEs) need to deliver ads to the right audience, evaluate their effectiveness, and make improvements within their limited advertising budgets. Therefore, there is a need for methods that improve the accuracy of ad targeting and enable continuous improvement based on user feedback.

[0441] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0442] In this invention, the server includes communication means for receiving and analyzing user input, analysis means for analyzing the received user data to understand the user's interests and objectives, and means for generating prompt sentences from user input using generative artificial intelligence. This enables the generation and delivery of targeted advertisements based on the user's specific needs, and continuous optimization of advertisement content and delivery accuracy through feedback.

[0443] "User input" refers to information that users provide to the chat interface of an advertising system, and it serves as the basic data for generating advertisements.

[0444] "Communication means" refers to the means of sending user input from a terminal to a server, and is a technology that enables accurate data transfer.

[0445] "Analysis means" refers to methods for analyzing received user data and understanding their interests and objectives.

[0446] "Generative artificial intelligence" refers to an AI model that generates prompt text from user input and uses that information to create targeted advertisements.

[0447] A "prompt" is a set of instructions or messages that form the basis of the generated advertisement, and it serves as the starting point for the AI ​​model to create the advertisement content.

[0448] An "advertising generation method" is a means for generating advertisements in a format suitable for a specific target audience based on a prompt message.

[0449] "Delivery method" refers to the means by which generated advertisements are delivered to target users, and it plays a role in determining the appropriate delivery timing and format.

[0450] "Learning methods" refer to technologies used to collect feedback from target users and improve the accuracy of ad delivery through analysis of that feedback.

[0451] This invention is designed as a system for effectively generating and delivering targeted advertisements. Users first access the system's chat interface using their own devices and input information about the products or services they wish to advertise. The system requires a computer and an internet connection as basic hardware.

[0452] The terminal plays the role of transmitting the entered information to the server in real time. The server is a computer server that analyzes user input using artificial intelligence technologies such as OpenAI as a generative AI model. This model understands the user's intent based on the input information and generates prompt messages. These prompt messages serve as a starting point for customizing targeted advertisements.

[0453] Specifically, the generative AI model generates a prompt such as, "Create a targeted ad to promote our new coffee menu." Based on this instruction, the server identifies target users and determines what kind of ad content is best suited for them. If the user is the owner of a local cafe, the server can target consumers with specific attributes living in the neighborhood (e.g., "coffee lovers" or "young people in their 20s") and generate and deliver ads for discount campaigns on the new menu.

[0454] The generated advertisements are sent to the target user's device using a delivery method. The device receives the advertisement and displays it in a format that is easy for the user to view. For example, visually appealing ad formats using vibrant images and catchy slogans may be employed.

[0455] Furthermore, user responses and feedback to advertisements are sent from the device to the server. This feedback data is used by the server to evaluate the effectiveness of the advertisements and to improve future advertising campaigns. The server uses this data to update its AI model and optimize ad delivery. This allows sole proprietors and small businesses to use their advertising budgets efficiently and achieve maximum results.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] Users access the system's chat interface using their own devices. Here, users enter detailed information about the products or services they wish to advertise. This input data is collected as initial information and sent from the interface to the user's device.

[0459] Step 2:

[0460] The terminal transmits user-entered information to the server in real time. The entered information is sent to the server as text data. The server receives this data and prepares for initial analysis.

[0461] Step 3:

[0462] The server analyzes the received user data. This analysis utilizes a generative AI model. Specifically, the server analyzes text data and generates prompts that are optimal for the user's needs and targeted advertisements. These generated prompts form the basis for the next ad generation step.

[0463] Step 4:

[0464] The server uses the generated prompt to identify the target user group and generate ad content. The specific content and messaging of the targeted ad campaign are determined via a generative artificial intelligence model. The ads generated at this stage are prepared as data for delivery.

[0465] Step 5:

[0466] The server uses a delivery method to distribute the generated advertising content to the target users. The advertisement is sent to the target user's device at the appropriate time and in the appropriate format. After delivery, the advertisement is visually presented to the user by the device.

[0467] Step 6:

[0468] User responses and feedback to advertisements are sent to the server via the device. This feedback data is compiled and analyzed on the server as data to evaluate the effectiveness of the advertisements. This allows for an understanding of the actual results of the advertising campaign.

[0469] Step 7:

[0470] The server uses collected feedback data to improve the generated AI model and optimize ad delivery. This process continuously improves targeting accuracy and messaging content for future ad deliveries. This helps users achieve more effective advertising results.

[0471] (Application Example 1)

[0472] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0473] This invention aims to provide an efficient system that enables sole proprietors and small and medium-sized enterprises to overcome the limitations of conventional advertising methods and deliver advertisements more effectively to their target customers. In particular, it addresses the challenge of maximizing the use of limited advertising budgets by providing a consistent system from ad generation and delivery to optimization through feedback.

[0474] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0475] In this invention, the server includes communication means for receiving and processing information from a user terminal, analysis means for analyzing information about the input product or service and identifying potential customer segments, and advertising generation means for automatically generating and transmitting advertisements directed at target customers based on the analysis results. This makes it possible for users to easily create targeted advertisements and effectively reach their target customers.

[0476] A "user terminal" is a device used to access the advertising delivery system and input information, and includes, for example, smartphones and personal computers.

[0477] "Communication means" refers to the function of sending information from the user's terminal to the server and receiving advertisements and feedback from the server to the user's terminal.

[0478] "Analysis means" refers to a function that analyzes information provided by users and processes it to identify potential customer segments.

[0479] An "ad generation method" is a function that handles the process of automatically creating and sending ads that are optimal for the target customer based on analyzed data.

[0480] "Distribution method" refers to the function used to effectively deliver generated advertisements to specific customer segments.

[0481] "Control means" refers to a process for collecting and analyzing received response information and using that information to improve the effectiveness of advertising activities.

[0482] "Generative artificial intelligence" refers to a method that optimizes ad generation and delivery by analyzing data and learning patterns.

[0483] This invention provides a system for effectively delivering targeted advertising to sole proprietors and small and medium-sized enterprises. The system mainly consists of a user terminal, a server, and a generative AI model. A specific example of this system is described below.

[0484] Users access the system interface using user devices such as smartphones or personal computers. Users input information about the products or services they wish to offer and submit it to the platform. The entered information is then transferred to the server via communication means.

[0485] Upon receiving this information, the server analyzes the data using analytical tools. Generative AI models are used in the analysis to identify the most suitable potential customer segment based on the user's information. GPT-4 is used as an example of a generative AI model. This model generates optimal advertising content using specific prompt sentences.

[0486] Based on the analysis results, the ad generation system activates and automatically creates ads targeted at the identified customers. The generated ads are then sent to the customers' devices in real time via the delivery system.

[0487] After an advertisement is delivered, customer responses are sent back to the server. The server uses control mechanisms to aggregate and analyze this response information. Based on this feedback, advertising activities are continuously optimized.

[0488] For example, if a local florist wants to promote a new bouquet, the user would enter information such as "New spring bouquet, for those in their 20s and 30s, 10% off first purchase" as a prompt. This information is processed by the server and delivered as a customized advertisement to nearby flower-loving customers.

[0489] Example of a prompt:

[0490] Please generate an ad. The target audience is local users in their 20s and 30s who love flowers, and the ad aims to introduce new flower bouquets. Please include a 10% off promotion.

[0491] Thus, the system of the present invention enables users to maximize the effectiveness of their advertising and achieve appropriate approaches to their customers.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The user's device receives information about the advertisement content entered by the user. Specifically, it accesses the interface on a smartphone or computer and enters information such as a prompt message: "New spring flower bouquets, for people in their 20s and 30s, 10% off first purchase." The entered information is then prepared to be sent to the server via a communication method.

[0495] Step 2:

[0496] The server processes the prompt messages received from the user terminal using an analysis tool. It analyzes the input prompt messages and applies a generative AI model (e.g., GPT-4) to identify potential customer segments. Specifically, it performs text analysis, and the analysis results generate a profile of the target customer.

[0497] Step 3:

[0498] The server's ad generation mechanism automatically generates ads based on analysis results. It uses a generation AI model to create optimal ads and build specific ad content targeted at specific customers. At this stage, the generated ad text and visual elements are obtained as output.

[0499] Step 4:

[0500] The generated advertisements are sent in real time to the target customers' devices via the server's delivery system. Specifically, the method of ad delivery is determined based on specific location information and past behavioral data. As a result of delivery, the advertisements are displayed to the specified target audience.

[0501] Step 5:

[0502] Customer reactions to advertisements viewed on their devices are sent back to the server. User actions and behavioral data (clicks, dwell time, etc.) are collected as specific inputs and sent to the server via communication means.

[0503] Step 6:

[0504] The server analyzes collected customer responses using control mechanisms to evaluate the effectiveness of the advertisements. Using the collected data as a medium, it quantifies the advertising's effectiveness and identifies areas for improvement. Based on this analysis, advertising activities are optimized for future campaigns.

[0505] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0506] This invention provides a more sophisticated advertising delivery system that incorporates an emotion engine that recognizes user emotions. This system enables sole proprietors and small businesses to highly target and optimize their advertising campaigns.

[0507] The user accesses the chat interface using their device and enters information about the product or service they want to advertise. The device captures this user input and activates an emotion engine. This engine analyzes the user's input to recognize their emotional state and sends it to the server along with the input data.

[0508] The server analyzes the received user data and sentiment data. Using a generative AI model, it builds targeting profiles based on user interests and emotions. Based on these profiles, it generates advertisements that best suit a specific emotional state. For example, if a user is showing positive emotions, it creates advertisements that further enhance those emotions, while for users showing negative emotions, it suggests encouraging advertisements.

[0509] The generated advertisements are delivered from the server to the target user's device, which then displays them in a user-friendly format. After the advertisement is displayed, the user's feedback is analyzed again using an emotion engine to verify the effectiveness of the advertisement content. The server analyzes this feedback and changes in emotion to improve the AI ​​model.

[0510] For example, if an education-related company that holds online seminars uses this system, when a user enters their opinion about the seminar content, the emotion engine recognizes from that opinion whether the user is excited or anxious. The server then uses this emotion information to deliver advertisements encouraging excited users to participate in the next level of the seminar, and encouraging advertisements that reinforce the value of the seminar to anxious users, thereby conducting effective promotion.

[0511] In this way, by using an emotion engine, more precise ad delivery can be achieved than before, enabling sole proprietors and small businesses to reach their target audience cost-effectively and increase sales.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The user opens a chat interface using their device and enters details about the product or service they want to advertise. The device receives this input data.

[0515] Step 2:

[0516] The device sends user input information to the emotion engine, which then analyzes the user's emotions based on that information. The emotion engine uses an emotion recognition algorithm to determine whether the emotional state is positive, negative, or neutral.

[0517] Step 3:

[0518] The device sends user data, including analyzed sentiment data, to the server. This user data includes detailed product information and emotional state.

[0519] Step 4:

[0520] The server analyzes the received data and uses a generative AI model to build user profiles. These profiles reflect the user's interests and emotional state and are used for targeting.

[0521] Step 5:

[0522] The server generates optimal advertisements based on the user's profile and specific emotional state. For example, it creates advertisements that further stimulate purchasing intent for positive users and selects advertisements that provide a sense of security for negative users.

[0523] Step 6:

[0524] The server delivers the generated advertisements to the target user's device. The device then displays the advertisements to the user visually and effectively.

[0525] Step 7:

[0526] Users input their reactions and opinions to the displayed advertisements into their devices. The devices then send this feedback to the server.

[0527] Step 8:

[0528] The server analyzes the feedback and evaluates the user's reaction to the advertisement and their emotional changes. Using this information, the server updates the AI ​​model and optimizes it to improve the accuracy of ad delivery.

[0529] (Example 2)

[0530] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0531] In modern advertising systems, it is difficult to flexibly respond to the diverse emotional states of users and provide advertisements tailored to individual needs. Traditional advertising systems do not take user emotions into consideration, resulting in low targeting accuracy and limited advertising effectiveness.

[0532] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0533] In this invention, the server includes communication means for receiving, analyzing, and recognizing user input and emotional state; analysis means for analyzing user data, including the obtained emotional data, to generate a user profile; and advertising generation means for generating targeted advertisements based on the user's emotions using a generation AI model. This enables sophisticated targeting that takes user emotions into account, maximizing the effectiveness of advertising.

[0534] "Communication methods" refer to the processes and technologies necessary to receive information input by users and to analyze that information.

[0535] "Emotional state" refers to data that indicates the user's psychological state and mood, analyzed based on the user's input.

[0536] "Analysis methods" refer to the technologies and processes used to objectively understand users' interests and needs by analyzing acquired user data and sentiment data.

[0537] A "generative AI model" is a model that utilizes artificial intelligence technology to perform analysis based on user data and generate specific outputs.

[0538] "Ad generation methods" refer to technologies and processes for automatically creating optimal advertisements based on user profiles and sentiment data.

[0539] "Delivery method" refers to the technology and processes used to send and display generated advertisements on a user's device in an appropriate format.

[0540] "Learning methods" refer to techniques and technologies that measure the effectiveness of advertisements and use feedback data to improve and optimize the system's accuracy.

[0541] This invention relates to an advertising delivery system that takes user emotions into consideration. This enables advertising campaigns with higher targeting accuracy. The system is composed of three main components: the user, the terminal, and the server.

[0542] First, the user accesses a specific interface using their device and enters information about the product or service they wish to advertise. The device is equipped with a communication method and an emotion engine that receives the user's input and performs sentiment analysis. This emotion engine uses natural language processing technology to understand the user's emotional state from their input and generates data based on that understanding.

[0543] The terminal sends the acquired emotion data and user input to the server. The server uses analytical tools to generate a user profile based on this data. In this process, a generative AI model is utilized to construct a profile based on the user's interests and objectives. An example of a prompt message used by the generative AI model is, "The user is showing positive emotions. Generate an advertisement that corresponds to this emotion."

[0544] The server then generates optimal advertisements based on the user's profile and sentiment data. Here, ad generation tools are used to automatically create ads tailored to the user's different emotional states. This process includes the generation of ad copy by a generative AI model.

[0545] The generated advertisements are delivered from the server to the user via the device. The device utilizes various delivery methods to ensure that the received advertisements are displayed in the most optimal way for the user. Specifically, it presents the advertisements in a format that enhances the user experience, such as through on-screen display or audio playback.

[0546] After an ad is displayed, the device collects user reactions and feedback, and this data is sent to a server. The server analyzes this feedback and uses learning mechanisms in conjunction with the emotion engine to optimize the effectiveness of the ad. In this way, the AI ​​model continues to evolve based on user feedback, and the accuracy of the ads improves.

[0547] This system allows sole proprietors and small and medium-sized enterprises to reach their target audience effectively and at low cost, aiming to increase sales.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The user inputs information about the product or service they wish to advertise through their device. This input is captured in text format via the device's communication method. This is the input for processing. The device uses this input information to perform sentiment analysis, recognize the user's emotional state, and generate sentiment data. The output consists of the analyzed sentiment data and the user's input information.

[0551] Step 2:

[0552] The terminal sends the generated emotion data and user input information to the server. The input here refers to all the information sent from the terminal. The server receives this data and uses analytical tools to perform data calculations to structure the user data. The output is detailed user profile information necessary for profile generation.

[0553] Step 3:

[0554] The server uses a generative AI model based on the received user profile information to generate targeted advertisements. The input here is user profile information, and the server uses the generative AI model to perform data calculations, such as generating ad content based on the user's interests and emotions. Prompts such as, "The user is showing positive emotions. Generate an ad corresponding to this emotion," are used. The output is customized ad content.

[0555] Step 4:

[0556] The generated advertisement is delivered from the server to the device. The input is the generated advertisement content. The device processes this data to display it in the most optimal format for the user, such as displaying it on the screen or playing audio. The output is the advertisement in a viewable state from the user's perspective.

[0557] Step 5:

[0558] After a user watches an advertisement, the device collects their reactions and feedback. The input is user feedback data, which the device formats for transmission to the server. The output is feedback data used to evaluate the effectiveness of the advertisement.

[0559] Step 6:

[0560] The server receives and analyzes feedback data. The input is feedback information sent from the terminal. The server utilizes an emotion engine to link the feedback with emotion data and executes a learning process to optimize the effectiveness of the advertisement. The output is an analysis result that helps improve ad display.

[0561] (Application Example 2)

[0562] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0563] Traditional advertising delivery systems have difficulty taking user emotions into account, resulting in insufficient targeting accuracy. Furthermore, the lack of sufficient feedback loops for optimizing advertising effectiveness means that building effective advertising strategies is time-consuming and resource-intensive.

[0564] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0565] In this invention, the server includes information communication means for receiving and analyzing user input, emotion analysis means for analyzing the received user information to understand the emotional state, and ad generation means for generating and delivering ads based on the user's emotions. This enables accurate ad delivery that responds to the user's emotions.

[0566] "Information and communication means" refers to a device or system that has the function of receiving user input and analyzing it.

[0567] "Emotional analysis means" refers to a technology or process for analyzing received user information and understanding their emotional state.

[0568] An "advertising generation method" is a mechanism for creating and delivering appropriate advertisements based on user emotions.

[0569] "Display means" refers to a device or technology used to visually present a generated advertisement to a target user.

[0570] "Learning methods" refer to algorithms and methods for collecting and analyzing user feedback to optimize the effectiveness of ad delivery.

[0571] "Adjustment methods" refer to techniques for modifying generated advertisements based on user history information to achieve more precise targeting.

[0572] "Artificial intelligence technology" refers to advanced computing methods used to generate user profiles and improve the accuracy of advertising targeting.

[0573] The system that implements this application performs sentiment analysis on the user's smartphone or other device, and generates and displays advertisements based on that analysis. The server uses the following information and communication means, sentiment analysis means, advertisement generation means, and display means.

[0574] The server first receives input from the user via information and communication means. This input is sent in the form of text, audio data, etc. Next, the sentiment analysis means analyzes this data using a natural language processing library (e.g., Google NLP API) to understand the user's emotional state. Sentiment recognition AI (e.g., Microsoft Azure Emotion API) is used for this sentiment evaluation.

[0575] Based on the acquired sentiment information, the server uses ad generation tools to create advertisements and deliver them in a format suitable for the user's device. This process also incorporates adjustment mechanisms to tailor ad content based on the user's past history. For example, users exhibiting positive sentiments will receive advertisements for corresponding products and services. To support this process, artificial intelligence technology (generative AI models) is used to create user profiles.

[0576] The user's device visually displays advertisements sent from the server through a display mechanism. After the user views the advertisement, feedback is collected through the device and analyzed by the server's learning mechanism. This data contributes to optimizing ad delivery across the entire system.

[0577] For example, if a user enters "I like this outfit" while using an online shopping app, the sentiment analysis system will capture that feeling of excitement and display advertisements for similar fashion items and accessories. This is expected to further reinforce the user's positive response. An example of a prompt used in this system would be: "User review: 'This product is great!' We will analyze this review and suggest relevant positive advertisements."

[0578] Thus, the system of the present invention can provide a highly personalized advertising experience by understanding the user's emotional state and generating targeted advertisements based on that state.

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] Users enter their thoughts and reviews on their devices. This input can be in text or voice format, and the device sends this data to a server. The input data is raw data used for sentiment evaluation.

[0582] Step 2:

[0583] The server analyzes the received input data using a natural language processing library (Google NLP API) and uses emotion recognition AI (Microsoft Azure Emotion API) to understand the user's emotional state. This analysis outputs the user's emotional state (positive, negative, etc.) in text format based on the input data.

[0584] Step 3:

[0585] The server uses a generative AI model to generate appropriate ad profiles based on the emotional state it has identified. The input is the user's emotional state, and the output is an ad profile tailored to that state. This profile serves as the foundational data for generating targeted ads.

[0586] Step 4:

[0587] The server creates advertisements using an ad generation mechanism and delivers them to the user's device via a display mechanism. A prompt is used to generate the ad content. The input is an ad profile, and the output is ad data that can be displayed on the device.

[0588] Step 5:

[0589] The user's device visually presents the displayed advertisement to the user. The user views the advertisement, and their subsequent actions and reactions are also recorded by the device. This record becomes feedback data.

[0590] Step 6:

[0591] Feedback data is sent from the terminal to the server, where the server's learning mechanism analyzes it. The input is feedback on user reactions and behaviors, and the output is improvement data to enhance the effectiveness of ad delivery. This optimizes the advertising strategy.

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

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

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

[0595] [Fourth Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

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

[0609] This invention aims to realize a system that enables sole proprietors and small and medium-sized enterprises to effectively deliver targeted advertising. The configuration and functions of this system are described below.

[0610] First, the user accesses the system's chat interface using their device. The process begins with the user entering information about the product or service they wish to advertise into the interface. The device then transmits the entered information to the server in real time.

[0611] The server analyzes the received user data. This analysis uses a generative AI model and applies algorithms to understand the user's interests and objectives. Based on the analysis results, the server identifies target users and generates optimal advertisements.

[0612] The generated advertisements are delivered from the server to the target user's device. The device then plays the role of displaying the advertisements in a user-friendly format and effectively delivering the information.

[0613] Furthermore, user reactions and feedback are sent to the server via the device. The server aggregates this feedback and evaluates the effectiveness of the advertisement. Based on the feedback, the server improves the AI ​​model and optimizes it for the next ad delivery.

[0614] As a concrete example, consider a local cafe owner who wants to promote a new menu item. The owner enters information about the new product into a chat interface, and this information is sent to a server. The server identifies a target audience of young coffee lovers living in the cafe's neighborhood and generates an advertisement for a discount campaign on the new menu item. This advertisement is delivered to the devices of users with the specified attributes, effectively promoting the new product.

[0615] The defining feature of this system is its ability to dynamically optimize ad delivery based on user feedback. As a result, it provides a system that allows sole proprietors and small businesses to efficiently utilize their advertising budgets.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] Users access a chat interface using their device and enter information about the products or services they want to advertise. The device receives this input and formats it as data.

[0619] Step 2:

[0620] The device sends formatted user input data to the server. The transmitted data includes details about the advertised products and services.

[0621] Step 3:

[0622] The server analyzes the received data and uses a generated AI model to identify the user's interests and objectives. The server also refers to trend information and historical data stored in the database to identify the target user group.

[0623] Step 4:

[0624] The server generates advertisements tailored to the most suitable target users based on the analysis results. These advertisements are designed to capture the target audience's interest.

[0625] Step 5:

[0626] The server delivers the generated advertisements to the target user's device. The device displays the advertisements in a format that is easy for the user to view.

[0627] Step 6:

[0628] Users view advertisements and input their reactions and opinions based on their content into their devices. The devices then send this feedback back to the server.

[0629] Step 7:

[0630] The server analyzes the received feedback and evaluates the effectiveness of ad delivery. The server uses this information to update the AI ​​model and further optimize future ad delivery.

[0631] (Example 1)

[0632] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] There is a need to achieve efficient and effective targeted advertising delivery while maximizing the return on investment of advertising. In particular, sole proprietors and small and medium-sized enterprises (SMEs) need to deliver ads to the right audience, evaluate their effectiveness, and make improvements within their limited advertising budgets. Therefore, there is a need for methods that improve the accuracy of ad targeting and enable continuous improvement based on user feedback.

[0634] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0635] In this invention, the server includes communication means for receiving and analyzing user input, analysis means for analyzing the received user data to understand the user's interests and objectives, and means for generating prompt sentences from user input using generative artificial intelligence. This enables the generation and delivery of targeted advertisements based on the user's specific needs, and continuous optimization of advertisement content and delivery accuracy through feedback.

[0636] "User input" refers to information that users provide to the chat interface of an advertising system, and it serves as the basic data for generating advertisements.

[0637] "Communication means" refers to the means of sending user input from a terminal to a server, and is a technology that enables accurate data transfer.

[0638] "Analysis means" refers to methods for analyzing received user data and understanding their interests and objectives.

[0639] "Generative artificial intelligence" refers to an AI model that generates prompt text from user input and uses that information to create targeted advertisements.

[0640] A "prompt" is a set of instructions or messages that form the basis of the generated advertisement, and it serves as the starting point for the AI ​​model to create the advertisement content.

[0641] An "advertising generation method" is a means for generating advertisements in a format suitable for a specific target audience based on a prompt message.

[0642] "Delivery method" refers to the means by which generated advertisements are delivered to target users, and it plays a role in determining the appropriate delivery timing and format.

[0643] "Learning methods" refer to technologies used to collect feedback from target users and improve the accuracy of ad delivery through analysis of that feedback.

[0644] This invention is designed as a system for effectively generating and delivering targeted advertisements. Users first access the system's chat interface using their own devices and input information about the products or services they wish to advertise. The system requires a computer and an internet connection as basic hardware.

[0645] The terminal plays the role of transmitting the entered information to the server in real time. The server is a computer server that analyzes user input using artificial intelligence technologies such as OpenAI as a generative AI model. This model understands the user's intent based on the input information and generates prompt messages. These prompt messages serve as a starting point for customizing targeted advertisements.

[0646] Specifically, the generative AI model generates a prompt such as, "Create a targeted ad to promote our new coffee menu." Based on this instruction, the server identifies target users and determines what kind of ad content is best suited for them. If the user is the owner of a local cafe, the server can target consumers with specific attributes living in the neighborhood (e.g., "coffee lovers" or "young people in their 20s") and generate and deliver ads for discount campaigns on the new menu.

[0647] The generated advertisements are sent to the target user's device using a delivery method. The device receives the advertisement and displays it in a format that is easy for the user to view. For example, visually appealing ad formats using vibrant images and catchy slogans may be employed.

[0648] Furthermore, user responses and feedback to advertisements are sent from the device to the server. This feedback data is used by the server to evaluate the effectiveness of the advertisements and to improve future advertising campaigns. The server uses this data to update its AI model and optimize ad delivery. This allows sole proprietors and small businesses to use their advertising budgets efficiently and achieve maximum results.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] Users access the system's chat interface using their own devices. Here, users enter detailed information about the products or services they wish to advertise. This input data is collected as initial information and sent from the interface to the user's device.

[0652] Step 2:

[0653] The terminal transmits user-entered information to the server in real time. The entered information is sent to the server as text data. The server receives this data and prepares for initial analysis.

[0654] Step 3:

[0655] The server analyzes the received user data. This analysis utilizes a generative AI model. Specifically, the server analyzes text data and generates prompts that are optimal for the user's needs and targeted advertisements. These generated prompts form the basis for the next ad generation step.

[0656] Step 4:

[0657] The server uses the generated prompt to identify the target user group and generate ad content. The specific content and messaging of the targeted ad campaign are determined via a generative artificial intelligence model. The ads generated at this stage are prepared as data for delivery.

[0658] Step 5:

[0659] The server uses a delivery method to distribute the generated advertising content to the target users. The advertisement is sent to the target user's device at the appropriate time and in the appropriate format. After delivery, the advertisement is visually presented to the user by the device.

[0660] Step 6:

[0661] User responses and feedback to advertisements are sent to the server via the device. This feedback data is compiled and analyzed on the server as data to evaluate the effectiveness of the advertisements. This allows for an understanding of the actual results of the advertising campaign.

[0662] Step 7:

[0663] The server uses collected feedback data to improve the generated AI model and optimize ad delivery. This process continuously improves targeting accuracy and messaging content for future ad deliveries. This helps users achieve more effective advertising results.

[0664] (Application Example 1)

[0665] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] This invention aims to provide an efficient system that enables sole proprietors and small and medium-sized enterprises to overcome the limitations of conventional advertising methods and deliver advertisements more effectively to their target customers. In particular, it addresses the challenge of maximizing the use of limited advertising budgets by providing a consistent system from ad generation and delivery to optimization through feedback.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0668] In this invention, the server includes communication means for receiving and processing information from a user terminal, analysis means for analyzing information about the input product or service and identifying potential customer segments, and advertising generation means for automatically generating and transmitting advertisements directed at target customers based on the analysis results. This makes it possible for users to easily create targeted advertisements and effectively reach their target customers.

[0669] A "user terminal" is a device used to access the advertising delivery system and input information, and includes, for example, smartphones and personal computers.

[0670] "Communication means" refers to the function of sending information from the user's terminal to the server and receiving advertisements and feedback from the server to the user's terminal.

[0671] "Analysis means" refers to a function that analyzes information provided by users and processes it to identify potential customer segments.

[0672] An "ad generation method" is a function that handles the process of automatically creating and sending ads that are optimal for the target customer based on analyzed data.

[0673] "Distribution method" refers to the function used to effectively deliver generated advertisements to specific customer segments.

[0674] "Control means" refers to a process for collecting and analyzing received response information and using that information to improve the effectiveness of advertising activities.

[0675] "Generative artificial intelligence" refers to a method that optimizes ad generation and delivery by analyzing data and learning patterns.

[0676] This invention provides a system for effectively delivering targeted advertising to sole proprietors and small and medium-sized enterprises. The system mainly consists of a user terminal, a server, and a generative AI model. A specific example of this system is described below.

[0677] Users access the system interface using user devices such as smartphones or personal computers. Users input information about the products or services they wish to offer and submit it to the platform. The entered information is then transferred to the server via communication means.

[0678] Upon receiving this information, the server analyzes the data using analytical tools. Generative AI models are used in the analysis to identify the most suitable potential customer segment based on the user's information. GPT-4 is used as an example of a generative AI model. This model generates optimal advertising content using specific prompt sentences.

[0679] Based on the analysis results, the ad generation system activates and automatically creates ads targeted at the identified customers. The generated ads are then sent to the customers' devices in real time via the delivery system.

[0680] After an advertisement is delivered, customer responses are sent back to the server. The server uses control mechanisms to aggregate and analyze this response information. Based on this feedback, advertising activities are continuously optimized.

[0681] For example, if a local florist wants to promote a new bouquet, the user would enter information such as "New spring bouquet, for those in their 20s and 30s, 10% off first purchase" as a prompt. This information is processed by the server and delivered as a customized advertisement to nearby flower-loving customers.

[0682] Example of a prompt:

[0683] Please generate an ad. The target audience is local users in their 20s and 30s who love flowers, and the ad aims to introduce new flower bouquets. Please include a 10% off promotion.

[0684] Thus, the system of the present invention enables users to maximize the effectiveness of their advertising and achieve appropriate approaches to their customers.

[0685] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0686] Step 1:

[0687] The user's device receives information about the advertisement content entered by the user. Specifically, it accesses the interface on a smartphone or computer and enters information such as a prompt message: "New spring flower bouquets, for people in their 20s and 30s, 10% off first purchase." The entered information is then prepared to be sent to the server via a communication method.

[0688] Step 2:

[0689] The server processes the prompt messages received from the user terminal using an analysis tool. It analyzes the input prompt messages and applies a generative AI model (e.g., GPT-4) to identify potential customer segments. Specifically, it performs text analysis, and the analysis results generate a profile of the target customer.

[0690] Step 3:

[0691] The server's ad generation mechanism automatically generates ads based on analysis results. It uses a generation AI model to create optimal ads and build specific ad content targeted at specific customers. At this stage, the generated ad text and visual elements are obtained as output.

[0692] Step 4:

[0693] The generated advertisements are sent in real time to the target customers' devices via the server's delivery system. Specifically, the method of ad delivery is determined based on specific location information and past behavioral data. As a result of delivery, the advertisements are displayed to the specified target audience.

[0694] Step 5:

[0695] Customer reactions to advertisements viewed on their devices are sent back to the server. User actions and behavioral data (clicks, dwell time, etc.) are collected as specific inputs and sent to the server via communication means.

[0696] Step 6:

[0697] The server analyzes collected customer responses using control mechanisms to evaluate the effectiveness of the advertisements. Using the collected data as a medium, it quantifies the advertising's effectiveness and identifies areas for improvement. Based on this analysis, advertising activities are optimized for future campaigns.

[0698] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0699] This invention provides a more sophisticated advertising delivery system that incorporates an emotion engine that recognizes user emotions. This system enables sole proprietors and small businesses to highly target and optimize their advertising campaigns.

[0700] The user accesses the chat interface using their device and enters information about the product or service they want to advertise. The device captures this user input and activates an emotion engine. This engine analyzes the user's input to recognize their emotional state and sends it to the server along with the input data.

[0701] The server analyzes the received user data and sentiment data. Using a generative AI model, it builds targeting profiles based on user interests and emotions. Based on these profiles, it generates advertisements that best suit a specific emotional state. For example, if a user is showing positive emotions, it creates advertisements that further enhance those emotions, while for users showing negative emotions, it suggests encouraging advertisements.

[0702] The generated advertisements are delivered from the server to the target user's device, which then displays them in a user-friendly format. After the advertisement is displayed, the user's feedback is analyzed again using an emotion engine to verify the effectiveness of the advertisement content. The server analyzes this feedback and changes in emotion to improve the AI ​​model.

[0703] For example, if an education-related company that holds online seminars uses this system, when a user enters their opinion about the seminar content, the emotion engine recognizes from that opinion whether the user is excited or anxious. The server then uses this emotion information to deliver advertisements encouraging excited users to participate in the next level of the seminar, and encouraging advertisements that reinforce the value of the seminar to anxious users, thereby conducting effective promotion.

[0704] In this way, by using an emotion engine, more precise ad delivery can be achieved than before, enabling sole proprietors and small businesses to reach their target audience cost-effectively and increase sales.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The user opens a chat interface using their device and enters details about the product or service they want to advertise. The device receives this input data.

[0708] Step 2:

[0709] The device sends user input information to the emotion engine, which then analyzes the user's emotions based on that information. The emotion engine uses an emotion recognition algorithm to determine whether the emotional state is positive, negative, or neutral.

[0710] Step 3:

[0711] The device sends user data, including analyzed sentiment data, to the server. This user data includes detailed product information and emotional state.

[0712] Step 4:

[0713] The server analyzes the received data and uses a generative AI model to build user profiles. These profiles reflect the user's interests and emotional state and are used for targeting.

[0714] Step 5:

[0715] The server generates optimal advertisements based on the user's profile and specific emotional state. For example, it creates advertisements that further stimulate purchasing intent for positive users and selects advertisements that provide a sense of security for negative users.

[0716] Step 6:

[0717] The server delivers the generated advertisements to the target user's device. The device then displays the advertisements to the user visually and effectively.

[0718] Step 7:

[0719] Users input their reactions and opinions to the displayed advertisements into their devices. The devices then send this feedback to the server.

[0720] Step 8:

[0721] The server analyzes the feedback and evaluates the user's reaction to the advertisement and their emotional changes. Using this information, the server updates the AI ​​model and optimizes it to improve the accuracy of ad delivery.

[0722] (Example 2)

[0723] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] In modern advertising systems, it is difficult to flexibly respond to the diverse emotional states of users and provide advertisements tailored to individual needs. Traditional advertising systems do not take user emotions into consideration, resulting in low targeting accuracy and limited advertising effectiveness.

[0725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0726] In this invention, the server includes communication means for receiving, analyzing, and recognizing user input and emotional state; analysis means for analyzing user data, including the obtained emotional data, to generate a user profile; and advertising generation means for generating targeted advertisements based on the user's emotions using a generation AI model. This enables sophisticated targeting that takes user emotions into account, maximizing the effectiveness of advertising.

[0727] "Communication methods" refer to the processes and technologies necessary to receive information input by users and to analyze that information.

[0728] "Emotional state" refers to data that indicates the user's psychological state and mood, analyzed based on the user's input.

[0729] "Analysis methods" refer to the technologies and processes used to objectively understand users' interests and needs by analyzing acquired user data and sentiment data.

[0730] A "generative AI model" is a model that utilizes artificial intelligence technology to perform analysis based on user data and generate specific outputs.

[0731] "Ad generation methods" refer to technologies and processes for automatically creating optimal advertisements based on user profiles and sentiment data.

[0732] "Delivery method" refers to the technology and processes used to send and display generated advertisements on a user's device in an appropriate format.

[0733] "Learning methods" refer to techniques and technologies that measure the effectiveness of advertisements and use feedback data to improve and optimize the system's accuracy.

[0734] This invention relates to an advertising delivery system that takes user emotions into consideration. This enables advertising campaigns with higher targeting accuracy. The system is composed of three main components: the user, the terminal, and the server.

[0735] First, the user accesses a specific interface using their device and enters information about the product or service they wish to advertise. The device is equipped with a communication method and an emotion engine that receives the user's input and performs sentiment analysis. This emotion engine uses natural language processing technology to understand the user's emotional state from their input and generates data based on that understanding.

[0736] The terminal sends the acquired emotion data and user input to the server. The server uses analytical tools to generate a user profile based on this data. In this process, a generative AI model is utilized to construct a profile based on the user's interests and objectives. An example of a prompt message used by the generative AI model is, "The user is showing positive emotions. Generate an advertisement that corresponds to this emotion."

[0737] The server then generates optimal advertisements based on the user's profile and sentiment data. Here, ad generation tools are used to automatically create ads tailored to the user's different emotional states. This process includes the generation of ad copy by a generative AI model.

[0738] The generated advertisements are delivered from the server to the user via the device. The device utilizes various delivery methods to ensure that the received advertisements are displayed in the most optimal way for the user. Specifically, it presents the advertisements in a format that enhances the user experience, such as through on-screen display or audio playback.

[0739] After an ad is displayed, the device collects user reactions and feedback, and this data is sent to a server. The server analyzes this feedback and uses learning mechanisms in conjunction with the emotion engine to optimize the effectiveness of the ad. In this way, the AI ​​model continues to evolve based on user feedback, and the accuracy of the ads improves.

[0740] This system allows sole proprietors and small and medium-sized enterprises to reach their target audience effectively and at low cost, aiming to increase sales.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The user inputs information about the product or service they wish to advertise through their device. This input is captured in text format via the device's communication method. This is the input for processing. The device uses this input information to perform sentiment analysis, recognize the user's emotional state, and generate sentiment data. The output consists of the analyzed sentiment data and the user's input information.

[0744] Step 2:

[0745] The terminal sends the generated emotion data and user input information to the server. The input here refers to all the information sent from the terminal. The server receives this data and uses analytical tools to perform data calculations to structure the user data. The output is detailed user profile information necessary for profile generation.

[0746] Step 3:

[0747] The server uses a generative AI model based on the received user profile information to generate targeted advertisements. The input here is user profile information, and the server uses the generative AI model to perform data calculations, such as generating ad content based on the user's interests and emotions. Prompts such as, "The user is showing positive emotions. Generate an ad corresponding to this emotion," are used. The output is customized ad content.

[0748] Step 4:

[0749] The generated advertisement is delivered from the server to the device. The input is the generated advertisement content. The device processes this data to display it in the most optimal format for the user, such as displaying it on the screen or playing audio. The output is the advertisement in a viewable state from the user's perspective.

[0750] Step 5:

[0751] After a user watches an advertisement, the device collects their reactions and feedback. The input is user feedback data, which the device formats for transmission to the server. The output is feedback data used to evaluate the effectiveness of the advertisement.

[0752] Step 6:

[0753] The server receives and analyzes feedback data. The input is feedback information sent from the terminal. The server utilizes an emotion engine to link the feedback with emotion data and executes a learning process to optimize the effectiveness of the advertisement. The output is an analysis result that helps improve ad display.

[0754] (Application Example 2)

[0755] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] Traditional advertising delivery systems have difficulty taking user emotions into account, resulting in insufficient targeting accuracy. Furthermore, the lack of sufficient feedback loops for optimizing advertising effectiveness means that building effective advertising strategies is time-consuming and resource-intensive.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0758] In this invention, the server includes information communication means for receiving and analyzing user input, emotion analysis means for analyzing the received user information to understand the emotional state, and ad generation means for generating and delivering ads based on the user's emotions. This enables accurate ad delivery that responds to the user's emotions.

[0759] "Information and communication means" refers to a device or system that has the function of receiving user input and analyzing it.

[0760] "Emotional analysis means" refers to a technology or process for analyzing received user information and understanding their emotional state.

[0761] An "advertising generation method" is a mechanism for creating and delivering appropriate advertisements based on user emotions.

[0762] "Display means" refers to a device or technology used to visually present a generated advertisement to a target user.

[0763] "Learning methods" refer to algorithms and methods for collecting and analyzing user feedback to optimize the effectiveness of ad delivery.

[0764] "Adjustment methods" refer to techniques for modifying generated advertisements based on user history information to achieve more precise targeting.

[0765] "Artificial intelligence technology" refers to advanced computing methods used to generate user profiles and improve the accuracy of advertising targeting.

[0766] The system that implements this application performs sentiment analysis on the user's smartphone or other device, and generates and displays advertisements based on that analysis. The server uses the following information and communication means, sentiment analysis means, advertisement generation means, and display means.

[0767] The server first receives input from the user via information and communication means. This input is sent in the form of text, audio data, etc. Next, the sentiment analysis means analyzes this data using a natural language processing library (e.g., Google NLP API) to understand the user's emotional state. Sentiment recognition AI (e.g., Microsoft Azure Emotion API) is used for this sentiment evaluation.

[0768] Based on the acquired sentiment information, the server uses ad generation tools to create advertisements and deliver them in a format suitable for the user's device. This process also incorporates adjustment mechanisms to tailor ad content based on the user's past history. For example, users exhibiting positive sentiments will receive advertisements for corresponding products and services. To support this process, artificial intelligence technology (generative AI models) is used to create user profiles.

[0769] The user's device visually displays advertisements sent from the server through a display mechanism. After the user views the advertisement, feedback is collected through the device and analyzed by the server's learning mechanism. This data contributes to optimizing ad delivery across the entire system.

[0770] For example, if a user enters "I like this outfit" while using an online shopping app, the sentiment analysis system will capture that feeling of excitement and display advertisements for similar fashion items and accessories. This is expected to further reinforce the user's positive response. An example of a prompt used in this system would be: "User review: 'This product is great!' We will analyze this review and suggest relevant positive advertisements."

[0771] Thus, the system of the present invention can provide a highly personalized advertising experience by understanding the user's emotional state and generating targeted advertisements based on that state.

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] Users enter their thoughts and reviews on their devices. This input can be in text or voice format, and the device sends this data to a server. The input data is raw data used for sentiment evaluation.

[0775] Step 2:

[0776] The server analyzes the received input data using a natural language processing library (Google NLP API) and uses emotion recognition AI (Microsoft Azure Emotion API) to understand the user's emotional state. This analysis outputs the user's emotional state (positive, negative, etc.) in text format based on the input data.

[0777] Step 3:

[0778] The server uses a generative AI model to generate appropriate ad profiles based on the emotional state it has identified. The input is the user's emotional state, and the output is an ad profile tailored to that state. This profile serves as the foundational data for generating targeted ads.

[0779] Step 4:

[0780] The server creates advertisements using an ad generation mechanism and delivers them to the user's device via a display mechanism. A prompt is used to generate the ad content. The input is an ad profile, and the output is ad data that can be displayed on the device.

[0781] Step 5:

[0782] The user's device visually presents the displayed advertisement to the user. The user views the advertisement, and their subsequent actions and reactions are also recorded by the device. This record becomes feedback data.

[0783] Step 6:

[0784] Feedback data is sent from the terminal to the server, where the server's learning mechanism analyzes it. The input is feedback on user reactions and behaviors, and the output is improvement data to enhance the effectiveness of ad delivery. This optimizes the advertising strategy.

[0785] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0786] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0787] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0788] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0789] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0790] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0791] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0792] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0793] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0794] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0795] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0796] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0797] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0798] 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.

[0799] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0800] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0801] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0802] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0803] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0804] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0805] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] A means of communication for receiving and analyzing user input,

[0809] An analytical method that analyzes received user data to understand the user's interests and objectives,

[0810] An ad generation means for generating and delivering ads to the optimal target based on the analysis results,

[0811] A distribution method for delivering generated advertisements to target users,

[0812] A learning method for collecting and analyzing feedback from target users to optimize ad delivery,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, further comprising adjustment means for adjusting generated advertisements based on the user's past behavioral data.

[0816] (Claim 3)

[0817] The system according to claim 1, comprising means for generating a user profile using an artificial intelligence model.

[0818] "Example 1"

[0819] (Claim 1)

[0820] A means of communication for receiving and analyzing user input,

[0821] An analytical method that analyzes received user data to understand the user's interests and objectives,

[0822] A means for generating prompt sentences from user input using generative artificial intelligence,

[0823] An ad generation means for identifying and generating targeted ads based on prompt messages,

[0824] A distribution method for delivering generated advertisements to target users,

[0825] A learning method for collecting and analyzing feedback from target users to optimize ad delivery and generation artificial intelligence,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising adjustment means for adjusting generated advertisements based on the user's past behavioral data.

[0829] (Claim 3)

[0830] The system according to claim 1, comprising means for generating a user profile using an artificial intelligence model.

[0831] "Application Example 1"

[0832] (Claim 1)

[0833] A means of communication for receiving and processing information from a user terminal,

[0834] An analytical means for analyzing information about products or services entered and identifying potential customer segments,

[0835] An advertising generation means that automatically generates and sends advertisements to target customers based on the analysis results,

[0836] A delivery method for providing generated advertisements to a specific customer segment,

[0837] A control means that collects received response information, analyzes it, and continuously updates advertising activities,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for dynamically changing generated advertisements based on the user's location information and environmental data.

[0841] (Claim 3)

[0842] The system according to claim 1, comprising means for improving targeted advertisements using generative artificial intelligence to increase users' purchasing intent.

[0843] "Example 2 of combining an emotion engine"

[0844] (Claim 1)

[0845] A means of communication for receiving and analyzing user input and recognizing emotional states,

[0846] An analysis means for analyzing user data, including obtained emotional data, to generate a user profile,

[0847] An advertising generation method for generating targeted advertisements based on user emotions using a generative AI model,

[0848] A delivery method for adjusting and delivering generated advertisements based on the user's emotional state,

[0849] A learning method to improve the accuracy of ad delivery by collecting user feedback, analyzing it using an emotion engine, and

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, further comprising adjustment means for adjusting generated advertisements based on the user's past behavioral data and sentiment data.

[0853] (Claim 3)

[0854] The system according to claim 1, comprising means for generating a profile based on user interest and sentiment data using an artificial intelligence model.

[0855] "Application example 2 when combining with an emotional engine"

[0856] (Claim 1)

[0857] Information and communication means for receiving and analyzing user input,

[0858] A sentiment analysis method that analyzes received user information to understand the emotional state,

[0859] An ad generation method for generating and delivering ads based on user emotions,

[0860] A display means for visually delivering generated advertisements to target users,

[0861] A learning method for collecting and analyzing feedback from target users to optimize ad delivery,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising adjustment means for adjusting generated advertisements based on the user's history information.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising means for generating a user profile using artificial intelligence technology. [Explanation of symbols]

[0867] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of communication for receiving and analyzing user input, An analytical method that analyzes received user data to understand the user's interests and objectives, An ad generation means for generating and delivering ads to the optimal target based on the analysis results, A distribution method for delivering generated advertisements to target users, A learning method for collecting and analyzing feedback from target users to optimize ad delivery, A system that includes this.

2. The system according to claim 1, further comprising adjustment means for adjusting generated advertisements based on the user's past behavioral data.

3. The system according to claim 1, comprising means for generating a user profile using an artificial intelligence model.

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